require(likert) > data(pisaitems) > > ##### Item 24: Reading Attitudes > items24 <- pisaitems[,substr(names(pisaitems), 1,5) == 'ST24Q'] > > items24 <- rename(items24, c( + ST24Q01="I read only if I have to.", + ST24Q02="Reading is one of my favorite hobbies.", + ST24Q03="I like talking about books with other people.", + ST24Q04="I find it hard to finish books.", + ST24Q05="I feel happy if I receive a book as a present.", + ST24Q06="For me, reading is a waste of time.", + ST24Q07="I enjoy going to a bookstore or a library.", + ST24Q08="I read only to get information that I need.", + ST24Q09="I cannot sit still and read for more than a few minutes.", + ST24Q10="I like to express my opinions about books I have read.", + ST24Q11="I like to exchange books with my friends.")) > l24g <- likert(items24[,1:2], grouping=pisaitems$CNT) > plot(l24g)

> dat<- data.frame(t=seq(0, 2*pi, by=0.1) ) > xhrt <- function(t) 16*sin(t)^3 > yhrt <- function(t) 13*cos(t)-5*cos(2*t)-2*cos(3*t)-cos(4*t) > dat$y=yhrt(dat$t) > dat$x=xhrt(dat$t) > with(dat, plot(x,y, type="l")) > with(dat, polygon(x,y, col="darkred"))

ixx <- rep(c(10, 30, 40), 2); plot.new() par(mfrow=c(2,3)); colschemes <- list(); colschemes[1:3] <- 'temperature' colschemes[4:6] <- list(c('blue', 'gray', 'red')) for (i in 1:length(ixx)) { par(mar=c(0,0,0,0)) index <- ixx[i] eventsub <- subsetEventData(edata_whales, index=index); plot.bammdata(eventsub, method='polar', pal= colschemes[[i]], par.reset=FALSE, lwd=3) addBAMMshifts(eventsub, method='polar', index=1, col='white', bg='black', cex=5, par.reset=FALSE) }

library(BAMMtools) data(whales, events.whales) edata_whales <- getEventData(whales, events.whales, burnin=0.1) plot.bammdata(edata_whales, lwd=3, method="polar", pal="temperature") data(primates, events.primates) ed <- getEventData(primates, events.primates, burnin=0.25, type = 'trait') par(mfrow=c(1,3), mar=c(1, 0.5, 0.5, 0.5), xpd=TRUE) q <- plot.bammdata(ed, tau=0.001, breaksmethod='linear', lwd=2) addBAMMshifts(ed, par.reset=FALSE, cex=2) title(sub='linear',cex.sub=2, line=-1) addBAMMlegend(q, location=c(0, 1, 140, 220)) q <- plot.bammdata(ed, tau=0.001, breaksmethod='linear', color.interval=c(NA,0.12), lwd=2) addBAMMshifts(ed, par.reset=FALSE, cex=2) title(sub='linear - color.interval',cex.sub=2, line=-1) addBAMMlegend(q, location=c(0, 1, 140, 220)) q <- plot.bammdata(ed, tau=0.001, breaksmethod='jenks', lwd=2) addBAMMshifts(ed, par.reset=FALSE, cex=2) title(sub='jenks',cex.sub=2, line=-1) addBAMMlegend(q, location=c(0, 1, 140, 220))

> library(ggnetwork) > set.seed(10312016) > ggplot(ggnetwork(em.net, arrow.gap = 0.02, layout = "fruchtermanreingold"), + aes(x, y, xend = xend, yend = yend)) + + geom_edges( + aes(color = curr_empl_type), + alpha = 0.25, + arrow = arrow(length = unit(5, "pt"), + type = "closed"), + curvature = 0.05) + + geom_nodes(aes(color = curr_empl_type), + size = 4) + + scale_color_brewer("Employment Type", + palette = "Set1") + + theme_blank() + + theme(legend.position = "bottom")

library(tidyverse) library(rvest) library(magrittr) library(ggmap) library(stringr) ref:https://www.r-bloggers.com/how-to-make-a-global-map-in-r-step-by-step/

library(gplots) > > #Build the matrix data to look like a correlation matrix > x <- matrix(rnorm(64), nrow=8) > x <- (x - min(x))/(max(x) - min(x)) #Scale the data to be between 0 and 1 > for (i in 1:8) x[i, i] <- 1.0 #Make the diagonal all 1's > > #Format the data for the plot > xval <- formatC(x, format="f", digits=2) > pal <- colorRampPalette(c(rgb(0.96,0.96,1), rgb(0.1,0.1,0.9)), space = "rgb") > > #Plot the matrix > x_hm <- heatmap.2(x, Rowv=FALSE, Colv=FALSE, dendrogram="none", main="by Volkan OBAN using R \n 8 X 8 Matrix Using Heatmap.2", xlab="Columns", ylab="Rows", col=pal, tracecol="#303030", trace="none", cellnote=xval, notecol="black", notecex=0.8, keysize = 1.3, margins=c(5, 5))

set.seed(1231) # Random scale-free diffusion network x <- rdiffnet(1000, 4, seed.graph="scale-free", seed.p.adopt = .025, rewire = FALSE, seed.nodes = "central", rgraph.arg=list(self=FALSE, m=4), threshold.dist = function(id) runif(1,.2,.4)) # Diffusion map (no random toa) dm0 <- diffusionMap(x, kde2d.args=list(n=150, h=1), layout=igraph::layout_with_fr) # Random diffnet.toa(x) <- sample(x$toa, size = nnodes(x)) # Diffusion map (random toa) dm1 <- diffusionMap(x, layout = dm0$coords, kde2d.args=list(n=150, h=.5)) oldpar <- par(no.readonly = TRUE) col <- colorRampPalette(blues9)(100) par(mfrow=c(1,2), oma=c(1,0,0,0), cex=.8) image(dm0, col=col, main="Non-random Times of Adoption\nAdoption from the core.") image(dm1, col=col, main="Random Times of Adoption") par(mfrow=c(1,1)) mtext("Both networks have the same distribution on times of adoption", 1, outer = TRUE)

g<-matrix(0,50,50) g[1,]<-1; g[,1]<-1 #Create a star gplot(g) gplot(rewire.ws(g,0.05))

gplot(rgws(1,100,1,2,1))

library(arules) > rules.all <- apriori(titanic.raw) > load("titanic.raw.rdata") > library(arulesViz) > plot(rules.all) plot(rules.all,main=" ", method = "graph", control = list(type = "items"))

> net.bg <- sample_pa(80) > > V(net.bg)$size <- 8 > > V(net.bg)$frame.color <- "firebrick3" > > V(net.bg)$color <- "hotpink" > > V(net.bg)$label <- "" > l <- layout_in_circle(net.bg) > > plot(net.bg)

metro_map <- ggmap::get_map(location = c(left = -77.22257, bottom = 39.05721, right = -77.11271, top = 39.14247)) ggmap::ggmap(metro_map) + geom_net(data = tripnet, layout.alg = NULL, labelon = TRUE, vjust = -0.5, ealpha = 0.5, aes(from_id = from_id, to_id = to_id, x = long, y = lat, linewidth = n / 15, colour = Metro)) + scale_colour_manual("Metro Station", values = c("grey40", "darkorange")) + theme_net() %+replace% theme(aspect.ratio=NULL, legend.position = "bottom") + coord_map() ref:https://cran.r-project.org/web/packages/ggCompNet/vignettes/examples-from-paper.html

> data(bikes, package = 'geomnet') > # data step for geomnet > tripnet <- fortify(as.edgedf(bikes$trips), bikes$stations[,c(2,1,3:5)]) > tripnet$Metro = FALSE > idx <- grep("Metro", tripnet$from_id) > tripnet$Metro[idx] <- TRUE > > # plot the bike sharing network shown in Figure 7b > set.seed(1232016) > ggplot(aes(from_id = from_id, to_id = to_id), data = tripnet) + + geom_net(aes(linewidth = n / 15, colour = Metro), + labelon = TRUE, repel = TRUE) + + theme_net() + + xlim(c(-0.1, 1.1)) + + scale_colour_manual("Metro Station", values = c("grey40", "darkorange")) + + theme(legend.position = "bottom")

data(football, package = 'geomnet') rownames(football$vertices) <- football$vertices$label # create network fb.net <- network::network(football$edges[, 1:2], directed = TRUE) # create node attribute (what conference is team in?) fb.net %v% "conf" <- football$vertices[ network.vertex.names(fb.net), "value" ] # create edge attribute (between teams in same conference?) network::set.edge.attribute( fb.net, "same.conf", football$edges$same.conf) set.seed(5232011) ggnet2(fb.net, mode = "fruchtermanreingold", color = "conf", palette = "Paired", color.legend = "Conference", edge.color = c("color", "grey75")) --- ftnet <- fortify(as.edgedf(football$edges), football$vertices) ftnet$schools <- ifelse( ftnet$value == "Independents", ftnet$from_id, "") # create data plot set.seed(5232011) ggplot(data = ftnet, aes(from_id = from_id, to_id = to_id)) + geom_net(layout.alg = 'fruchtermanreingold', aes(colour = value, group = value, linetype = factor(same.conf != 1), label = schools), linewidth = 0.5, size = 5, vjust = -0.75, alpha = 0.3) + theme_net() + theme(legend.position = "bottom") + scale_colour_brewer("Conference", palette = "Paired") + guides(linetype = FALSE)

> library(ggnet) > data(email, package = 'geomnet') > > # create node attribute data > em.cet <- as.character( + email$nodes$CurrentEmploymentType) > names(em.cet) = email$nodes$label > > # remove the emails sent to all employees > edges <- subset(email$edges, nrecipients < 54) > # create network > em.net <- edges[, c("From", "to") ] > em.net <- network::network(em.net, directed = TRUE) > # create employee type node attribute > em.net %v% "curr_empl_type" <- + em.cet[ network.vertex.names(em.net) ] > set.seed(10312016) > ggnet2(em.net, color = "curr_empl_type", + size = 4, palette = "Set1", + arrow.size = 5, arrow.gap = 0.02, + edge.alpha = 0.25, mode = "fruchtermanreingold", + edge.color = c("color", "grey50"), + color.legend = "Employment Type") + ggtitle("by Volkan OBAN using R - ggnet") + + theme(legend.position = "bottom") > email$edges <- email$edges[, c(1,5,2:4,6:9)] > emailnet <- fortify( + as.edgedf(subset(email$edges, nrecipients < 54)), + email$nodes) > set.seed(10312016) > ggplot(data = emailnet, + aes(from_id = from_id, to_id = to_id)) + + geom_net(layout.alg = "fruchtermanreingold", + aes(colour = CurrentEmploymentType, + group = CurrentEmploymentType, + linewidth = 3 * (...samegroup.. / 8 + .125)), + ealpha = 0.25, + size = 4, curvature = 0.05, + directed = TRUE, arrowsize = 0.5) + + scale_colour_brewer("Employment Type", palette = "Set1") + + theme_net() + ggtitle("by Volkan OBAN using R - ggnet") + + theme(legend.position = "bottom") > set.seed(10312016) > ggplot(data = emailnet, + aes(from_id = from_id, to_id = to_id)) + + geom_net(layout.alg = "fruchtermanreingold", + aes(colour = CurrentEmploymentType, + group = CurrentEmploymentType, + linewidth = 3 * (...samegroup.. / 8 + .125)), + ealpha = 0.25, + size = 4, curvature = 0.05, + directed = TRUE, arrowsize = 0.5) + + scale_colour_brewer("Employment Type", palette = "Set1") + + theme_net() + + theme(legend.position = "bottom") >

> library(geomnet) > data(madmen, package = "geomnet") > > # code for geom_net > # data step: merge edges and nodes by the "from" column > > MMnet <- fortify(as.edgedf(madmen$edges), madmen$vertices) set.seed(10052016) ggplot(data = MMnet, aes(from_id = from_id, to_id = to_id)) + geom_net(aes(colour = Gender), layout.alg = "kamadakawai", size = 2, labelon = TRUE, vjust = -0.6, ecolour = "grey60", directed =FALSE, fontsize = 3, ealpha = 0.5) + scale_colour_manual(values = c("#FF69B4", "#0099ff")) + xlim(c(-0.05, 1.05)) + theme_net() + theme(legend.position = "bottom")

fit <- lm(mpg ~ hp, data = mtcars) d <- mtcars fit <- lm(mpg ~ hp, data = d) d$predicted <- predict(fit) # Save the predicted values d$residuals <- residuals(fit) # Save the residual values # Quick look at the actual, predicted, and residual values library(dplyr) d %>% select(mpg, predicted, residuals) %>% head() ggplot(d, aes(x = hp, y = mpg)) + geom_smooth(method = "lm", se = FALSE, color = "lightgrey") + geom_segment(aes(xend = hp, yend = predicted), alpha = .2) + # > Color adjustments made here... geom_point(aes(color = abs(residuals))) + # Color mapped to abs(residuals) scale_color_continuous(low = "black", high = "red") + # Colors to use here guides(color = FALSE) + # Color legend removed # < geom_point(aes(y = predicted), shape = 1) + theme_bw() and // another visualization ggplot(d, aes(x = hp, y = mpg)) + geom_smooth(method = "lm", se = FALSE, color = "lightgrey") + geom_segment(aes(xend = hp, yend = predicted), alpha = .2) + # > Color AND size adjustments made here... geom_point(aes(color = abs(residuals), size = abs(residuals))) + # size also mapped scale_color_continuous(low = "black", high = "red") + guides(color = FALSE, size = FALSE) + # Size legend also removed # < geom_point(aes(y = predicted), shape = 1) + theme_bw()

library(dplyr) titanic_tbl <- dplyr::tbl_df(Titanic) titanic_tbl <- titanic_tbl %>% mutate_each(funs(factor), Class:Survived) by_class_survival <- titanic_tbl %>% group_by(Class, Survived) %>% summarize(Count = sum(n)) titanic.mat <- matrix(by_class_survival$Count, nrow = 4, ncol = 2) dimnames(titanic.mat ) <- list(Class = levels(titanic_tbl$Class), Survival = levels(titanic_tbl$Survived)) print(titanic.mat) groupColors <- c("#2171b5", "#6baed6", "#bdd7e7", "#bababa", "#d7191c", "#1a9641") chorddiag(titanic.mat, type = "bipartite", groupColors = groupColors, tickInterval = 50)

library(migest) demo(cfplot_nat, package = "migest", ask = FALSE)

library("migest") demo(cfplot_reg2, package = "migest", ask = FALSE)

library(dplyr) > library(ggplot2) > > # Read data from the web > url = "https://gist.githubusercontent.com/stephenturner/806e31fce55a8b7175af/raw/1a507c4c3f9f1baaa3a69187223ff3d3050628d4/results.txt" > > results = read.table(url, header=TRUE) > results = mutate(results, sig=ifelse(results$padj<0.05, "FDR<0.05", "Not Sig")) > > p = ggplot(results, aes(log2FoldChange, -log10(pvalue))) + + geom_point(aes(col=sig)) + ggtitle("by Volkan OBAN using R") + + scale_color_manual(values=c("darkblue", "purple")) > p > p+geom_text(data=filter(results, padj<0.05), aes(label=Gene)) > library(ggrepel) > > p+geom_text_repel(data=filter(results, padj<0.05), aes(label=Gene)) > library(ggthemes) > library(ggrepel) > > p+geom_text_repel(data=filter(results, padj<0.05), aes(label=Gene)) + theme_wsj() + scale_colour_wsj("colors6", "") or > p+geom_text_repel(data=filter(results, padj<0.05), aes(label=Gene)) + theme_solarized(light = FALSE) + + scale_colour_solarized("red")

library(dplyr) library(ggplot2) # Read data from the web url = "https://gist.githubusercontent.com/stephenturner/806e31fce55a8b7175af/raw/1a507c4c3f9f1baaa3a69187223ff3d3050628d4/results.txt" results = read.table(url, header=TRUE) results = mutate(results, sig=ifelse(results$padj<0.05, "FDR<0.05", "Not Sig")) p = ggplot(results, aes(log2FoldChange, -log10(pvalue))) + geom_point(aes(col=sig)) + scale_color_manual(values=c("red", "black")) p p+geom_text(data=filter(results, padj<0.05), aes(label=Gene))

code: library(RColorBrewer) # Create Data days=rep(c("monday","tuesday","wenesday","thursday","friday","saturday","sunday") , each=120) time=rep (rep( paste(seq(0,22,2),seq(2,24,2),sep="-") , each=10 ) , 7) value=rep ( rep(seq(0,22,2) , each=10 ) , 7)+rnorm(mean=10, sd=10 , length(time)) data=data.frame(days, time, value) # Create a color palette my_colors = brewer.pal(9, "Blues") my_colors = colorRampPalette(my_colors)(12) # Make the boxplot boxplot(data$value ~ data$time+data$days , xaxt="n" , xlab="" , col=my_colors , pch=20 , cex=0.3 , ylab="value" ) abline(v= seq(0, 12*7, 12) +0.5 , col="grey") axis(1, labels=unique(days) , at=seq(6,12*7,12) ) # Add general trend a=aggregate(data$value , by=list(data$time, data$days) , mean) lines(a[,3], type="l" , col="red" , lwd=2)

ref. and codes: http://timelyportfolio.blogspot.com.tr/2013/06/r-plotting-financial-time-series.html

> library(dygraphs) > dygraph(ldeaths) %>% + dyRangeSelector() %>% + dyUnzoom() > library(xts) > data(sample_matrix) > library(dygraphs) > dygraph(sample_matrix) %>% + dyCandlestick() > library(xts) > data(sample_matrix) > library(dygraphs) > dygraph(sample_matrix, main = "by Volkan OBAN using R - dygraphs- Candlestick") %>% dyCandlestick()

library(quantmod) library(dygraphs) tickers <- c("AAPL", "MSFT") getSymbols(tickers) closePrices <- do.call(merge, lapply(tickers, function(x) Cl(get(x)))) dateWindow <- c("2008-01-01", "2009-01-01") dygraph(closePrices, main = "Value", group = "stock") %>% dyRebase(value = 100) %>% dyRangeSelector(dateWindow = dateWindow) dygraph(closePrices, main = "Percent", group = "stock") %>% dyRebase(percent = TRUE) %>% dyRangeSelector(dateWindow = dateWindow) dygraph(closePrices, main = "None", group = "stock") %>% dyRangeSelector(dateWindow = dateWindow)

> data(USJudgeRatings) > symnum( cU <- cor(USJudgeRatings) ) hM <- format(round(cU, 2)) > hM heatmap.2(cU, Rowv=FALSE,main=" Volkan OBAN using R - gplots heatmap.2", symm=TRUE, col=rev(heat.colors(16)), + distfun=function(c) as.dist(1 - c), trace="none", + cellnote=hM)

f1=jitter(sample(c(2,3),1));f2=jitter(sample(c(2,3),1));f3=jitter(sample(c(2,3),1));f4=jitter(sample(c(2,3),1)) d1=runif(1,0,1e-02);d2=runif(1,0,1e-02);d3=runif(1,0,1e-02);d4=runif(1,0,1e-02) p1=runif(1,0,pi);p2=runif(1,0,pi);p3=runif(1,0,pi);p4=runif(1,0,pi) xt = function(t) exp(-d1*t)*sin(t*f1+p1)+exp(-d2*t)*sin(t*f2+p2) yt = function(t) exp(-d3*t)*sin(t*f3+p3)+exp(-d4*t)*sin(t*f4+p4) t=seq(1, 100, by=.001) dat=data.frame(t=t, x=xt(t), y=yt(t)) with(dat, plot(x,y, type="l", xlim =c(-2,2), ylim =c(-2,2), xlab = "", ylab = "", xaxt='n', yaxt='n'))

> library(mvtnorm) > k <- 100 # Number of samples for each correlation > N <- 20 # Size of the samples > r <- seq(-1, 1, by=.2) # The true correlations > n <- length(r) > rr <- matrix(NA, nr=n, nc=k) > for (i in 1:n) { + for (j in 1:k) { + x <- rmvnorm(N, sigma=matrix(c(1, r[i], r[i], 1), nr=2, nc=2)) + rr[i,j] <- cor( x[,1], x[,2] ) + } + } > estimated.correlation <- as.vector(rr) > true.correlation <- r[row(rr)] > boxplot(estimated.correlation ~ true.correlation, + col = "purple", + xlab = "True correlation", main="y Volkan OBAN using R", + ylab = "Estimated correlation" ) > library(mvtnorm) > k <- 100 # Number of samples for each correlation > N <- 20 # Size of the samples > r <- seq(-1, 1, by=.2) # The true correlations > n <- length(r) > rr <- matrix(NA, nr=n, nc=k) > for (i in 1:n) { + for (j in 1:k) { + x <- rmvnorm(N, sigma=matrix(c(1, r[i], r[i], 1), nr=2, nc=2)) + rr[i,j] <- cor( x[,1], x[,2] ) + } + } > estimated.correlation <- as.vector(rr) > true.correlation <- r[row(rr)] > boxplot(estimated.correlation ~ true.correlation, + col = "lightpink3", + xlab = "True correlation", main="by Volkan OBAN using R", + ylab = "Estimated correlation" )

ggplot(diamonds, aes(x = cut, y = price, fill = clarity)) + + geom_boxplot() + + facet_wrap(~ clarity, scale = "free")

> library(ggplot2) > > # create fake dataset with additional attributes - sex, sample, and temperature > x <- data.frame(values = c(runif(100, min = 0), runif(100), runif(100, max = 3), runif(100)), letter = rep(c('o', 'v'), each = 100), sample = rep(c('VVV', 'OOO'), each = 200), s = sample(c('1984', '1990', '2000', '2019'), 400, replace = TRUE) ) > > > ggplot(x, aes(x = sample, y = values, fill = letter)) + + geom_boxplot() + + facet_wrap(~ s)

> p<- ggplot(diamonds, aes(x=cut, y=price, fill=cut)) > p + geom_boxplot() + facet_wrap(~clarity, scales="free")

require (ggplot2) > require (plyr) > library(reshape2) > > set.seed(1234) > x<- rnorm(100) > y.1<-rnorm(80) > y.2<-rnorm(60) > y.3<-rnorm(75) > y.4<-rnorm(105) > y.5<-rnorm(80) > y.6<-rnorm(90) > df<- (as.data.frame(cbind(x,y.1,y.2,y.3,y.4,y.5,y.6))) ggplot(dfmelt, aes(value, x, group = round_any(x, 0.5), fill=variable))+ + geom_boxplot() + + geom_jitter() + + facet_wrap(~variable)

N <- 100 i <- sample(3, N, replace=TRUE) x <- matrix(rnorm(N*3),ncol=3) lab <- c("small", "bigger", "biggest") scatterplot3js(x, color=rainbow(N), labels=lab[i], size=i, renderer="canvas")

> data(flights) > # Approximate locations as factors > dest <- factor(sprintf("%.2f:%.2f",flights[,3], flights[,4])) > # A table of destination frequencies > freq <- sort(table(dest), decreasing=TRUE) > # The most frequent destinations in these data, possibly hub airports? > frequent_destinations <- names(freq)[1:10] > # Subset the flight data by destination frequency > idx <- dest %in% frequent_destinations > frequent_flights <- flights[idx, ] > # Lat/long and counts of frequent flights > ll <- unique(frequent_flights[,3:4]) > # Plot frequent destinations as bars, and the flights to and from > # them as arcs. Adjust arc width and color by frequency. > globejs(lat=ll[,1], long=ll[,2], arcs=frequent_flights, + arcsHeight=0.3, arcsLwd=2, arcsColor="#FFFFFF", arcsOpacity=0.15, + atmosphere=TRUE, color="#000080", pointsize=0.5) >

> library(maps) > data(world.cities) > caps <- subset(world.cities, capital == 1) > caps$population <- prettyNum(caps$pop, big.mark = ",") > figure(width = 800, height = 450,title="by Volkan OBAN using R - rbokeh -- data(world.cities)", padding_factor = 0) %>% + ly_map("world", col = "darkblue") %>% + ly_points(long, lat, data = caps, size = 5, + hover = c(name, country.etc, population))

library(wordcloud) > > #Create a list of words (Random words concerning my work) > a=c("VOLKAN OBAN","Mathematics","Data Science","Machine Learning","scikit-learn","solution","MLib","Apache Spark","Analysis","Big Data","Science","Statistics","Data", "Programming","ggplot2","matplotlib-seaborn","Volkan","VOLKAN","Istanbul","kNN","R", "R","Data-Viz","Python","kmeans","Programming","Graph Theory ","Operations Research", "Predictive Analytics","Clustering","Data Science","Prescriptive Analytics","Analytics","Classification") > > #I give a frequency to each word of this list > b=sample(seq(0,1,0.01) , length(a) , replace=TRUE) > par(bg="deeppink4") > wordcloud(a , b , col=terrain.colors(length(a) , alpha=0.9) , rot.per=0.3 )

x = seq(-50, 50, by = 1) y = -(x^2) # set graphic parameters op = par(bg = 'black', mar = rep(0.5, 4)) # Plot plot(y, x, type = 'n') lines(y, x, lwd = 2*runif(1), col = hsv(0.08, 1, 1, alpha = runif(1, 0.5, 0.9))) for (i in seq(10, 2500, 10)) { lines(y-i, x, lwd = 2*runif(1), col = hsv(0.08, 1, 1, alpha = runif(1, 0.5, 0.9))) } for (i in seq(500, 600, 10)) { lines(y - i, x, lwd = 2*runif(1), col = hsv(0, 1, 1, alpha = runif(1, 0.5, 0.9))) } for (i in seq(2000, 2300, 10)) { lines(y - i, x, lwd = 2*runif(1), col = hsv(0, 1, 1, alpha = runif(1, 0 .5, 0.9))) } for (i in seq(100, 150, 10)) { lines(y - i, x, lwd = 2*runif(1), col = hsv(0, 1, 1, alpha = runif(1, 0.5, 0.9))) } # signature legend("bottomright", legend="© Gaston Sanchez", bty = "n", text.col="gray70")

library(RColorBrewer) > > # Classic palette BuPu, with 4 colors > coul = brewer.pal(4, "BuPu") > > # I can add more tones to this palette : > coul = colorRampPalette(coul)(25) > > # Plot it > pie(rep(1, length(coul)), col = coul , main=" R - piechart - RColorBrewer ")

require(plot3D) Zorunlu paket yükleniyor: plot3D > lon <- seq(165.5, 188.5, length.out = 30) > lat <- seq(-38.5, -10, length.out = 30) > xy <- table(cut(quakes$long, lon), + cut(quakes$lat, lat)) > xmid <- 0.5*(lon[-1] + lon[-length(lon)]) > ymid <- 0.5*(lat[-1] + lat[-length(lat)]) > > par (mar = par("mar") + c(0, 0, 0, 2)) > hist3D(x = xmid, y = ymid, z = xy, + zlim = c(-20, 40), main = " Earth quakes", + ylab = "latitude", xlab = "longitude", + zlab = "counts", bty= "g", phi = 5, theta = 25, + shade = 0.2, col = "white", border = "black", + d = 1, ticktype = "detailed") > > with (quakes, scatter3D(x = long, y = lat, + z = rep(-20, length.out = length(long)), + colvar = quakes$depth, col = gg.col(100), + add = TRUE, pch = 18, clab = c("depth", "m"), + colkey = list(length = 0.5, width = 0.5, + dist = 0.05, cex.axis = 0.8, cex.clab = 0.8) + ))

ggplot(train, aes(Outlet_Identifier, Item_Outlet_Sales)) + geom_boxplot(fill = "mediumpurple4")+ + scale_y_continuous("Item Outlet Sales", breaks= seq(0,15000, by=500))+ + labs(title = " R - ggplot2", x = "Outlet Identifier") data:https://docs.google.com/spreadsheets/d/1PR5StHxg2jlMCb4IUilGSEwhylXn-3q3EJucSaVolCU/edit#gid=0

train<-read.csv(mart.csv) Error in read.table(file = file, header = header, sep = sep, quote = quote, : object 'mart.csv' not found > train <- read.csv(file="mart.csv", header=TRUE, sep=",") > ggplot(train, aes(Item_Visibility, Item_MRP)) + geom_point(aes(color = Item_Type)) + + scale_x_continuous("Item Visibility", breaks = seq(0,0.35,0.05))+ + scale_y_continuous("Item MRP", breaks = seq(0,270,by = 30))+ + theme_bw() data:https://docs.google.com/spreadsheets/d/1PR5StHxg2jlMCb4IUilGSEwhylXn-3q3EJucSaVolCU/edit#gid=0

ref: https://www.r-bloggers.com/improved-net-stacked-distribution-graphs-via-ggplot2-trickery/

library("ggplot2") > data <- read.csv("ggplot-data.csv", header=TRUE, nrows=200) > gg <- ggplot(data, aes(x=Keyword)) > gg <- gg + geom_bar(aes(weight=Traffic, fill=Country) + coord_flip() + ) > gg > data$kw <- reorder(data$Keyword, data$Traffic) > gg <- ggplot(data, aes(x=kw)) > > gg <- gg + geom_bar(aes(weight=Traffic, fill=Country)) + coord_flip() > > gg > gg <- ggplot(data, aes(x=kw)) > > gg <- gg + geom_bar(aes(weight=Traffic, fill=Country)) + coord_flip() > > gg

> c <- ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar() > c + facet_wrap(~cut, scales = "free_y") + coord_flip(

library(ggplot2) > df <- structure(c(106487, 495681, 1597442, + 2452577, 2065141, 2271925, 4735484, 3555352, + 8056040, 4321887, 2463194, 347566, 621147, + 1325727, 1123492, 800368, 761550, 1359737, + 1073726, 36, 53, 141, 41538, 64759, 124160, + 69942, 74862, 323543, 247236, 112059, 16595, + 37028, 153249, 427642, 1588178, 2738157, + 2795672, 2265696, 11951, 33424, 62469, + 74720, 166607, 404044, 426967, 38972, 361888, + 1143671, 1516716, 160037, 354804, 996944, + 1716374, 1982735, 3615225, 4486806, 3037122, + 17, 54, 55, 210, 312, 358, 857, 350, 7368, + 8443, 6286, 1750, 7367, 14092, 28954, 80779, + 176893, 354939, 446792, 33333, 69911, 53144, + 29169, 18005, 11704, 13363, 18028, 46547, + 14574, 8954, 2483, 14693, 25467, 25215, + 41254, 46237, 98263, 185986), .Dim = c(19, + 5), .Dimnames = list(c("1820-30", "1831-40", + "1841-50", "1851-60", "1861-70", "1871-80", + "1881-90", "1891-00", "1901-10", "1911-20", + "1921-30", "1931-40", "1941-50", "1951-60", + "1961-70", "1971-80", "1981-90", "1991-00", + "2001-06"), c("Europe", "Asia", "Americas", + "Africa", "Oceania"))) > library(reshape) Attaching package: ‘reshape’ The following objects are masked from ‘package:plyr’: rename, round_any The following object is masked from ‘package:Matrix’: expand > df.m <- melt(df) > df.m <- rename(df.m, c(X1 = "Period", X2 = "Region")) a <- ggplot(df.m, aes(x = Period, y = value/1e+06, + fill = Region)) + options(title = "Migration to the United States by Source Region (1820-2006)") + + labs(x = NULL, y = "Number of People (in millions)n", + fill = NULL) > b <- a + geom_bar(stat = "identity", position = "stack") > b c <- b+ facet_grid(Region ~ .) + options(legend.position = "none") > c > total <- cast(df.m, Period ~ ., sum) > total <- rename(total, c(`(all)` = "value")) > total$Region <- "Total" > df.m.t <- rbind(total, df.m) > c1 <- c %+% df.m > total <- cast(df.m, Period ~ ., sum) > total <- rename(total, c(`(all)` = "value")) > total$Region <- "Total" > df.m.t <- rbind(total, df.m) > c1 <- c %+% df.m > c1 > c2 <- c1 + facet_grid(Region ~ ., scale = "free_y") > c2

ibrary(ggplot2) > library(ggthemes) > library(extrafont) Registering fonts with R > library(plyr) Attaching package: ‘plyr’ The following object is masked from ‘package:network’: is.discrete > library(scales) Attaching package: ‘scales’ The following object is masked _by_ ‘.GlobalEnv’: cscale > charts.data <- read.csv("data.csv") > p <- ggplot() + geom_bar(aes(y = percentage, x = year, fill = product), data = charts.data,stat="identity") p <- p + geom_text(data=charts.data, aes(x = year, y = percentage, + label = paste0(percentage,"%")), size=4) p

Year <- c(rep(c("1984-01", "1987-05", "1990-06", "2005-01"), each = 4)) Category <- c(rep(c("V", "O", "R", "D"), times = 4)) Frequency <- c(174, 248, 201, 326, 215, 428, 309, 365, 419, 652, 231, 695, 144, 452, 281, 210) Data <- data.frame(Year, Category, Frequency) ggplot(Data, aes(x = Year, y = Frequency, fill = Category, label = Frequency)) + geom_bar(stat = "identity") + geom_text(size = 3, position = position_stack(vjust = 0.5))

> ggplot(diamonds, aes(clarity)) + + geom_bar(aes(fill = cut)) + + facet_grid(cut ~ .)

ref and code :https://www.r-bloggers.com/gauge-chart-in-r/

gg.gauge <- function(pos,breaks=c(0,42,58,100)) { + require(ggplot2) + get.poly <- function(a,b,r1=0.5,r2=1.0) { + th.start <- pi*(1-a/100) + th.end <- pi*(1-b/100) + th <- seq(th.start,th.end,length=100) + x <- c(r1*cos(th),rev(r2*cos(th))) + y <- c(r1*sin(th),rev(r2*sin(th))) + return(data.frame(x,y)) + } + ggplot()+ ggtitle("by Volkan OBAN using R \n Gauge") + + geom_polygon(data=get.poly(breaks[1],breaks[2]),aes(x,y),fill="green")+ + geom_polygon(data=get.poly(breaks[2],breaks[3]),aes(x,y),fill="pink")+ + geom_polygon(data=get.poly(breaks[3],breaks[4]),aes(x,y),fill="purple")+ + geom_polygon(data=get.poly(pos-1,pos+1,0.2),aes(x,y))+ + geom_text(data=as.data.frame(breaks), size=5, fontface="bold", vjust=0, + aes(x=1.1*cos(pi*(1-breaks/100)),y=1.1*sin(pi*(1-breaks/100)),label=paste0(breaks,"%")))+ + annotate("text",x=0,y=0,label=pos,vjust=0,size=8,fontface="bold")+ + coord_fixed()+ + theme_bw()+ + theme(axis.text=element_blank(), + axis.title=element_blank(), + axis.ticks=element_blank(), + panel.grid=element_blank(), + panel.border=element_blank()) + } > gg.gauge(52,breaks=c(0,42,58,100) + + ) > library(gridExtra) > grid.newpage() > grid.draw(arrangeGrob(gg.gauge(22),gg.gauge(36), + gg.gauge(71),gg.gauge(95),ncol=2))

gg.gauge <- function(pos,breaks=c(0,42,58,100)) { + require(ggplot2) + get.poly <- function(a,b,r1=0.5,r2=1.0) { + th.start <- pi*(1-a/100) + th.end <- pi*(1-b/100) + th <- seq(th.start,th.end,length=100) + x <- c(r1*cos(th),rev(r2*cos(th))) + y <- c(r1*sin(th),rev(r2*sin(th))) + return(data.frame(x,y)) + } + ggplot()+ ggtitle("by Volkan OBAN using R \n Gauge") + + geom_polygon(data=get.poly(breaks[1],breaks[2]),aes(x,y),fill="green")+ + geom_polygon(data=get.poly(breaks[2],breaks[3]),aes(x,y),fill="pink")+ + geom_polygon(data=get.poly(breaks[3],breaks[4]),aes(x,y),fill="purple")+ + geom_polygon(data=get.poly(pos-1,pos+1,0.2),aes(x,y))+ + geom_text(data=as.data.frame(breaks), size=5, fontface="bold", vjust=0, + aes(x=1.1*cos(pi*(1-breaks/100)),y=1.1*sin(pi*(1-breaks/100)),label=paste0(breaks,"%")))+ + annotate("text",x=0,y=0,label=pos,vjust=0,size=8,fontface="bold")+ + coord_fixed()+ + theme_bw()+ + theme(axis.text=element_blank(), + axis.title=element_blank(), + axis.ticks=element_blank(), + panel.grid=element_blank(), + panel.border=element_blank()) + } > gg.gauge(52,breaks=c(0,42,58,100) + + )

> spec <- " + digraph { 'VOLKAN OBAN \n Data Scientist ' } + [1]: LETTERS[1] + " > > > grViz(replace_in_spec(spec))

> spec <- " + digraph { '@1' } + [1]: LETTERS[1] + " > grViz(replace_in_spec(spec)) > spec <- " + digraph a_nice_graph { + node [fontname = Arial] + a [label = 'by VOLKAN OBAN using R '] + b [label = 'Mathematics'] + c [label = 'Data Science'] + d [label = 'Analytics'] + e [label = 'Programming'] + f [label = 'Machine Learning'] + g [label = 'Python'] + h [label = 'Statistics'] + i [label = 'R'] + j [label = 'Istanbul'] + a -> { b c d e f g h i j} + } + [1]: 'top' + [2]: 10:20 + " > grViz(replace_in_spec(spec)) >

> ggthemr('lilac') >ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + + geom_boxplot() + + coord_flip() +

sea ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + + geom_boxplot() + + coord_flip()

.................... ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")

> ggthemr('lilac') > ggplot(data = diamonds) + + geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge") + ggtitle("by Volkan OBAN using R - ggplot2 and ggthemr packages /data(diamonds)")

> ggthemr('sea') > ggplot(data = diamonds) + + geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge") + ggtitle("by Volkan OBAN using R - ggplot2 and ggthemr packages /data(diamonds)")

ref: http://handsondatascience.com/ClustersO.pdf

> ds<-map_data("world") > p <- ggplot(ds, aes( x=long, y=lat, group=group)) > p <-p + geom_polygon() + ggtitle("by Volkan OBAN using R - ggmap") > p > > p <- ggplot(ds, aes(x=long, y=lat, group=group, fill=region)) > p <- p + geom_polygon() > p <- p + geom_polygon() > p <- p + theme(legend.position = "none") > p

> library(rattle) # Load weather dataset. Normalise names normVarNames(). Rattle: A free graphical interface for data mining with R. Version 4.1.0 Copyright (c) 2006-2015 Togaware Pty Ltd. Type 'rattle()' to shake, rattle, and roll your data. > library(randomForest) # Impute missing using na.roughfix(). randomForest 4.6-12 Type rfNews() to see new features/changes/bug fixes. > # Identify the dataset. > dsname <- "weather" > ds <- get(dsname) > names(ds) <- normVarNames(names(ds)) > vars <- names(ds) > target <- "rain_tomorrow" > risk <- "risk_mm" > id <- c("date", "location") > # Ignore the IDs and the risk variable. > ignore <- union(id, if (exists("risk")) risk) > # Ignore variables which are completely missing. > mvc <- sapply(ds[vars], function(x) sum(is.na(x))) # Missing value count. > mvn <- names(ds)[(which(mvc == nrow(ds)))] # Missing var names. > ignore <- union(ignore, mvn) > # Initialise the variables > vars <- setdiff(vars, ignore) > # Variable roles. > inputc <- setdiff(vars, target) > inputi <- sapply(inputc, function(x) which(x == names(ds)), USE.NAMES=FALSE) > numi <- intersect(inputi, which(sapply(ds, is.numeric))) > numc <- names(ds)[numi] > cati <- intersect(inputi, which(sapply(ds, is.factor))) > catc <- names(ds)[cati] > # Impute missing values, but do this wisely - understand why missing. > if (sum(is.na(ds[vars]))) ds[vars] <- na.roughfix(ds[vars]) > # Number of observations. > nobs <- nrow(ds) > model <- m.km <- kmeans(ds, 10) > model <- m.kms <- kmeans(scale(ds[numi]), 10) > model$size [1] 34 54 15 70 24 32 30 44 43 20 > library(ggplot2) > library(reshape) Attaching package: ‘reshape’ The following object is masked from ‘package:Matrix’: expand > nclust <- 4 > model <- m.kms <- kmeans(scale(ds[numi]), nclust) > dscm <- melt(model$centers) > names(dscm) <- c("Cluster", "Variable", "Value") > dscm$Cluster <- factor(dscm$Cluster) > dscm$Order <- as.vector(sapply(1:length(numi), rep, nclust)) > p <- ggplot(dscm, + aes(x=reorder(Variable, Order), + y=Value, group=Cluster, colour=Cluster)) > p <- p + coord_polar() > p <- p + geom_point() > p <- p + geom_path() > p <- p + labs(x=NULL, y=NULL) > p <- p + theme(axis.ticks.y=element_blank(), axis.text.y = element_blank()) > p >

> set.seed(32297) d <- data.frame(x=runif(100),y=runif(100)) > clus <- kmeans(d,centers=5) > d$cluster <- clus$cluster > library('ggplot2') > library('grDevices') > h <- do.call(rbind, + lapply(unique(clus$cluster), + function(c) { f <- subset(d,cluster==c); f[chull(f),]})) > ggplot() + + geom_text(data=d,aes(label=cluster,x=x,y=y, + color=cluster),size=3) + + geom_polygon(data=h,aes(x=x,y=y,group=cluster,fill=as.factor(cluster)), + alpha=0.4,linetype=0) + + theme(legend.position = "none")

> library(wordcloud) > > #Create a list of words (Random words concerning my work) > a=c("Volkan OBAN","Clustering","Turkey","Istanbul","Classification","Istanbul Technical University","Mathematics", + "Data Science","Analysis","Machine Learning","Science","Statistics","Data", + "Programming","Clustering","Recommedation","Visualization","Spark","Business","VOLKAN","R", "R", + "Data-Viz","Python","Linux","Programming","Graphs","Numbers", "Big Data", + "Computing","Data-Science","Analytics","GitHub","OBAN") > > #I give a frequency to each word of this list > b=sample(seq(0,1,0.01) , length(a) , replace=TRUE) > > #The package will automatically make the wordcloud ! (I add a black background) > par(bg="hotpink4") > wordcloud(a , b , col=terrain.colors(length(a) , alpha=0.9) , rot.per=0.3 ) >

> moxbuller = function(n) { + u = runif(n) + v = runif(n) + x = cos(2*pi*u)*sqrt(-2*log(v)) + y = sin(2*pi*v)*sqrt(-2*log(u)) + r = list(x=x, y=y) + return(r) + } > r = moxbuller(50000) > par(bg="aliceblue") > par(mar=c(0,0,0,0)) > plot(r$x,r$y, pch=".", col="hotpink4",main=" \n by Volkan OBAN using R", cex=1.2)

library(magrittr) > > add_line_points2 <- function(plot, df, ...) { + plot + + geom_line(aes(x = Time, y = weight, group = Chick), ..., data = df) + + geom_point(aes(x = Time, y = weight), ..., data = df) + } > > (plot4 <- ggplot() %>% add_line_points2(diet1) + %>% add_line_points2(diet2, colour = "red")

> library(ggplot2) > > data(ChickWeight) > diet1 <- subset(ChickWeight, Diet == 1) > diet2 <- subset(ChickWeight, Diet == 2) > add_line <- function(df) { + geom_line(aes(x = Time, y = weight, group = Chick), data = df) + } > > add_points <- function(df) { + geom_point(aes(x = Time, y = weight), data = df) + } > > add_line_points <- function(df) { + add_line(df) + add_points(df) + } (p <- ggplot(aes(x = Time, y = weight, group = Chick, colour = Diet), + data = ChickWeight) + + geom_line() + geom_point())

library(ggplot2) > > data(ChickWeight) > diet1 <- subset(ChickWeight, Diet == 1) > diet2 <- subset(ChickWeight, Diet == 2) > add_line <- function(df) { + geom_line(aes(x = Time, y = weight, group = Chick), data = df) + } > > add_points <- function(df) { + geom_point(aes(x = Time, y = weight), data = df) + } > > add_line_points <- function(df) { + add_line(df) + add_points(df) p <- ggplot(aes(x = Time, y = weight, group = Chick), data = diet1) + + geom_line() + geom_point()

cloud(Sepal.Length ~ Petal.Length * Petal.Width | Species, data = iris, screen = list(x = -90, y = 70),main="by Volkan OBAN using R", distance = .4, zoom = .6)

library(quantmod) > tckrs <- c("SPY", "QQQ", "GDX", "DBO", "VWO") > getSymbols(tckrs, from = "2007-01-01" SPY.Close <- SPY[,4] > QQQ.Close <- QQQ[,4] > GDX.Close <- GDX[,4] > DBO.Close <- DBO[,4] > VWO.Close <- VWO[,4] > SPY1 <- as.numeric(SPY.Close[1]) > QQQ1 <- as.numeric(QQQ.Close[1]) > GDX1 <- as.numeric(GDX.Close[1]) > DBO1 <- as.numeric(DBO.Close[1]) > VWO1 <- as.numeric(VWO.Close[1] + ) > SPY <- SPY.Close/SPY1 > QQQ <- QQQ.Close/QQQ1 > GDX <- GDX.Close/GDX1 > DBO <- DBO.Close/DBO1 > VWO <- VWO.Close/VWO1 > basket <- cbind(SPY, QQQ, GDX, DBO, VWO + ) > zoo.basket <- as.zoo(basket + ) > tsRainbow <- rainbow(ncol(zoo.basket)) > # Plot the overlayed series > plot(x = zoo.basket, ylab = "Cumulative Return", main = "by Volkan OBAN using R \n Cumulative Returns", + col = tsRainbow, screens = 1) > # Set a legend in the upper left hand corner to match color to return series > legend(x = "topleft", legend = c("SPY", "QQQ", "GDX", "DBO", "VWO"), lty = 1,col = tsRainbow)

> library(ggcyto) > data(GvHD) > fs <- GvHD[subset(pData(GvHD), Patient %in%5:7 & Visit %in% c(5:6))[["name"]]] > fr <- fs[[1]] > p <- ggcyto(fs, aes(x = `FSC-H`)) > p <- ggcyto(fs, aes(x = `FSC-H`, y = `SSC-H`)) + ggtitle("by Volkan OBAN using R") > p <- p + geom_hex(bins = 128) > p Warning message: Removed 257 rows containing missing values (geom_hex). > p + scale_fill_gradientn(colours = rainbow(7), trans = "sqrt") > library(knitr) > library(RColorBrewer) > p + scale_fill_gradientn(colours = brewer.pal(n=8,name="PiYG"),trans="sqrt")

library(ggcyto) data(GvHD) fs <- GvHD[subset(pData(GvHD), Patient %in%5:7 & Visit %in% c(5:6))[["name"]]] fr <- fs[[1]] p1 <- ggplot(mapping = aes(x = `FSC-H`, y = `SSC-H`)) + myColor_scale_fill + facet_grid(Patient~Visit) p1 + stat_binhex(data = fs, bin = 64)

> p<-ggplot(diamonds, aes(cut, price)) + + geom_boxplot() + + coord_flip() + theme_solarized() + + scale_colour_solarized("purple") + ggtitle("by Volkan OBAN using R \n data(diamonds) ") + theme(plot.title = element_text(size = 12, face = "bold") + ) > p

> data(iris) > m2 <- svm(Species~., data = iris) > plot(m2, iris, Petal.Width ~ Petal.Length, + slice = list(Sepal.Width = 3, Sepal.Length = 4))

u1 <- cos(y) * cos(x) v1 <- cos(y) * sin(x) u2 <- sin(y) * sin(x) v2 <- sin(y) * cos(x) field <- stack(u, u1, u2, v, v1, v2) names(field) <- c('u', 'u1', 'u2', 'v', 'v1', 'v2') vectorplot(field, isField='dXY', narrows=300, lwd.arrows=.4, par.settings=BTCTheme(), layout=c(3, 1)) ## uLayer and vLayer define which layers contain ## horizontal and vertical components, respectively vectorplot(field, isField='dXY', narrows=300, uLayer=1:3, vLayer=6:4)

u1 <- cos(y) * cos(x) v1 <- cos(y) * sin(x) u2 <- sin(y) * sin(x) v2 <- sin(y) * cos(x) field <- stack(u, u1, u2, v, v1, v2) names(field) <- c('u', 'u1', 'u2', 'v', 'v1', 'v2') vectorplot(field, isField='dXY', narrows=300, lwd.arrows=.4, par.settings=BTCTheme(), layout=c(3, 1)) ## uLayer and vLayer define which layers contain ## horizontal and vertical components, respectively vectorplot(field, isField='dXY', narrows=300, uLayer=1:3, vLayer=6:4)

library(SWMPr) library(oce) # clean input data, one hour time step, subset, fill gaps dat <- qaqc(apadbwq) %>% setstep(timestep = 60) %>% subset(., subset = c('2013-01-01 0:0', '2013-12-31 0:0'), select = 'depth') %>% na.approx(maxgap = 1e6) # get model datsl <- as.sealevel(elevation = dat$depth, time = dat$datetimestamp) mod <- tidem(t = datsl) # add predictions to observed data dat$Estimated <- predict(mod) # plot ggplot(dat, aes(x = datetimestamp, y = Estimated)) + geom_line() + theme_bw()

constituents <- c('M2', 'S2', 'N2', 'K2', 'K1', 'O1', 'P1') # loop through tidal components, predict each with tidem preds <- sapply(constituents, function(x){ mod <- tidem(t = datsl, constituent = x) pred <- predict(mod) pred - mean(pred) }) # combine prediction, sum, add time data predall <- rowSums(preds) + mean(datsl[['elevation']]) preds <- data.frame(time = datsl[['time']], preds, Estimated = predall) head(preds) mod <- tidem(t = datsl) Note: the record is too short to fit for constituents: SA PI1 S1 PSI1 GAM2 H1 H2 T2 R2 > > # get components of interest > amps <- data.frame(mod@data[c('name', 'amplitude')]) %>% + filter(name %in% constituents) %>% + arrange(amplitude) > amps name amplitude 1 K2 0.01091190 2 N2 0.01342395 3 S2 0.02904518 4 P1 0.04100388 5 O1 0.11142455 6 M2 0.12005114 7 K1 0.12865764 > dat$Estimated <- predict(mod) > > # plot one month > ggplot(dat, aes(x = datetimestamp, y = depth)) + + geom_point() + + geom_line(aes(y = Estimated), colour = 'blue') + + scale_x_datetime(limits = as.POSIXct(c('2013-07-01', '2013-07-31'))) + + scale_y_continuous(limits = c(0.9, 2)) + + theme_bw()

library(SWMPr) Warning message: package ‘SWMPr’ was built under R version 3.3.3 > library(oce) > > # clean, one hour time step, subset, fill gaps > dat <- qaqc(apadbwq) %>% + setstep(timestep = 60) %>% + subset(subset = c('2013-01-01 0:0', '2013-12-31 0:0'), select = 'depth') %>% + na.approx(maxgap = 1e6) > datsl <- as.sealevel(elevation = dat$depth, time = dat$datetimestamp) > plot(datsl,main="by Volkan OBAN using R")

> library("graphics") > # Mosaic plot of observed values > mosaicplot(housetasks, las=2, col="steelblue", + main = " \n housetasks - observed counts")

> require(maps) > Tur = map_data('world', region = 'Turkey') > ggplot(Tur, aes(x = long, y = lat, group = group)) + + geom_polygon(fill = 'red', colour = 'black') +ggtitle("TURKEY- TÜRKİYE CENNETİM"

> c <- ggplot(diamonds, aes(carat, price)) > c + geom_bin2d() > require(hexbin) > c + geom_hex() > c + geom_hex(bins = 10)

> wdata = data.frame( + s = factor(rep(c("F", "M"), each=200)), + weight = c(rnorm(200, 55), rnorm(200, 58))) a <- ggplot(wdata, aes(x = weight)) > a + geom_dotplot()

> set.seed(1234) > wdata = data.frame( + s = factor(rep(c("F", "M"), each=200)), + weight = c(rnorm(200, 55), rnorm(200, 58))) > head(wdata) s weight 1 F 53.79293 2 F 55.27743 3 F 56.08444 4 F 52.65430 5 F 55.42912 6 F 55.50606 > qplot(s, weight, data = wdata, geom = "dotplot", + stackdir = "center", binaxis = "y", dotsize = 0.5)

> df <- data.frame( + group = c("X", "Y", "Z"), + value = c(37, 43, 20) + ) > head(df) group value 1 X 37 2 Y 43 3 Z 20 > library(ggplot2) > bp<- ggplot(df, aes(x="", y=value, fill=group))+ + geom_bar(width = 1, stat = "identity") > bp > bp<- ggplot(df, aes(x="", y=value, fill=group))+ + geom_bar(width = 1, stat = "identity") > pie <- bp + coord_polar("y", start=0) > pie > pie + scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9")) > ggplot(PlantGrowth, aes(x=factor(1), fill=group))+ + geom_bar(width = 1)+ + coord_polar("y") > ggplot(PlantGrowth, aes(x=factor(1), fill=group))+ + geom_bar(width = 1)+ + coord_polar("y") > blank_theme <- theme_minimal()+ + theme( + axis.title.x = element_blank(), + axis.title.y = element_blank(), + panel.border = element_blank(), + panel.grid=element_blank(), + axis.ticks = element_blank(), + plot.title=element_text(size=14, face="bold") + ) > library(scales) > pie + scale_fill_grey() + blank_theme + + theme(axis.text.x=element_blank()) + + geom_text(aes(y = value/3 + c(0, cumsum(value)[-length(value)]), + label = percent(value/100)), size=5 + ) > pie + scale_fill_brewer("Blues") + blank_theme + + theme(axis.text.x=element_blank())+ + geom_text(aes(y = value/3 + c(0, cumsum(value)[-length(value)]), + label = percent(value/100)), size=5) >

correlation matrix > mydata <- mtcars[, c(1,3,4,5,6,7)] > cormat <- round(cor(mydata),2) > library(reshape2) > melted_cormat <- melt(cormat) > head(melted_cormat) library(ggplot2) > ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+ geom_tile(color = "white")+ scale_fill_gradient2(low = "purple", high = "red", mid = "white", midpoint = 0, limit = c(-1,1), space = "Lab", name="Pearson\n Correlation") + theme_minimal()+ theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 12, hjust = 1))+ coord_fixed()

> ohio <- midwest %>% + filter(state == "OH") %>% + select(county, percollege) %>% + arrange(percollege) %>% + mutate(Avg = mean(percollege, na.rm = TRUE), + Above = ifelse(percollege - Avg > 0, TRUE, FALSE), + county = factor(county, levels = .$county) ggplot(ohio, aes(percollege, county, color = Above)) + + geom_segment(aes(x = Avg, y = county, xend = percollege, yend = county), color = "grey50") + ggtitle("preprared by Volkan OBAN using R - ggplot2 - data(midwest) ") + + geom_point()

> library(rworldmap) > newmap <- getMap(resolution = "high") > plot(newmap,main=" R - rworldmap", + xlim = c(-20, 59), + ylim = c(35, 71), + asp = 1)

> data <- t(iris[,1:4]) > varAnnot <- as.matrix(iris[,5]) > colnames(varAnnot) <- "Species" > canvasXpress(t(data),varAnnot=varAnnot, graphType='Scatter3D', colorBy='Species')

> data <- t(iris[,1:4]) > smpAnnot <- as.matrix(iris[,5]) > colnames(smpAnnot) <- "Species" > canvasXpress(data,title="by Volkan OBAN using R \n canvasXpress package", smpAnnot=smpAnnot, graphType='Boxplot', groupingFactors=list('Species')) > # or > canvasXpress(data,title="by Volkan OBAN using R \n canvasXpress package",smpAnnot=smpAnnot, graphType='Boxplot', afterRender=list(list('groupSamples', list('Species'))))

> library(ggplot2) > g <- ggplot(mpg, aes(manufacturer)) > g + geom_bar(aes(fill=class), width = 0.5) + + theme(axis.text.x = element_text(angle=65, vjust=0.6)) + + labs(title="by Volkan OBAN using R", + subtitle=" Categorywise Bar Chart \n Manufacturer of vehicles", + caption="Source: Manufacturers from 'mpg' dataset")

qmap(location = "izmir")

gridExtra::grid.arrange( + waffle(c(Volkan=50, Oban=50), rows=5,title="by Volkan OBAN using R - gridExtra and waffle packages", xlab="R-waffle package"), + waffle(c(Oban=25, Volkan=75), rows=5), waffle(c(Oban=7, Volkan=93), rows=5), waffle(c(Oban=42, Volkan=58), rows=5), waffle(c(Oban=2, Volkan=98), rows=5), waffle(c(oban=63, Volkan=37), rows=5),waffle(c(Oban=2, Volkan=98), rows=5), waffle(c(oban=75, Volkan=25), rows=5),waffle(c(Oban=15, Volkan=85), rows=5), waffle(c(oban=63, Volkan=37), rows=5),waffle(c(Oban=0, Volkan=100), rows=5), waffle(c(oban=100, Volkan=0), rows=5) )

> slices <- c(18, 12, 4, 16, 8, 9, 12) > labels <- c("A", "B", "C", "X", "V", "O", "Z") > library(plotrix) > pie3D(slices,labels=labels,explode=0.1, main=" 3D- explodated Pie Chart")

ref: https://www.r-bloggers.com/introduction-to-ggraph-layouts/

> library(gridExtra) > dat <- list( 'Normal'=rnorm(50),'Dense normal'= rnorm(500),'Bimodal'=c(rnorm(100), rnorm(100,5)), 'Trimodal'=c(rnorm(100), rnorm(100,5),rnorm(100,-3)) + ) > labs<-rep(names(dat),sapply(dat,length)) > labs<-factor(labs,levels=unique(labs)) > dat<-unlist(dat) > > > > > > p1<-ggplot(mapping=aes(labs, dat)) +geom_quasirandom(method='smiley',alpha=.2) + ggtitle('Default (n/5)') + labs(x='Volkan OBAN') > p2<-ggplot(mapping=aes(labs, dat)) + geom_quasirandom(method='smiley',nbins=50,alpha=.2) +ggtitle('nbins=50') > p3<-ggplot(mapping=aes(labs, dat)) +geom_quasirandom(method='smiley',nbins=100,alpha=.2) + ggtitle('nbins=100') > p4<-ggplot(mapping=aes(labs, dat)) +geom_quasirandom(method='smiley',nbins=250,alpha=.2) +ggtitle('nbins=250') > grid.arrange(p1, p2, p3, p4, ncol=1) >

ref: https://cran.r-project.org/web/packages/psych/psych.pdf

ref: http://rpubs.com/danmirman/plotting_factor_analysis

pp <- ggtree(tree) %>% phylopic("79ad5f09-cf21-4c89-8e7d-0c82a00ce728", print(pp)

ref : https://bioconductor.org/packages/devel/bioc/manuals/ggtree/man/ggtree.pdf

library(mlrMBO) fun = makeSingleObjectiveFunction( name = "SineMixture", fn = function(x) sin(x[1])*cos(x[2])/2 + 0.04 * sum(x^2), par.set = makeNumericParamSet(id = "x", len = 2, lower = -5, upper = 5) ) ctrl = makeMBOControl() # For this numeric optimization we are going to use the Expected Improvement as infill criterion: ctrl = setMBOControlInfill(ctrl, crit = crit.ei) # We will allow for exactly 25 evaluations of the objective function: ctrl = setMBOControlTermination(ctrl, max.evals = 25L) library(ggplot2) des = generateDesign(n = 8L, par.set = getParamSet(fun), fun = lhs::randomLHS) autoplot(fun, render.levels = TRUE) + geom_point(data = des)

set.seed(1) library(mlrMBO) fun = makeSingleObjectiveFunction( name = "SineMixture", fn = function(x) sin(x[1])*cos(x[2])/2 + 0.04 * sum(x^2), par.set = makeNumericParamSet(id = "x", len = 2, lower = -5, upper = 5) ) library(plot3D) plot3D(fun, contour = TRUE, lightning = TRUE)

rocketData <- data.frame( x = c(1,1,2,2), y = c(1,2,2,3) ) rocketData <- do.call(rbind, lapply(seq_len(500)-1, function(i) { rocketData$y <- rocketData$y - c(0,i/500); rocketData$group <- i+1; rocketData })) rocketData2 <- data.frame( x = c(2, 2.25, 2), y = c(2, 2.5, 3) ) rocketData2 <- do.call(rbind, lapply(seq_len(500)-1, function(i) { rocketData2$x[2] <- rocketData2$x[2] - i*0.25/500; rocketData2$group <- i+1 + 500; rocketData2 })) ggplot() + geom_link(aes(x=2, y=2, xend=3, yend=3, alpha=..index.., size = ..index..), colour='goldenrod', n=500) + geom_bezier(aes(x=x, y=y, group=group, colour=..index..), data=rocketData) + geom_bezier(aes(x=y, y=x, group=group, colour=..index..), data=rocketData) + geom_bezier(aes(x=x, y=y, group=group, colour=1), data=rocketData2) + geom_bezier(aes(x=y, y=x, group=group, colour=1), data=rocketData2) + geom_text(aes(x=1.65, y=1.65, label='vvv', angle=45), colour='white', size=15) + coord_fixed() + scale_x_reverse() + scale_y_reverse() + scale_alpha(range=c(1, 0), guide='none') + scale_size_continuous(range=c(20, 0.1), trans='exp', guide='none') + scale_color_continuous(guide='none') + xlab('') + ylab('') + ggtitle('ggforce: ggplot2') + theme(plot.title = element_text(size = 20))

MASS package data(iris)

M <- cor(mtcars) ord <- corrMatOrder(M, order = "AOE") M2 <- M[ord,ord] corrplot.mixed(M2) corrplot.mixed(M2, lower = "ellipse", upper = "circle") corrplot.mixed(M2, lower = "square", upper = "circle") corrplot.mixed(M2, lower = "shade", upper = "circle") corrplot.mixed(M2, tl.pos = "lt") corrplot.mixed(M2, tl.pos = "lt", diag = "u") corrplot.mixed(M2, tl.pos = "lt", , diag = "l")

data(mtcars) M <- cor(mtcars) ## different color series col1 <- colorRampPalette(c("#7F0000","red","#FF7F00","yellow","white", "cyan", "#007FFF", "blue","#00007F")) col2 <- colorRampPalette(c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FFFFFF", "#D1E5F0", "#92C5DE", "#4393C3", "#2166AC", "#053061")) col3 <- colorRampPalette(c("red", "white", "blue")) col4 <- colorRampPalette(c("#7F0000","red","#FF7F00","yellow","#7FFF7F", "cyan", "#007FFF", "blue","#00007F")) wb <- c("white","black") par(ask = TRUE) ## different color scale and methods to display corr-matrix corrplot(M, method = "number", col = "black", cl.pos = "n") corrplot(M, method = "number") corrplot(M) corrplot(M, order = "AOE") corrplot(M, order = "AOE", addCoef.col = "grey") corrplot(M, order = "AOE", col = col1(20), cl.length = 21, addCoef.col = "grey") corrplot(M, order = "AOE", col = col1(10), addCoef.col = "grey") corrplot(M, order = "AOE", col = col2(200)) corrplot(M, order = "AOE", col = col2(200), addCoef.col = "grey") corrplot(M, order = "AOE", col = col2(20), cl.length = 21, addCoef.col = "grey") corrplot(M, order = "AOE", col = col2(10), addCoef.col = "grey")

> world <- map_data("world") Attaching package: ‘maps’ The following object is masked from ‘package:plyr’: ozone > worldmap <- ggplot(world, aes(long, lat, group = group)) + + geom_path() + + scale_y_continuous(NULL, breaks = (-2:3) * 30, labels = NULL) + + scale_x_continuous(NULL, breaks = (-4:4) * 45, labels = NULL) > > worldmap + coord_map() > # Some crazier projections > worldmap + coord_map("ortho") > worldmap + coord_map("stereographic")

> world <- map_data("world") Attaching package: ‘maps’ The following object is masked from ‘package:plyr’: ozone > worldmap <- ggplot(world, aes(long, lat, group = group)) + + geom_path() + + scale_y_continuous(NULL, breaks = (-2:3) * 30, labels = NULL) + + scale_x_continuous(NULL, breaks = (-4:4) * 45, labels = NULL) > > worldmap + coord_map() > # Some crazier projections > worldmap + coord_map("ortho") > worldmap + coord_map("stereographic")

ref: http://jdobr.es/blog/data-vis-with-ggplot/

ref: http://jdobr.es/blog/data-vis-with-ggplot/

data(sdfsample) (sdfset <- sdfsample) ## Plot single compound structure plotStruc(sdfset[[1]]) ## Plot several compounds structures plot(sdfset[1:4]) ## Highlighting substructures (here all rings) myrings <- as.numeric(gsub(".*_", "", unique(unlist(rings(sdfset[1]))))) plot(sdfset[1], colbonds=myrings) ## Customize plot plot(sdfset[1:4], griddim=c(2,2), print_cid=letters[1:4], print=FALSE, noHbonds=FALSE)

## Import SDFset sample set data(sdfsample) (sdfset <- sdfsample) ## Plot single compound structure plotStruc(sdfset[[1]]) ## Plot several compounds structures plot(sdfset[1:4]) ## Highlighting substructures (here all rings) myrings <- as.numeric(gsub(".*_", "", unique(unlist(rings(sdfset[1]))))) plot(sdfset[1], colbonds=myrings)

data(sdfsample) (sdfset <- sdfsample) ## Plot single compound structure plotStruc(sdfset[[1]]) ## Plot several compounds structures plot(sdfset[1:4])

data(sdfsample) sdfset <- sdfsample ## Create bond matrix for first two molecules in sdfset conMA(sdfset[1:2], exclude=c("H")) ## Return bond matrix for first molecule and plot its structure with atom numbering conMA(sdfset[[1]], exclude=c("H")) plot(sdfset[1], atomnum = TRUE, noHbonds=FALSE , no_print_atoms = "", atomcex=0.8) ref:https://www.bioconductor.org/packages/devel/bioc/manuals/ChemmineR/man/ChemmineR.pdf

dsmall <- diamonds[sample(nrow(diamonds), 1000), ] > library(grid) > a <- ggplot(dsmall, aes(color, price/carat)) + geom_jitter(size=4, alpha = I(1 / 1.5), aes(color=color)) > b <- ggplot(dsmall, aes(color, price/carat, color=color)) + geom_boxplot() > c <- ggplot(dsmall, aes(color, price/carat, fill=color)) + geom_boxplot() + theme(legend.position = "none") > grid.newpage() # Open a new page on grid device > pushViewport(viewport(layout = grid.layout(2, 2))) # Assign to device viewport with 2 by 2 grid layout > print(a, vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2)) > print(b, vp = viewport(layout.pos.row = 2, layout.pos.col = 1)) > print(c, vp = viewport(layout.pos.row = 2, layout.pos.col = 2, width=0.3, height=0.3, x=0.8, y=0.8))

df <- data.frame(group = rep(c("Above", "Below"), each=10), x = rep(1:10, 2), y = c(runif(10, 0, 1), runif(10, -1, 0))) > p <- ggplot(df, aes(x=x, y=y, fill=group)) + geom_bar(stat="identity", position="identity") > p

ref: https://learnr.wordpress.com/page/4/ Dikesh Jariwala

R-bloggers # Read flight list flights <- read.csv("flights.csv", stringsAsFactors = FALSE) # Lookup coordinates library(ggmap) airports <- unique(c(flights$From, flights$To)) coords <- geocode(airports) airports <- data.frame(airport=airports, coords) flights <- merge(flights, airports, by.x="To", by.y="airport") flights <- merge(flights, airports, by.x="From", by.y="airport") # Plot flight routes library(ggplot2) library(ggrepel) worldmap <- borders("world", colour="#efede1", fill="#efede1") # create a layer of borders ggplot() + worldmap + geom_curve(data=flights, aes(x = lon.x, y = lat.x, xend = lon.y, yend = lat.y), col = "#b29e7d", size = 1, curvature = .2) + geom_point(data=airports, aes(x = lon, y = lat), col = "#970027") + geom_text_repel(data=airports, aes(x = lon, y = lat, label = airport), col = "black", size = 2, segment.color = NA) + theme(panel.background = element_rect(fill="white"), axis.line = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank() )

Funnel

qgraph(dat.3[-c(1:3,13:15),],layout=L.3,nNodes=30,directed=FALSE,edge.labels=TRUE,esize=14)

> dat.3 <- matrix(c(1:15*2-1,1:15*2),,2) > dat.3 <- cbind(dat.3,round(seq(-0.7,0.7,length=15),1)) > L.3 <- matrix(1:30,nrow=2) > # Different esize: > qgraph(dat.3,layout=L.3,directed=FALSE,edge.labels=TRUE,esize=14) > qgraph(dat.3[-c(1:3,13:15),],layout=L.3,nNodes=30,directed=FALSE, + edge.labels=TRUE,esize=14) > > qgraph(dat.3,layout=L.3,directed=FALSE,edge.labels=TRUE,esize=14,maximum=1) > title("by Volkan OBAN using R-qgraph package",line=2.5)

> library(explodingboxplotR) > > # use this to replicate > # from ?boxplot > #boxplot(count ~ spray, data = InsectSprays, col = "lightgray") > > exploding_boxplot( + data.frame( + rowname = rownames(InsectSprays), + InsectSprays, + stringsAsFactors = FALSE), + y = "count", + group = "spray", + color = "spray", + label = "rowname" + )

z <- seq(-10, 10, 0.1) x <- cos(z) y <- sin(z) scatterplot3js(x, y, z, color=rainbow(length(z)))

N <- 100 i <- sample(3, N, replace=TRUE) x <- matrix(rnorm(N*3),ncol=3) lab <- c("small", "bigger", "biggest") scatterplot3js(x, color=rainbow(N), labels=lab[i], size=i, renderer="canvas")

data(flights) # Approximate locations as factors dest <- factor(sprintf("%.2f:%.2f",flights[,3], flights[,4])) # A table of destination frequencies freq <- sort(table(dest), decreasing=TRUE) # The most frequent destinations in these data, possibly hub airports? frequent_destinations <- names(freq)[1:10] # Subset the flight data by destination frequency idx <- dest %in% frequent_destinations frequent_flights <- flights[idx, ] # Lat/long and counts of frequent flights ll <- unique(frequent_flights[,3:4]) # Plot frequent destinations as bars, and the flights to and from # them as arcs. Adjust arc width and color by frequency. globejs(lat=ll[,1], long=ll[,2], arcs=frequent_flights, arcsHeight=0.3, arcsLwd=2, arcsColor="#ffff00", arcsOpacity=0.15, atmosphere=TRUE, color="#00aaff", pointsize=0.5)

library(rgdal) library(threejs) # Download MODIS 16-day 1 degree Vegetation Index data manually from # http://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD13A2_M_NDVI # or use the following cached copy from May 25, 2014 cache <- tempfile() writeBin( readBin( url("http://illposed.net/nycr2015/MOD13A2_E_NDVI_2014-05-25_rgb_360x180.TIFF", open="rb"), what="raw", n=1e6), con=cache) x <- readGDAL(cache) # Obtain lat/long coordinates and model values as a data.frame x <- as.data.frame(cbind(coordinates(x), x@data[,1])) names(x) <- c("long","lat","value") # Remove ocean areas and NA values x <- x[x$value < 255,] x <- na.exclude(x) # Cut the values up into levels corresponding to the # 99th, 95th, 90th, percentiles and then all the rest. x$q <- as.numeric( cut(x$value, breaks=quantile(x$value, probs=c(0,0.90,0.95,0.99,1)), include.lowest=TRUE)) # Colors for each level col = c("#0055ff","#00aaff","#00ffaa","#aaff00")[x$q] # bling out the data globejs(lat=x$lat, long=x$long, val=x$q^3, # Bar height color=col, pointsize=0.5, atmosphere=TRUE)

library(DiagrammeR) > > create_random_graph(140, 100, set_seed = 23) %>% + join_node_attrs(get_w_connected_cmpts(.)) %>% + select_nodes_by_id(get_articulation_points(.)) %>% + set_node_attrs_ws("peripheries", 2) %>% + set_node_attrs_ws("width", 0.65) %>% + set_node_attrs_ws("height", 0.65) %>% + set_node_attrs_ws("penwidth", 3) %>% + clear_selection() %>% + add_global_graph_attrs( + attr = + c("color", "penwidth", "width", "height"), + value = + c("gray80", "3", "0.5", "0.5"), + attr_type = + c("edge", "edge", "node", "node")) %>% + colorize_node_attrs( + node_attr_from = "wc_component", + node_attr_to = "fillcolor", + alpha = 80) %>% + set_node_attr_to_display() %>% + select_nodes_by_degree("deg >= 3") %>% + trav_both_edge() %>% + set_edge_attrs_ws("penwidth", 4) %>% + set_edge_attrs_ws("color", "gray60") %>% + clear_selection() %>% + render_graph()

ref. and code: https://www.r-bloggers.com/giving-a-thematic-touch-to-your-interactive-chart/

ref: https://www.r-bloggers.com/giving-a-thematic-touch-to-your-interactive-chart/

data(world.map.simplified, package = "spnet") graph.map.plot.position(world.map.simplified) graph.map.plot.position(world.map.simplified, cex = 0.4) graph.map.plot.position(world.map.simplified, label = 'ID ', cex = 0.3)

ref: https://cran.r-project.org/web/packages/ndtv/ndtv.pdf

data(McFarland_cls33_10_16_96) coords<-plot(cls33_10_16_96) # center layout coords with 100 unit area layout.center(coords,xlim=c(0,100),ylim=c(0,100)) # rescale layout coords to unit interval layout.normalize(coords)

ref:https://cran.r-project.org/web/packages/qgraph/qgraph.pdf

library(networkDynamicData) data(vanDeBunt_students) times<-get.change.times(vanDeBunt_students) vanDProj<-timeProjectedNetwork(vanDeBunt_students,onsets = times,termini = times) # plot it with gray for the time edges plot(vanDProj, arrowhead.cex = 0, edge.col=ifelse(vanDProj%e%'edge.type'=='within_slice','black','gray'),vertex.cex=0.7,mode='kamadakawai')

library(geomnet) > library(dplyr) > # create plot > ggplot(data = soccernet, aes(from_id = home, to_id = away)) + + geom_net(aes(colour = div, group = div), ealpha = .25, + layout.alg = 'fruchtermanreingold') + + facet_wrap(~season) + + theme_net()

> ggplot(data = lesmisnet, aes(from_id = from, to_id = to, + linewidth = degree / 5 + 0.1 )) + + geom_net(aes(size = degree, alpha = degree), + colour = "grey30", ecolour = "grey60", + layout.alg = "fruchtermanreingold", labelon = TRUE, vjust = -0.75) + + scale_alpha(range = c(0.3, 1)) + theme_net() + ggtitle("by Volkan OBAN using R - geomnet")

data(football) ftnet <- fortify(as.edgedf(football$edges), football$vertices) p <- ggplot(data=ftnet, aes(from_id=from_id, to_id=to_id)) p + geom_net(aes(colour=value), linewidth=0.75, size=4.5, ecolour="grey80") + scale_colour_brewer("Conference", palette="Paired") + theme_net() + theme(legend.position="bottom")

emailnet <- fortify(emailedges, email$nodes, group = "day") Joining edge and node information by from_id and label respectively. > ggplot(data = emailnet, aes(from_id = from, to_id = to_id)) + + geom_net(aes(colour= CurrentEmploymentType), linewidth=0.5, fiteach=TRUE) + + scale_colour_brewer(palette="Set2") + facet_wrap(~day, nrow=2) + theme(legend.position="bottom") + ggtitle("by Volkan OBAN using R - geomnet")

emailedges <- as.edgedf(subset(email$edges, nrecipients < 54)) emailnet <- fortify(emailedges, email$nodes) #no facets ggplot(data = emailnet, aes(from_id = from_id, to_id = to_id)) + geom_net(aes(colour= CurrentEmploymentType), linewidth=0.5) + scale_colour_brewer(palette="Set2")

data(theme_elements) TEnet <- fortify(as.edgedf(theme_elements$edges[,c(2,1)]), theme_elements$vertices) ggplot(data = TEnet, aes(from_id = from_id, to_id = to_id)) + geom_net(labelon=TRUE, vjust=-0.5)

library(geomnet) Zorunlu paket yükleniyor: ggplot2 > data(blood) > p <- ggplot(data = blood$edges, aes(from_id = from, to_id = to)) > p + geom_net(vertices=blood$vertices, aes(colour=..type..)) + theme_net() > > bloodnet <- fortify(as.edgedf(blood$edges), blood$vertices) Using from as the from node column and to as the to node column. If this is not correct, rewrite dat so that the first 2 columns are from and to node, respectively. Joining edge and node information by from_id and label respectively. > p <- ggplot(data = bloodnet, aes(from_id = from_id, to_id = to_id)) > p + geom_net() > p + geom_net(aes(colour=rho)) + theme_net() > p + geom_net(aes(colour=rho), labelon=TRUE, vjust = -0.5) > p + geom_net(aes(colour=rho, linetype = group_to, label = from_id), + vjust=-0.5, labelcolour="black", directed=TRUE) + + theme_net() + ggtitle(" prepared by VOLKAN OBAN using R \n geomnet package") > p + geom_net(colour = "orange", layout.alg = 'circle', size = 6) > p + geom_net(colour = "orange", layout.alg = 'circle', size = 6, linewidth=.75) > p + geom_net(colour = "orange", layout.alg = 'circle', size = 0, linewidth=.75,directed = TRUE) > p + geom_net(aes(size=Predominance, colour=rho, shape=rho, linetype=group_to),linewidth=0.75, labelon =TRUE, labelcolour="black") + + facet_wrap(~Ethnicity) + + scale_colour_brewer(palette="Set2")

library(geomnet) data(blood) p <- ggplot(data = blood$edges, aes(from_id = from, to_id = to)) p + geom_net(vertices=blood$vertices, aes(colour=..type..)) + theme_net() bloodnet <- fortify(as.edgedf(blood$edges), blood$vertices) p <- ggplot(data = bloodnet, aes(from_id = from_id, to_id = to_id)) p + geom_net() p + geom_net(aes(colour=rho)) + theme_net() p + geom_net(aes(colour=rho), labelon=TRUE, vjust = -0.5) p + geom_net(aes(colour=rho, linetype = group_to, label = from_id), vjust=-0.5, labelcolour="black", directed=TRUE) + theme_net()

latinSq(20) ref:http://rstudio-pubs-static.s3.amazonaws.com/1915_bd5807659c42432a9929af403b2bda5c.html

require(reshape2) ## Loading required package: reshape2 require(ggplot2) ## Loading required package: ggplot2 require(RColorBrewer) ## Loading required package: RColorBrewer latinSq = function(n) { v = rep(NA, n^2) v[n * (1:n) - (n - 1)] = 1:n mem = 1 for (i in 1:(n^2)) { if (!is.na(v[i])) mem = ifelse(v[i] < n, v[i] + 1, 1) if (is.na(v[i])) { v[i] = mem mem = ifelse(mem < n, mem + 1, 1) } } dim(v) = c(n, n) lsqm = melt(v) if (n != 7) gg <- ggplot(lsqm, aes(x = Var1, y = Var2, fill = value, label = LETTERS[value])) if (n == 7) { LATINSQ = c("L", "A", "T", "I", "N", "S", "Q")[lsqm$value] lsqm = data.frame(lsqm, LATINSQ) gg <- ggplot(lsqm, aes(x = Var1, y = Var2, fill = value, label = LATINSQ)) } ggPrint <- gg + geom_tile() + geom_text() + scale_fill_gradientn(colours = brewer.pal(n, "Spectral")) + theme_bw() + theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) ggPrint } latinSq(6)

data(oldFrench) oldFrench.ca = corres.fnc(oldFrench) oldFrench.ca summary(oldFrench.ca, head = TRUE) plot(oldFrench.ca) # more readable plot data(oldFrenchMeta) plot(oldFrench.ca, rlabels = oldFrenchMeta$Genre, rcol = as.numeric(oldFrenchMeta$Genre), rcex = 0.5, extreme = 0.1, ccol = "blue") # create subset of proze texts prose = oldFrench[oldFrenchMeta$Genre=="prose" & !is.na(oldFrenchMeta$Year),] proseinfo = oldFrenchMeta[oldFrenchMeta$Genre=="prose" & !is.na(oldFrenchMeta$Year),] proseinfo$Period = as.factor(proseinfo$Year <= 1250) prose.ca = corres.fnc(prose) plot(prose.ca, addcol = FALSE, rcol = as.numeric(proseinfo$Period) + 1, rlabels = proseinfo$Year, rcex = 0.7) # and add supplementary data for texts with unknown date of composition proseSup = oldFrench[oldFrenchMeta$Genre == "prose" & is.na(oldFrenchMeta$Year),] corsup.fnc(prose.ca, bycol = FALSE, supp = proseSup, font = 2, cex = 0.8, labels = substr(rownames(proseSup), 1, 4))

library("psych") library("qgraph") # Load BFI data: data(bfi) bfi <- bfi[, 1:25] # Groups and names object (not needed really, but make the plots easier to # interpret): Names <- scan("http://sachaepskamp.com/files/BFIitems.txt", what = "character", sep = "\n") # Create groups object: Groups <- rep(c("A", "C", "E", "N", "O"), each = 5) # Compute correlations: cor_bfi <- cor_auto(bfi) # Plot correlation network: graph_cor <- qgraph(cor_bfi, layout = "spring", nodeNames = Names, groups = Groups, legend.cex = 0.6, DoNotPlot = TRUE) # Plot partial correlation network: graph_pcor <- qgraph(cor_bfi, graph = "concentration", layout = "spring", nodeNames = Names, groups = Groups, legend.cex = 0.6, DoNotPlot = TRUE) # Plot glasso network: graph_glas <- qgraph(cor_bfi, graph = "glasso", sampleSize = nrow(bfi), layout = "spring", nodeNames = Names, legend.cex = 0.6, groups = Groups, legend.cex = 0.7, GLratio = 2)

library(meta) library(metafor) UT_CT <- structure(list(HedgesG = c(0.423967347, 0.463106494, 0.24028285, 0.859968212, 0.700832432, -0.47267567, 1.478756303, -0.0956, 0.3216, 0.246, -0.276444701, -0.0888, -0.0883, 0.507049057, 0.2715, 0.4705, 0.3825, 0.172067039, -0.503812571, -0.373979221, 0.268963583, 0.338268088, 0.179899652, -0.559086162, -0.0901, 0.0688, -0.211118367, 1.212322358, 0.575640797, -0.345344262, 0.929063226, 0.997507389, -0.205137778, -0.25576051, -0.498009871, -0.330754639, 0.624634361, 0.667445161, 0.626010596, 0.03, 0.089677431, 0.30608501, -0.365244026, -0.051468156, 0.27, 0.355, 0.775529648, 1.041749533, -0.096, -0.143722066, 0.0953, -0.5481, 0.865, -0.738, -0.3701, -0.6209, 0.2206, 0, 0.43, -0.008883176), SE = c(0.328686052, 0.26286584, 0.204602057, 0.333714062, 0.380311448, 0.250787154, 0.40690344, 0.155084096, 0.223830293, 0.156204994, 0.319656905, 0.318168825, 0.318166748, 0.315652397, 0.214242853, 0.221133444, 0.237907545, 0.293797292, 0.301387511, 0.261597221, 0.249257982, 0.328900502, 0.233733134, 0.206587525, 0.35614549, 0.200541797, 0.171667711, 0.269412515, 0.288276271, 0.292372285, 0.33215153, 0.293760287, 0.336350481, 0.211909603, 0.23109561, 0.247283673, 0.306012425, 0.257261725, 0.326419813, 0.316, 0.247090732, 0.248441017, 0.280785825, 0.355341625, 0.2749, 0.27, 0.289786359, 0.402131319, 0.160312195, 0.157579079, 0.32046, 0.450998, 0.6359, 0.476, 0.1857, 0.2022, 0.302, 0.2455, 0.3162, 0.100200227), InverseSE = c(3.042416897, 3.804221963, 4.887536399, 2.996577349, 2.629423875, 3.987445069, 2.457585512, 6.448114433, 4.467670516, 6.401843997, 3.128354129, 3.142985494, 3.143006003, 3.168041834, 4.66760028, 4.522156316, 4.203313517, 3.403707343, 3.317987517, 3.822670572, 4.011907632, 3.040433186, 4.278383572, 4.840563347, 2.807841257, 4.986491677, 5.825207274, 3.711780056, 3.468894601, 3.420296833, 3.010674074, 3.404136109, 2.973089248, 4.718993315, 4.327213305, 4.04393864, 3.267841172, 3.887092026, 3.063539526, 3.164556962, 4.047096352, 4.025100251, 3.561433345, 2.814193243, 3.637686431, 3.703703704, 3.450818054, 2.486749858, 6.237828616, 6.346020071, 3.120514261, 2.217304733, 1.572574304, 2.100840336, 5.385029618, 4.945598417, 3.311258278, 4.073319756, 3.162555345, 9.980017326), Ap = c(1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Blocked = c(0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 999L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 999L, 0L, 1L, 0L, 1L, 1L), Complexity = c(48L, 60L, 36L, 48L, 48L, 48L, 48L, 48L, 48L, 30L, 48L, 48L, 48L, 48L, 48L, 48L, 48L, 40L, 40L, 48L, 48L, 60L, 48L, 48L, 48L, 48L, 48L, 108L, 108L, 36L, 48L, 48L, 48L, 48L, 48L, 48L, 48L, 48L, 160L, 48L, 48L, 36L, 44L, 48L, 144L, 144L, 48L, 36L, 48L, 40L, 48L, 48L, 48L, 75L, 48L, 48L, 96L, 48L, 48L, 48L), PresTime = c(4, 999, 2.5, 8, 8, 5, 4.5, 6, 4, 4, 8, 2, 999, 8, 8, 999, 999, 4, 999, 4, 8, 4, 8, 4, 8.8, 8.8, 999, 999, 999, 3.5, 7, 2.5, 2.5, 8, 8, 8, 10, 14, 999, 999, 999, 999, 999, 999, 4, 4, 4, 999, 4, 999, 4, 4, 4, 4, 999, 4, 999, 8, 4, 4), DelDur = c(3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 8L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 3L, 3L, 3L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 999L, 4L, 3L, 3L, 3L, 3L, 3L, 5L, 3L, 3L, 4L, 3L, 3L), DistTask = c(3L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 4L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 2L)), .Names = c("HedgesG", "SE", "InverseSE", "Ap", "Blocked", "Complexity", "PresTime", "DelDur", "DistTask"), class = "data.frame", row.names = c(NA, -60L)) # Code for Trim and Fill procedure, to fill in missing effect sizes. tf1 <- trimfill(UT_CT$HedgesG, UT_CT$SE) op <- par(cex.main = 1.5, mar = c(5, 6, 4, 5) + 0.1, mgp = c(3.5, 1, 0), cex.lab = 1.5, font.lab = 2, cex.axis = 1.3, bty = "n", las = 1) funnel(tf1, yaxis = "invse", xlab = "", ylab = "", contour = 0.95, xlim = c(-2, 2), ylim = c(1, 12), cex = 2, col = "black", col.contour = "lightgray", ref = 0, axes = F) axis(1) axis(2) par(las = 0) mtext("Hedges' G", side = 1, line = 2.5, cex = 1.5) mtext("Inverse of Standard Error", side = 2, line = 3, cex = 1.5) par(op)

library("psych") library("qgraph") # Load BFI data: data(bfi) bfi <- bfi[, 1:25] # Groups and names object (not needed really, but make the plots easier to # interpret): Names <- scan("http://sachaepskamp.com/files/BFIitems.txt", what = "character", sep = "\n") # Create groups object: Groups <- rep(c("A", "C", "E", "N", "O"), each = 5) # Compute correlations: cor_bfi <- cor_auto(bfi) # Plot correlation network: graph_cor <- qgraph(cor_bfi, layout = "spring", nodeNames = Names, groups = Groups, legend.cex = 0.6, DoNotPlot = TRUE) # Plot partial correlation network: graph_pcor <- qgraph(cor_bfi, graph = "concentration", layout = "spring", nodeNames = Names, groups = Groups, legend.cex = 0.6, DoNotPlot = TRUE) # Plot glasso network: graph_glas <- qgraph(cor_bfi, graph = "glasso", sampleSize = nrow(bfi), layout = "spring", nodeNames = Names, legend.cex = 0.6, groups = Groups, legend.cex = 0.7, GLratio = 2, DoNotPlot = TRUE) # centrality plot (all graphs): centralityPlot(list(r = graph_cor, `Partial r` = graph_pcor, glasso = graph_glas), labels = Names) + labs(colour = "") + theme_bw() + theme(legend.position = "bottom")

> FacVar1 = as.factor(rep(c("level1", "level2"), 25)) > FacVar2 = as.factor(rep(c("levelA", "levelB", "levelC"), 17)[-51]) > FacVar3 = as.factor(rep(c("levelI", "levelII", "levelIII", "levelIV"), 13)[-c(51:52)]) > > ## 4 Numeric Vars > set.seed(123) > NumVar1 = round(rnorm(n = 50, mean = 1000, sd = 50), digits = 2) ## Normal distribution > set.seed(123) > NumVar2 = round(runif(n = 50, min = 500, max = 1500), digits = 2) ## Uniform distribution > set.seed(123) > NumVar3 = round(rexp(n = 50, rate = 0.001)) ## Exponential distribution > NumVar4 = 2001:2050 > > simData = data.frame(FacVar1, FacVar2, FacVar3, NumVar1, NumVar2, NumVar3, NumVar4) > plot(simData$NumVar1,main="by VOLKAN OBAN using R", type = "o", ylim = c(0, max(simData$NumVar1, simData$NumVar2))) ## index plot with one variable > lines(simData$NumVar2, type = "o", lty = 2, col = "purple") >

library(streamgraph) > library(viridis) > > stocks_url <- "http://infographics.economist.com/2015/tech_stocks/data/stocks.csv" > stocks <- read.csv(stocks_url, stringsAsFactors=FALSE) > > stock_colors <- viridis_pal()(100) > stocks %>% + mutate(date=as.Date(quarter, format="%m/%d/%y")) %>% + streamgraph(key="ticker", value="nominal", offset="expand") %>% + sg_fill_manual(stock_colors) %>% + sg_axis_x(tick_interval=10, tick_units="year") %>% + sg_legend(TRUE, "Ticker: ")

ref: https://mran.microsoft.com/web/packages/ggmap/ggmap.pdf

mtcars A tool for exploring correlations. It makes it possible to easily perform routine tasks when exploring correlation matrices such as ignoring the diagonal, focusing on the correlations of certain variables against others, or rearranging and visualising the matrix in terms of the strength of the correlations

require(igraph) gr <- graph_from_data_frame(flare$edges, vertices = flare$vertices) ggraph(gr, 'treemap', weight = 'size') + geom_node_tile() # We can color by modifying the graph gr <- tree_apply(gr, function(node, parent, depth, tree) { if (depth == 1) { tree <- set_vertex_attr(tree, 'Class', node, V(tree)$shortName[node]) } else if (depth > 1) { tree <- set_vertex_attr(tree, 'Class', node, V(tree)$Class[parent]) } tree })

> require(igraph) > flareGraph <- graph_from_data_frame(flare$edges, vertices = flare$vertices) > ggraph(flareGraph, 'dendrogram', circular = TRUE) + + geom_edge_diagonal0() + + geom_node_text(aes(filter = leaf, angle = node_angle(x, y), label = shortName), + hjust = 'outward', size = 2) + + expand_limits(x = c(-1.3, 1.3), y = c(-1.3, 1.3)) > require(igraph) > flareGraph <- graph_from_data_frame(flare$edges, vertices = flare$vertices) > ggraph(flareGraph, 'dendrogram', circular = TRUE) + + geom_edge_diagonal0() + + geom_node_text(aes(filter = leaf, angle = node_angle(x, y), label = shortName), + hjust = 'outward', size = 2) + + expand_limits(x = c(-1.3, 1.3), y = c(-1.3, 1.3))

> library(igraph) > graph <- graph_from_data_frame(highschool) > ggraph(graph) + geom_edge_link() + geom_node_point() + theme_graph() Using `nicely` as default layout > > library(ggforce) > sizes <- sample(10, 100, TRUE) > position <- pack_circles(sizes) > data <- data.frame(x = position[,1], y = position[,2], r = sqrt(sizes/pi)) > ggplot() + + geom_circle(aes(x0 = x, y0 = y, r = r), data = data, fill = 'steelblue') + + geom_circle(aes(x0 = 0, y0 = 0, r = attr(position, 'enclosing_radius'))) + + geom_polygon(aes(x = x, y = y), + data = data[attr(position, 'front_chain'), ], + fill = NA, + colour = 'black')

require(igraph) gr <- graph_from_data_frame(flare$edges, vertices = flare$vertices) ggraph(gr, 'treemap', weight = 'size') + geom_node_tile() # We can color by modifying the graph gr <- tree_apply(gr, function(node, parent, depth, tree) { if (depth == 1) { tree <- set_vertex_attr(tree, 'Class', node, V(tree)$shortName[node]) } else if (depth > 1) { tree <- set_vertex_attr(tree, 'Class', node, V(tree)$Class[parent]) } tree }) ggraph(gr, 'treemap', weight = 'size') + geom_node_tile(aes(fill = Class, filter = leaf, alpha = depth), colour = NA) + geom_node_tile(aes(size = depth), colour = 'white') + scale_alpha(range = c(1, 0.5), guide = 'none') + scale_size(range = c(4, 0.2), guide = 'none')

> require(igraph) > gr <- graph_from_data_frame(flare$edges, vertices = flare$vertices) > ggraph(gr, 'circlepack', weight = 'size') + geom_node_circle() + coord_fixed()

> library(igraph) > gr <- graph_from_data_frame(highschool) > V(gr)$popularity <- as.character(cut(degree(gr, mode = 'in'), breaks = 3, + labels = c('low', 'medium', 'high'))) > ggraph(gr) + + geom_edge_link() + geom_node_point() + + facet_nodes(~popularity)

> gr <- graph_from_data_frame(highschool) > ggraph(gr) + + geom_edge_link() + + geom_node_point() + + facet_edges(~year) Using `nicely` as default layout > > library(igraph) > gr <- graph_from_data_frame(highschool) > ggraph(gr) + + geom_edge_link() + + geom_node_point() + + facet_edges(~year)

variety=c(rep("soldur" , 40), rep("silur" , 40), rep("lloyd" , 40), rep("pescadou" , 40) , rep("X4582" , 40) , rep("Dudur" , 40) , rep("Classic" , 40)) treatment= rep(c(rep("high" , 20) , rep("low" , 20)) , 7) note=c( rep(c(sample(0:4, 20 , replace=T) , sample(1:6, 20 , replace=T)),2), rep(c(sample(5:7, 20 , replace=T), sample(5:9, 20 , replace=T)),2), c(sample(0:4, 20 , replace=T) , sample(2:5, 20 , replace=T), rep(c(sample(6:8, 20 , replace=T) , sample(7:10, 20 , replace=T)),2) )) data=data.frame(variety, treatment , note) new_order <- with(data, reorder(variety , note, mean , na.rm=T)) # Then I make the boxplot, asking to use the 2 factors : variety (in the good order) AND treatment : par(mar=c(3,4,3,1)) myplot=boxplot(note ~ treatment*new_order , data=data , boxwex=0.4 , ylab="sickness", main="sickness of several wheat lines" , col=c("slateblue1" , "tomato") , xaxt="n") # To add the label of x axis my_names=sapply(strsplit(myplot$names , '\\.') , function(x) x[[2]] ) my_names=my_names[seq(1 , length(my_names) , 2)] axis(1, at = seq(1.5 , 14 , 2), labels = my_names , tick=FALSE , cex=0.3) for(i in seq(0.5 , 20 , 2)){ abline(v=i,lty=1, col="grey")} # Add a legend legend("bottomright", legend = c("High treatment", "Low treatment"), col=c("slateblue1" , "tomato"), pch = 15, bty = "n", pt.cex = 3, cex = 1.2, horiz = F, inset = c(0.1, 0.1))

m <- matrix(c(1,1,1, 2,3,4, 5,6,7), ncol=3, by=T) l <- layout(m) layout.show(l) # show layout to doublecheck # layout cells are filled in the order of the numbers # set par, e.g. mar each time if required for (i in 1:7) { par(mar=c(i,i,i,i)) hist(rnorm(100), col=i) }

bmgraph(swomen, layout = "bip3", cex = 3, tcex = .8, pch = c(19, 15), lwd = 1.5, vcol = 2:3) ref:https://github.com/mplex/multigraph

> swomen <- read.dl(file = "http://moreno.ss.uci.edu/davis.dat") > bmgraph(swomen,main="\n prepared by Volkan OBAN using R \n multigraph package

floflies <- read.dl(file = "http://moreno.ss.uci.edu/padgett.dat") multigraph(floflies, directed = FALSE, layout = "force", seed = 2, cex = 6, tcex = .7, pos = 0, vcol = 8,ecol = 1, lwd = 2, bwd = .5, lty = 2:1, pch = 13)

theme_calc()

ref:https://www.r-bloggers.com/how-to-create-a-data-visualization-from-the-new-york-times-in-r/

set.seed(1234) wdata = data.frame( sex = factor(rep(c("F", "M"), each=200)), weight = c(rnorm(200, 55), rnorm(200, 58))) head(wdata, 4) gghistogram(wdata, x = "weight", add = "mean", rug = TRUE, fill = "sex", palette = c("#00AFBB", "#E7B800"), add_density = TRUE)

data("ToothGrowth") df <- ToothGrowth ggdotplot(df, "dose", "len", add = "boxplot", color = "dose", fill = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"))

ggdotchart(df, x = "mpg", main="by VOLKAN OBAN", label = "name", group = "cyl", color = "cyl",palette = "Dark2" )

data("mtcars") df <- mtcars df$cyl <- as.factor(df$cyl) df$name <- rownames(df) head(df[, c("wt", "mpg", "cyl")], 3) # Basic plot ggdotchart(df, x = "mpg", label = "name" )

wdata = data.frame( + sex = factor(rep(c("F", "M"), each=200)), + weight = c(rnorm(200, 55), rnorm(200, 58))) > head(wdata, 4) sex weight 1 F 53.79293 2 F 55.27743 3 F 56.08444 4 F 52.65430 > > ggdensity(wdata, x = "weight", fill = "lightgray", + add = "mean", rug = TRUE) > ggdensity(wdata, x = "weight", + add = "mean", rug = TRUE, + color = "sex", fill = "sex", + palette = c("#00AFBB", "#E7B800"))

df <- ToothGrowth ggboxplot(df, "dose", "len", fill = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"))

> data("ToothGrowth") > df <- ToothGrowth ggboxplot(df, x = "dose", y = "len", add = "jitter", shape = "dose")

data(diff_express) ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"), fdr = 0.05, fc = 2, size = 0.4, palette = c("#B31B21", "#1465AC", "darkgray"), genenames = as.vector(diff_express$name), legend = "top", top = 20, font.label = c("bold", 11), label.rectangle = TRUE, font.legend = "bold", font.main = "bold", ggtheme = ggplot2::theme_minimal())

ggviolin(df, x = "dose", y = "len", fill = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"), add = "boxplot", add.params = list(fill = "white"))

sp <- ggplot(faithful, aes(x=eruptions, y=waiting)) + geom_point() sp + geom_density_2d() # Gradient de couleur sp + stat_density_2d(aes(fill = ..level..), geom="polygon") # Changer le gradient de couleur sp + stat_density_2d(aes(fill = ..level..), geom="polygon")+ scale_fill_gradient(low="blue", high="red")

sp <- ggplot(faithful, aes(x=eruptions, y=waiting)) + geom_point() sp + geom_density_2d() # Gradient de couleur sp + stat_density_2d(aes(fill = ..level..), geom="polygon") # Changer le gradient de couleur sp + stat_density_2d(aes(fill = ..level..), geom="polygon")+ scale_fill_gradient(low="blue", high="red")

library(ggplot2) d <- diamonds levels(d$cut) <- list(A="Fair", B="Good", " "="space1", C="Very Good", D="Ideal", " "="space2", E="Premium") ggplot(d, aes(x=cut, y=depth)) + geom_boxplot(color="black", size=0.2) + theme_bw() + scale_x_discrete(breaks = c("A", "B", " ", "C", "D", " ", "E"), drop=FALSE) + coord_flip()

if (require(dygraphs) && require(xts)) { mydata <- xts(rnorm(365), order.by = as.Date("2017-01-01") + 0:364) manipulateWidget( dygraph(mydata) %>% dyShading(from=period[1], to = period[2], color = "#CCEBD6"), period = mwDateRange(c("2017-03-01", "2017-04-01"), min = "2017-01-01", max = "2017-12-31") ) }

ref:https://cran.rstudio.com/web/packages/manipulateWidget/manipulateWidget.pdf

set.seed(34903490) x = rnorm(50) y = 0.5*x^2 + 2*x + rnorm(length(x)) frm = data.frame(x=x,y=y,yC=y>=as.numeric(quantile(y,probs=0.8))) frm$absY <- abs(frm$y) frm$posY = frm$y > 0 frm$costX = 1 WVPlots::DoubleHistogramPlot(frm, "x", "yC", title="Example double histogram plot")

library(BatchGetSymbols) first.date <- Sys.Date()-150 last.date <- Sys.Date() tickers <- c('FB','NYSE:MMM','PETR4.SA','abcdef') l.out <- BatchGetSymbols(tickers = tickers, first.date = first.date, last.date = last.date) library(ggplot2) p <- ggplot(l.out$df.tickers, aes(x = ref.date, y = price.close)) p <- p + geom_line() p <- p + facet_wrap(~ticker, scales = 'free_y') print(p)

require(stick) > set.seed(68331) > plotStick(x = runif(100), y = runif(100))

> require(igraph) > ws_graph <- watts.strogatz.game(1, 50, 4, 0.05) > edgebundle(ws_graph,tension = 0.1,fontsize = 18,padding=40)

ref: https://github.com/garthtarr/edgebundleR

require(huge) data("stockdata") # generate returns sequences X = log(stockdata$data[2:1258,]/stockdata$data[1:1257,]) # perform some regularisation out.huge = huge(cor(X),method = "glasso",lambda=0.56,verbose = FALSE) # identify the linkages adj.mat = as.matrix(out.huge$path[[1]]) # format the colnames nodenames = paste(gsub("","",stockdata$info[,2]),stockdata$info[,1],sep=".") head(cbind(stockdata$info[,2],stockdata$info[,1],nodenames)) colnames(adj.mat) = rownames(adj.mat) = nodenames # restrict attention to the connected stocks: adj.mat = adj.mat[rowSums(adj.mat)>0,colSums(adj.mat)>0] # plot the result edgebundle(adj.mat,tension=0.8,fontsize = 10)

> data <- data.frame( + id = 1:4, + content = c("geldim" , "gördüm" ,"dünya", "gideceğim"), + start = c("1984-01-24", "2010-01-11", "2020-12-20", "2016-02-14 15:00:00"), + end = c(NA,NA, "2016-02-04", NA)) > > timevis(data)

> dygraph(lungDeaths) > dygraph(lungDeaths,main = "prepared by VOLKAN OBAN using R \n dygraphs package") %>% + dySeries("mdeaths", label = "Male") %>% + dySeries("fdeaths", label = "Female") %>% + dyOptions(stackedGraph = TRUE) %>% + dyRangeSelector(height = 20)

library(ggplot2) # Create a Violin plot ggplot(diamonds, aes(x = cut, y = price, fill = clarity)) + geom_violin(trim= FALSE) + scale_y_log10() + facet_wrap(~ clarity)

ggplot(diamonds, aes(x = cut, y = price, fill = clarity)) + geom_violin() + scale_y_log10()

> ggplot(diamonds, aes(x = cut, y = price, fill = cut)) + + geom_violin() + scale_y_log10() + + geom_boxplot(width = 0.2)

gplot(ChickWeight, aes(x = Diet, y = weight, color = Diet)) + + geom_violin(fill = "pink") + + geom_jitter(position = position_jitter(0.2)) + + theme(legend.position = "top")

> ggplot(ChickWeight, aes(x = Diet, y = weight)) + geom_boxplot(notch = TRUE) + geom_jitter(position = position_jitter(0.5), aes(colour = Diet)

> data(airquality) > # prepare the data > temp <- airquality$Temp > > # gererate normal distribution with same mean and sd > tempNorm <- rnorm(200,mean=mean(temp, na.rm=TRUE), sd = sd(temp, na.rm=TRUE)) > > # make a list > x <- list("temp"=temp, "norm"=tempNorm) > stripchart(x, + main="prepared by VOLKAN OBAN using R \n Multiple stripchart for comparision", + xlab="Degree Fahrenheit", + ylab="Temperature", + method="jitter", + col=c("purple","red"), + pch=16 + )

p <- ggplot(mpg, aes(cyl, hwy)) p + geom_point() p + geom_jitter() p + geom_jitter(aes(colour = class))

df = data.frame(y = rnorm(500), x = sample(LETTERS[1:5],500,replace=T) library(lattice) boxplot(y ~ x, data = ddf, lwd = 2,xlab='x', ylab = 'y') stripchart(y ~ x, vertical = TRUE, data = ddf,method = "jitter", add = TRUE, pch = 20, col = 'purple')

ref:https://science.nature.nps.gov/im/datamgmt/statistics/r/advanced/latticegraphics.cfm

> year <- function(x) as.POSIXlt(x)$year + 1900 > qplot(unemploy / pop, uempmed, data = economics, + geom = c("point", "path")) + ggtitle("prepared by VOLKAN OBAN using R-ggplot2 - data(economics) ")

qplot(color, price / carat, data = diamonds, geom = "jitter",alpha = I(1 / 5) )

> library(arules) > data("AdultUCI") > dframe = AdultUCI[, c("education", "hours-per-week")] > colnames(dframe) = c("education", "hours_per_week") > library(ggplot2) > ggplot(dframe, aes(x=education, y=hours_per_week)) + + geom_point(colour="lightblue", alpha=0.1, position="jitter") + + geom_boxplot(outlier.size=0.5, alpha=0.2) + coord_flip()

library(qrage) > data(links) > #Data that determines the color of the nodes > data(nodeColor) > #Data that determines the size of the node > data(nodeValues) > #Create graph > qrage(links=links,nodeColor=nodeColor,nodeValue=nodeValues,cut=0.1) >

library(dplyr) iris %>% tbl_df %>% filter( Sepal.Width > 3 ) %>% rpivotTable

> library(sjmisc) > data(efc) > sjp.grpfrq(efc$e17age, efc$e16sex, show.values = FALSE) > > sjp.grpfrq(efc$e17age, efc$e42dep, intr.var = efc$e16sex, type = "box")

> data(worldgeojson, package = "highcharter") > data("GNI2014", package = "treemap") > highchart(type = "map") %>% + hc_add_series_map(map = worldgeojson, df = GNI2014, value = "GNI", joinBy = "iso3") %>% + hc_colorAxis(stops = color_stops()) %>% + hc_tooltip(useHTML = TRUE, headerFormat = "", + pointFormat = "this is {point.name} and have {point.population} people with gni of {point.GNI}")

> hciconarray(c("nice", "good"), c(10, 20)) > hciconarray(c("nice", "good"), c(10, 20), size = 10) > hciconarray(c("nice", "good"), c(100, 200), icons = "child") > hciconarray(c("car", "truck", "plane"), c(75, 30, 20), icons = c("car", "truck", "plane")) %>% + hc_add_theme( + hc_theme_merge( + hc_theme_flatdark(), + hc_theme_null(chart = list(backgroundColor = "#34495e")) + ) + )

> hciconarray(c("nice", "good"), c(10, 20)) > hciconarray(c("nice", "good"), c(10, 20), size = 10) > hciconarray(c("nice", "good"), c(100, 200), icons = "child") > hciconarray(c("car", "truck", "plane"), c(75, 30, 20), icons = c("car", "truck", "plane")) %>% + hc_add_theme( + hc_theme_merge( + hc_theme_flatdark(), + hc_theme_null(chart = list(backgroundColor = "#34495e")) + ) + )

http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html#Marginal%20Histogram%20/%20Boxplot

library(ggplot2) theme_set(theme_bw()) # plot g <- ggplot(mpg, aes(manufacturer, cty)) g + geom_boxplot() + geom_dotplot(binaxis='y', stackdir='center', dotsize = .5, fill="red") + theme(axis.text.x = element_text(angle=65, vjust=0.6)) + labs(title="Box plot + Dot plot", subtitle="City Mileage vs Class: Each dot represents 1 row in source data", caption="Source: mpg", x="Class of Vehicle", y="City Mileage")

df = structure(list(Affiliation = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L), .Label = c("BMI", "CCS", "CS", "Epi", "Genom", "HSE", "HSR", "HPR"), class = "factor"), count = structure(c(4L, 21L, 14L, 20L, 11L, 13L, 19L, 15L, 5L, 22L, 17L, 24L, 9L, 12L, 18L, 16L, 1L, 10L, 7L, 23L, 2L, 3L, 8L, 6L), .Label = c("15", "26", "27", "32", "40", "41", "42", "58", "62", "63", "70", "88", "89", "96", "99", "112", "125", "160", "164", "172", "176", "178", "200", "628"), class = "factor"), Year = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("2014", "2015", "2016"), class = "factor")), .Names = c("Affiliation", "count", "Year"), row.names = c(NA, 24L), class = "data.frame") ggplot(df, aes(x = Affiliation, y = count, fill = Year, group = Year)) + geom_bar(position = position_dodge(width = 0.9), stat = "identity", alpha = 1, size = 1, width = 0.05) + geom_text(aes(label = count), position = position_dodge(width = 0.9), vjust = -0.25) + scale_fill_brewer(palette = "Set1") df$count <- as.numeric(as.character(df$count)) gg <- ggplot(df, aes(Affiliation, count)) gg <- gg + geom_segment(aes(xend=Affiliation, yend=0)) gg <- gg + geom_point() gg <- gg + geom_text(aes(label=count, y=count+25), vjust=0, size=3) gg <- gg + scale_y_continuous(expand=c(0,0), limits=c(0, 800)) gg <- gg + facet_wrap(~Year) gg <- gg + labs(x=NULL, y=NULL) gg <- gg + theme_bw() gg <- gg + theme(strip.background=element_blank()) gg <- gg + theme(strip.text=element_text(hjust=0)) gg <- gg + theme(panel.grid.major.x=element_blank()) gg <- gg + theme(panel.grid.minor.y=element_blank()) gg <- gg + theme(axis.ticks=element_blank()) gg <- gg + theme(axis.text.x=element_text(size=8)) gg <- gg + theme(axis.text.y=element_text(size=8, vjust=c(0, 0.5, 0.5, 0.5, 1))) gg

n <- rnorm(130, 10, 3) p <- rpois(110, 4) u <- runif(300, 0, 20) l <- rlnorm(130, log(2)) g <- rgamma(140, 3) e <- rexp(160) violins(list(e=e, p=p,u=u,n=n,l=l,g=g), ylim=c(0,20), col=c('purple','lightblue','lightgreen','red','orange','yellow'), stats=TRUE)

> ds = read.csv("http://www.math.smith.edu/r/data/help.csv") > smallds = subset(ds, female==1) > boxplot(pcs~homeless, data=smallds, + horizontal=TRUE) > stripchart(round(pcs)~homeless, + method='stack', data=smallds, + add=TRUE) + title("by VOLKAN OBAN")

library(DescTools) library(Sleuth3) attach(ex0923) PlotBubble( x= Educ, y = AFQT, area = Income2005/1000, col = SetAlpha(as.numeric(Gender)), border = "burlywood", inches = .5, xlab = "Education", ylab = "AFQT test score") title(main = "Income, proportional to circle area") legend("left", c("Female","Male"), text.col = c(1:2), cex =.9, bty = "n")

> library(corrplot) > library(Sleuth2) > attach(ex1713) >y = cor(ex1713[, 2:6]) > par(mfrow = c(2,2)) > corrplot(y) # default method is "circle" > corrplot(y, method = "color") > corrplot(y, method = "number") > corrplot(y, method = "ellipse", type = "lower"

> library(corrgram) > col.corrgram <- function(ncol){ + colorRampPalette(c("darkgoldenrod4", "burlywood1", + "darkkhaki", "darkgreen"))(ncol)} > corrgram(mtcars, order=TRUE, lower.panel=panel.shade, + upper.panel=panel.pie, text.panel=panel.txt, + main="prepared by Volkan OBAN using R-corrgram \n Correlogram of Car Mileage Data (PC2/PC1 Order)")

> corrgram(mtcars, order=TRUE, lower.panel=panel.ellipse, + upper.panel=panel.pts, text.panel=panel.txt, + diag.panel=panel.minmax, + main="prepared by Volkan OBAN using R-corrgram \n Car Milage Data in PC2/PC1 Order")

library(plotrix) #Build the matrix data to look like a correlation matrix n <- 8 x <- matrix(runif(n*n), nrow=n) xmin <- 0 xmax <- 1 for (i in 1:n) x[i, i] <- 1.0 #Make the diagonal all 1's #Generate the palette for the matrix and the legend. Generate labels for the legend palmat <- color.scale(x, c(1, 0.4), c(1, 0.4), c(0.96, 1)) palleg <- color.gradient(c(1, 0.4), c(1, 0.4), c(0.96, 1), nslices=100) lableg <- c(formatC(xmin, format="f", digits=2), formatC(1*(xmax-xmin)/4, format="f", digits=2), formatC(2*(xmax-xmin)/4, format="f", digits=2), formatC(3*(xmax-xmin)/4, format="f", digits=2), formatC(xmax, format="f", digits=2)) #Set up the plot area and plot the matrix par(mar=c(5, 5, 5, 8)) color2D.matplot(x, cellcolors=palmat, main=paste(n, " X ", n, " Matrix Using Color2D.matplot", sep=""), show.values=2, vcol=rgb(0,0,0), axes=FALSE, vcex=0.7) axis(1, at=seq(1, n, 1)-0.5, labels=seq(1, n, 1), tck=-0.01, padj=-1) #In the axis() statement below, note that the labels are decreasing. This is because #the above color2D.matplot() statement has "axes=FALSE" and a normal axis() #statement was used. axis(2, at=seq(1, n, 1)-0.5, labels=seq(n, 1, -1), tck=-0.01, padj=0.7) #Plot the legend pardat <- par() color.legend(pardat$usr[2]+0.5, 0, pardat$usr[2]+1, pardat$usr[2], paste(" ", lableg, sep=""), palleg, align="rb", gradient="y", cex=0.7)

> library(gplots) > > #Build the matrix data to look like a correlation matrix > x <- matrix(rnorm(64), nrow=8) > x <- (x - min(x))/(max(x) - min(x)) #Scale the data to be between 0 and 1 > for (i in 1:8) x[i, i] <- 1.0 #Make the diagonal all 1's > > #Format the data for the plot > xval <- formatC(x, format="f", digits=2) > pal <- colorRampPalette(c(rgb(0.96,0.96,1), rgb(0.1,0.1,0.9)), space = "rgb") > > #Plot the matrix > x_hm <- heatmap.2(x, Rowv=FALSE, Colv=FALSE, dendrogram="none", main="8 X 8 Matrix Using Heatmap.2", xlab="using R-gplots", ylab="", col=pal, tracecol="#303030", trace="none", cellnote=xval, notecol="black", notecex=0.8, keysize = 1.5, margins=c(5, 5))

library(neuralnet) mod <- neuralnet(Y1 ~ X1 + X2 + X3, data = neuraldat, hidden = 5) plotnet(mod,main="by Volkan OBAN") ## using caret library(caret) mod <- train(Y1 ~ X1 + X2 + X3, method = 'nnet', data = neuraldat, linout = TRUE) plotnet(mod) ## a more complicated network with categorical response AND <- c(rep(0, 7), 1) OR <- c(0, rep(1, 7)) binary_data <- data.frame(expand.grid(c(0, 1), c(0, 1), c(0, 1)), AND, OR) mod <- neuralnet(AND + OR ~ Var1 + Var2 + Var3, binary_data, hidden = c(6, 12, 8), rep = 10, err.fct = 'ce', linear.output = FALSE) plotnet(mod,main="by Volkan OBAN") ## recreate the previous example with numeric inputs # get the weights and structure in the right format wts <- neuralweights(mod) struct <- wts$struct wts <- unlist(wts$wts) # plot plotnet(wts, struct = struct,main="by Volkan OBAN") ## color input nodes by relative importance mod <- nnet(Y1 ~ X1 + X2 + X3, data = neuraldat, size = 5) rel_imp <- garson(mod, bar_plot = FALSE)$rel_imp cols <- colorRampPalette(c('lightgreen', 'darkgreen'))(3)[rank(rel_imp)] plotnet(mod, circle_col = list(cols, 'lightblue'),main="by Volkan OBAN")

bwplot(gcsescore ~ gender | factor(score), Chem97, layout = c(6, 1))

data(Chem97, package = "mlmRev") bwplot(factor(score) ~ gcsescore | gender, Chem97)

ref: http://bioconnector.org/bims8382/r-ggplot2.html

ref:http://bioconnector.org/bims8382/r-ggplot2.html

ref:http://www.ats.ucla.edu/stat/r/dae/mlogit.htm

http://www.sthda.com/english/wiki/hcpc-hierarchical-clustering-on-principal-components-hybrid-approach-2-2-unsupervised-machine-learning

> set.seed(123) > # K-means clustering > km.res <- kmeans(scale(USArrests), 4, nstart = 25) > # Use clusplot function > library(cluster) > clusplot(scale(USArrests), km.res$cluster, main = "Cluster plot", + color=TRUE, labels = 2, lines = 0) > library("factoextra") > # Visualize kmeans clustering > fviz_cluster(km.res, USArrests)

> library(ggplot2) > library(dplyr) > data(diamonds) > diamonds %>% + ggplot(aes(x=carat,y=price)) + + geom_point(alpha=0.5) + + facet_grid(~ cut) + + stat_smooth(method = lm, formula = y ~ poly(x,2)) + + theme_bw()

library(CatterPlots) meow <- multicat(xs=x, ys=rnorm(21), cat=c(1,2,3,4,5,6,7,8,9,10), catcolor=list(c(0,0,0,1)), canvas=c(-0.1,1.1, -0.1, 1.1), xlab="some cats", ylab="other cats", main="Random Cats") ref:https://github.com/Gibbsdavidl/CatterPlots

> ggpairs(iris, upper=list(continuous="density"), lower=list(continuous="smooth",combo="facetdensity"), color="Species")

> require(GGally) > ggpairs(iris, color='Species', alpha=0.4)

dist <- data.frame(value=rnorm(10000, 1:4), group=1:4) myBoxplot + scale_fill_discrete(breaks=c("1","3","2","4"), labels=c("Dist 1","Dist 3","Dist 2","Dist 4")) myBoxplot + theme_bw()

aa <- data.frame(value=rnorm(dist <- data.frame(value=rnorm(10000, 1:4), group=1:4)10000, 1:4), group=1:4) ggplot(aa, aes(x=group, y=value, color=group)) + geom_jitter(alpha=0.5)

https://rpubs.com/ikochergin/177292

dist <- data.frame(value=rnorm(10000, 1:4), group=1:4) ggplot(dist, aes(x=group, y=value, color=group)) + geom_jitter(alpha=0.2,shape=21)

ggplot(data=myMovieData, aes(Type,Budget)) + geom_jitter() + geom_boxplot(alpha=I(0.6))+ scale_y_log10()

library(ggplot2movies) glimpse(movies) d1 <-data.frame(movies[movies$Action==1, c("budget", "Short", "year")]) d1$Type <- "Animation" d2 <-data.frame(movies[movies$Animation==1, c("budget", "Short", "year")]) d2$Type <- "Animation" d3 <-data.frame(movies[movies$Comedy==1, c("budget", "Short", "year")]) d3$Type <- "Comedy" d4 <-data.frame(movies[movies$Drama==1, c("budget", "Short", "year")]) d4$Type <- "Drama" d5 <-data.frame(movies[movies$Documentary==1, c("budget", "Short", "year")]) d5$Type <- "Documentary" d6 <-data.frame(movies[movies$Romance==1, c("budget", "Short", "year")]) d6$Type <- "Romance" myMovieData <- rbind(d1, d2, d3, d4, d5, d6) names(myMovieData) <- c("Budget", "Short", "Year", "Type" ) glimpse(myMovieData) qplot(Type,Budget, data=myMovieData, geom=c("boxplot","jitter"), log="y")

data(world.cities) capitals <- subset(world.cities, capital == 1) capitals.big <- subset(capitals, pop > 3000000) ggplot(capitals.big, aes(long, lat)) + borders("world") + geom_point(aes(size = pop)) + geom_text(aes(long, lat,label=country.etc),hjust=-0.2,size=4) ggplot(capitals.big, aes(long, lat)) + borders("world") + geom_point(aes(size = pop)) + geom_text(aes(long, lat,label=country.etc),hjust=-0.2,size=4) + coord_map(projection = "ortho", orientation=c(41, 20, 0))

> data(world.cities) > capitals <- subset(world.cities, capital == 1) > ggplot(capitals, aes(long, lat)) + borders("world", fill="cornflowerblue", col="darkorchid") + geom_point(aes(size = pop),col="blueviolet")

p1 <- ggplot(diamonds, aes(x=carat, y=price, color=cut)) + geom_point() + geom_smooth() + theme(legend.position="none") + labs(title="legend.position='none'") # remove legend p2 <- ggplot(diamonds, aes(x=carat, y=price, color=cut)) + geom_point() + geom_smooth() + theme(legend.position="top") + labs(title="legend.position='top'") # legend at top p3 <- ggplot(diamonds, aes(x=carat, y=price, color=cut)) + geom_point() + geom_smooth() + labs(title="legend.position='coords inside plot'") + theme(legend.justification=c(1,0), legend.position=c(1,0)) # legend inside the plot. grid.arrange(p1, p2, p3, ncol=3)

ref: http://sharpsightlabs.com/blog/small-multiples-ggplot/

library(ggplot2) library(grid) library(reshape2) options(stringsAsFactors=FALSE) # Generating synthetic data here tpl <- c('1st', '2nd', '3rd', '4th', '5th') dat <- data.frame(foo=as.factor(sample(tpl, 1000, replace=TRUE)), bar=as.factor(sample(tpl, 1000, replace=TRUE)), effect=runif(1000, 0.1, 0.7)) # Just doing a cross-tabulation ctab <- melt(table(subset(dat, select=c('foo', 'bar'))), id.vars='foo') ctab$y <- rep(0.8, dim(ctab)[1]) # Just conducting ANOVA tests here tests <- c() for (q in levels(dat$bar)) { test <- aov(effect ~ foo, data=subset(dat, bar == q)) tests <- c(tests, sprintf('p-value: ~%.4f', summary(test)[[1]][['Pr(>F)']][[1]])) } tests <- data.frame(p.value=tests, bar=levels(dat$bar), x=rep(1, 5), y=rep(0, 5)) ggplot(dat, mapping=aes(y=effect)) + geom_boxplot(mapping=aes(x=foo)) + geom_text(data=tests, aes(x=x, y=y, label=p.value), hjust=0.1, vjust=0.1) + geom_text(data=ctab, aes(x=foo, y=y, label=value), vjust=0.7) + xlab('2000 Census White Pop. Proportion Quintile') + ylab('Vegetation Cover Proportion') + labs(title='Vegetation Cover by 2000 Census Tract, Pop. Density Quintiles') + facet_wrap(~ bar) + theme_bw() + theme(text=element_text(size=16), plot.margin=unit(c(0.5, 0.2, 0.5, 0), 'cm'), panel.grid.major.y=element_line(color='gray'), panel.grid.major.x=element_blank())

ggplot(mpg, aes(displ, hwy)) + geom_point(data = transform(mpg, class = NULL), colour = "grey85") + geom_point() + facet_wrap(~class)

p <- qplot(price, data = diamonds, geom = "histogram", binwidth = 1000) + ggtitle("by Volkan OBAN using R - ggplot2") > p + facet_wrap(~ color) > p + facet_wrap(~ color, scales = "free_y") > p <- qplot(displ, hwy, data = mpg) > p + facet_wrap(~ cyl) > p + facet_wrap(~ cyl, scales = "free") > cyl6 <- subset(mpg, cyl == 6) > p + geom_point(data = cyl6, colour = "red", size = 1) + + facet_wrap(~ cyl) > p + geom_point(data = transform(cyl6, cyl = 7), colour = "red") + + facet_wrap(~ cyl) > p + geom_point(data = transform(cyl6, cyl = NULL), colour = "red") + + facet_wrap(~ cyl) >

d <- ggplot(diamonds, aes(carat, price, fill = ..density..)) + xlim(0, 2) + stat_binhex(na.rm = TRUE) + opts(aspect.ratio = 1) d + facet_wrap(~ color) d + facet_wrap(~ color, ncol = 1) d + facet_wrap(~ color, ncol = 4) d + facet_wrap(~ color, nrow = 1) d + facet_wrap(~ color, nrow = 3) # Using multiple variables continues to wrap the long ribbon of # plots into 2d - the ribbon just gets longer # d + facet_wrap(~ color + cut)

https://www.ling.upenn.edu/~joseff/rstudy/summer2010_ggplot2_intro.html

ggplot(mpg, aes(class, hwy, fill = factor(year)))+ + geom_boxplot() > ggplot(mpg, aes(reorder(class, hwy, median), hwy, fill = factor(year)))+ geom_boxplot()

> p <- ggplot(mpg, aes(displ, hwy)) > > p + geom_point() + stat_smooth() `geom_smooth()` using method = 'loess' > p + geom_point() + stat_smooth(method = "lm") > > library(MASS) Attaching package: ‘MASS’ The following object is masked from ‘package:plotly’: select The following object is masked from ‘package:dplyr’: select > p + geom_point() + stat_smooth(method = "rlm") > p + stat_smooth(geom = "point")+stat_smooth(geom = "errorbar")

d <- data.frame(x=c(1,1,2),y=c(1,2,2)*100) > gg <- ggplot(d,aes(x,y)) > gg <- ggplot(mpg, aes(displ, hwy)) > gg + geom_encircle(data=subset(mpg, hwy>40)) + geom_point() > gg + geom_encircle(aes(group=manufacturer)) + geom_point() > gg + geom_encircle(aes(group=manufacturer,fill=manufacturer),alpha=0.4)+ + geom_point() > gg + geom_encircle(aes(group=manufacturer,fill=manufacturer),alpha=0.4)+ + geom_point()

library(igraph) ref:https://rdatamining.wordpress.com/2012/05/17/an-example-of-social-network-analysis-with-r-using-package-igraph/

> p <- figure(width = 1000) %>% + ly_points(date, Freq, data = flightfreq, + hover = list(date, Freq, dow), size = 5) %>% + ly_abline(v = as.Date("2001-09-11")) > p >

> figure(data = lattice::singer) %>% + ly_points(catjitter(voice.part), jitter(height), color = "black") %>% + ly_boxplot(voice.part, height, with_outliers = FALSE)

idx <- split(1:150, iris$Species) figs <- lapply(idx, function(x) { figure(width = 300, height = 300) %>% ly_points(Sepal.Length, Sepal.Width, data = iris[x, ], hover = list(Sepal.Length, Sepal.Width)) }) # 1 row, 3 columns grid_plot(figs) # specify xlim and ylim to be applied to all panels grid_plot(figs, xlim = c(4, 8), ylim = c(1.5, 4.5)) # unnamed list will remove labels grid_plot(unname(figs)) # 2 rows, 2 columns grid_plot(figs, nrow = 2) # x and y axis with same (and linked) limits grid_plot(figs, same_axes = TRUE) # x axis with same (and linked) limits grid_plot(figs, same_axes = c(TRUE, FALSE), nrow = 2) # x axis with same (and linked) limits and custom xlim grid_plot(figs, same_axes = c(TRUE, FALSE), xlim = c(5, 7), nrow = 2) # send lists instead of specifying nrow and ncol grid_plot(list( c(list(figs[[1]]), list(figs[[3]])), c(list(NULL), list(figs[[2]])) )) # a null entry will be skipped in the grid figs2 <- figs figs2[1] <- list(NULL) grid_plot(figs2, nrow = 2) # with themes grid_plot(figs) %>% theme_title(text_color = "red") %>% theme_plot(background_fill_color = "#E6E6E6", outline_line_color = "white") %>% theme_grid(c("x", "y"), grid_line_color = "white", minor_grid_line_color = "white", minor_grid_line_alpha = 0.4) %>% theme_axis(c("x", "y"), axis_line_color = "white", major_label_text_color = "#7F7F7F", major_tick_line_color = "#7F7F7F", minor_tick_line_alpha = 0, num_minor_ticks = 2) # themes again grid_plot(figs) %>% set_theme(bk_ggplot_theme) # link data across plots in the grid (try box_select tool) # (data sources must be the same) tools <- c("pan", "wheel_zoom", "box_zoom", "box_select", "reset") p1 <- figure(tools = tools, width = 500, height = 500) %>% ly_points(Sepal.Length, Sepal.Width, data = iris, color = Species) p2 <- figure(tools = tools, width = 500, height = 500) %>% ly_points(Petal.Length, Petal.Width, data = iris, color = Species) grid_plot(list(p1, p2), same_axes = TRUE, link_data = TRUE)

> circos.clear() > layout(matrix(1:9, 3, 3)) > for(i in 1:9) { + factors = 1:8 + par(mar = c(0.5, 0.5, 0.5, 0.5)) + circos.par(cell.padding = c(0, 0, 0, 0)) + circos.initialize(factors, xlim = c(0, 1)) + circos.trackPlotRegion(ylim = c(0, 1), track.height = 0.05, + bg.col = rand_color(8), bg.border = NA) + for(i in 1:20) { + se = sample(1:8, 2) + circos.link(se[1], runif(2), se[2], runif(2), + col = rand_color(1, transparency = 0.4), border = NA) + } + circos.clear() + }

> library(heatmaply) > heatmaply(iris[,-5], k_row = 3, k_col = 2,main="by VOLKAN OBAN using R \n heatmaply package-data(iris)") > heatmaply(cor(iris[,-5])) > heatmaply(cor(iris[,-5]), limits = c(-1,1)) > heatmaply(mtcars, k_row = 3, k_col = 2)

library(ggplot2) > library(reshape) > data(HairEyeColor) > P=t(HairEyeColor[,,2]) > Pm=melt(P) ggplot(Pm, aes(Eye, Hair, fill=value)) + geom_tile() + geom_text(aes(label=Pm$value),colour="white")+ theme(axis.text.x=element_text(size = 15),axis.text.y=element_text(size = 15))

library(ggplot2) library(ggthemes) library(data.table) library(ggTimeSeries) set.seed(1) dtData = data.table( DateCol = seq( as.Date("1/01/2014", "%d/%m/%Y"), as.Date("31/12/2015", "%d/%m/%Y"), "days" ), ValueCol = runif(730) ) dtData[, ValueCol := ValueCol + (strftime(DateCol,"%u") %in% c(6,7) * runif(1) * 0.75), .I] dtData[, ValueCol := ValueCol + (abs(as.numeric(strftime(DateCol,"%m")) - 6.5)) * runif(1) * 0.75, .I] dtData[, CategCol := letters[1 + round(ValueCol * 7)]] # base plot p2 = ggplot_calendar_heatmap( dtData, 'DateCol', 'CategCol' ) # adding some formatting p2 + xlab('') + ylab('') + facet_wrap(~Year, ncol = 1)

set.seed(1) dfData = data.frame(x = 1:1000, y = cumsum(rnorm(1000))) # base plot p1 = ggplot_horizon(dfData, 'x', 'y') p1 + xlab('') + ylab('') + scale_fill_continuous(low = 'green', high = 'red') + coord_fixed( 0.5 * diff(range(dfData$x)) / diff(range(dfData$y)))

set.seed(1) dfData = data.frame(x = 1:100, y = cumsum(rnorm(100))) # base plot p1 = ggplot_waterfall( dtData = dfData, 'x', 'y' ) # adding some formatting p1 + xlab('') + ylab('')

ref:https://cran.r-project.org/web/packages/plotKML/plotKML.pdf data(eberg) data(eberg_grid) data(eberg_zones) data(eberg_contours) library(sp) coordinates(eberg) <- ~X+Y proj4string(eberg) <- CRS("+init=epsg:31467") gridded(eberg_grid) <- ~x+y proj4string(eberg_grid) <- CRS("+init=epsg:31467") # visualize the maps: data(SAGA_pal) l.sp <- list("sp.lines", eberg_contours, col="black") ## Not run: spplot(eberg_grid["DEMSRT6"], col.regions = SAGA_pal[[1]], sp.layout=l.sp) spplot(eberg_zones, sp.layout=list("sp.points", eberg, col="black", pch="+"))

library(ggplot2) library(reshape) require(PerformanceAnalytics) data(edhec) ed=data.frame(edhec) ed$date=as.Date(rownames(ed)) m=melt(ed,id="date") m$variable=gsub('\\.',' ',m$variable) ggplot(m,aes(date,0,fill=value))+geom_tile(aes(height=max(m$value)-min(m$value)))+geom_line(aes(x=date,y=value))+facet_grid(variable~.)+ scale_fill_gradient2(low="red",high="blue")+ylab("value") +xlab("Date \n by VOLKAN OBAN using R")

df=iris > m=as.matrix(cbind(df$Petal.Length, df$Petal.Width),ncol=2) > cl=(kmeans(m,3)) > df$cluster=factor(cl$cluster) > centers=as.data.frame(cl$centers) > library(ggplot2) > > ggplot(data=df, aes(x=Petal.Length, y=Petal.Width, color=cluster )) + + geom_point() + + geom_point(data=centers, aes(x=V1,y=V2, color='Center')) + + geom_point(data=centers, aes(x=V1,y=V2, color='Center'), size=52, alpha=.3,)

> data(BreastCancer) > # create a bar plot of each categorical attribute > par(mfrow=c(2,4)) > for(i in 2:9) { + counts <- table(BreastCancer[,i]) + name <- names(BreastCancer)[i] + barplot(counts, main=name) + }

> library(ggplot2) > library(dplyr) > library(tidyr) > > dfr <- data.frame(x=factor(1:20),y1=runif(n=20)) %>% + mutate(y2=1-y1) %>% + gather(variable,value,-x) ggplot(dfr,aes(x=x,y=value,fill=variable))+ geom_bar(stat="identity")+ labs(title=" title")+ theme(legend.position="top", legend.justification="right")

ref: http://timelyportfolio.blogspot.com.tr/

ref: http://timelyportfolio.blogspot.com.tr/

data(tao, package = "VIM") aggr(tao)

library(circlize) library(RColorBrewer) library(ComplexHeatmap) lt = readRDS(paste0(system.file(package = "ComplexHeatmap"), "/extdata/meth.rds")) list2env(lt, envir = environment()) ha = HeatmapAnnotation(df = data.frame(type = c(rep("Tumor", 10), rep("Control", 10))), col = list(type = c("Tumor" = "red", "Control" = "blue"))) ha2 = HeatmapAnnotation(df = data.frame(type = c(rep("Tumor", 10), rep("Control", 10))), col = list(type = c("Tumor" = "red", "Control" = "blue")), show_legend = FALSE) # column order of the methylation matrix which will be assigned to the expressio matrix column_tree = hclust(dist(t(meth))) ht_list = Heatmap(meth, name = "methylation", col = colorRamp2(c(0, 0.5, 1), c("blue", "white", "red")), cluster_columns = column_tree, top_annotation = ha, column_names_gp = gpar(fontsize = 8), km = 5, column_title = "Methylation", column_title_gp = gpar(fontsize = 10), row_title_gp = gpar(fontsize = 10)) + Heatmap(direction, name = "direction", col = c("hyper" = "red", "hypo" = "blue"), column_names_gp = gpar(fontsize = 8)) + Heatmap(expr[, column_tree$order], name = "expression", col = colorRamp2(c(-2, 0, 2), c("green", "white", "red")), cluster_columns = FALSE, top_annotation = ha2, column_names_gp = gpar(fontsize = 8), column_title = "Expression", column_title_gp = gpar(fontsize = 10)) + Heatmap(cor_pvalue, name = "-log10(cor_p)", col = colorRamp2(c(0, 2, 4), c("white", "white", "red")), column_names_gp = gpar(fontsize = 8)) + Heatmap(gene_type, name = "gene type", col = brewer.pal(length(unique(gene_type)), "Set1"), column_names_gp = gpar(fontsize = 8)) + Heatmap(anno, name = "anno_gene", col = brewer.pal(length(unique(anno)), "Set2"), column_names_gp = gpar(fontsize = 8)) + Heatmap(dist, name = "dist_tss", col = colorRamp2(c(0, 10000), c("black", "white")), column_names_gp = gpar(fontsize = 8)) + Heatmap(enhancer, name = "anno_enhancer", col = colorRamp2(c(0, 1), c("white", "orange")), cluster_columns = FALSE, column_names_gp = gpar(fontsize = 8), column_title = "Enhancer", column_title_gp = gpar(fontsize = 10)) ht_global_opt(heatmap_legend_title_gp = gpar(fontsize = 8, fontface = "bold"), heatmap_legend_labels_gp = gpar(fontsize = 8)) draw(ht_list, newpage = FALSE, column_title = "prepared by Volkan OBAN using R-ComplexHeatmap \n Correspondence between methylation, expression and other genomic features", column_title_gp = gpar(fontsize = 12, fontface = "bold"), heatmap_legend_side = "bottom") invisible(ht_global_opt(RESET = TRUE))

library(ComplexHeatmap) library(MASS) library(pvclust) data(Boston) boston.pv <- pvclust(Boston, nboot=100) plot(boston.pv)

mat = readRDS(paste0(system.file("extdata", package = "ComplexHeatmap"), "/measles.rds")) ha1 = HeatmapAnnotation(dist1 = anno_barplot(colSums(mat), bar_width = 1, gp = gpar(col = NA, fill = "#FFE200"), border = FALSE, axis = TRUE)) ha2 = rowAnnotation(dist2 = anno_barplot(rowSums(mat), bar_width = 1, gp = gpar(col = NA, fill = "#FFE200"), border = FALSE, which = "row", axis = TRUE), width = unit(1, "cm")) ha_column = HeatmapAnnotation(cn = function(index) { year = as.numeric(colnames(mat)) which_decade = which(year %% 10 == 0) grid.text(year[which_decade], which_decade/length(year), 1, just = c("center", "top")) }) Heatmap(mat, name = "cases", col = colorRamp2(c(0, 800, 1000, 127000), c("white", "cornflowerblue", "yellow", "red")), cluster_columns = FALSE, show_row_dend = FALSE, rect_gp = gpar(col= "white"), show_column_names = FALSE, row_names_side = "left", row_names_gp = gpar(fontsize = 10), column_title = 'Measles cases in US states 1930-2001\nVaccine introduced 1961', top_annotation = ha1, top_annotation_height = unit(1, "cm"), bottom_annotation = ha_column, bottom_annotation_height = grobHeight(textGrob("1900"))) + ha2 decorate_heatmap_body("cases", { i = which(colnames(mat) == "1961") x = i/ncol(mat) grid.lines(c(x, x), c(0, 1), gp = gpar(lwd = 2)) grid.text("Vaccine introduced", x, unit(1, "npc") + unit(5, "mm")) })

marginplot(sleep[c("Gest","Dream")],pch = c(20),col=c("purple","yellow","pink"))

> x<- c(1774, 1706, 1288, 1276, 2350, 1821, 1712, 1654, 1680, 1451, + 1275, 2140, 1747, 1749, 1770, 1797, 1485, 1299, 2330, 1822, 1627, + 1847, 1797, 1452, 1328, 2363, 1998, 1864, 2088, 2084, 594, 884, + 1968, 1858, 1640, 1823, 1938, 1490, 1312, 2312, 1937, 1617, 1643, + 1468, 1381, 1276, 2228, 1756, 1465, 1716, 1601, 1340, 1192, 2231, + 1768, 1623, 1444, 1575, 1375, 1267, 2475, 1630, 1505, 1810, 1601, + 1123, 1324, 2245, 1844, 1613, 1710, 1546, 1290, 1366, 2427, 1783, + 1588, 1505, 1398, 1226, 1321, 2299, 1047, 1735, 1633, 1508, 1323, + 1317, 2323, 1826, 1615, 1750, 1572, 1273, 1365, 2373, 2074, 1809, + 1889, 1521, 1314, 1512, 2462, 1836, 1750, 1808, 1585, 1387, 1428, + 2176, 1732, 1752, 1665, 1425, 1028, 1194, 2159, 1840, 1684, 1711, + 1653, 1360, 1422, 2328, 1798, 1723, 1827, 1499, 1289, 1476, 2219, + 1824, 1606, 1627, 1459, 1324, 1354, 2150, 1728, 1743, 1697, 1511, + 1285, 1426, 2076, 1792, 1519, 1478, 1191, 1122, 1241, 2105, 1818, + 1599, 1663, 1319, 1219, 1452, 2091, 1771, 1710, 2000, 1518, 1479, + 1586, 1848, 2113, 1648, 1542, 1220, 1299, 1452, 2290, 1944, 1701, + 1709, 1462, 1312, 1365, 2326, 1971, 1709, 1700, 1687, 1493, 1523, + 2382, 1938, 1658, 1713, 1525, 1413, 1363, 2349, 1923, 1726, 1862, + 1686, 1534, 1280, 2233, 1733, 1520, 1537, 1569, 1367, 1129, 2024, + 1645, 1510, 1469, 1533, 1281, 1212, 2099, 1769, 1684, 1842, 1654, + 1369, 1353, 2415, 1948, 1841, 1928, 1790, 1547, 1465, 2260, 1895, + 1700, 1838, 1614, 1528, 1268, 2192, 1705, 1494, 1697, 1588, 1324, + 1193, 2049, 1672, 1801, 1487, 1319, 1289, 1302, 2316, 1945, 1771, + 2027, 2053, 1639, 1372, 2198, 1692, 1546, 1809, 1787, 1360, 1182, + 2157, 1690, 1494, 1731, 1633, 1299, 1291, 2164, 1667, 1535, 1822, + 1813, 1510, 1396, 2308, 2110, 2128, 2316, 2249, 1789, 1886, 2463, + 2257, 2212, 2608, 2284, 2034, 1996, 2686, 2459, 2340, 2383, 2507, + 2304, 2740, 1869, 654, 1068, 1720, 1904, 1666, 1877, 2100, 504, + 1482, 1686, 1707, 1306, 1417, 2135, 1787, 1675, 1934, 1931, 1456) > y=auto.arima(x) > plot(forecast(y,h=30)) > points(1:length(x),fitted(y),type="l",col="green" + ) > library(caret) > fit <- nnetar(x) > plot(forecast(fit,h=60) > points(1:length(x),fitted(fit),type="l",col="green") >

tm_shape(World, bbox = "Turkey") + + tm_borders("grey20") + + tm_grid(projection="longlat", labels.size = .5) + + tm_text("name", size="AREA") + + tm_compass(position = c(.65, .15), color.light = "grey90") + + tm_credits("Eckert IV projection", position = c(.85, 0)) + + tm_style_classic(inner.margins=c(.04,.03, .02, .01), legend.position = c("left", "bottom"), + legend.frame = TRUE, bg.color="lightblue", legend.bg.color="lightblue", title="by Volkan OBAN using R- tmap \n TURKEY", + earth.boundary = TRUE, space.color="grey90") >

irisct <- ctree(Species ~ .,data = iris) irisct plot(irisct) table(predict(irisct), iris$Species)

library(rgdal) par(mfrow=c(1,3)) plot(Sepal.Length, Sepal.Width, col='skyblue', pch=1) title("Sepal.Length vs Sepal.Width") plot(Sepal.Length, Petal.Length, col='magenta', pch=2) title("Sepal.Length vs Petal.Length") plot(Sepal.Length, Petal.Width, col='red', pch=3) title("Sepal.Length vs Petal.Width")

library(GGally) ds = read.csv("http://www.math.smith.edu/r/data/help.csv") ds$sex = as.factor(ifelse(ds$female==1, "female", "male")) ds$housing = as.factor(ifelse(ds$homeless==1, "homeless", "housed")) smallds = subset(ds, select=c("housing", "sex", "i1", "cesd")) ggpairs(smallds, diag=list(continuous="density", discrete="bar"), axisLabels="show")

P <- ggplot(data = mpg,aes(cty, hwy,color=class))+geom_point(size=3) + facet_wrap(~ manufacturer,scales="free")+ + labs(title=" prepared by Volkan OBAN \n data = mpg --ggplot2 and ggthemes packages \n Plot With Facets") > P > P +scale_colour_Publication()+ theme_Publication()

(qplot(carat, price, data=dsamp, colour=cut) + theme_economist() + scale_colour_economist() + ggtitle("Diamonds Are Forever"))

dtemp <- data.frame(months = factor(rep(substr(month.name,1,3), 4), levels = substr(month.name,1,3)), city = rep(c("Tokyo", "New York", "Berlin", "London"), each = 12), temp = c(7.0, 6.9, 9.5, 14.5, 18.2, 21.5, 25.2, 26.5, 23.3, 18.3, 13.9, 9.6, -0.2, 0.8, 5.7, 11.3, 17.0, 22.0, 24.8, 24.1, 20.1, 14.1, 8.6, 2.5, -0.9, 0.6, 3.5, 8.4, 13.5, 17.0, 18.6, 17.9, 14.3, 9.0, 3.9, 1.0, 3.9, 4.2, 5.7, 8.5, 11.9, 15.2, 17.0, 16.6, 14.2, 10.3, 6.6, 4.8)) ggplot(dtemp, aes(x = months, y = temp, group = city, color = city)) + geom_line() + geom_point(size = 1.1) + ggtitle("Monthly Average Temperature") + theme_hc() + scale_colour_hc()

ggplot(diamonds, aes(x = clarity, fill = cut)) + geom_bar() + scale_fill_ptol() + theme_minimal()

library(ggmap) # example of map of Dhangadhi, Nepal dhanmap1 = get_map(location = c(lon = 29.016896, lat = 41.118085 ,zoom = 12, maptype = 'roadmap', source = "google") dhanmap1 = ggmap(dhanmap1) dhanmap1

set.seed (78888) rectheat = sample(c(rnorm (10, 5,1), NA, NA), 150, replace = T) circlefill = rectheat*10 + rnorm (length (rectheat), 0, 3) circlesize = rectheat*1.5 + rnorm (length (rectheat), 0, 3) myd <- data.frame (rowv = rep (1:10, 15), columnv = rep(1:15, each = 10), rectheat, circlesize, circlefill) require(ggplot2) pl1 <- ggplot(myd, aes(y = factor(rowv), x = factor(columnv))) + geom_tile(aes(fill = rectheat)) + scale_fill_continuous(low = "blue", high = "green") pl1 + geom_point(aes(colour = circlefill, size =circlesize)) + scale_color_gradient(low = "yellow", high = "red")+ scale_size(range = c(1, 20))+ theme_bw()

> x<-rnorm(1000) > g<-round(runif(1000)) > g2<-round(runif(1000)) > box.plot.ade(x, g, g2, vnames=list(c("subgroup 1","subgroup 2"), + c("group 1", "group 2")), wall=0, count='N: ?', means=TRUE) > box.plot.ade(x, g, g2, vnames=list(c("subgroup 1","subgroup 2"), + c("group 1", "group 2")), wall=1, type="violin") > box.plot.ade(x, g, g2, vnames=list(c("subgroup 1","subgroup 2"), + c("group 1", "group 2")), wall=2, type="percentile") > box.plot.ade(x, g, g2, vnames=list(c("subgroup 1","subgroup 2"), + c("group 1", "group 2")), wall=3, type="sd")

> x<-rnorm(1000) > g<-round(runif(1000)) > g2<-round(runif(1000)) > box.plot.ade(x, g, g2, vnames=list(c("subgroup 1","subgroup 2"), + c("group 1", "group 2")), wall=0, count='N: ?', means=TRUE) > box.plot.ade(x, g, g2, vnames=list(c("subgroup 1","subgroup 2"), + c("group 1", "group 2")), wall=1, type="violin") > box.plot.ade(x, g, g2, vnames=list(c("subgroup 1","subgroup 2"), + c("group 1", "group 2")), wall=2, type="percentile") > box.plot.ade(x, g, g2, vnames=list(c("subgroup 1","subgroup 2"), + c("group 1", "group 2")), wall=3, type="sd")

> x<-rnorm(1000) > g<-round(runif(1000)) > g2<-round(runif(1000)) > box.plot.ade(x, g, g2, vnames=list(c("subgroup 1","subgroup 2"), + c("group 1", "group 2")), wall=0, count='N: ?', means=TRUE) > box.plot.ade(x, g, g2, vnames=list(c("subgroup 1","subgroup 2"), + c("group 1", "group 2")), wall=1, type="violin") > box.plot.ade(x, g, g2, vnames=list(c("subgroup 1","subgroup 2"), + c("group 1", "group 2")), wall=2, type="percentile") > box.plot.ade(x, g, g2, vnames=list(c("subgroup 1","subgroup 2"), + c("group 1", "group 2")), wall=3, type="sd")

x<- round(runif(1000, 0.5, 10.5)) bar.plot.ade(x, btext='Uniform distribution', gradient=TRUE) x<-rbinom(1000, 1, 0.75) y<-rbinom(1000, 1, 0.30) z<-rbinom(1000, 1, 0.50) bar.plot.ade(x,y,z) bar.plot.ade(x,y,z, wall=4, form='c', main='Bar-Plot')

> ggplot(mtcars,aes(x = cyl, y = mpg)) + geom_violin(fill = "pink") + geom_point(aes(size = carb), colour = "blue", position = "jitter") + xlab("cyl") + ylab ("mpg")

> library(rworldmap) > d <- data.frame( + country=c("Turkey", "Italy", "Germany", "AZERBAIJAN","SPAIN"), + value=c(-2, -1, 0, 1, 2)) n <- joinCountryData2Map(d, joinCode="NAME", nameJoinColumn="country") mapCountryData(n, nameColumnToPlot="value", mapTitle="prepared by Volkan OBAN using R \n ",xlim=c(-20, 70), ylim=c(15, 70),colourPalette="red2White",addLegend=TRUE,oceanCol="lightblue", missingCountryCol="purple")

> library(rworldmap) > d <- data.frame( + country=c("Turkey", "France", "Germany", "Italy", "Netherlands"), + value=c(-2, -1, 0, 1, 2)) > n <- joinCountryData2Map(d, joinCode="NAME", nameJoinColumn="country") > mapCountryData(n, nameColumnToPlot="value", mapTitle="World"

library(ggplot2) library(ggthemes) ggplot(mtcars, aes(wt, mpg)) + geom_point() + geom_rug() + theme_tufte(ticks=F) + xlab("Car weight (lb/1000)") + ylab("Miles per gallon of fuel") + theme(axis.title.x = element_text(vjust=-0.5), axis.title.y = element_text(vjust=1))

testlen<-c(rnorm(36)*2+5) testpos<-seq(0,350,by=10) polar.plot(testlen,testpos,main="Test Polar Plot",lwd=3,line.col=4) polar.plot(testlen,testpos,main="Test Clockwise Polar Plot", start=90,clockwise=TRUE,lwd=3,line.col=4)

testcp<-list("",40) for(i in 1:40) testcp[[i]]<-rnorm(sample(1:8,1)*50) segs<-get.segs(testcp) centipede.plot(segs,main="Test centipede plot",vgrid=0)

ref: http://genoplotr.r-forge.r-project.org/code/barto_seg_plots.R

> featurePlot(x = iris[, 1:4], + y = iris$Species, + plot = "box", + ## Pass in options to bwplot() + scales = list(y = list(relation="free"), + x = list(rot = 90)), + layout = c(4,1 ) ,main=" feature Plot", + auto.key = list(columns = 2))

library(AppliedPredictiveModeling) > transparentTheme(trans = .4) > library(caret) > featurePlot(x = iris[, 1:4], + y = iris$Species, + plot = "pairs",main="prepared by Volkan OBAN using R", + auto.key = list(columns = 3))

ref: https://shiring.github.io/machine_learning/2016/11/27/flu_outcome_ML_post

ggplot(data = fluH7N9.china.2013_gather, aes(x = Date, y = age, fill = outcome)) + stat_density2d(aes(alpha = ..level..), geom = "polygon") + geom_jitter(aes(color = outcome, shape = gender), size = 1.5) + geom_rug(aes(color = outcome)) + labs( fill = "Outcome", color = "Outcome", alpha = "Level", shape = "Gender", x = "Date in 2013", y = "Age", title = "2013 Influenza A H7N9 cases in China", subtitle = "Dataset from 'outbreaks' package (Kucharski et al. 2014)", caption = "" ) + facet_grid(Group ~ province) + my_theme() + scale_shape_manual(values = c(15, 16, 17)) + scale_color_brewer(palette="Set1", na.value = "grey50") + scale_fill_brewer(palette="Set1")

tree1 <- rpart(survived~., data=ptitanic) par(mfrow=c(4,3)) for(iframe in 1:nrow(tree1$frame)) { cols <- ifelse(1:nrow(tree1$frame) <= iframe, "black", "gray") prp(tree1, col=cols, branch.col=cols, split.col=cols) }

heat.tree <- function(tree, low.is.green=FALSE, ...) { # dots args passed to prp y <- tree$frame$yval if(low.is.green) y <- -y max <- max(y) min <- min(y) cols <- rainbow(99, end=.36)[ ifelse(y > y[1], (y-y[1]) * (99-50) / (max-y[1]) + 50, (y-min) * (50-1) / (y[1]-min) + 1)] prp(tree, branch.col=cols, box.col=cols, ...) } data(ptitanic) tree <- rpart(age ~ ., data=ptitanic) heat.tree(tree, type=4, varlen=0, faclen=0, fallen.leaves=TRUE)

http://machinelearningmastery.com/machine-learning-in-r-step-by-step/

R Data Viz. colored dendrogram in R. # load code of A2R function source("https://lnkd.in/gkjzrrE") A2Rplot... ref: https://rpubs.com/gaston/dendrograms

http://www.sthda.com/english/wiki/beautiful-dendrogram-visualizations-in-r-5-must-known-methods-unsupervised-machine-learning

http://www.sthda.com/english/wiki/beautiful-dendrogram-visualizations-in-r-5-must-known-methods-unsupervised-machine-learning

http://www.sthda.com/english/wiki/beautiful-dendrogram-visualizations-in-r-5-must-known-methods-unsupervised-machine-learning

https://rpubs.com/gaston/dendrograms http://www.sthda.com/english/wiki/beautiful-dendrogram-visualizations-in-r-5-must-known-methods-unsupervised-machine-learning

library(plotly) library(zoo) # Trends Data trends <- read.csv("https://cdn.rawgit.com/plotly/datasets/master/Move%20to%20Canada.csv", check.names = F, stringsAsFactors = F) trends.zoo <- zoo(trends[,-1], order.by = as.Date(trends[,1], format = "%d/%m/%Y")) trends.zoo <- aggregate(trends.zoo, as.yearmon, mean) trends <- data.frame(Date = index(trends.zoo), coredata(trends.zoo)) # Immigration Data immi <- read.csv("https://cdn.rawgit.com/plotly/datasets/master/Canada%20Immigration.csv", stringsAsFactors = F) labels <- format(as.yearmon(trends$Date), "%Y") labels <- as.character(sapply(labels, function(x){ unlist(strsplit(x, "20"))[2] })) test <- labels[1] for(i in 2:length(labels)){ if(labels[i] == test) { labels[i] <- "" }else{ test <- labels[i] } } labels[1] <- "2004" hovertext1 <- paste0("Date:<b>", trends$Date, "</b><br>", "From US:<b>", trends$From.US, "</b><br>") hovertext2 <- paste0("Date:<b>", trends$Date, "</b><br>", "From Britain:<b>", trends$From.Britain, "</b><br>") p <- plot_ly(data = trends, x = ~Date) %>% # Time series chart add_lines(y = ~From.US, line = list(color = "#00526d", width = 4), hoverinfo = "text", text = hovertext1, name = "From US") %>% add_lines(y = ~From.Britain, line = list(color = "#de6e6e", width = 4), hoverinfo = "text", text = hovertext2, name = "From Britain") %>% add_markers(x = c(as.yearmon("2004-11-01"), as.yearmon("2016-03-01")), y = c(24, 44), marker = list(size = 15, color = "#00526d"), showlegend = F) %>% add_markers(x = c(as.yearmon("2008-07-01"), as.yearmon("2016-07-01")), y = c(27, 45), marker = list(size = 15, color = "#de6e6e"), showlegend = F) %>% # Markers for legend add_markers(x = c(as.yearmon("2005-01-01"), as.yearmon("2005-01-01")), y = c(40, 33.33), marker = list(size = 15, color = "#00526d"), showlegend = F) %>% add_markers(x = c(as.yearmon("2005-01-01"), as.yearmon("2005-01-01")), y = c(36.67, 30), marker = list(size = 15, color = "#de6e6e"), showlegend = F) %>% add_text(x = c(as.yearmon("2004-11-01"), as.yearmon("2016-03-01")), y = c(24, 44), text = c("<b>1</b>", "<b>3</b>"), textfont = list(color = "white", size = 8), showlegend = F) %>% add_text(x = c(as.yearmon("2008-07-01"), as.yearmon("2016-07-01")), y = c(27, 45), text = c("<b>2</b>", "<b>4</b>"), textfont = list(color = "white", size = 8), showlegend = F) %>% # Text for legend add_text(x = c(as.yearmon("2005-01-01"), as.yearmon("2005-01-01"), as.yearmon("2005-01-01"), as.yearmon("2005-01-01")), y = c(40, 36.67, 33.33, 30), text = c("<b>1</b>", "<b>2</b>", "<b>3</b>", "<b>4</b>"), textfont = list(color = "white", size = 8), showlegend = F) %>% # Bar chart add_bars(data = immi, x = ~Year, y = ~USA, yaxis = "y2", xaxis = "x2", showlegend = F, marker = list(color = "#00526d"), name = "USA") %>% add_bars(data = immi, x = ~Year, y = ~UK, yaxis = "y2", xaxis = "x2", showlegend = F, marker = list(color = "#de6e6e"), name = "UK") %>% layout(legend = list(x = 0.8, y = 0.36, orientation = "h", font = list(size = 10), bgcolor = "transparent"), yaxis = list(domain = c(0.4, 0.95), side = "right", title = "", ticklen = 0, gridwidth = 2), xaxis = list(showgrid = F, ticklen = 4, nticks = 100, ticks = "outside", tickmode = "array", tickvals = trends$Date, ticktext = labels, tickangle = 0, title = ""), yaxis2 = list(domain = c(0, 0.3), gridwidth = 2, side = "right"), xaxis2 = list(anchor = "free", position = 0), # Annotations annotations = list( list(xref = "paper", yref = "paper", xanchor = "left", yanchor = "right", x = 0, y = 1, showarrow = F, text = "<b>Your home and native land?</b>", font = list(size = 18, family = "Balto")), list(xref = "paper", yref = "paper", xanchor = "left", yanchor = "right", x = 0, y = 0.95, showarrow = F, align = "left", text = "<b>Google search volume for <i>'Move to Canada'</i></b><br>^{100 is peak volume<br><b>Note</b> that monthly averages are used}", font = list(size = 13, family = "Arial")), list(xref = "plot", yref = "plot", xanchor = "left", yanchor = "right", x = as.yearmon("2005-03-01"), y = 40, showarrow = F, align = "left", text = "<b>George W. Bush is re-elected</b>", font = list(size = 12, family = "Arial"), bgcolor = "white"), list(xref = "plot", yref = "plot", xanchor = "left", yanchor = "right", x = as.yearmon("2005-03-01"), y = 36.67, showarrow = F, align = "left", text = "<b>Canadian minister visits Britain, ecourages skilled workers to move</b>", font = list(size = 12, family = "Arial"), bgcolor = "white"), list(xref = "plot", yref = "plot", xanchor = "left", yanchor = "right", x = as.yearmon("2005-03-01"), y = 33.33, showarrow = F, align = "left", text = "<b>Super tuesday: Donald Trump wins 7 out of 11 republican primaries</b>", font = list(size = 12, family = "Arial"), bgcolor = "white"), list(xref = "plot", yref = "plot", xanchor = "left", yanchor = "right", x = as.yearmon("2005-03-01"), y = 30, showarrow = F, align = "left", text = "<b>Britain votes 52-48% to leave the Europen Union</b>", font = list(size = 12, family = "Arial"), bgcolor = "white"), list(xref = "paper", yref = "paper", xanchor = "left", yanchor = "right", x = 0, y = 0.3, showarrow = F, align = "left", text = "<b>Annual immigration to Canada</b>", font = list(size = 12, family = "Arial")), list(xref = "paper", yref = "paper", xanchor = "left", yanchor = "right", x = 0, y = -0.07, showarrow = F, align = "left", text = "<b>Source:</b> Google trends and national statistics", font = list(size = 12, family = "Arial")), list(xref = "paper", yref = "paper", xanchor = "left", yanchor = "right", x = 0.85, y = 0.98, showarrow = F, align = "left", text = 'Inspired by <a href = "http://www.economist.com/blogs/graphicdetail/2016/07/daily-chart">The economist</a>', font = list(size = 12, family = "Arial"))), paper_bgcolor = "#f2f2f2", margin = list(l = 18, r = 30, t = 18), width = 1024,height = 600) print(p)

library(MASS) library(RColorBrewer) library(class) mycols <- brewer.pal(8, "Dark2")[c(3,2)] sink("classification-out.txt") ######### # Plots showing decision boundaries s <- sqrt(1/5) set.seed(30) makeX <- function(M, n=100, sigma=diag(2)*s) { z <- sample(1:nrow(M), n, replace=TRUE) m <- M[z,] return(t(apply(m,1,function(mu) mvrnorm(1,mu,sigma)))) } M0 <- mvrnorm(10, c(1,0), diag(2)) # generate 10 means x0 <- makeX(M0) ## the final values for y0=blue M1 <- mvrnorm(10, c(0,1), diag(2)) x1 <- makeX(M1) x <- rbind(x0, x1) y <- c(rep(0,100), rep(1,100)) cols <- mycols[y+1] GS <- 75 # put data in a Gs x Gs grid XLIM <- range(x[,1]) tmpx <- seq(XLIM[1], XLIM[2], len=GS) YLIM <- range(x[,2]) tmpy <- seq(YLIM[1], YLIM[2], len=GS) newx <- expand.grid(tmpx, tmpy) # KNN (1) yhat <- knn(x, newx, y, k=1) colshat <- mycols[as.numeric(yhat)] plot(x, xlab="X1", ylab="X2", xlim=XLIM, ylim=YLIM, type="n") points(newx, col=colshat, pch=".") contour(tmpx, tmpy, matrix(as.numeric(yhat),GS,GS), levels=c(1,2), add=TRUE, drawlabels=FALSE) points(x, col=cols) title("KNN (1)")

library(rpart) library(rpart.plot) data(ptitanic) set.seed(123) tree <- rpart(survived ~ ., data = ptitanic, control = rpart.control(cp = 0.0001)) bestcp <- tree$cptable[which.min(tree$cptable[,"xerror"]),"CP"] # Step3: Prune the tree using the best cp. tree.pruned <- prune(tree, cp = bestcp) conf.matrix <- table(ptitanic$survived, predict(tree.pruned,type="class")) rownames(conf.matrix) <- paste("Actual", rownames(conf.matrix), sep = ":") colnames(conf.matrix) <- paste("Pred", colnames(conf.matrix), sep = ":") print(conf.matrix) plot(tree.pruned) text(tree.pruned, cex = 0.8, use.n = TRUE, xpd = TRUE) prp(tree.pruned, faclen = 0, cex = 0.8, extra = 1)

par(mfrow = c(2,3)) beeswarm(distributions, col = 2:4, main = 'corral = "none" (default)') beeswarm(distributions, col = 2:4, corral = "gutter", main = 'corral = "gutter"') beeswarm(distributions, col = 2:4, corral = "wrap", main = 'corral = "wrap"') beeswarm(distributions, col = 2:4, corral = "random", main = 'corral = "random"') beeswarm(distributions, col = 2:4, corral = "omit", main = 'corral = "omit"')

> distributions <- list(runif = runif(100, min = -3, max = 3), + rnorm = rnorm(100), + rlnorm = rlnorm(100, sdlog = 0.5)) > beeswarm(distributions, xlab="prepared by VOLKAN OBAN using R-beeswarm", col = 2:4)

Make.Funny.Plot <- function(x){ unique.vals <- length(unique(x)) N <- length(x) N.val <- min(N/20,unique.vals) if(unique.vals>N.val){ x <- ave(x,cut(x,N.val),FUN=min) x <- signif(x,4) } # construct the outline of the plot outline <- as.vector(table(x)) outline <- outline/max(outline) # determine some correction to make the V shape, # based on the range y.corr <- diff(range(x))*0.05 # Get the unique values yval <- sort(unique(x)) plot(c(-1,1),c(min(yval),max(yval)), type="n",xaxt="n",xlab="") for(i in 1:length(yval)){ n <- sum(x==yval[i]) x.plot <- seq(-outline[i],outline[i],length=n) y.plot <- yval[i]+abs(x.plot)*y.corr points(x.plot,y.plot,pch=19,cex=0.5) } } x <- rnorm(1000) Make.Funny.Plot(x) boxplot(x, add = T, at = 0, col="#0000ff22") # my thanks goes to Greg Snow for the tip on the transparency colour (from 2007): https://stat.ethz.ch/pipermail/r-help/2007-October/142934.html

library(beeswarm) > data(breast) > beeswarm(time_survival ~ ER, data = breast, + pch = 16, pwcol = 1 + as.numeric(event_survival), + xlab = "beeswarm package", ylab = "Follow-up time (months)", + labels = c("ER neg", "ER pos")) > legend("topright", legend = c("Yes", "No"), + title = "Censored", pch = 16, col = 1:2

p<-ggplot(mapping=aes(labs, dat)) + + geom_quasirandom(method='frowney',alpha=.2) + + ggtitle('prepared by Volkan OBAN using R \n smiley') + labs(x='') + + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) > p

p2<-ggplot(mapping=aes(labs, dat)) + + geom_quasirandom(method='pseudorandom',alpha=.2) + + ggtitle('prepared by Volkan OBAN using R \n pseudorandom') + labs(x='') + + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

ggplot(mpg,aes(class, hwy)) + geom_quasirandom() + ggtitle(" prepared in R by Volkan OBAN \n ggbeeswarm package" ) + theme(plot.title = element_text(size = rel(1), colour = "purple"))

ggplot2 ggplot(mpg,aes(class, hwy)) + geom_beeswarm(cex=1.1) + ggtitle(" prepared in R by Volkan OBAN \n ggbeeswarm package" ) + theme(plot.title = element_text(size = rel(1), colour = "purple"))

ggplot(economics_long, aes(date, value01)) + + geom_line(aes(linetype = variable)) + ggtitle("prepared by Volkan OBAN using R -- ggplot2and ggthemes packages ") + theme_wsj() + scale_colour_wsj("colors6", "") + theme(plot.title = element_text(size = rel(0.5), colour = "blue"))

ggplot(economics_long, aes(date, value01)) + + geom_line(aes(linetype = variable)) + ggtitle("prepared by Volkan OBAN using R -- ggplot2and ggthemes packages ") + theme_wsj() + scale_colour_wsj("colors6", "")

ggplot(economics_long, aes(date, value01)) + + geom_line(aes(linetype = variable)) + ggtitle("prepared by Volkan OBAN using R -- ggplot2and ggthemes packages ") + theme_solarized(light = FALSE) + scale_colour_solarized("red")

ggplot(economics_long, aes(date, value)) + + geom_line() + + facet_wrap(~variable, scales = "free_y", nrow = 2, switch = "x") + + theme(strip.background = element_blank()) + theme_solarized(light = FALSE) + + scale_colour_solarized("red")

data(iris) iris.x <- iris[, 1:4] cl3 <- pam(iris.x, 3)$clustering op <- par(mfrow= c(2,2)) clusplot(iris.x, cl3, color = TRUE) U <- par("usr") ## zoom in : rect(0,-1, 2,1, border = "orange", lwd=2) clusplot(iris.x, cl3, color = TRUE, xlim = c(0,2), ylim = c(-1,1)) box(col="orange",lwd=2); mtext("sub region", font = 4, cex = 2) ## or zoom out : clusplot(iris.x, cl3, color = TRUE, xlim = c(-4,4), ylim = c(-4,4)) mtext("`super' region", font = 4, cex = 2) rect(U[1],U[3], U[2],U[4], lwd=2, lty = 3) https://stat.ethz.ch/R-manual/R-devel/library/cluster/html/clusplot.default.html

ggpairs(iris, upper=list(continuous="density"), lower=list(continuous="smooth"))

dist <- data.frame(value=rnorm(10000, 1:4), group=1:4) ggplot(dist, aes(x=value, fill=as.factor(group))) + geom_histogram(alpha=0.5) +geom_vline(data = subset(dist, group=="1"), aes(xintercept=median(value)), color="black",linetype="dashed", size=1) + geom_text(data = subset(dist, group =="1"),aes(x=median(value),y=350,label=round(median(value), digit=3)),hjust=-0.2) +facet_grid(.~group)

> dist <- data.frame(value=rnorm(10000, 1:4), group=1:4) > ggplot(dist, aes(x=group, y=value, color=group)) + geom_jitter(alpha=0.5)

mg <- ggplot(mtcars, aes(x = mpg, y = wt)) + geom_point() mg + facet_grid(vs + am ~ gear) mg + facet_grid(vs + am ~ gear, margins = TRUE)

ggplot(mpg, aes(drv, model)) + geom_point() + facet_grid(manufacturer ~ ., scales = "free", space = "free") + theme(strip.text.y = element_text(angle = 0))

qplot(circumference,age, data=Orange, geom=c("line","point"), facets=~Tree)

> library(ggplot2) > boxplot(circumference~Tree, data=Orange) > qplot(Tree,circumference, data=Orange, geom=c("boxplot","point"))

library(plotly) library(quantmod) # Download some data getSymbols(Symbols = c("AAPL", "MSFT")) ds <- data.frame(Date = index(AAPL), AAPL[,6], MSFT[,6]) p <- plot_ly(ds, x = ~Date) %>% add_lines(y = ~AAPL.Adjusted, name = "Apple") %>% add_lines(y = ~MSFT.Adjusted, name = "Microsoft") %>% layout( title = "Stock Prices", xaxis = list( rangeselector = list( buttons = list( list( count = 3, label = "3 mo", step = "month", stepmode = "backward"), list( count = 6, label = "6 mo", step = "month", stepmode = "backward"), list( count = 1, label = "1 yr", step = "year", stepmode = "backward"), list( count = 1, label = "YTD", step = "year", stepmode = "todate"), list(step = "all"))), rangeslider = list(type = "date")), yaxis = list(title = "Price"))

p <- plot_ly( + plotly::hobbs, r = ~r, t = ~t, color = ~nms, alpha = 0.5, type = "scatter" + ) > layout(p, title = "prepared by Volkan OBAN using R-plotly \n Hobbs-Pearson Trials", plot_bgcolor = toRGB("blue")

> library(plotly) > p <- plot_ly( + plotly::mic, r = ~r, t = ~t, color = ~nms, alpha = 0.5, type = "scatter" + ) > layout(p, title = "prepared by Volkan OBAN using R-plotly \n Mic Patterns", orientation = -90)

pm = ggpairs(data=tips, + columns=1:3, + upper = list(continuous = "density"), + lower = list(combo = "facetdensity"), + title="tips data", + colour = "sex") pm

https://github.com/fawda123/ggord

https://github.com/fawda123/ggord

library(ggord) library(FactoMineR) > data("tea") > tea <- tea[, c('Tea', 'sugar', 'price', 'age_Q', 'sex')] > > ord <- MCA(tea[, -1], graph = FALSE) > > ggord(ord, tea$Tea)

> m3a <- glmer(remission ~ Age + LengthofStay + FamilyHx + IL6 + CRP + + CancerStage + Experience + (1 | DID) + (1 | HID), + data = hdp, family = binomial, nAGQ=1) Warning message: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.400012 (tol = 0.001, component 1) > dotplot(ranef(m3a, which = "DID", postVar = TRUE), scales = list(y = list(alternating = 0)))

ref. and data: http://www.ats.ucla.edu/stat/r/dae/melogit.htm

ref: http://www.ats.ucla.edu/stat/r/dae/melogit.htm

p2 <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) + geom_point() + facet_wrap( ~ cyl, ncol=2, scales = "free") + guides(colour="none") + theme() grid.arrange(tableGrob(mtcars[1:4, 1:4]), p2, ncol=2, widths=c(1.5, 1), clip=FALSE)

> > moxbuller = function(n) { + u = runif(n) + v = runif(n) + x = cos(2*pi*u)*sqrt(-2*log(v)) + y = sin(2*pi*v)*sqrt(-2*log(u)) + r = list(x=x, y=y) + return(r) + } > r = moxbuller(50000) > par(bg="white") > par(mar=c(0,0,0,0)) > plot(r$x,r$y, pch=".", col="red", main=" \n \n \n prepared in R by VOLKAN OBAN", cex=1.2)

library(lattice) library(psych) d <- colMeans(msq[,c(2,7,34,36,42,43,46,55,68)],na.rm = T)*10 barchart(sort(d), xlab="", ylab="", col = "grey", origin=1, border = "transparent", box.ratio=0.5, panel = function(x,y,...) { panel.barchart(x,y,...) panel.abline(v=seq(1,6,1), col="white", lwd=3)}, par.settings = list(axis.line = list(col = "transparent"))) ltext(current.panel.limits()$xlim[2]-50, adj=1, current.panel.limits()$ylim[1]-100, "Average scores\non negative emotion traits\nfrom 3896 participants\n(Watson et al., 1988)")

#--Load previously saved data: path <- "http://www.sr.bham.ac.uk/~ajrs/R/datasets" a <- load(url(paste(path,"middayweather.RData",sep="/"))) close(url(paste(path,"middayweather.RData",sep="/"))) #--Load extra library: ## if not already installed, then run: # install.packages("ggplot2") require(ggplot2) #--Calculate month from date & create factor: middayweather$month <- with(middayweather, factor(as.POSIXlt(Date)$mon, label=month.abb)) #--Calculate year from date: middayweather$year <- with(middayweather, 1900 + as.POSIXlt(Date)$year) #--Only use complete years: middayweather <- subset(middayweather, year %in% 2007:2009) #-----Plot data as boxplot summary for each month: theme_set(theme_gray(base_size = 11)) p <- ggplot(data=middayweather, aes(month, T.out)) + geom_boxplot() + facet_wrap( ~ year, nrow=1) + scale_x_discrete(breaks=month.abb[c(1, 4, 7, 10)]) + xlab("") + ylab(as.expression(expression( paste("Temperature (", degree,"C)") )))

> coplot(ll.dm, data = quakes, number = c(3, 7), overlap = c(-.5, .1),xlab = "long", bar.bg = c(fac = "blue"))

coplot(breaks ~ Index | wool * tension, data = warpbreaks, + col = "red", bg = "black", pch = 21,xlab = "Index", + bar.bg = c(fac = "purple"))

> par(mfrow=c(1,3)) > mysplits = split(mtcars,mtcars$cyl) > maxmpg = max(mtcars$mpg) > for (ii in 1:length(mysplits)) { + tmpdf <- mysplits[[ii]] + auto <- tmpdf[tmpdf$am == 0,] + man <- tmpdf[tmpdf$am == 1,] + plot(tmpdf$wt , tmpdf$mpg,type="n", + main=paste(names(mysplits[ii])," Cylinders"), + ylim=c(0,maxmpg), xlab="wt",ylab="MPG") + points(auto$wt,auto$mpg,col="blue",pch=19) + points(man$wt,man$mpg,col="red",pch=19) + grid() + legend("topright", inset=0.05, c("manual","auto"), pch = 19, col=c("red","blue")) }

> defect.counts <- c(12,29,18,3,34,4) > names(defect.counts) <- c("Weather","Overslept", "Alarm Failure", + "Time Change","Traffic","Other") > df.defects <- data.frame(defect.counts) > > df.defects library(qcc) pareto.chart(defect.counts)

ref:http://analyzecore.com/2015/04/01/cohort-analysis-and-lifecycle-grids-mixed-segmentation-with-r/

library(dplyr) library(reshape2) library(ggplot2) library(scales) library(gridExtra) # creating data sample set.seed(10) cohorts <- data.frame(cohort = paste('cohort', formatC(c(1:36), width=2, format='d', flag='0'), sep = '_'), Y_00 = sample(c(1300:1500), 36, replace = TRUE), Y_01 = c(sample(c(800:1000), 36, replace = TRUE)), Y_02 = c(sample(c(600:800), 24, replace = TRUE), rep(NA, 12)), Y_03 = c(sample(c(400:500), 12, replace = TRUE), rep(NA, 24))) # simulating seasonality (Black Friday) cohorts[c(11, 23, 35), 2] <- as.integer(cohorts[c(11, 23, 35), 2] * 1.25) cohorts[c(11, 23, 35), 3] <- as.integer(cohorts[c(11, 23, 35), 3] * 1.10) cohorts[c(11, 23, 35), 4] <- as.integer(cohorts[c(11, 23, 35), 4] * 1.07) # calculating retention rate and preparing data for plotting df_plot <- melt(cohorts, id.vars = 'cohort', value.name = 'number', variable.name = 'year_of_LT') df_plot <- df_plot %>% group_by(cohort) %>% arrange(year_of_LT) %>% mutate(number_prev_year = lag(number), number_Y_00 = number[which(year_of_LT == 'Y_00')]) %>% ungroup() %>% mutate(ret_rate_prev_year = number / number_prev_year, ret_rate = number / number_Y_00, year_cohort = paste(year_of_LT, cohort, sep = '-')) ##### The first way for plotting cycle plot via scaling # calculating the coefficient for scaling 2nd axis k <- max(df_plot$number_prev_year[df_plot$year_of_LT == 'Y_01'] * 1.15) / min(df_plot$ret_rate[df_plot$year_of_LT == 'Y_01']) # retention rate cycle plot ggplot(na.omit(df_plot), aes(x = year_cohort, y = ret_rate, group = year_of_LT, color = year_of_LT)) + theme_bw() + geom_point(size = 4) + geom_text(aes(label = percent(round(ret_rate, 2))), size = 4, hjust = 0.4, vjust = -0.6, fontface = "plain") + # smooth method can be changed (e.g. for "lm") geom_smooth(size = 2.5, method = 'loess', color = 'darkred', aes(fill = year_of_LT)) + geom_bar(aes(y = number_prev_year / k, fill = year_of_LT), alpha = 0.2, stat = 'identity') + geom_bar(aes(y = number / k, fill = year_of_LT), alpha = 0.6, stat = 'identity') + geom_text(aes(y = 0, label = cohort), color = 'white', angle = 90, size = 4, hjust = -0.05, vjust = 0.4) + geom_text(aes(y = number_prev_year / k, label = number_prev_year), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + geom_text(aes(y = number / k, label = number), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_text(size=16), axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(x = 'Year of Lifetime by Cohorts', y = 'Number of Customers / Retention Rate') + ggtitle("Customer Retention Rate - Cycle plot") ##### The second way for plotting cycle plot via multi-plotting # plot #1 - Retention rate p1 <- ggplot(na.omit(df_plot), aes(x = year_cohort, y = ret_rate, group = year_of_LT, color = year_of_LT)) + theme_bw() + geom_point(size = 4) + geom_text(aes(label = percent(round(ret_rate, 2))), size = 4, hjust = 0.4, vjust = -0.6, fontface = "plain") + geom_smooth(size = 2.5, method = 'loess', color = 'darkred', aes(fill = year_of_LT)) + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_blank(), axis.title.y = element_text(size=18, face="bold"), axis.text = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(y = 'Retention Rate') + ggtitle("Customer Retention Rate - Cycle plot") # plot #2 - number of customers p2 <- ggplot(na.omit(df_plot), aes(x = year_cohort, group = year_of_LT, color = year_of_LT)) + theme_bw() + geom_bar(aes(y = number_prev_year, fill = year_of_LT), alpha = 0.2, stat = 'identity') + geom_bar(aes(y = number, fill = year_of_LT), alpha = 0.6, stat = 'identity') + geom_text(aes(y = number_prev_year, label = number_prev_year), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + geom_text(aes(y = number, label = number), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + geom_text(aes(y = 0, label = cohort), color = 'white', angle = 90, size = 4, hjust = -0.05, vjust = 0.4) + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + scale_y_continuous(limits = c(0, max(df_plot$number_Y_00 * 1.1))) + labs(x = 'Year of Lifetime by Cohorts', y = 'Number of Customers') # multiplot grid.arrange(p1, p2, ncol = 1) # retention rate bubble chart ggplot(na.omit(df_plot), aes(x = cohort, y = ret_rate, group = cohort, color = year_of_LT)) + theme_bw() + scale_size(range = c(15, 40)) + geom_line(size = 2, alpha = 0.3) + geom_point(aes(size = number_prev_year), alpha = 0.3) + geom_point(aes(size = number), alpha = 0.8) + geom_smooth(linetype = 2, size = 2, method = 'loess', aes(group = year_of_LT, fill = year_of_LT), alpha = 0.2) + geom_text(aes(label = paste0(number, '/', number_prev_year, '\n', percent(round(ret_rate, 2)))), color = 'white', size = 3, hjust = 0.5, vjust = 0.5, fontface = "plain") + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_text(size=16), axis.text.x = element_text(size=10, angle=90, hjust=.5, vjust=.5, face="plain"), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(x = 'Cohorts', y = 'Retention Rate by Year of Lifetime') + ggtitle("Customer Retention Rate - Bubble chart") # retention rate falling drops chart ggplot(df_plot, aes(x = cohort, y = ret_rate, group = cohort, color = year_of_LT)) + theme_bw() + scale_size(range = c(15, 40)) + scale_y_continuous(limits = c(0, 1)) + geom_line(size = 2, alpha = 0.3) + geom_point(aes(size = number), alpha = 0.8) + geom_text(aes(label = paste0(number, '\n', percent(round(ret_rate, 2)))), color = 'white', size = 3, hjust = 0.5, vjust = 0.5, fontface = "plain") + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_text(size=16), axis.text.x = element_text(size=10, angle=90, hjust=.5, vjust=.5, face="plain"), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(x = 'Cohorts', y = 'Retention Rate by Year of Lifetime') + ggtitle("Customer Retention Rate - Falling Drops chart")

library(dplyr) library(reshape2) library(ggplot2) library(scales) library(gridExtra) # creating data sample set.seed(10) cohorts <- data.frame(cohort = paste('cohort', formatC(c(1:36), width=2, format='d', flag='0'), sep = '_'), Y_00 = sample(c(1300:1500), 36, replace = TRUE), Y_01 = c(sample(c(800:1000), 36, replace = TRUE)), Y_02 = c(sample(c(600:800), 24, replace = TRUE), rep(NA, 12)), Y_03 = c(sample(c(400:500), 12, replace = TRUE), rep(NA, 24))) # simulating seasonality (Black Friday) cohorts[c(11, 23, 35), 2] <- as.integer(cohorts[c(11, 23, 35), 2] * 1.25) cohorts[c(11, 23, 35), 3] <- as.integer(cohorts[c(11, 23, 35), 3] * 1.10) cohorts[c(11, 23, 35), 4] <- as.integer(cohorts[c(11, 23, 35), 4] * 1.07) # calculating retention rate and preparing data for plotting df_plot <- melt(cohorts, id.vars = 'cohort', value.name = 'number', variable.name = 'year_of_LT') df_plot <- df_plot %>% group_by(cohort) %>% arrange(year_of_LT) %>% mutate(number_prev_year = lag(number), number_Y_00 = number[which(year_of_LT == 'Y_00')]) %>% ungroup() %>% mutate(ret_rate_prev_year = number / number_prev_year, ret_rate = number / number_Y_00, year_cohort = paste(year_of_LT, cohort, sep = '-')) ##### The first way for plotting cycle plot via scaling # calculating the coefficient for scaling 2nd axis k <- max(df_plot$number_prev_year[df_plot$year_of_LT == 'Y_01'] * 1.15) / min(df_plot$ret_rate[df_plot$year_of_LT == 'Y_01']) # retention rate cycle plot ggplot(na.omit(df_plot), aes(x = year_cohort, y = ret_rate, group = year_of_LT, color = year_of_LT)) + theme_bw() + geom_point(size = 4) + geom_text(aes(label = percent(round(ret_rate, 2))), size = 4, hjust = 0.4, vjust = -0.6, fontface = "plain") + # smooth method can be changed (e.g. for "lm") geom_smooth(size = 2.5, method = 'loess', color = 'darkred', aes(fill = year_of_LT)) + geom_bar(aes(y = number_prev_year / k, fill = year_of_LT), alpha = 0.2, stat = 'identity') + geom_bar(aes(y = number / k, fill = year_of_LT), alpha = 0.6, stat = 'identity') + geom_text(aes(y = 0, label = cohort), color = 'white', angle = 90, size = 4, hjust = -0.05, vjust = 0.4) + geom_text(aes(y = number_prev_year / k, label = number_prev_year), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + geom_text(aes(y = number / k, label = number), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_text(size=16), axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(x = 'Year of Lifetime by Cohorts', y = 'Number of Customers / Retention Rate') + ggtitle("Customer Retention Rate - Cycle plot") ##### The second way for plotting cycle plot via multi-plotting # plot #1 - Retention rate p1 <- ggplot(na.omit(df_plot), aes(x = year_cohort, y = ret_rate, group = year_of_LT, color = year_of_LT)) + theme_bw() + geom_point(size = 4) + geom_text(aes(label = percent(round(ret_rate, 2))), size = 4, hjust = 0.4, vjust = -0.6, fontface = "plain") + geom_smooth(size = 2.5, method = 'loess', color = 'darkred', aes(fill = year_of_LT)) + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_blank(), axis.title.y = element_text(size=18, face="bold"), axis.text = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(y = 'Retention Rate') + ggtitle("Customer Retention Rate - Cycle plot") # plot #2 - number of customers p2 <- ggplot(na.omit(df_plot), aes(x = year_cohort, group = year_of_LT, color = year_of_LT)) + theme_bw() + geom_bar(aes(y = number_prev_year, fill = year_of_LT), alpha = 0.2, stat = 'identity') + geom_bar(aes(y = number, fill = year_of_LT), alpha = 0.6, stat = 'identity') + geom_text(aes(y = number_prev_year, label = number_prev_year), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + geom_text(aes(y = number, label = number), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + geom_text(aes(y = 0, label = cohort), color = 'white', angle = 90, size = 4, hjust = -0.05, vjust = 0.4) + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + scale_y_continuous(limits = c(0, max(df_plot$number_Y_00 * 1.1))) + labs(x = 'Year of Lifetime by Cohorts', y = 'Number of Customers') # multiplot grid.arrange(p1, p2, ncol = 1) # retention rate bubble chart ggplot(na.omit(df_plot), aes(x = cohort, y = ret_rate, group = cohort, color = year_of_LT)) + theme_bw() + scale_size(range = c(15, 40)) + geom_line(size = 2, alpha = 0.3) + geom_point(aes(size = number_prev_year), alpha = 0.3) + geom_point(aes(size = number), alpha = 0.8) + geom_smooth(linetype = 2, size = 2, method = 'loess', aes(group = year_of_LT, fill = year_of_LT), alpha = 0.2) + geom_text(aes(label = paste0(number, '/', number_prev_year, '\n', percent(round(ret_rate, 2)))), color = 'white', size = 3, hjust = 0.5, vjust = 0.5, fontface = "plain") + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_text(size=16), axis.text.x = element_text(size=10, angle=90, hjust=.5, vjust=.5, face="plain"), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(x = 'Cohorts', y = 'Retention Rate by Year of Lifetime') + ggtitle("Customer Retention Rate - Bubble chart") # retention rate falling drops chart ggplot(df_plot, aes(x = cohort, y = ret_rate, group = cohort, color = year_of_LT)) + theme_bw() + scale_size(range = c(15, 40)) + scale_y_continuous(limits = c(0, 1)) + geom_line(size = 2, alpha = 0.3) + geom_point(aes(size = number), alpha = 0.8) + geom_text(aes(label = paste0(number, '\n', percent(round(ret_rate, 2)))), color = 'white', size = 3, hjust = 0.5, vjust = 0.5, fontface = "plain") + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_text(size=16), axis.text.x = element_text(size=10, angle=90, hjust=.5, vjust=.5, face="plain"), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(x = 'Cohorts', y = 'Retention Rate by Year of Lifetime') + ggtitle("Customer Retention Rate - Falling Drops chart")

library(dplyr) library(reshape2) library(ggplot2) library(scales) library(gridExtra) # creating data sample set.seed(10) cohorts <- data.frame(cohort = paste('cohort', formatC(c(1:36), width=2, format='d', flag='0'), sep = '_'), Y_00 = sample(c(1300:1500), 36, replace = TRUE), Y_01 = c(sample(c(800:1000), 36, replace = TRUE)), Y_02 = c(sample(c(600:800), 24, replace = TRUE), rep(NA, 12)), Y_03 = c(sample(c(400:500), 12, replace = TRUE), rep(NA, 24))) # simulating seasonality (Black Friday) cohorts[c(11, 23, 35), 2] <- as.integer(cohorts[c(11, 23, 35), 2] * 1.25) cohorts[c(11, 23, 35), 3] <- as.integer(cohorts[c(11, 23, 35), 3] * 1.10) cohorts[c(11, 23, 35), 4] <- as.integer(cohorts[c(11, 23, 35), 4] * 1.07) # calculating retention rate and preparing data for plotting df_plot <- melt(cohorts, id.vars = 'cohort', value.name = 'number', variable.name = 'year_of_LT') df_plot <- df_plot %>% group_by(cohort) %>% arrange(year_of_LT) %>% mutate(number_prev_year = lag(number), number_Y_00 = number[which(year_of_LT == 'Y_00')]) %>% ungroup() %>% mutate(ret_rate_prev_year = number / number_prev_year, ret_rate = number / number_Y_00, year_cohort = paste(year_of_LT, cohort, sep = '-')) ##### The first way for plotting cycle plot via scaling # calculating the coefficient for scaling 2nd axis k <- max(df_plot$number_prev_year[df_plot$year_of_LT == 'Y_01'] * 1.15) / min(df_plot$ret_rate[df_plot$year_of_LT == 'Y_01']) # retention rate cycle plot ggplot(na.omit(df_plot), aes(x = year_cohort, y = ret_rate, group = year_of_LT, color = year_of_LT)) + theme_bw() + geom_point(size = 4) + geom_text(aes(label = percent(round(ret_rate, 2))), size = 4, hjust = 0.4, vjust = -0.6, fontface = "plain") + # smooth method can be changed (e.g. for "lm") geom_smooth(size = 2.5, method = 'loess', color = 'darkred', aes(fill = year_of_LT)) + geom_bar(aes(y = number_prev_year / k, fill = year_of_LT), alpha = 0.2, stat = 'identity') + geom_bar(aes(y = number / k, fill = year_of_LT), alpha = 0.6, stat = 'identity') + geom_text(aes(y = 0, label = cohort), color = 'white', angle = 90, size = 4, hjust = -0.05, vjust = 0.4) + geom_text(aes(y = number_prev_year / k, label = number_prev_year), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + geom_text(aes(y = number / k, label = number), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_text(size=16), axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(x = 'Year of Lifetime by Cohorts', y = 'Number of Customers / Retention Rate') + ggtitle("Customer Retention Rate - Cycle plot") ##### The second way for plotting cycle plot via multi-plotting # plot #1 - Retention rate p1 <- ggplot(na.omit(df_plot), aes(x = year_cohort, y = ret_rate, group = year_of_LT, color = year_of_LT)) + theme_bw() + geom_point(size = 4) + geom_text(aes(label = percent(round(ret_rate, 2))), size = 4, hjust = 0.4, vjust = -0.6, fontface = "plain") + geom_smooth(size = 2.5, method = 'loess', color = 'darkred', aes(fill = year_of_LT)) + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_blank(), axis.title.y = element_text(size=18, face="bold"), axis.text = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(y = 'Retention Rate') + ggtitle("Customer Retention Rate - Cycle plot") # plot #2 - number of customers p2 <- ggplot(na.omit(df_plot), aes(x = year_cohort, group = year_of_LT, color = year_of_LT)) + theme_bw() + geom_bar(aes(y = number_prev_year, fill = year_of_LT), alpha = 0.2, stat = 'identity') + geom_bar(aes(y = number, fill = year_of_LT), alpha = 0.6, stat = 'identity') + geom_text(aes(y = number_prev_year, label = number_prev_year), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + geom_text(aes(y = number, label = number), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + geom_text(aes(y = 0, label = cohort), color = 'white', angle = 90, size = 4, hjust = -0.05, vjust = 0.4) + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + scale_y_continuous(limits = c(0, max(df_plot$number_Y_00 * 1.1))) + labs(x = 'Year of Lifetime by Cohorts', y = 'Number of Customers') # multiplot grid.arrange(p1, p2, ncol = 1) # retention rate bubble chart ggplot(na.omit(df_plot), aes(x = cohort, y = ret_rate, group = cohort, color = year_of_LT)) + theme_bw() + scale_size(range = c(15, 40)) + geom_line(size = 2, alpha = 0.3) + geom_point(aes(size = number_prev_year), alpha = 0.3) + geom_point(aes(size = number), alpha = 0.8) + geom_smooth(linetype = 2, size = 2, method = 'loess', aes(group = year_of_LT, fill = year_of_LT), alpha = 0.2) + geom_text(aes(label = paste0(number, '/', number_prev_year, '\n', percent(round(ret_rate, 2)))), color = 'white', size = 3, hjust = 0.5, vjust = 0.5, fontface = "plain") + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_text(size=16), axis.text.x = element_text(size=10, angle=90, hjust=.5, vjust=.5, face="plain"), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(x = 'Cohorts', y = 'Retention Rate by Year of Lifetime') + ggtitle("Customer Retention Rate - Bubble chart") # retention rate falling drops chart ggplot(df_plot, aes(x = cohort, y = ret_rate, group = cohort, color = year_of_LT)) + theme_bw() + scale_size(range = c(15, 40)) + scale_y_continuous(limits = c(0, 1)) + geom_line(size = 2, alpha = 0.3) + geom_point(aes(size = number), alpha = 0.8) + geom_text(aes(label = paste0(number, '\n', percent(round(ret_rate, 2)))), color = 'white', size = 3, hjust = 0.5, vjust = 0.5, fontface = "plain") + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_text(size=16), axis.text.x = element_text(size=10, angle=90, hjust=.5, vjust=.5, face="plain"), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(x = 'Cohorts', y = 'Retention Rate by Year of Lifetime') + ggtitle("Customer Retention Rate - Falling Drops chart")

library(dplyr) library(reshape2) library(ggplot2) library(scales) library(gridExtra) # creating data sample set.seed(10) cohorts <- data.frame(cohort = paste('cohort', formatC(c(1:36), width=2, format='d', flag='0'), sep = '_'), Y_00 = sample(c(1300:1500), 36, replace = TRUE), Y_01 = c(sample(c(800:1000), 36, replace = TRUE)), Y_02 = c(sample(c(600:800), 24, replace = TRUE), rep(NA, 12)), Y_03 = c(sample(c(400:500), 12, replace = TRUE), rep(NA, 24))) # simulating seasonality (Black Friday) cohorts[c(11, 23, 35), 2] <- as.integer(cohorts[c(11, 23, 35), 2] * 1.25) cohorts[c(11, 23, 35), 3] <- as.integer(cohorts[c(11, 23, 35), 3] * 1.10) cohorts[c(11, 23, 35), 4] <- as.integer(cohorts[c(11, 23, 35), 4] * 1.07) # calculating retention rate and preparing data for plotting df_plot <- melt(cohorts, id.vars = 'cohort', value.name = 'number', variable.name = 'year_of_LT') df_plot <- df_plot %>% group_by(cohort) %>% arrange(year_of_LT) %>% mutate(number_prev_year = lag(number), number_Y_00 = number[which(year_of_LT == 'Y_00')]) %>% ungroup() %>% mutate(ret_rate_prev_year = number / number_prev_year, ret_rate = number / number_Y_00, year_cohort = paste(year_of_LT, cohort, sep = '-')) ##### The first way for plotting cycle plot via scaling # calculating the coefficient for scaling 2nd axis k <- max(df_plot$number_prev_year[df_plot$year_of_LT == 'Y_01'] * 1.15) / min(df_plot$ret_rate[df_plot$year_of_LT == 'Y_01']) # retention rate cycle plot ggplot(na.omit(df_plot), aes(x = year_cohort, y = ret_rate, group = year_of_LT, color = year_of_LT)) + theme_bw() + geom_point(size = 4) + geom_text(aes(label = percent(round(ret_rate, 2))), size = 4, hjust = 0.4, vjust = -0.6, fontface = "plain") + # smooth method can be changed (e.g. for "lm") geom_smooth(size = 2.5, method = 'loess', color = 'darkred', aes(fill = year_of_LT)) + geom_bar(aes(y = number_prev_year / k, fill = year_of_LT), alpha = 0.2, stat = 'identity') + geom_bar(aes(y = number / k, fill = year_of_LT), alpha = 0.6, stat = 'identity') + geom_text(aes(y = 0, label = cohort), color = 'white', angle = 90, size = 4, hjust = -0.05, vjust = 0.4) + geom_text(aes(y = number_prev_year / k, label = number_prev_year), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + geom_text(aes(y = number / k, label = number), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_text(size=16), axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(x = 'Year of Lifetime by Cohorts', y = 'Number of Customers / Retention Rate') + ggtitle("Customer Retention Rate - Cycle plot") ##### The second way for plotting cycle plot via multi-plotting # plot #1 - Retention rate p1 <- ggplot(na.omit(df_plot), aes(x = year_cohort, y = ret_rate, group = year_of_LT, color = year_of_LT)) + theme_bw() + geom_point(size = 4) + geom_text(aes(label = percent(round(ret_rate, 2))), size = 4, hjust = 0.4, vjust = -0.6, fontface = "plain") + geom_smooth(size = 2.5, method = 'loess', color = 'darkred', aes(fill = year_of_LT)) + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_blank(), axis.title.y = element_text(size=18, face="bold"), axis.text = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(y = 'Retention Rate') + ggtitle("Customer Retention Rate - Cycle plot") # plot #2 - number of customers p2 <- ggplot(na.omit(df_plot), aes(x = year_cohort, group = year_of_LT, color = year_of_LT)) + theme_bw() + geom_bar(aes(y = number_prev_year, fill = year_of_LT), alpha = 0.2, stat = 'identity') + geom_bar(aes(y = number, fill = year_of_LT), alpha = 0.6, stat = 'identity') + geom_text(aes(y = number_prev_year, label = number_prev_year), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + geom_text(aes(y = number, label = number), angle = 90, size = 4, hjust = -0.1, vjust = 0.4) + geom_text(aes(y = 0, label = cohort), color = 'white', angle = 90, size = 4, hjust = -0.05, vjust = 0.4) + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + scale_y_continuous(limits = c(0, max(df_plot$number_Y_00 * 1.1))) + labs(x = 'Year of Lifetime by Cohorts', y = 'Number of Customers') # multiplot grid.arrange(p1, p2, ncol = 1) # retention rate bubble chart ggplot(na.omit(df_plot), aes(x = cohort, y = ret_rate, group = cohort, color = year_of_LT)) + theme_bw() + scale_size(range = c(15, 40)) + geom_line(size = 2, alpha = 0.3) + geom_point(aes(size = number_prev_year), alpha = 0.3) + geom_point(aes(size = number), alpha = 0.8) + geom_smooth(linetype = 2, size = 2, method = 'loess', aes(group = year_of_LT, fill = year_of_LT), alpha = 0.2) + geom_text(aes(label = paste0(number, '/', number_prev_year, '\n', percent(round(ret_rate, 2)))), color = 'white', size = 3, hjust = 0.5, vjust = 0.5, fontface = "plain") + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_text(size=16), axis.text.x = element_text(size=10, angle=90, hjust=.5, vjust=.5, face="plain"), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(x = 'Cohorts', y = 'Retention Rate by Year of Lifetime') + ggtitle("Customer Retention Rate - Bubble chart") # retention rate falling drops chart ggplot(df_plot, aes(x = cohort, y = ret_rate, group = cohort, color = year_of_LT)) + theme_bw() + scale_size(range = c(15, 40)) + scale_y_continuous(limits = c(0, 1)) + geom_line(size = 2, alpha = 0.3) + geom_point(aes(size = number), alpha = 0.8) + geom_text(aes(label = paste0(number, '\n', percent(round(ret_rate, 2)))), color = 'white', size = 3, hjust = 0.5, vjust = 0.5, fontface = "plain") + theme(legend.position='none', plot.title = element_text(size=20, face="bold", vjust=2), axis.title.x = element_text(size=18, face="bold"), axis.title.y = element_text(size=18, face="bold"), axis.text = element_text(size=16), axis.text.x = element_text(size=10, angle=90, hjust=.5, vjust=.5, face="plain"), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(x = 'Cohorts', y = 'Retention Rate by Year of Lifetime') + ggtitle("Customer Retention Rate - Falling Drops chart")

cohort.sum <- data.frame(cohort=c('Cohort01', 'Cohort02', 'Cohort03', 'Cohort04', 'Cohort05', 'Cohort06', 'Cohort07', 'Cohort08', 'Cohort09', 'Cohort10', 'Cohort11', 'Cohort12'), M1=c(270000,0,0,0,0,0,0,0,0,0,0,0), M2=c(85000,275000,0,0,0,0,0,0,0,0,0,0), M3=c(72000,63000,277000,0,0,0,0,0,0,0,0,0), M4=c(52000,42000,76000,361000,0,0,0,0,0,0,0,0), M5=c(50000,45000,60000,80000,288000,0,0,0,0,0,0,0), M6=c(51000,52000,55000,51000,58000,253000,0,0,0,0,0,0), M7=c(51000,69000,48000,45000,42000,54000,272000,0,0,0,0,0), M8=c(46000,85000,77000,41000,38000,37000,74000,352000,0,0,0,0), M9=c(38000,42000,72000,41000,31000,30000,49000,107000,285000,0,0,0), M10=c(39000,38000,45000,33000,34000,34000,46000,83000,69000,279000,0,0), M11=c(38000,42000,31000,32000,26000,28000,43000,82000,51000,87000,282000,0), M12=c(35000,35000,38000,45000,35000,32000,48000,44000,47000,52000,92000,500000)) ibrary(ggplot2) library(reshape2) #we need to melt data cohort.chart <- melt(cohort.sum, id.vars = "cohort") colnames(cohort.chart) <- c('cohort', 'month', 'revenue') #define palette blues <- colorRampPalette(c('red', 'black')) #plot data p <- ggplot(cohort.chart, aes(x=month, y=revenue, group=cohort)) p + geom_area(aes(fill = cohort)) + scale_fill_manual(values = blues(nrow(cohort.sum))) + ggtitle('Total revenue by Cohort')

bp <- figure( height = 400, width = 700 ) data(barley,package="lattice") # no facet to get started bp %>% ly_points( yield, variety, barley, color = year, hover = list( variety, yield ) ) # now show facet lapply( levels( barley$site ) ,function(s){ figure( height = 200, width = 700, title = s ) %>% ly_points( yield, variety # for fun do without dplyr , data = subset(barley, site == s) , color = year , hover = list( variety, yield ) , size = 6 ) } ) %>% grid_plot( nrow = length(.), ncol = 1, same_axes = T )

library(rbokeh) library(ggplot2) data(diamonds) bp <- figure( height = 400, width = 700 ) bp %>% ly_points(carat,price,diamonds,color=cut,size=5) # add hover bp %>% ly_points(carat,price,diamonds,color=cut,size=1,hover=list(cut,clarity,color)) # make it a hexbin bp %>% ly_hexbin( carat, price, diamonds ) # make it a hexbin with facet by color lapply( levels(unique(diamonds$color)) ,function(c){ figure( height = 300, width = 300, title = paste0("Color: ",c) ) %>% ly_hexbin( carat, log(price), diamonds[which(diamonds$color==c),] ) } ) %>% grid_plot( nrow = 3, ncol = 3, same_axes = T ) # histogram on diamonds bp %>% ly_hist( x = carat, data = diamonds, breaks = 2 ) # density on diamonds bp %>% ly_density( x = carat, data = diamonds ) # quantile on diamonds bp %>% ly_quantile(price,group = "color", diamonds) bp %>% ly_quantile(price,group = "color", diamonds, distn=qnorm) #demo a transform bp %>% ly_points( cyl, mpg^2, mtcars ) %>% # not transformed ly_points( cyl, mpg, mtcars, color = "red" ) %>% # axis need to come after layers specified y_axis( log = T ) # set vs map color bp %>% ly_points( mpg, wt, mtcars, color = "purple") bp %>% ly_points( mpg, wt, data.frame(name=rownames(mtcars),mtcars), color = cyl, hover = list(name)) # boxplot data("Oxboys", package = "nlme") bp %>% ly_boxplot( Occasion, height, Oxboys )

library(rbokeh) library(ggplot2) data(diamonds) bp <- figure( height = 400, width = 700 ) bp %>% ly_points(carat,price,diamonds,color=cut,size=5) # add hover bp %>% ly_points(carat,price,diamonds,color=cut,size=1,hover=list(cut,clarity,color)) # make it a hexbin bp %>% ly_hexbin( carat, price, diamonds ) # make it a hexbin with facet by color lapply( levels(unique(diamonds$color)) ,function(c){ figure( height = 300, width = 300, title = paste0("Color: ",c) ) %>% ly_hexbin( carat, log(price), diamonds[which(diamonds$color==c),] ) } ) %>% grid_plot( nrow = 3, ncol = 3, same_axes = T ) # histogram on diamonds bp %>% ly_hist( x = carat, data = diamonds, breaks = 2 ) # density on diamonds bp %>% ly_density( x = carat, data = diamonds ) # quantile on diamonds bp %>% ly_quantile(price,group = "color", diamonds) bp %>% ly_quantile(price,group = "color", diamonds, distn=qnorm) #demo a transform bp %>% ly_points( cyl, mpg^2, mtcars ) %>% # not transformed ly_points( cyl, mpg, mtcars, color = "red" ) %>% # axis need to come after layers specified y_axis( log = T ) # set vs map color bp %>% ly_points( mpg, wt, mtcars, color = "purple") bp %>% ly_points( mpg, wt, data.frame(name=rownames(mtcars),mtcars), color = cyl, hover = list(name)) # boxplot data("Oxboys", package = "nlme") bp %>% ly_boxplot( Occasion, height, Oxboys )

library(rbokeh) library(ggplot2) data(diamonds) bp <- figure( height = 400, width = 700 ) bp %>% ly_points(carat,price,diamonds,color=cut,size=5) # add hover bp %>% ly_points(carat,price,diamonds,color=cut,size=1,hover=list(cut,clarity,color)) # make it a hexbin bp %>% ly_hexbin( carat, price, diamonds ) # make it a hexbin with facet by color lapply( levels(unique(diamonds$color)) ,function(c){ figure( height = 300, width = 300, title = paste0("Color: ",c) ) %>% ly_hexbin( carat, log(price), diamonds[which(diamonds$color==c),] ) } ) %>% grid_plot( nrow = 3, ncol = 3, same_axes = T ) # histogram on diamonds bp %>% ly_hist( x = carat, data = diamonds, breaks = 2 ) # density on diamonds bp %>% ly_density( x = carat, data = diamonds ) # quantile on diamonds bp %>% ly_quantile(price,group = "color", diamonds) bp %>% ly_quantile(price,group = "color", diamonds, distn=qnorm) #demo a transform bp %>% ly_points( cyl, mpg^2, mtcars ) %>% # not transformed ly_points( cyl, mpg, mtcars, color = "red" ) %>% # axis need to come after layers specified y_axis( log = T ) # set vs map color bp %>% ly_points( mpg, wt, mtcars, color = "purple") bp %>% ly_points( mpg, wt, data.frame(name=rownames(mtcars),mtcars), color = cyl, hover = list(name)) # boxplot data("Oxboys", package = "nlme") bp %>% ly_boxplot( Occasion, height, Oxboys )

p <- ggplot(diamonds, aes(x=factor(color), y=carat)) > > # Boxplot of diamond carat as a function of diamond color > p + geom_boxplot() p + geom_boxplot() + coord_flip() > > # Plot3: Set aesthetics to fixed value > p + geom_boxplot(fill = "palegreen", color = "blue4", size=0.5, outlier.color = "blue4", outlier.size = 2) > > # Plot4: Vary fill by diamond color > p + geom_boxplot(aes(fill=factor(color))) > > # Plot5: Add more dimensions with new aesthetic mappings > p + geom_boxplot(aes(fill = factor(cut))

> library(plotly) > > p <- ggplot(mtcars, aes(x = factor(gear), y = mpg, color = cyl)) + + geom_boxplot() + + geom_jitter(size = 5) > > > ggplotly(p)

> library(ggplot2) > ggplot(diamonds, aes(cut, color)) + geom_jitter(aes(color = cut), size = 0.5)

df <- ToothGrowth > df$dose <- as.factor(df$dose) > data_summary <- function(data, varname, grps){ + require(plyr) + summary_func <- function(x, col){ + c(mean = mean(x[[col]], na.rm=TRUE), + sd = sd(x[[col]], na.rm=TRUE)) + } + data_sum<-ddply(data, grps, .fun=summary_func, varname) + data_sum <- rename(data_sum, c("mean" = varname)) + return(data_sum) + } > df2 <- data_summary(df, varname="len", grps= "dose") Loading required package: plyr Attaching package: ‘plyr’ The following objects are masked from ‘package:plotly’: arrange, mutate, rename, summarise The following object is masked from ‘package:network’: is.discrete The following object is masked from ‘package:graph’: join Warning message: package ‘plyr’ was built under R version 3.3.1 > # Convert dose to a factor variable > df2$dose=as.factor(df2$dose) > head(df2) dose len sd 1 0.5 10.605 4.499763 2 1 19.735 4.415436 3 2 26.100 3.774150 > f <- ggplot(df2, aes(x = dose, y = len, + ymin = len-sd, ymax = len+sd)) > f + geom_crossbar() > # color by groups > f + geom_crossbar(aes(color = dose)) > # Change color manually > f + geom_crossbar(aes(color = dose)) + + scale_color_manual(values = c("#999999", "#E69F00", "#56B4E9"))+ + theme_minimal() > # fill by groups and change color manually > f + geom_crossbar(aes(fill = dose)) + + scale_fill_manual(values = c("#999999", "#E69F00", "#56B4E9"))+ + theme_minimal() + ggtitle("prepared by Volkan OBAN in R") > library(ggthemes) > f + geom_crossbar() > # color by groups > f + geom_crossbar(aes(color = dose)) > # Change color manually > f + geom_crossbar(aes(color = dose)) + + scale_color_manual(values = c("#999999", "#E69F00", "#56B4E9"))+ + theme_minimal() > # fill by groups and change color manually > f + geom_crossbar(aes(fill = dose)) + + scale_fill_manual(values = c("#999999", "#E69F00", "#56B4E9"))+ theme_economist() + scale_colour_economist()

ggplot(diamonds, aes(cut, color)) + ggtitle("prepared in R by Volkan OBAN \n ggplot2 package-data=diamonds") + + geom_jitter(aes(color = cut), size = 0.5

x<- c(-1,-0.75,-0.5,-0.25,0,0.25,0.5,0.75,1) > y<- c(-1,-0.75,-0.5,-0.25,0,0.25,0.5,0.75,1) > z<- c(0.226598762, 0.132395904, 0.14051906, 0.208607098, 0.320840304, + 0.429423216, 0.54086732, 0.647792527, 0.256692375, + 0.256403273, 0.172881269, 0.121978079, 0.156718831, 0.17175081, + 0.32791861, 0.420194456, 0.493195109, 0.619020921, + 0.278066455, 0.199822296, 0.140827896, 0.140139205, 0.206984231, + 0.2684947, 0.340728872, 0.422645622, 0.501908648, + 0.285697424, 0.22749307, 0.16881002, 0.13354722, 0.149532449, + 0.213353293, 0.283777474, 0.355946993, 0.427175997, + 0.294521663, 0.236133131, 0.18710497, 0.14828074, 0.145457711, + 0.182992988, 0.228281887, 0.291865148, 0.341808458, + 0.271987072, 0.252962505, 0.201123092, 0.162942848, 0.14828074, + 0.167205292, 0.214481881, 0.27141981, 0.332162403, + 0.268966875, 0.253628745, 0.213509108, 0.180342353, 0.151623426, + 0.1617176, 0.192572929, 0.243404723, 0.301780548, + 0.284462825, 0.25473406, 0.215401758, 0.202840815, 0.171061666, + 0.160368388, 0.183680312, 0.226156887, 0.272598273, + 0.305655289, 0.247134344, 0.235118253, 0.214725129, 0.185684599, + 0.167917048, 0.184066896, 0.218763431, 0.256692375) > > model<-data.frame(x,y,z) z1 <- matrix(z, 9, 9) > persp(x, y, z1) > library(lattice) > g <- expand.grid(x = x, y = y) > g$z <- z > wireframe(z ~ x * y, data = g) > library(lattice) > g <- expand.grid(x = x, y = y) > g$z <- z > wireframe(z ~ x * y,main="Volkan OBAN",data = g) > library(lattice) > g <- expand.grid(x = x, y = y) > g$z <- z > wireframe(z ~ x * y,data = g) >

data(flea) ggscatmat(flea, columns = 2:4, color="species", alpha=0.8)

> library(ggplot2) > pm <- ggpairs(tips, mapping = aes(color = sex), columns = c("total_bill", "time", "tip")) > pm

> > data(tips, package = "reshape") > pm <- ggpairs(tips) > pm

swiss <- datasets::swiss # add a 'fake' column swiss$Residual <- seq_len(nrow(swiss)) # calculate all residuals prior to display residuals <- lapply(swiss[2:6], function(x) { summary(lm(Fertility ~ x, data = swiss))$residuals }) # calculate a consistent y range for all residuals y_range <- range(unlist(residuals)) # custom function to display continuous data. If the y variable is "Residual", do custom work. lm_or_resid <- function(data, mapping, ..., line_color = "red", line_size = 1) { if (as.character(mapping$y) != "Residual") { return(ggally_smooth_lm(data, mapping, ...)) } # make residual data to display resid_data <- data.frame( x = data[[as.character(mapping$x)]], y = residuals[[as.character(mapping$x)]] ) ggplot(data = data, mapping = mapping) + geom_hline(yintercept = 0, color = line_color, size = line_size) + ylim(y_range) + geom_point(data = resid_data, mapping = aes(x = x, y = y), ...) } # plot the data ggduo( swiss, 2:6, c(1,7), types = list(continuous = lm_or_resid) )

> library(GGally) > psych <- read.csv("http://www.ats.ucla.edu/stat/data/mmreg.csv") > colnames(psych) <- c("Control", "Concept", "Motivation", "Read", "Write", "Math", "Science", "Sex") > psych <- data.frame( + Motivation = psych$Motivation, + Self.Concept = psych$Concept, + Locus.of.Control = psych$Control, + Read = psych$Read, + Write = psych$Write, + Math = psych$Math, + Science = psych$Science, + Sex = c("0" = "Male", "1" = "Female")[as.character(psych$Sex)] + ) > ggduo( + psych, 1:3, 4:8, + types = list(continuous = "smooth_lm"), + title = "Between Academic and Psychological Variable Correlation", + xlab = "Psychological", + ylab = "Academic" + )

map = suppressMessages(get_map(location = 'Turkey', zoom = 4)) > ggmap(map)

> suppressMessages(library(maps)) > suppressMessages(library(ggmap)) > suppressMessages(library(mapproj) map1 = suppressMessages(get_map( + location = 'Maslak', zoom = 14, #zoom-in level + maptype="satellite")) #map type > ggmap(map1)

> data(tips, package = "reshape") > plotList <- list( + qplot(total_bill, tip, data = subset(tips, smoker == "No" & sex == "Female")) + + facet_grid(time ~ day), + qplot(total_bill, tip, data = subset(tips, smoker == "Yes" & sex == "Female")) + + facet_grid(time ~ day), + qplot(total_bill, tip, data = subset(tips, smoker == "No" & sex == "Male")) + + facet_grid(time ~ day), + qplot(total_bill, tip, data = subset(tips, smoker == "Yes" & sex == "Male")) + + facet_grid(time ~ day) pm <- ggmatrix( plotList, nrow = 2, ncol = 2, yAxisLabels = c("Female", "Male"), xAxisLabels = c("Non Smoker", "Smoker"), title = "Total Bill vs Tip", showStrips = NULL # default ) pm

> library(GGally) > psych <- read.csv("http://www.ats.ucla.edu/stat/data/mmreg.csv") > colnames(psych) <- c("Control", "Concept", "Motivation", "Read", "Write", "Math", "Science", "Sex") > psych <- data.frame( + Motivation = psych$Motivation, + Self.Concept = psych$Concept, + Locus.of.Control = psych$Control, + Read = psych$Read, + Write = psych$Write, + Math = psych$Math, + Science = psych$Science, + Sex = c("0" = "Male", "1" = "Female")[as.character(psych$Sex)] + ) > ggpairs(psych, 4:8, title = "prepared by Volkan OBAN using R-GGally pack \n Within Academic Variables")

> library(GGally) > psych <- read.csv("http://www.ats.ucla.edu/stat/data/mmreg.csv") > colnames(psych) <- c("Control", "Concept", "Motivation", "Read", "Write", "Math", "Science", "Sex") > psych <- data.frame( + Motivation = psych$Motivation, + Self.Concept = psych$Concept, + Locus.of.Control = psych$Control, + Read = psych$Read, + Write = psych$Write, + Math = psych$Math, + Science = psych$Science, + Sex = c("0" = "Male", "1" = "Female")[as.character(psych$Sex)] + ) > ggpairs(psych, 1:3, title = " Within Psychological Variables")

data(efc) > # Function call when label attributes are attached > sjp.xtab(efc$e42dep, efc$e16sex) > sjp.xtab(efc$e42dep, efc$e16sex, title="prepared in R by Volkan OBAN")

library(sjPlot) > library(sjmisc) > # init default theme for plots > sjp.setTheme(geom.label.size = 2.5, axis.title.size = .9, axis.textsize = .9) sjp.frq(dummy, title = "prepared by VOLKAN OBAN using R") > dummy <- set_labels(dummy, c("very low", "low", "mid", "hi")) > dummy <- set_label(dummy, "Humidity") > # check structure of dummy > str(dummy) atomic [1:200] 2 2 3 4 3 3 2 3 2 3 ... - attr(*, "labels")= Named num [1:4] 1 2 3 4 ..- attr(*, "names")= chr [1:4] "very low" "low" "mid" "hi" - attr(*, "label")= chr "Humidity" > sjp.frq(dummy, title = "prepared by VOLKAN OBAN using R") >

data(iris) > pairs(iris[1:5], main = "Iris Data", pch = 21, bg = c("red", "green3", "blue")[unclass(iris$Species)])

> data(mpg) > g<-ggplot(mpg, aes(displ, hwy, color=factor(year))) > > g+geom_point() > g+geom_point()+facet_grid(drv~cyl, margins=TRUE)

options(repr.plot.width = 8) > options(repr.plot.height = 6) > > ggplot(clim, aes(Year, CO2))+geom_line(color='black')+geom_point(color='red') +ggtitle("Carbondioxide Concentration")+xlab('prepared in R-ggplot2 by VOLKAN OBAN')+ylab('ppm')+ + geom_vline(xintercept = c(1990,2000),colour="green", linetype = "longdash") >

> clim<-read.csv("climate_change.csv") > options(repr.plot.width = 8) > options(repr.plot.height = 6) > library(ggplot2) > ggplot(clim, aes(Year, Temp))+geom_line(color='green')+geom_point()+ggtitle('Temperature Change')+xlab('prepared in R-ggplot2 by VOLKAN OBAN')+ylab('Temperature')+stat_smooth(colour='blue', span=0.2)

dsub <- diamonds[ sample(nrow(diamonds), 1000), ] ggplot(dsub, aes(x = cut, y = carat, fill = clarity)) + + geom_boxplot(outlier.size = 0) + + geom_point(pch = 21, position = position_jitterdodge())

> p <- ggplot(ToothGrowth, aes(x=factor(dose), y=len, fill=supp)) + + scale_fill_manual(values=c("#FF0000", "white")) + ggtitle("prepared in R by Volkan OBAN ") > p + geom_dotplot(binaxis="y", position="dodge", stackdir="center", binwidth=2)

g <- ggplot(dt, aes_string(x="Group", y="Duration")) + coord_flip() + geom_boxplot(aes(ymin=..lower.., ymax=..upper..), fatten=1.1, lwd=.1, outlier.shape=NA) + geom_dotplot(data=dt[EndType==1], aes(fill=EndType), fill="black", binaxis="y", stackdir="up", method="histodot", binwidth=15, dotsize=.5) + geom_dotplot(data=dt[EndType==0], aes(fill=EndType), fill="white", binaxis="y", stackdir="down", method="histodot", binwidth=15, dotsize=.5) > print(g)

> # Creating basic Jitter > ggplot(ChickWeight, aes(x = Diet, y = weight)) + + geom_boxplot(notch = TRUE) + + geom_jitter(position = position_jitter(0.5), aes(colour = Diet)) + ggtitle("prepared in R by Volkan OBAN \n ggplot Density Plot \n data(ChickWeight) ")

ggplot(data = diamonds, aes(x = price, fill = cut)) + geom_histogram(binwidth = 250, color = "black")+ ggtitle("prepared in R by Volkan OBAN \n ggplot Density Plot \n data(diamonds) ")

ggplot(data = diamonds, aes(x = price, fill = cut)) + geom_histogram(binwidth = 250, color = "midnightblue") + theme(legend.position = "top")

> ggplot(data = diamonds, aes(x = price, fill = cut)) + geom_density(adjust = 1/5, color = "midnightblue") + facet_wrap(~ cut, scale = "free") + labs(title="GGPLOT Density Plot", x="Price in Dollars", y="Density") + theme_dark()

ggplot(data = diamonds, aes(x = price, fill = cut)) + geom_density(adjust = 1/5, color = "midnightblue") + theme(legend.position = "top")

library(ggplot2) > > # Create a Dot plot > ggplot(airquality, aes(x = Wind, fill = factor(Month))) + + geom_dotplot(binwidth = 1.5)

library(ggplot2) > > # Create a Dot plot > ggplot(airquality, aes(x = factor(Month), fill = factor(Month), + y = Wind)) + + geom_dotplot(binaxis = "y", stackdir = "center", + color = "gold") + ggtitle("prepared in R by Volkan OBAN \n ggplot2 - geom_dotplot")

ggplot(diamonds, aes(carat, depth)) + geom_boxplot(aes(group = plyr::round_any(carat, 0.1))) + xlim(NA, 2.05) + ggtitle("prepared by Volkan OBAN using R-ggplot2")

ggplot(mpg, aes(drv, displ, fill = drv)) + geom_dotplot(binaxis = "y", stackdir = "center") + ggtitle("prepared by Volkan OBAN using R-ggplot2")

ggplot(diamonds, aes(carat, depth)) + geom_boxplot(aes(group = plyr::round_any(carat, 0.1))) + xlim(NA, 2.05)

> data(Oxboys, package = "nlme") > head(Oxboys) library(ggplot2) ggplot(Oxboys, aes(Occasion, height)) + geom_boxplot() + geom_line(aes(group = Subject), colour = "#3366FF", alpha = 0.5)

library(plotly) > p <- plot_ly(midwest, x = ~percollege, color = ~state, type = "box") %>% layout( title = "prepared in R-plotly by VOLKAN OBAN") > p

t<- c(97,93,91,87,86,85,80,78,69,68,67,65,63,62,59,59,54,51,48,45,43,43,38,33,30,27,25,20,18,15,12,7,3,3,3) > hist(t, main=" colorful histogram \n Distribution of Player Ratings",xlim = c(0,99), breaks=c(seq(2,99,2)), col = c("darkred", "deepskyblue3", "red", "purple","mediumorchid1","darkorange2","black","navyblue"))

> library(manhattanly) > manhattanly(HapMap, + snp = "SNP", gene = "GENE", title="prepared in R-manhattanly pack. by Volkan OBAN \n Manhattan Plot", + annotation1 = "ZSCORE", annotation2 = "EFFECTSIZE", + highlight = significantSNP)

p <- plot_ly(y = ~rnorm(50), type = "box") %>% + add_trace(y = ~rnorm(50, 1)) %>% + add_trace(y=~runif(numcases,min,max+1)) %>% + add_trace(y = ~rnorm(80, 1)) %>% + add_trace(y = ~rnorm(100, 1)) %>% + add_trace(y = ~rnorm(78, 1)) %>% + add_trace(y = ~rnorm(98, 1)) %>% + add_trace(y = ~rnorm(45, 1)) %>% + add_trace(y = ~rnorm(95, 1)) %>% + add_trace(y = ~qbinom(0.2, 10, 1/3)) %>% + add_trace(y = ~rnorm(95, 1)) %>% + add_trace(y = ~runif(numcases,min,max/2)) %>% + + add_trace(y = ~~runif(numcases,min,max+2) %>% + add_trace(y = ~pnorm(27.4, mean=50, sd=20)) %>% + add_trace(y = ~runif(numcases,min,max+1)) %>% + layout( title = "prepared in R-plotly by VOLKAN OBAN")) > p

ggplot2 ggthemes > data = data.frame(student = c("VOLKAN", "OBAN", "GÜL", "GÜLCE", "SEMRA"), + percentile = c(25, 95, 54, 70, 99) ) plot + geom_pointrange(aes(ymin = 0, ymax = 100)) + coord_flip() + ggtitle("prepared in R by VOLKAN OBAN") + theme_economist() + scale_colour_economist()

ref:https://github.com/ebommes/plots/blob/master/sparktable.R > options(stringsAsFactors = FALSE) Error: could not find function "ptions" > > library(grid) > library(gtable) > library(ggplot2) > library(ggthemes) > > randu <- function(n) { + r <- sample(1:5, 1) + if(r == 1) return(rnorm(n)); + if(r == 2) return(runif(n)); + if(r == 3) return(rbinom(n, 10, 0.5)); + if(r == 4) return(rpois(n, 0.8)); + if(r == 5) return(rexp(n)); + } > > test_intg <- function(x) { + if(class(x) != 'numeric') return(TRUE); + perc <- length(unique(x)) / length(x) + + if(perc <= 0.05) { + return(TRUE) + } else { + return(FALSE) + } + } > > sumry <- function(df) { + df_mean <- sapply(df, mean) + df_median <- sapply(df, median) + df_sd <- sapply(df, sd) + df_min <- sapply(df, min) + df_max <- sapply(df, max) + data.frame(Variable = names(df), + Mean = format(round(df_mean, 3), nsmall = 3), + Median = format(round(df_median, 3), nsmall = 3), + St.Dev = format(round(df_sd, 3), nsmall = 3), + Min = format(round(df_min, 3), nsmall = 3), + Max = format(round(df_max, 3), nsmall = 3)) + } > > theme_spark <- function() { + theme_tufte() + + theme(axis.title = element_blank(), + axis.text.y = element_blank(), + axis.ticks = element_blank(), + axis.text.x = element_blank()) + } > > tplotter <- function(x) { + df_tmp <- data.frame(x = c(1:length(x)), y = x) + + if(test_intg(x) == TRUE) { + p <- ggplot(df_tmp, aes(x = x, y = y)) + + theme_spark() + + geom_hline(yintercept = 0, colour = 'darkgrey') + + geom_point(fill = 'black', size = 0.1) + } else { + p <- ggplot(df_tmp, aes(x = x, y = y)) + + theme_spark() + + geom_hline(yintercept = 0, colour = 'darkgrey') + + geom_line(colour = 'black') + } + + return(ggplotGrob(p)) + } > > dplotter <- function(x) { + df_tmp <- data.frame(x = c(1:length(x)), y = x) + + if(test_intg(x) == TRUE) { + p <- ggplot(df_tmp, aes(x = y)) + + theme_spark() + + geom_bar(fill = 'black', width = 0.25) + } else { + p <- ggplot(df_tmp, aes(x = y)) + + theme_spark() + + geom_density(color = 'black') + } + + return(ggplotGrob(p)) + } > > bplotter <- function(x) { + df_tmp <- data.frame(x = c(1:length(x)), y = x) + + p <- ggplot(df_tmp, aes(x = y, y = y)) + + theme_spark() + + coord_flip() + ggtitle("prepared in R by VOLKAN OBAN") + + geom_tufteboxplot(median.type = 'line', whisker.type = 'line', + hoffset = 0, width = 3, voffset = 0.02) + + return(ggplotGrob(p)) + } > > n <- 50 > set.seed(1234) > df <- data.frame(a = randu(n), b = randu(n), c = randu(n), d = randu(n), + e = randu(n), f = randu(n), g = randu(n), h = randu(n)) > > df_sumry <- sumry(df) > > df.names <- names(df_sumry) > m <- ncol(df_sumry) > n <- nrow(df_sumry) > > funs <- c('tplotter', 'bplotter', 'dplotter') > > gtab <- gtable(unit(rep(1, m + length(funs)), 'null'), unit(rep(1, n + 1), 'null')) > > # fill text > for(i in 1:n) { + for(j in 1:ncol(df_sumry)) { + if(i == 1) { + gtab <- gtable_add_grob(gtab, textGrob(df.names[j]), + t = i, l = j, r = j) + } + + gtab <- gtable_add_grob(gtab, textGrob(df_sumry[i, j]), + t = i + 1, l = j, r = j) + } + + for(j in 1:length(funs)) { + gtab <- gtable_add_grob(gtab, do.call(funs[j], list(df[, i])), + t = i + 1, l = m + j, r = m + j) + } + + } > > gtab <- gtable_add_grob(gtab, textGrob(paste('# Obs. =', nrow(df))), + t = 1, l = m + 1, r = m + length(funs)) > > dev.new(width = 0.79 * (m + 3), height = 0.42 * n) NULL > grid.draw(gtab)

count <- 3000 > > x <- c() > y <- c() > z <- c() > c <- c() > > for (i in 1:count) { + r <- i * (count - i) + x <- c(x, r * cos(i / 30)) + y <- c(y, r * sin(i / 30)) + z <- c(z, i) + c <- c(c, i) + } > > data <- data.frame(x, y, z, c) > > p <- plot_ly(data, x = ~x, y = ~y, z = ~z, type = 'scatter3d', mode = 'lines', + line = list(width = 4, color = ~c, colorscale = list(c(0,'#BA52ED'), c(1,'#FCB040')))) > > p

library("maps") require(ggplot2) library(ggsubplot) world.map <- map("world", plot = FALSE, fill = TRUE) world_map <- map_data("world") require(lattice) require(latticeExtra) # Calculate the mean longitude and latitude per region (places where subplots are plotted) library(plyr) cntr <- ddply(world_map,.(region),summarize,long=mean(long),lat=mean(lat)) # example data myd <- data.frame (region = c("USA","China","USSR","Brazil", "Australia","India", "Nepal", "Canada", "South Africa", "South Korea", "Philippines", "Mexico", "Finland", "Egypt", "Chile", "Greenland"), frequency = c(501, 350, 233, 40, 350, 150, 180, 430, 233, 120, 96, 87, 340, 83, 99, 89)) subsetcntr <- subset(cntr, region %in% c("USA","China","USSR","Brazil", "Australia","India", "Nepal", "Canada", "South Africa", "South Korea", "Philippines", "Mexico", "Finland", "Egypt", "Chile", "Greenland")) simdat <- merge(subsetcntr, myd) colnames(simdat) <- c( "region","long","lat", "myvar" ) panel.3dmap <- function(..., rot.mat, distance, xlim, ylim, zlim, xlim.scaled, ylim.scaled, zlim.scaled) { scaled.val <- function(x, original, scaled) { scaled[1] + (x - original[1]) * diff(scaled)/diff(original) } m <- ltransform3dto3d(rbind(scaled.val(world.map$x, xlim, xlim.scaled), scaled.val(world.map$y, ylim, ylim.scaled), zlim.scaled[1]), rot.mat, distance) panel.lines(m[1, ], m[2, ], col = "green4") } p2 <- cloud(myvar ~ long + lat, simdat, panel.3d.cloud = function(...) { panel.3dmap(...) panel.3dscatter(...) }, type = "h", col = "purple", scales = list(draw = FALSE), zoom = 1.1, xlim = world.map$range[1:2], ylim = world.map$range[3:4], xlab = NULL, ylab = NULL, zlab = NULL, aspect = c(diff(world.map$range[3:4])/diff(world.map$range[1:2]), 0.3), panel.aspect = 0.75, lwd = 2, screen = list(z = 30, x = -60), par.settings = list(axis.line = list(col = "transparent"), box.3d = list(col = "transparent", alpha = 0))) p2

m <- leaflet() %>% addTiles() garnishMap(m, "addMouseCoordinates") # same as garnishMap(m, addMouseCoordinates) ## add more than one with named argument library(raster) m1 <- garnishMap(m, addMouseCoordinates, mapview:::addHomeButton, ext = extent(breweries91)) m1 ## even more flexible m2 <- garnishMap(m1, addCircleMarkers, data = breweries91) garnishMap(m2, addPolygons, data = gadmCHE, popup = popupTable(gadmCHE), fillOpacity = 0.8, color = "black", fillColor = "#BEBEBE")

m <- leaflet() %>% addTiles() garnishMap(m, "addMouseCoordinates") # same as garnishMap(m, addMouseCoordinates) ## add more than one with named argument library(raster) m1 <- garnishMap(m, addMouseCoordinates, mapview:::addHomeButton, ext = extent(breweries91)) m1 ## even more flexible m2 <- garnishMap(m1, addCircleMarkers, data = breweries91) garnishMap(m2, addPolygons, data = gadmCHE, popup = popupTable(gadmCHE), fillOpacity = 0.8, color = "black", fillColor = "#BEBEBE")

wordcloud2(demoFreq, figPath = "em.png", size = 1.5, color = "white", backgroundColor="black")

require(quantmod) require(ggplot2) require(reshape2) require(plyr) require(scales) # Download some Data, e.g. the CBOE VIX getSymbols("^VIX",src="yahoo") # Make a dataframe dat<-data.frame(date=index(VIX),VIX) # We will facet by year ~ month, and each subgraph will # show week-of-month versus weekday # the year is simple dat$year<-as.numeric(as.POSIXlt(dat$date)$year+1900) # the month too dat$month<-as.numeric(as.POSIXlt(dat$date)$mon+1) # but turn months into ordered facors to control the appearance/ordering in the presentation dat$monthf<-factor(dat$month,levels=as.character(1:12),labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE) # the day of week is again easily found dat$weekday = as.POSIXlt(dat$date)$wday # again turn into factors to control appearance/abbreviation and ordering # I use the reverse function rev here to order the week top down in the graph # you can cut it out to reverse week order dat$weekdayf<-factor(dat$weekday,levels=rev(1:7),labels=rev(c("Mon","Tue","Wed","Thu","Fri","Sat","Sun")),ordered=TRUE) # the monthweek part is a bit trickier # first a factor which cuts the data into month chunks dat$yearmonth<-as.yearmon(dat$date) dat$yearmonthf<-factor(dat$yearmonth) # then find the "week of year" for each day dat$week <- as.numeric(format(dat$date,"%W")) # and now for each monthblock we normalize the week to start at 1 dat<-ddply(dat,.(yearmonthf),transform,monthweek=1+week-min(week)) # Now for the plot P<- ggplot(dat, aes(monthweek, weekdayf, fill = VIX.Close)) + geom_tile(colour = "white") + facet_grid(year~monthf) + scale_fill_gradient(low="red", high="yellow") + options(title = "Time-Series Calendar Heatmap") + xlab("Week of Month") + ylab("") P

library(dplyr) library(tidyr) library(rvest) library(rcdimple) # Get the table from the Census database with rvest url <- "http://www.census.gov/population/international/data/idb/region.php?N=%20Results%20&T=10&A=separate&RT=0&Y=2015,2020,2025,2030,2035,2040,2045,2050&R=-1&C=IN" df <- url %>% html() %>% html_nodes("table") %>% html_table() %>% data.frame() names(df) <- c("Year", "Age", "total", "Male", "Female", "percent", "pctMale", "pctFemale", "sexratio") cols <- c(1, 3:9) df[,cols] <- apply(df[,cols], 2, function(x) as.numeric(as.character(gsub(",", "", x)))) # Format the table with dplyr and tidyr df1 <- df %>% mutate(Order = 1:nrow(df), Male = -1 * Male) %>% filter(Age != "Total") %>% select(Year, Age, Male, Female, Order) %>% gather(Gender, Population, -Age, -Order, -Year) max_x <- plyr::round_any(max(df1$Population), 10000, f = ceiling) min_x <- plyr::round_any(min(df1$Population), 10000, f = floor) # Build the chart with rcdimple df1 %>% dimple(x = "Population", y = "Age", group = "Gender", type = 'bar', storyboard = "Year") %>% yAxis(type = "addCategoryAxis", orderRule = "Order") %>% xAxis(type = "addMeasureAxis", overrideMax = max_x, overrideMin = min_x) %>% default_colors(c("green", "orange")) %>% add_legend() %>% add_title(html = "<h3 style='font-family:Helvetica; text-align: center;'>India's population, 2015-2050</h3>") %>% # Here, I'll pass in some JS code to make all the values on the X-axis and in the tooltip absolute values tack(., options = list( chart = htmlwidgets::JS(" function(){ var self = this; // x axis should be first or [0] but filter to make sure self.axes.filter(function(ax){ return ax.position == 'x' })[0] // now we have our x axis set _getFormat as before ._getFormat = function () { return function(d) { return d3.format(',.0f')(Math.abs(d) / 1000000) + 'm'; }; }; // return self to return our chart return self; } ")) )

source('https://raw.githubusercontent.com/walkerke/teaching-with-datavis/master/pyramids/rcharts_pyramids.R') d> library(rCharts) d> nPyramid('QA', 2014, colors = c('darkred', 'silver')) d> nPyramid('QA', 2014,colors = c('darkred', 'silver'))

huron <- data.frame(year = 1875:1972, level = as.vector(LakeHuron)) h <- ggplot(huron, aes(year)) h + geom_ribbon(aes(ymin=0, ymax=level)) h + geom_ribbon(aes(ymin = level - 1, ymax = level + 1), fill = "grey70") + geom_line(aes(y = level)

> # Baseball data example, from Friendly & Kwan (2009) > if (require(vcd) && require(perturb)) { + # model, with transformed variables + Baseball$logsal <- log(Baseball$sal87) + Baseball$years7 <- pmin(Baseball$years,7) + base.mod <- lm(logsal ~ years+atbat+hits+homeruns+runs+rbi+walks, data=Baseball) + if (require(car)) { + # examine variance inflation factors + vif(base.mod) + } + # corresponds to SAS: / collinoint option + cd <- colldiag(base.mod, add.intercept=FALSE, center=TRUE) + # simplified display + print(cd, fuzz=.3) + tableplot(cd) + } Zorunlu paket yükleniyor: vcd Attaching package: ‘vcd’ The following object is masked from ‘package:raster’: mosaic Zorunlu paket yükleniyor: perturb Warning message: In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, : there is no package called ‘perturb’ > install.packages("perturb") Installing package into ‘C:/Users/lenovo/Documents/R/win-library/3.3’ (as ‘lib’ is unspecified) trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.3/perturb_2.05.zip' Content type 'application/zip' length 45706 bytes (44 KB) downloaded 44 KB package ‘perturb’ successfully unpacked and MD5 sums checked The downloaded binary packages are in C:\Users\lenovo\AppData\Local\Temp\Rtmp2hu8MR\downloaded_packages > library(perturb) Attaching package: ‘perturb’ The following object is masked from ‘package:raster’: reclassify > # Baseball data example, from Friendly & Kwan (2009) > if (require(vcd) && require(perturb)) { + # model, with transformed variables + Baseball$logsal <- log(Baseball$sal87) + Baseball$years7 <- pmin(Baseball$years,7) + base.mod <- lm(logsal ~ years+atbat+hits+homeruns+runs+rbi+walks, data=Baseball) + if (require(car)) { + # examine variance inflation factors + vif(base.mod) + } + # corresponds to SAS: / collinoint option + cd <- colldiag(base.mod, add.intercept=FALSE, center=TRUE) + # simplified display + print(cd, fuzz=.3) + tableplot(cd) + }

df <- data.frame(x = rnorm(15000),y=rnorm(15000)) ggplot(df,aes(x=x,y=y)) + geom_point() + geom_density2d()

library(cartography) # Load data data(nuts2006) # set margins opar <- par(mar = c(0,0,1.2,0)) # Compute the compound annual growth rate nuts2.df$cagr <- (((nuts2.df$pop2008 / nuts2.df$pop1999)^(1/9)) - 1) * 100 # Plot a layer with the extent of the EU28 countries with only a background color plot(nuts0.spdf, border = NA, col = NA, bg = "#A6CAE0") # Plot non european space plot(world.spdf, col = "#E3DEBF", border=NA, add=TRUE) # Plot Nuts2 regions plot(nuts2.spdf, col = "grey60",border = "white", lwd=0.4, add=TRUE) # Set a custom color palette cols <- carto.pal(pal1 = "blue.pal", n1 = 2, pal2 = "red.pal", n2 = 4) # Plot symbols with choropleth coloration propSymbolsChoroLayer(spdf = nuts2.spdf, df = nuts2.df, var = "pop2008", # field in df to plot the symbols sizes inches = 0.1, # set the symbols sizes var2 = "cagr", # field in df to plot the colors col = cols, # symbols colors breaks = c(-2.43,-1,0,0.5,1,2,3.1), # breaks border = "grey50", # border colors of the symbols lwd = 0.75, # symbols width legend.var.pos = "topright", # legend position legend.var.values.rnd = -3, # legend value legend.var.title.txt = "Total Population", # size legend title legend.var.style = "e", # legend type legend.var2.pos = "right", # legend position legend.var2.title.txt = "Compound Annual\nGrowth Rate") # legend title # layout layoutLayer(title = "Demographic trends, 1999-2008", coltitle = "black", sources = "Eurostat, 2011", scale = NULL, author = "cartography", frame ="", col = NA) par(opar)

library(chorddiag) > m <- matrix(c(11975, 5871, 8916, 2868, + 1951, 10048, 2060, 6171, + 8010, 16145, 8090, 8045, + 1013, 990, 940, 6907), + byrow = TRUE, + nrow = 4, ncol = 4) > haircolors <- c("black", "blonde", "brown", "red") > dimnames(m) <- list(have = haircolors, + prefer = haircolors) > m > groupColors <- c("#000000", "#FFDD89", "#957244", "#F26223") > chorddiag(m, groupColors = groupColors, groupnamePadding = 40) >

data(chats) chatsw <- data.frame(t(chats)) chatscoa <- dudi.coa(chatsw, scann = FALSE) par(mfrow = c(2,2)) table.cont(chatsw, abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, x = chatscoa$c1[,1], y = chatscoa$l1[,1], abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, x = chatscoa$c1[,1], y = chatscoa$l1[,1], abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, clabel.c = 1.5) par(mfrow = c(1,1)) ## Not run: data(rpjdl) w <- data.frame(t(rpjdl$fau)) wcoa <- dudi.coa(w, scann = FALSE) table.cont(w, abmean.y = TRUE, x = wcoa$c1[,1], y = rank(wcoa$l1[,1]), csi = 0.2, clabel.c = 0, row.labels = rpjdl$lalab, clabel.r = 0.75)

library(plotrix) testdf<-data.frame(Before=c(10,7,5,9),During=c(8,6,2,5),After=c(5,3,4,3)) rownames(testdf)<-c("Red","Green","Blue","Lightblue") barp(testdf,main="Test addtable2plot",ylab="Value", names.arg=colnames(testdf),col=2:5) # show most of the options addtable2plot(0.7 ,8,testdf,bty="o",display.rownames=TRUE,hlines=TRUE, vlines=TRUE,title="The table")

x <- seq(1, 100, 1) y <- matrix(20*100, nrow=100, ncol=20) for (i in 1:20) { y[, i] <- cumsum(rnorm(100)) } #Build the table df <- data.frame(x=x, y=y) head(df) #Plot the table matplot(df[, 1], df[, 2:21], type="l", main="Twenty Random Walks", xlab="x", ylab="y") grid()

library(dplyr) > library(ggplot2) > library(reshape2) > > #simulating dataset > cohorts <- data.frame() > set.seed(10) > for (i in c(1:100)) { + coh <- data.frame(cohort=i, + date=c(i:100), + week.lt=c(1:(100-i+1)), + num=replicate(1, sample(c(1:40), 100-i+1, rep=TRUE)), + av=replicate(1, sample(c(5:10), 100-i+1, rep=TRUE))) + coh$num[coh$week.lt==1] <- sample(c(90:100), 1, rep=TRUE) + ifelse(max(coh$date)>1, coh$num[coh$week.lt==2] <- sample(c(75:90), 1, rep=TRUE), NA) + ifelse(max(coh$date)>2, coh$num[coh$week.lt==3] <- sample(c(60:75), 1, rep=TRUE), NA) + ifelse(max(coh$date)>3, coh$num[coh$week.lt==4] <- sample(c(40:60), 1, rep=TRUE), NA) + ifelse(max(coh$date)>34, + {coh$num[coh$date==35] <- sample(c(60:85), 1, rep=TRUE) + coh$av[coh$date==35] <- 4}, + NA) + ifelse(max(coh$date)>47, + {coh$num[coh$date==48] <- sample(c(60:85), 1, rep=TRUE) + coh$av[coh$date==48] <- 4}, + NA) + ifelse(max(coh$date)>86, + {coh$num[coh$date==87] <- sample(c(60:85), 1, rep=TRUE) + coh$av[coh$date==87] <- 4}, + NA) + ifelse(max(coh$date)>99, + {coh$num[coh$date==100] <- sample(c(60:85), 1, rep=TRUE) + coh$av[coh$date==100] <- 4}, + NA) + coh$gr.marg <- coh$av*coh$num + cohorts <- rbind(cohorts, coh) + } > > cohorts$cohort <- formatC(cohorts$cohort, width=3, format='d', flag='0') > cohorts$cohort <- paste('coh:week:', cohorts$cohort, sep='') > cohorts$date <- formatC(cohorts$date, width=3, format='d', flag='0') > cohorts$date <- paste('cal_week:', cohorts$date, sep='') > cohorts$week.lt <- formatC(cohorts$week.lt, width=3, format='d', flag='0') > cohorts$week.lt <- paste('week:', cohorts$week.lt, sep='') > > #calculating CLV to date > cohorts <- cohorts %>% + group_by(cohort) %>% + mutate(clv=cumsum(gr.marg)/num[week.lt=='week:001']) > > #color palette > cols <- c("#e7f0fa", "#c9e2f6", "#95cbee", "#0099dc", "#4ab04a", "#ffd73e", "#eec73a", "#e29421", "#e29421", "#f05336", "#ce472e") > > #Heatmap based on Number of active customers > t <- max(cohorts$num) > > ggplot(cohorts, aes(y=cohort, x=date, fill=num)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Cohort Activity Heatmap (number of customers who purchased - calendar view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > ggplot(cohorts, aes(y=cohort, x=week.lt, fill=num)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Cohort Activity Heatmap (number of customers who purchased - lifetime view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > # Heatmap based on Gross margin > t <- max(cohorts$gr.marg) > > ggplot(cohorts, aes(y=cohort, x=date, fill=gr.marg)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on Gross margin (calendar view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > ggplot(cohorts, aes(y=cohort, x=week.lt, fill=gr.marg)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on Gross margin (lifetime view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > # Heatmap of per customer gross margin > t <- max(cohorts$av) > > ggplot(cohorts, aes(y=cohort, x=date, fill=av)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("Heatmap based on per customer gross margin (calendar view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > ggplot(cohorts, aes(y=cohort, x=week.lt, fill=av)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on per customer gross margin (lifetime view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > # Heatmap of CLV to date > t <- max(cohorts$clv) > > ggplot(cohorts, aes(y=cohort, x=date, fill=clv)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on CLV to date of customers who ever purchased (calendar view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > ggplot(cohorts, aes(y=cohort, x=week.lt, fill=clv)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on CLV to date of customers who ever purchased (lifetime view)")

library(dplyr) > library(ggplot2) > library(reshape2) > > #simulating dataset > cohorts <- data.frame() > set.seed(10) > for (i in c(1:100)) { + coh <- data.frame(cohort=i, + date=c(i:100), + week.lt=c(1:(100-i+1)), + num=replicate(1, sample(c(1:40), 100-i+1, rep=TRUE)), + av=replicate(1, sample(c(5:10), 100-i+1, rep=TRUE))) + coh$num[coh$week.lt==1] <- sample(c(90:100), 1, rep=TRUE) + ifelse(max(coh$date)>1, coh$num[coh$week.lt==2] <- sample(c(75:90), 1, rep=TRUE), NA) + ifelse(max(coh$date)>2, coh$num[coh$week.lt==3] <- sample(c(60:75), 1, rep=TRUE), NA) + ifelse(max(coh$date)>3, coh$num[coh$week.lt==4] <- sample(c(40:60), 1, rep=TRUE), NA) + ifelse(max(coh$date)>34, + {coh$num[coh$date==35] <- sample(c(60:85), 1, rep=TRUE) + coh$av[coh$date==35] <- 4}, + NA) + ifelse(max(coh$date)>47, + {coh$num[coh$date==48] <- sample(c(60:85), 1, rep=TRUE) + coh$av[coh$date==48] <- 4}, + NA) + ifelse(max(coh$date)>86, + {coh$num[coh$date==87] <- sample(c(60:85), 1, rep=TRUE) + coh$av[coh$date==87] <- 4}, + NA) + ifelse(max(coh$date)>99, + {coh$num[coh$date==100] <- sample(c(60:85), 1, rep=TRUE) + coh$av[coh$date==100] <- 4}, + NA) + coh$gr.marg <- coh$av*coh$num + cohorts <- rbind(cohorts, coh) + } > > cohorts$cohort <- formatC(cohorts$cohort, width=3, format='d', flag='0') > cohorts$cohort <- paste('coh:week:', cohorts$cohort, sep='') > cohorts$date <- formatC(cohorts$date, width=3, format='d', flag='0') > cohorts$date <- paste('cal_week:', cohorts$date, sep='') > cohorts$week.lt <- formatC(cohorts$week.lt, width=3, format='d', flag='0') > cohorts$week.lt <- paste('week:', cohorts$week.lt, sep='') > > #calculating CLV to date > cohorts <- cohorts %>% + group_by(cohort) %>% + mutate(clv=cumsum(gr.marg)/num[week.lt=='week:001']) > > #color palette > cols <- c("#e7f0fa", "#c9e2f6", "#95cbee", "#0099dc", "#4ab04a", "#ffd73e", "#eec73a", "#e29421", "#e29421", "#f05336", "#ce472e") > > #Heatmap based on Number of active customers > t <- max(cohorts$num) > > ggplot(cohorts, aes(y=cohort, x=date, fill=num)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Cohort Activity Heatmap (number of customers who purchased - calendar view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > ggplot(cohorts, aes(y=cohort, x=week.lt, fill=num)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Cohort Activity Heatmap (number of customers who purchased - lifetime view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > # Heatmap based on Gross margin > t <- max(cohorts$gr.marg) > > ggplot(cohorts, aes(y=cohort, x=date, fill=gr.marg)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on Gross margin (calendar view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > ggplot(cohorts, aes(y=cohort, x=week.lt, fill=gr.marg)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on Gross margin (lifetime view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > # Heatmap of per customer gross margin > t <- max(cohorts$av) > > ggplot(cohorts, aes(y=cohort, x=date, fill=av)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("Heatmap based on per customer gross margin (calendar view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > ggplot(cohorts, aes(y=cohort, x=week.lt, fill=av)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on per customer gross margin (lifetime view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > # Heatmap of CLV to date > t <- max(cohorts$clv) > > ggplot(cohorts, aes(y=cohort, x=date, fill=clv)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on CLV to date of customers who ever purchased (calendar view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > ggplot(cohorts, aes(y=cohort, x=week.lt, fill=clv)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on CLV to date of customers who ever purchased (lifetime view)")

library(dplyr) > library(ggplot2) > library(reshape2) > > #simulating dataset > cohorts <- data.frame() > set.seed(10) > for (i in c(1:100)) { + coh <- data.frame(cohort=i, + date=c(i:100), + week.lt=c(1:(100-i+1)), + num=replicate(1, sample(c(1:40), 100-i+1, rep=TRUE)), + av=replicate(1, sample(c(5:10), 100-i+1, rep=TRUE))) + coh$num[coh$week.lt==1] <- sample(c(90:100), 1, rep=TRUE) + ifelse(max(coh$date)>1, coh$num[coh$week.lt==2] <- sample(c(75:90), 1, rep=TRUE), NA) + ifelse(max(coh$date)>2, coh$num[coh$week.lt==3] <- sample(c(60:75), 1, rep=TRUE), NA) + ifelse(max(coh$date)>3, coh$num[coh$week.lt==4] <- sample(c(40:60), 1, rep=TRUE), NA) + ifelse(max(coh$date)>34, + {coh$num[coh$date==35] <- sample(c(60:85), 1, rep=TRUE) + coh$av[coh$date==35] <- 4}, + NA) + ifelse(max(coh$date)>47, + {coh$num[coh$date==48] <- sample(c(60:85), 1, rep=TRUE) + coh$av[coh$date==48] <- 4}, + NA) + ifelse(max(coh$date)>86, + {coh$num[coh$date==87] <- sample(c(60:85), 1, rep=TRUE) + coh$av[coh$date==87] <- 4}, + NA) + ifelse(max(coh$date)>99, + {coh$num[coh$date==100] <- sample(c(60:85), 1, rep=TRUE) + coh$av[coh$date==100] <- 4}, + NA) + coh$gr.marg <- coh$av*coh$num + cohorts <- rbind(cohorts, coh) + } > > cohorts$cohort <- formatC(cohorts$cohort, width=3, format='d', flag='0') > cohorts$cohort <- paste('coh:week:', cohorts$cohort, sep='') > cohorts$date <- formatC(cohorts$date, width=3, format='d', flag='0') > cohorts$date <- paste('cal_week:', cohorts$date, sep='') > cohorts$week.lt <- formatC(cohorts$week.lt, width=3, format='d', flag='0') > cohorts$week.lt <- paste('week:', cohorts$week.lt, sep='') > > #calculating CLV to date > cohorts <- cohorts %>% + group_by(cohort) %>% + mutate(clv=cumsum(gr.marg)/num[week.lt=='week:001']) > > #color palette > cols <- c("#e7f0fa", "#c9e2f6", "#95cbee", "#0099dc", "#4ab04a", "#ffd73e", "#eec73a", "#e29421", "#e29421", "#f05336", "#ce472e") > > #Heatmap based on Number of active customers > t <- max(cohorts$num) > > ggplot(cohorts, aes(y=cohort, x=date, fill=num)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Cohort Activity Heatmap (number of customers who purchased - calendar view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > ggplot(cohorts, aes(y=cohort, x=week.lt, fill=num)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Cohort Activity Heatmap (number of customers who purchased - lifetime view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > # Heatmap based on Gross margin > t <- max(cohorts$gr.marg) > > ggplot(cohorts, aes(y=cohort, x=date, fill=gr.marg)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on Gross margin (calendar view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > ggplot(cohorts, aes(y=cohort, x=week.lt, fill=gr.marg)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on Gross margin (lifetime view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > # Heatmap of per customer gross margin > t <- max(cohorts$av) > > ggplot(cohorts, aes(y=cohort, x=date, fill=av)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("Heatmap based on per customer gross margin (calendar view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > ggplot(cohorts, aes(y=cohort, x=week.lt, fill=av)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on per customer gross margin (lifetime view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > # Heatmap of CLV to date > t <- max(cohorts$clv) > > ggplot(cohorts, aes(y=cohort, x=date, fill=clv)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on CLV to date of customers who ever purchased (calendar view)") Warning: Ignoring unknown parameters: linewidth Warning messages: 1: In if (!guide$label) zeroGrob() else { : the condition has length > 1 and only the first element will be used 2: In if (!guide$ticks) zeroGrob() else { : the condition has length > 1 and only the first element will be used > > ggplot(cohorts, aes(y=cohort, x=week.lt, fill=clv)) + + theme_minimal() + + geom_tile(colour="white", linewidth=2, width=.9, height=.9) + + scale_fill_gradientn(colours=cols, limits=c(0, t), + breaks=seq(0, t, by=t/4), + labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)), + guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) + + theme(legend.position='bottom', + legend.direction="horizontal", + plot.title = element_text(size=20, face="bold", vjust=2), + axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) + + ggtitle("prepared in R by VOLKAN OBAN \n Cohort Analysis with Heatmap \n Heatmap based on CLV to date of customers who ever purchased (lifetime view)")

CV_1 <- 0.2 CV_2 <- 0.3 Mean <- 65 sigma_1 <- sqrt(log(1 + CV_1^2)) mu_1 <- log(Mean) - sigma_1^2 / 2 sigma_2 <- sqrt(log(1 + CV_2^2)) mu_2 <- log(Mean) - sigma_2^2 / 2 q <- c(0.25, 0.5, 0.75, 0.9, 0.95) SummaryTable <- data.frame( Quantile=paste0(100*q,"%ile"), Loss_1=round(qlnorm(q, mu_1, sigma_1),1), Loss_2=round(qlnorm(q, mu_2, sigma_2),1) ) # Create a plot library(ggplot2) plt <- ggplot(data.frame(x=c(20, 150)), aes(x)) + stat_function(fun=function(x) dlnorm(x, mu_1, sigma_1), aes(colour="CV_1")) + stat_function(fun=function(x) dlnorm(x, mu_2, sigma_2), aes(colour="CV_2")) + scale_colour_discrete(name = "CV", labels=c(expression(CV[1]), expression(CV[2]))) + xlab("Loss") + ylab("Density") + ggtitle(paste0("Two log-normal distributions with same mean of ", Mean,", but different CVs")) # Create a table plot library(gridExtra) names(SummaryTable) <- c("Quantile", expression(Loss(CV[1])), expression(Loss(CV[2]))) # Set theme to allow for plotmath expressions tt <- ttheme_default(colhead=list(fg_params = list(parse=TRUE))) tbl <- tableGrob(SummaryTable, rows=NULL, theme=tt) # Plot chart and table into one object grid.arrange(plt, tbl, nrow=2, as.table=TRUE, heights=c(3,1))

> science <- c( 32914, 11566, 4989, 3743, 968, 814, 673, 482, 286, 281 ) > bw <- c( 1694, 1167, 1108, 988, 919, 639, 596, 591, 580, 544 ) > lol <- c( 22627, 18100, 17688, 14374, 13459, 12045, 4711, 3779, 3670, 3393 ) > fashion <- c( 955, 581, 486, 435, 402, 303, 279, 279, 278, 275 ) > architecture <- c( 1426, 461, 433, 251, 230, 219, 194, 194, 175, 167 ) > art <- c( 7492, 2965, 2761, 1316, 544, 435, 413, 331, 307, 296 ) > require(RColorBrewer) Zorunlu paket yükleniyor: RColorBrewer > accent = brewer.pal(8, "Accent") > leg.txt <- c("science", "black & white", "lol", "fashion", "architecture", "art") > leg.col <- c(accent[1], accent[2], accent[3], accent[4], accent[5], accent[6]) > par(bg="#fafaff") > plot(science, type="s", log="y", lwd=2, col=accent[1], xlab="x-th most popular blog post", ylab="# likes", main="prepared in R by VOLKAN OBAN \n Distribution of LIKES on tumblr", cex.axis=.8, col.main="#444444", col.axis="#333333", fg="#332211") > points(bw, type="s", lwd=2, col=accent[2]) > points(lol, type="s", lwd=2, col=accent[3]) > points(fashion, type="s", lwd=3, col=accent[4]) > points(architecture, type="s", lwd=2, col=accent[5]) > points(art, type="s", lwd=2, col=accent[6]) legend("topright", leg.txt, fill=leg.col, title="TAG", text.col="#393939", title.col="#222222", border="#f0ffff", box.col="#666666"

library(broom) library(dplyr) library(ggplot2) iris_sub <- select(iris, x1 = Petal.Length, x2 = Petal.Width) kclusts <- data.frame(k=1:6) %>% group_by(k) %>% do(kclust=kmeans(iris_sub, .$k)) clusters <- kclusts %>% group_by(k) %>% do(tidy(.$kclust[[1]])) assignments <- kclusts %>% group_by(k) %>% do(augment(.$kclust[[1]], iris_sub)) clusterings <- kclusts %>% group_by(k) %>% do(glance(.$kclust[[1]])) ggplot(assignments, aes(x = x1, y = x2)) + facet_wrap(~ k) + geom_point(aes(color=.cluster)) + geom_point(data=clusters, size=10, shape="x")

> co2dat <- data.frame( + y = co2, + x = floor(time(co2)), + m = rep(month.abb, 39)) figure(xlim = c(1958, 2010), title="prepared in R-rbokeh by VOLKAN OBAN") %>% + ly_lines(x, y, color = m, data = co2dat)

> wa_cancer <- droplevels(subset(latticeExtra::USCancerRates, state == "Washington")) > ## y axis sorted by male rate > ylim <- levels(with(wa_cancer, reorder(county, rate.male))) > > figure(ylim = ylim, width = 700,title="prepared in R by Volkan OBAN \n rbokeh package", height = 600, tools = "") %>% + ly_segments(LCL95.male, county, UCL95.male, + county, data = wa_cancer, color = NULL, width = 2) %>% + ly_points(rate.male, county, glyph = 16, data = wa_cancer)

> figure(xlab="prepared in R by VOLKAN OBAN \n rbokeh package", legend_location = "top_left") %>% + ly_quantile(Sepal.Length, group = Species, data = iris)

> doubles <- read.csv("https://gist.githubusercontent.com/hafen/77f25b556725b3d0066b/raw/10f0e811f09f2b9f0f9ccfb542e296dfac2761d4/doubles.csv") > > ly_baseball <- function(x) { + base_x <- c(90 * cos(pi/4), 0, 90 * cos(3 * pi/4), 0) + base_y <- c(90 * cos(pi/4), sqrt(90^2 + 90^2), 90 * sin(pi/4), 0) + distarc_x <- lapply(c(2:4) * 100, function(a) + seq(a * cos(3 * pi/4), a * cos(pi/4), length = 200)) + distarc_y <- lapply(distarc_x, function(x) + sqrt((x[1]/cos(3 * pi/4))^2 - x^2)) + + x %>% + ## boundary + ly_segments(c(0, 0), c(0, 0), c(-300, 300), c(300, 300), alpha = 0.4) %>% + ## bases + ly_crect(base_x, base_y, width = 10, height = 10, + angle = 45*pi/180, color = "black", alpha = 0.4) %>% + ## infield/outfield boundary + ly_curve(60.5 + sqrt(95^2 - x^2), + from = base_x[3] - 26, to = base_x[1] + 26, alpha = 0.4) %>% + ## distance arcs (ly_arc should work here and would be much simpler but doesn't) + ly_multi_line(distarc_x, distarc_y, alpha = 0.4) + } > > figure(xgrid = FALSE, ygrid = FALSE, width = 630, height = 540, + xlab = "Horizontal distance from home plate (ft.) \n prepared by Volkan OBAN using R-rbokeh package", + ylab = "Vertical distance from home plate (ft.)") %>% + ly_baseball() %>% + ly_hexbin(doubles, xbins = 50, shape = 0.77, alpha = 0.75, palette = "Spectral10")

> p <- figure(width = 800, height = 400,title="prepared by VOLKAN OBAN \n rbokeh packages in R") %>% + ly_lines(date, Freq, data = flightfreq, alpha = 0.3) %>% + ly_points(date, Freq, data = flightfreq, + hover = list(date, Freq, dow), size = 5) %>% + ly_abline(v = as.Date("2001-09-11")) > p

> library(lattice) > v<-dotplot(reorder(Var2,Freq)~Freq|Var1,data = as.data.frame.table(VADeaths),origin=0,type=c("p","h"),main="R Data Visualization \n lattice package-dotplot",xlab="Number of Deaths per 100 ") > v

TurtleGraphics package. > drawTriangle <- function(points) { + turtle_setpos(points[1,1], points[1,2]) + turtle_goto(points[2,1], points[2,2]) + turtle_goto(points[3,1], points[3,2]) + turtle_goto(points[1,1], points[1,2]) + } > getMid <- function(p1, p2) + (p1+p2)*0.5 > sierpinski <- function(points, degree){ + drawTriangle(points) + if (degree > 0) { + p1 <- matrix(c(points[1,], getMid(points[1,], points[2,]), + getMid(points[1,], points[3,])), nrow=3, byrow=TRUE) + sierpinski(p1, degree-1) + p2 <- matrix(c(points[2,], getMid(points[1,], points[2,]), + getMid(points[2,], points[3,])), nrow=3, byrow=TRUE) + sierpinski(p2, degree-1) + p3 <- matrix(c(points[3,], getMid(points[3,], points[2,]), + getMid(points[1,], points[3,])), nrow=3, byrow=TRUE) + sierpinski(p3, degree-1) + } + invisible(NULL) + } > turtle_init(520, 500, "clip") > turtle_do({ + p <- matrix(c(10, 10, 510, 10, 250, 448), nrow=3, byrow=TRUE) + turtle_col("red") + sierpinski(p, 6) + turtle_setpos(250, 448) + }) >

TurtleGraphics package

> BMat=rbind(c(0.333,0,0,0.333,-0.333,0),c(0.167,-0.289,0.289,0.167,-0.0830,0.144),c(0.167,0.289,-0.289,0.167,0.083,0.144),c(0.333,0,0,0.333,0.333,0)) > > # Initial conditions: > x=0 > y=0 > > plot(0,0,xlim=c(-0.5,0.5),ylim=c(0,1),col="white",main="prepared in R by VOLKAN OBAN \n Koch Snowflake") > COLOR=c("green","red","blue","yellow") > > for(j in 1:100) + { + i=sample(1:4,1) # ,prob=c(0.25,0.25,0.25,0.25) + x3=x + x=BMat[i,1]*x+BMat[i,2]*y+BMat[i,5] + y=BMat[i,3]*x3+BMat[i,4]*y+BMat[i,6] + points(x,y,pch=".",cex=1, col=COLOR[i]) + } > BMat=rbind(c(0.333,0,0,0.333,-0.333,0),c(0.167,-0.289,0.289,0.167,-0.0830,0.144),c(0.167,0.289,-0.289,0.167,0.083,0.144),c(0.333,0,0,0.333,0.333,0)) > > # Initial conditions: > x=0 > y=0 > > plot(0,0,xlim=c(-0.5,0.5),ylim=c(0,1),col="white",main="prepared in R by VOLKAN OBAN \n Koch Snowflake") > COLOR=c("green","red","blue","yellow") > > for(j in 1:2000) + { + i=sample(1:4,1) # ,prob=c(0.25,0.25,0.25,0.25) + x3=x + x=BMat[i,1]*x+BMat[i,2]*y+BMat[i,5] + y=BMat[i,3]*x3+BMat[i,4]*y+BMat[i,6] + points(x,y,pch=".",cex=1, col=COLOR[i]) + } >

KochSnowflakeExample <- function(){ iterate <- function(T,i){ A = T[ ,1]; B=T[ ,2]; C = T[,3]; if (i == 1){ d = (A + B)/2; h = (C-d); d = d-(1/3)*h; e = (2/3)*B + (1/3)*A; f = (1/3)*B + (2/3)*A; } if (i == 2){ d = B; e = (2/3)*B + (1/3)*C; f = (2/3)*B + (1/3)*A; } if (i == 3){ d = (B + C)/2; h = (A-d); d = d-(1/3)*h; e = (2/3)*C + (1/3)*B; f = (1/3)*C + (2/3)*B; } if (i == 4){ d = C; e = (2/3)*C + (1/3)*A; f = (2/3)*C + (1/3)*B; } if (i == 5){ d = (A + C)/2; h = (B-d); d = d-(1/3)*h; e = (2/3)*A + (1/3)*C; f = (1/3)*A + (2/3)*C; } if (i == 6){ d = A; e = (2/3)*A + (1/3)*C; f = (2/3)*A + (1/3)*B; } if (i == 0){ d = A; e = B; f = C; } Tnew = cbind(d,e,f) return(Tnew); #Return a smaller triangle. } draw <- function(T, col=rgb(0,0,0),border=rgb(0,0,0)){ polygon(T[1,],T[2,],col=col,border=border) } Iterate = function(T,v,col=rgb(0,0,0),border=rgb(0,0,0)){ for (i in v) T = iterate(T,i); draw(T,col=col,border=border); } #The vertices of the initial triangle: A = matrix(c(1,0),2,1); B = matrix(c(cos(2*pi/3), sin(2*pi/3)),2,1); C = matrix(c(cos(2*pi/3),-sin(2*pi/3)),2,1); T0 = cbind(A,B,C); plot(numeric(0),xlim=c(-1.1,1.1),ylim=c(-1.1,1.1),axes=FALSE,frame=FALSE,ann=FALSE); par(mar=c(0,0,0,0),bg=rgb(1,1,1)); par(usr=c(-1.1,1.1,-1.1,1.1)); #Draw snowflake: for (i in 0:6) for (j in 0:6) for (k in 0:6) for (l in 0:6) Iterate(T0,c(i,j,k,l)); } KochSnowflakeExample();

library(spt) (abc = st(45,75)) plot(abc, , iter=18)

> library("ggplot2") > library("ggthemes") >ggplot(diamonds, aes(price, fill = cut)) + + geom_histogram(binwidth = 500) + theme_economist() + scale_colour_economist()

> nn <- 100 > myData <- data.frame(X = rnorm(nn), + Y = rnorm(nn)) > myData$Z <- with(myData, X * Y) > myData$Y <- myData$Y > 0 > > # This plot serves only to make a "hard" test case > # comparing Windows GDI to cairographics. > zp1 <- ggplot(myData, # \/ Here's a handy little function + aes(x = X, fill = cut_number(Z, n = 10))) +ggtitle("prepared in R by VOLKAN OBAN") > zp1 <- zp1 + geom_abline(aes(intercept = X, slope = X), lwd = 1/5) > zp1 <- zp1 + geom_density(alpha = 2/3) > zp1 <- zp1 + theme_bw() > zp1 <- zp1 + facet_grid(~ Y) > zp1 <- zp1 + scale_fill_manual(values = colorRampPalette(rev(brewer.pal(11, "Spectral")))(10), + guide = "none") > print(zp1) > > ggsave(plot = zp1, "Standard ggsave.png", h = 9/3, w = 16/3) > ggsave(plot = zp1, "Cairo ggsave.png", h = 9/3, w = 16/3, type = "cairo-png")

library(beanplot) > beanplot(rnorm(100),rnorm(150), rnorm(180),rnorm(50), runif(85), runif(70),runif(30),col="red", xlab="prepared by VOLKAN OBAN using R-beanplot")

> crime <- read.csv("http://datasets.flowingdata.com/crimeRatesByState-formatted.csv") > crime.new <- crime[crime$state != "District of Columbia",] library(beanplot) beanplot(crime.new[,-1],col="purple", ylab="prepared by VOLKAN OBAN" )

crime <- read.csv("http://datasets.flowingdata.com/crimeRatesByState-formatted.csv") crime.new <- crime[crime$state != "District of Columbia",] > library(beanplot) > beanplot(crime.new[,-1])

library(tabplot) > require(ggplot2) > data(diamonds) > > tab <- tableplot(diamonds) > plot(tab, title="prepared in R by VOLKAN OBAN \n data(diamonds)", + fontsize=12, + legend.lines=7, + fontsize.title=16)

hist3D_fancy<- function(x, y, break.func = c("Sturges", "scott", "FD"), breaks = NULL, colvar = NULL, col="white", clab=NULL, phi = 5, theta = 25, ...){ # Compute the number of classes for a histogram break.func <- break.func [1] if(is.null(breaks)){ x.breaks <- switch(break.func, Sturges = nclass.Sturges(x), scott = nclass.scott(x), FD = nclass.FD(x)) y.breaks <- switch(break.func, Sturges = nclass.Sturges(y), scott = nclass.scott(y), FD = nclass.FD(y)) } else x.breaks <- y.breaks <- breaks # Cut x and y variables in bins for counting x.bin <- seq(min(x), max(x), length.out = x.breaks) y.bin <- seq(min(y), max(y), length.out = y.breaks) xy <- table(cut(x, x.bin), cut(y, y.bin)) z <- xy xmid <- 0.5*(x.bin[-1] + x.bin[-length(x.bin)]) ymid <- 0.5*(y.bin[-1] + y.bin[-length(y.bin)]) oldmar <- par("mar") par (mar = par("mar") + c(0, 0, 0, 2)) hist3D(x = xmid, y = ymid, z = xy, ..., zlim = c(-max(z)/2, max(z)), zlab = "counts", bty= "g", phi = phi, theta = theta, shade = 0.2, col = col, border = "black", d = 1, ticktype = "detailed") scatter3D(x, y, z = rep(-max(z)/2, length.out = length(x)), colvar = colvar, col = gg.col(100), add = TRUE, pch = 18, clab = clab, colkey = list(length = 0.5, width = 0.5, dist = 0.05, cex.axis = 0.8, cex.clab = 0.8) ) par(mar = oldmar) } data(iris) hist3D_fancy(iris$Sepal.Length, iris$Petal.Width, main="prepared by Volkan OBAN ", colvar=as.numeric(iris$Species))

set.seed(1234) > x <- sort(rnorm(10)) > y <- runif(10) > # Variable for coloring points > col.v <- sqrt(x^2 + y^2) > scatter2D(x, y, colvar = col.v, pch = 16, bty ="n", + type ="b") > CI <- list() > CI$x <- matrix(nrow = length(x), data = c(rep(0.25, 2*length(x)))) > scatter2D(x, y, colvar = col.v, pch = 16, bty ="n", cex = 1.5, + CI = CI, type = "b") > CI <- list() > CI$x <- matrix(nrow = length(x), data = c(rep(0.25, 2*length(x)))) > scatter2D(x, y, colvar = col.v, pch = 16, bty ="n", cex = 1.5, main="prepared in R bu Volkan OBAN - scatter2D function", + CI = CI, type = "b")

ibrary(plot3D) Warning message: In as.list(X) : reached elapsed time limit > > X <- seq(0, 2*pi, length.out = 50) > Y <- seq(-15, 6, length.out = 50) > M <- mesh(X, Y) > u <- M$x > v <- M$y > > # x, y and z grids > x <- (1.16 ^ v) * cos(v) * (1 + cos(u)) > y <- (-1.16 ^ v) * sin(v) * (1 + cos(u)) > z <- (-2 * 1.16 ^ v) * (1 + sin(u)) > > # full colored image > par(mai = c(0.01, 0.01, 0.01, 0.01)) > surf3D(x, y, z, colvar = z, + col = ramp.col(col = c("violet", "pink"), n = 100), + colkey = FALSE, shade = 0.5, alpha = 0.3, expand = 1.2, + box = FALSE, phi = 35, border = "black", theta = 70, + lighting = TRUE, ltheta = 560, lphi = -50)

layout(matrix(c(1,1,1,2,3,4),nrow=2,ncol=3,byrow=TRUE)) # plot a proximity.timeline illustrating infection spread proximity.timeline(toy_epi_sim,vertex.col = 'ndtvcol', spline.style='color.attribute', mode = 'sammon',default.dist=100, chain.direction='reverse') # plot 3 static cross-sectional networks # (beginning, middle and end) underneath for comparison plot(network.collapse(toy_epi_sim,at=1),vertex.col='ndtvcol', vertex.cex=2,main='toy_epi_sim network at t=1') plot(network.collapse(toy_epi_sim,at=17),vertex.col='ndtvcol', vertex.cex=2,main='toy_epi_sim network at=17') plot(network.collapse(toy_epi_sim,at=25),vertex.col='ndtvcol', vertex.cex=2,main='toy_epi_sim network at t=25') layout(1)

> library(animation) > library(picante) > library(nlme) > library(FD) > library(vegan) > library(permute) > library(geometry) > library(magic) > library(abind) > library(ape) > library(ade4) > fire <- function(centre = c(0, 0), r = 1:5, theta = seq(0, + 2 * pi, length = 100), l.col = rgb(1, 1, 0), lwd = 5, + ...) { + x <- centre[1] + outer(r, theta, function(r, theta) r * + sin(theta)) + y <- centre[2] + outer(r, theta, function(r, theta) r * + cos(theta)) + matplot(x, y, type = "l", lty = 1, col = l.col, add = T, + lwd = lwd, ...) + } > f <- function(centre = rbind(c(-7, 7), c(7, 6)), n = c(7, + 5), N = 20, l.col = c("rainbow", "green"), p.col = "red", + lwd = 5, ...) { + ani.options(interval = 0.1) + lwd = lwd + if (is.vector(centre) && length(n) == 1) { + r = 1:n + l = seq(0.1, 0.6, length = n) + matplot(centre[1], centre[2], col = p.col, ...) + for (r in r) { + fire(centre = centre, r = seq(r - l[r], r + l[r], + length = 10), theta = seq(0, 2 * pi, length = 10 * + r) + 1, l.col = rainbow(n)[r], lwd = lwd, ...) + } + } + else { + matplot(centre[, 1], centre[, 2], col = p.col, ...) + l = list() + for (i in 1:length(n)) l[i] = list(seq(0.1, 0.6, + length = n[i])) + if (length(l.col) == 1) + l.col = rep(l.col, length(n)) + r = 1:N + for (r in r) { + for (j in 1:length(n)) { + if (r%%(n[j] + 1) == 0) { + r1 = 1:n[j] + l1 = seq(0.1, 0.6, length = n[j]) + for (r1 in r1) { + fire(centre = centre[j, ], r = seq(r1 - + l1[r1], r1 + l1[r1], length = 10), theta = seq(0, + 2 * pi, length = 10 * r1) + 1, l.col = par("bg"), + lwd = lwd + 2) + } + } + else { + if (l.col[j] == "red") + fire(centre = centre[j, ], r = seq(r%%(n[j] + + 1) - l[[j]][r%%(n[j] + 1)], r%%(n[j] + + 1) + l[[j]][r%%(n[j] + 1)], length = 10), + theta = seq(0, 2 * pi, length = 10 * + r%%(n[j] + 1)) + 1, l.col = rgb(1, + r%%(n[j] + 1)/n[j], 0), lwd = lwd, + ...) + else if (l.col[j] == "green") + fire(centre = centre[j, ], r = seq(r%%(n[j] + + 1) - l[[j]][r%%(n[j] + 1)], r%%(n[j] + + 1) + l[[j]][r%%(n[j] + 1)], length = 10), + theta = seq(0, 2 * pi, length = 10 * + r%%(n[j] + 1)) + 1, l.col = rgb(1 - + r%%(n[j] + 1)/n[j], 1, 0), lwd = lwd, + ...) + else if (l.col[j] == "blue") + fire(centre = centre[j, ], r = seq(r%%(n[j] + + 1) - l[[j]][r%%(n[j] + 1)], r%%(n[j] + + 1) + l[[j]][r%%(n[j] + 1)], length = 10), + theta = seq(0, 2 * pi, length = 10 * + r%%(n[j] + 1)) + 1, l.col = rgb(r%%(n[j] + + 1)/n[j], 0, 1), lwd = lwd, ...) + else fire(centre = centre[j, ], r = seq(r%%(n[j] + + 1) - l[[j]][r%%(n[j] + 1)], r%%(n[j] + + 1) + l[[j]][r%%(n[j] + 1)], length = 10), + theta = seq(0, 2 * pi, length = 10 * r%%(n[j] + + 1)) + 1, l.col = rainbow(n[j])[r%%(n[j] + + 1)], lwd = lwd, ...) + } + ani.pause() + } + } + } + } > card <- function(N = 20, p.col = "green", bgcolour = "black", + lwd = 5, ...) { + ani.options(interval = 1) + for (i in 1:N) { + par(ann = F, bg = bgcolour, mar = rep(0, 4), pty = "s") + f(N = i, lwd = lwd, ...) + text(0, 0, "Happy New Year \n Happy New Year \n VOLKAN OBAN ", srt = 360 * i/N, col = rainbow(N)[i], + cex = 4.5 * i/N) + ani.pause() + } + } > ani.options(interval = 0.2) > card(N = 30, centre = rbind(c(-8, 8), c(8, 10), c(5, 0)), n = c(9, 5, 6), pch = 8, p.col = "green", l.col = c("rainbow", "red", "green"), xlim = c(-12, 12), ylim = c(-12,12))

Turkish flag.Türk Bayrağı. wordcloud2(demoFreq, figPath = "bayr.png", size = 1.5, color = "red", backgroundColor="white")

ATATÜRK

wordcloud2(demoFreq, figPath = "ata.png", size = 1.5, color = "black", backgroundColor="white")

Atatürk

wordcloud2(demoFreq, figPath = "pii.png", size = 1.5, color = "skyblue", backgroundColor="black")

wordcloud2(demoFreq, figPath = "atam.png", size = 1.5, color = "skyblue", backgroundColor="black")

> library(wordcloud2) > letterCloud( demoFreq, word = "itü", color='random-light' , backgroundColor="black")

library(wordcloud) wordcloud(c("HAPPY NEW YEAR", "2017","VOLKAN OBAN"), max.words =100,min.freq=3,scale=c(4,.5), random.order = FALSE,rot.per=.5,vfont=c("gothic english","plain"),colors=palette())

library(wordcloud2) > letterCloud(demoFreq, word = "HAPPY NEW YEAR - 2017 !", wordSize = 1)

library(wordcloud2) letterCloud( demoFreq, word = "2017 \ V. O. ", color='random-light' , backgroundColor="black")

titanic data set.

library(ggplot2) > #create data > set.seed(3) > > #time steps > t.step<-seq(0,20) > > #group names > grps<-letters[1:10] > > #random data for group values across time > grp.dat<-runif(length(t.step)*length(grps),5,15) > > #create data frame for use with plot > grp.dat<-matrix(grp.dat,nrow=length(t.step),ncol=length(grps)) > grp.dat<-data.frame(grp.dat,row.names=t.step) > names(grp.dat)<-grps > source("https://gist.github.com/fawda123/6589541/raw/8de8b1f26c7904ad5b32d56ce0902e1d93b89420/plot_area.r") > > plot.area(grp.dat)

> set.seed(3674) > k <- rep(1:3, each=30) > x <- k + rnorm(mean=10, sd=.2,n=90) > y <- -2*k + rnorm(mean=10, sd=.4,n=90) > z <- 3*k + rnorm(mean=10, sd=.6,n=90) > > dat <- data.frame(group=factor(k),x,y,z) > > library(GGally) > ggparcoord(dat,columns=1:4,groupColumn = 1)

> library(ggplot2) > ggplot(data = diamonds) + + geom_bar(mapping = aes(x = cut, fill = cut), width = 1) + + coord_polar() + + facet_wrap( ~ clarity) + ggtitle("prepared in R by Volkan OBAN") > ggplot(data = diamonds) + + geom_bar(mapping = aes(x = cut, fill = cut), width = 1) + + coord_polar() + + facet_wrap( ~ clarity) + ggtitle("prepared in R by Volkan OBAN \n data(diamonds)")

library(rasterVis) > alt <- getData('worldclim', var='alt', res=2.5) > a1 <- getData('GADM', country='Turkey', level=1) > oregon <- a1[a1$NAME_1 == 'Oregon',] > alt <- crop(alt, extent(oregon) + 0.5) > alt <- mask(alt, oregon) > levelplot(alt,main="prepared in R-rastervis package by Volkan OBAN \n TURKEY", par.settings=GrTheme)

> m <- matrix(c(25,16,12,8,4,13,9,5,21,17,1,22,18,14,10,19,15,6,2,23,7,3,24,20,11), nrow=5, ncol=5) > df <- expand.grid(x=1:ncol(m),y=1:nrow(m)) > df$val <- m[as.matrix(df[c('y','x')])] > library(plotrix) > xt <- xtabs(val ~ ., df[c(2,1,3)]) > color2D.matplot(xt, vcex = 3, show.values = 1, axes = FALSE, xlab = "Magic Square 5x5 ", ylab = "", cellcolors = rep("pink", length(xt)))

Code: m <- matrix(c(25,13,1,19,7,16,9,22,15,3,12,5,18,6,24,8,21,14,2,20,4,17,10,23,11), nrow=5, ncol=5) > df <- expand.grid(x = 1:ncol(m),y = 1:nrow(m)) > df$val <- m[as.matrix(df[c('y', 'x')])] > library(plotrix) > xt <- xtabs(val ~ ., df[c(2,1,3)]) > color2D.matplot(xt, vcex = 3, show.values = 1, axes = FALSE, xlab = "Magic Square 5x5 ", + ylab = "prepared by Volkan OBAN ", cellcolors = rep("white", length(xt)))

> x <- 1:400 > y <- sin(x/10) * exp(x * -0.01) > > plot(x, y) > x <- 1:400 > y <- sin(x/10) * exp(x * -0.01) > > plot(x, y)

data <- read.csv("http://bl.ocks.org/WillTurman/raw/4631136/data.csv", stringsAsFactors=FALSE) data$date <- as.Date(data$date, format="%m/%d/%y") streamgraph(data, interactive=TRUE) %>% sg_colors("Reds") dat <- read.csv("http://asbcllc.com/blog/2015/february/cre_stream_graph_test/data/cre_transaction-data.csv") dat %>% streamgraph("asset_class", "volume_billions", "year", interpolate="cardinal") %>% sg_axis_x(1, "year", "%Y") %>% sg_fill_brewer("PuOr") datatable(dat) dat %>% streamgraph("asset_class", "volume_billions", "year", offset="silhouette", interpolate="step") %>% sg_axis_x(1, "year", "%Y") %>% sg_fill_brewer("PuOr") dat %>% streamgraph("asset_class", "volume_billions", "year", offset="zero", interpolate="cardinal") %>% sg_axis_x(1, "year", "%Y") %>% sg_fill_brewer("PuOr") %>% sg_legend(TRUE, "Asset class: ") Now, who let that stacked bar chart get in here ;-) dat %>% streamgraph("asset_class", "volume_billions", "year", offset="zero", interpolate="step") %>% sg_axis_x(1, "year", "%Y") %>% sg_fill_brewer("PuOr") # get top 10 names for each year by sex babynames %>% group_by(year, sex) %>% top_n(10, n) -> dat1 # just look at female names and get the data for # the top n by all years to see how they "flow" babynames %>% filter(sex=="F", name %in% dat1$name) -> dat streamgraph(dat, "name", "n", "year") %>% sg_fill_tableau() %>% sg_axis_x(tick_units = "year", tick_interval = 10, tick_format = "%Y") %>% sg_legend(TRUE, "Name: ")

> library(rbokeh) > library(maps) > data(world.cities) > caps <- subset(world.cities, capital == 1) > caps$population <- prettyNum(caps$pop, big.mark = ",") > figure(width = 800, height = 450,title = "prepared by Volkan OBAN- rbokeh in R", padding_factor = 0) %>% + ly_map("world", col = "gray") %>% + ly_points(long, lat, data = caps, size = 5, + hover = c(name, country.etc, population))

https://rpubs.com/omicsdata/faceted_heatmap

stock <- "MSFT" start.date <- "2012-01-01" end.date <- Sys.Date() quote <- paste("http://ichart.finance.yahoo.com/table.csv?s=", stock, "&a=", substr(start.date,6,7), "&b=", substr(start.date, 9, 10), "&c=", substr(start.date, 1,4), "&d=", substr(end.date,6,7), "&e=", substr(end.date, 9, 10), "&f=", substr(end.date, 1,4), "&g=d&ignore=.csv", sep="") stock.data <- read.csv(quote, as.is=TRUE) stock.data$Date <- as.Date(stock.data$Date) ## Uncomment the next 3 lines to install the developer version of googleVis # install.packages(c("devtools","RJSONIO", "knitr", "shiny", "httpuv")) # library(devtools) # install_github("mages/googleVis") library(googleVis) plot( gvisCalendar(data=stock.data, datevar="Date", numvar="Adj.Close", options=list( title="Calendar heat map of MSFT adjsuted close", calendar="{cellSize:10, yearLabel:{fontSize:20, color:'#444444'}, focusedCellColor:{stroke:'red'}}", width=590, height=320), chartid="Calendar") ) library(lattice) > library(chron) > source("http://blog.revolutionanalytics.com/downloads/calendarHeat.R") > # Plot as calendar heatmap > calendarHeat(stock.data$Date, stock.data$Adj.Close, + varname="PREPARED BY VOLKAN OBAN \n MSFT Adjusted Close") > library(lattice) > library(chron) > source("http://blog.revolutionanalytics.com/downloads/calendarHeat.R") > # Plot as calendar heatmap > calendarHeat(stock.data$Date, stock.data$Adj.Close, + varname="\n PREPARED BY VOLKAN OBAN \n MSFT Adjusted Close") > library(lattice) > library(chron) > source("http://blog.revolutionanalytics.com/downloads/calendarHeat.R") > # Plot as calendar heatmap > calendarHeat(stock.data$Date, stock.data$Adj.Close, + varname="MSFT Adjusted Close \n PREPARED BY VOLKAN OBAN \n") >

ref:https://www.r-bloggers.com/calculating-auc-the-area-under-a-roc-curve/

category <- c(1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0) prediction <- rev(seq_along(category)) prediction[9:10] <- mean(prediction[9:10]) library(pROC) roc_obj <- roc(category, prediction) auc(roc_obj) ## Area under the curve: 0.825 roc_df <- data.frame( TPR=rev(roc_obj$sensitivities), FPR=rev(1 - roc_obj$specificities), labels=roc_obj$response, scores=roc_obj$predictor) rectangle <- function(x, y, width, height, density=12, angle=-45, ...) polygon(c(x,x,x+width,x+width), c(y,y+height,y+height,y), density=density, angle=angle, ...) roc_df <- transform(roc_df, dFPR = c(diff(FPR), 0), dTPR = c(diff(TPR), 0)) plot(0:10/10, 0:10/10, type='n', xlab="FPR", ylab="TPR") abline(h=0:10/10, col="lightblue") abline(v=0:10/10, col="lightblue") with(roc_df, { mapply(rectangle, x=FPR, y=0, width=dFPR, height=TPR, col="green", lwd=2) mapply(rectangle, x=FPR, y=TPR, width=dFPR, height=dTPR, col="blue", lwd=2) lines(FPR, TPR, type='b', lwd=3, col="red") }) ref:https://www.r-bloggers.com/calculating-auc-the-area-under-a-roc-curve/

library(lattice) dotplot(VADeaths,groups=FALSE,layout=c(1,4),aspect=0.7,origin=0,type=c("p","h"),main="prepared by Volkan OBAN \n dotplot/Lattice package \n Death Rates in Virginia-1940",xlab="Rate (per 100)" )

ref:https://rdrr.io/cran/strap/man/geoscalePhylo.html

DatePhylo geoscalePhylo

get_map ggmap functions

Tur.map = get_map(location = "Turkey", zoom = 5, color="bw") ## get MAP data p <- ggmap(Tur.map) > p

> library(plotly) > > x <- c('Produce<br>Revenue', 'Services<br>Rev.', 'Total<br>Revenue', 'Fixed<br>Costs', 'Variable<br>Costs', 'Total<br>Costs', 'Total') > y <- c(400, 660, 660, 590, 400, 400, 340) > base <- c(0, 430, 0, 570, 370, 370, 0) > revenue <- c(430, 260, 690, 0, 0, 0, 0) > costs <- c(0, 0, 0, 120, 200, 320, 0) > profit <- c(0, 0, 0, 0, 0, 0, 370) > text <- c('$430K', '$260K', '$690K ', '$-1 20K', '$-200K', '$-320K', '$370K') > data <- data.frame(x, base, revenue, costs, profit, text) > > #The default order will be alphabetized unless specified as below: > data$x <- factor(data$x, levels = data[["x"]]) > > p <- plot_ly(data, x = ~x, y = ~base, type = 'bar', marker = list(color = 'rgba(1,1,1, 0.0)')) %>% + add_trace(y = ~revenue, marker = list(color = 'rgba(55, 128, 191, 0.7)', + line = list(color = 'rgba(55, 128, 191, 0.7)', + width = 2))) %>% + add_trace(y = ~costs, marker = list(color = 'rgba(219, 64, 82, 0.7)', + line = list(color = 'rgba(219, 64, 82, 1.0)', + width = 2))) %>% + add_trace(y = ~profit, marker = list(color = 'rgba(50, 171, 96, 0.7)', + line = list(color = 'rgba(50, 171, 96, 1.0)', + width = 2))) %>% + layout(title = 'Annual Profit ', + xaxis = list(title = ""), + yaxis = list(title = "prepared by Volkan OBAN"), + barmode = 'stack', + paper_bgcolor = 'rgba(245, 246, 249, 1)', + plot_bgcolor = 'rgba(245, 246, 249, 1)', + showlegend = FALSE) %>% + add_annotations(text = text, + x = x, + y = y, + xref = "x", + yref = "y", + font = list(family = 'Arial', + size = 14, + color = 'rgba(245, 246, 249, 1)'), +

> library(devtools) > devtools::install_github("jjallaire/sigma") library(sigma) > data <- system.file("examples/ediaspora.gexf.xml", package = "sigma") > sigma(data)

library(devtools) install_github("ramnathv/rChartsCalendar") dat <- read.csv('http://t.co/mN2RgcyQFc')[,c('date', 'pts')] library(rChartsCalendar) r1 <- plotCalMap(x = 'date', y = 'pts', data = dat, domain = 'month', start = "2012-10-27", legend = seq(10, 50, 10), itemName = 'point', range = 7 ) library(quantmod) getSymbols("AAPL") xts_to_df <- function(xt){ data.frame( date = format(as.Date(index(xt)), '%Y-%m-%d'), coredata(xt) ) } dat = xts_to_df(AAPL) plotCalMap('date', 'AAPL.Adjusted', data = dat, domain = 'month', legend = seq(500, 700, 40), start = '2014-01-01', itemName = '$$' )

library(TimeProjection) >dates = timeSequence(from = '2012-01-01', to = '2012-12-31', by = 'day') > plotCalendarHeatmap(as.Date(dates), 1:366

stock.dailychange<-100*(diff(stock.data$Adj.Close,lag=1)/y[1:length(stock.data$Adj.Close)-1]) calendarHeat(stock.data$Date[1:length(stock.data$Date)-1], stock.dailychange, varname="SPY daily % changes(CL-CL)")

code source: https://github.com/iascchen/VisHealth/blob/master/R/calendarHeat.R then stock <- "MSFT" start.date <- "2006-01-12" end.date <- Sys.Date() quote <- paste("http://ichart.finance.yahoo.com/table.csv?s=", stock, "&a=", substr(start.date,6,7), "&b=", substr(start.date, 9, 10), "&c=", substr(start.date, 1,4), "&d=", substr(end.date,6,7), "&e=", substr(end.date, 9, 10), "&f=", substr(end.date, 1,4), "&g=d&ignore=.csv", sep="") stock.data <- read.csv(quote, as.is=TRUE) calendarHeat(stock.data$Date, stock.data$Adj.Close, varname="MSFT Adjusted Close")

library(qgraph) dat.3 <- matrix(c(1:15*2-1,1:15*2),,2) dat.3 <- cbind(dat.3,round(seq(-0.7,0.7,length=15),1)) # Create grid layout: L.3 <- matrix(1:30,nrow=2) # Different esize: qgraph(dat.3,layout=L.3,directed=FALSE,edge.labels=TRUE,esize=14)

# split into 2 rows and 2 cols split.screen(c(2,2)) # keep track of which screen we are # plotting to scr <- 1 # iterate over columns for (i in 1:ncol(df)) { # select screen screen(scr) # reduce margins par(mar=c(3,2,1,1)) # empty plot plot(1:nrow(df), 1:nrow(df), pch="", xlab=NA, ylab=NA, xaxt="n", yaxt="n", ylim=c(0,35), bty="n") # plot all data in grey for (j in 1:ncol(df)) { lines(1:nrow(df), df[,j], col="grey", lwd=3) } # plot selected in blue lines(1:nrow(df), df[,i], col="blue4", lwd=4) # add blobs points(c(1,nrow(df)), c(df[1,i], df[nrow(df),i]), pch=16, cex=2, col="blue4") # add numbers mtext(df[1,i], side=2, at=df[1,i], las=2) mtext(df[nrow(df),i], side=4, at=df[nrow(df),i], las=2) # add title title(colnames(df)[i]) # add axes if we are one of # the bottom two plots if (scr >= 3) { axis(side=1, at=1:nrow(df), tick=FALSE, labels=rownames(df)) } # next screen scr <- scr + 1 } # close multi-panel image close.screen(all=TRUE)

> library(PerformanceAnalytics) > library(psych) > d <- msq[,80:84] > chart.Boxplot(d, main = "", xlab="average personality rating (based on n=3896) \n prepared by Volkan OBAN", ylab="",element.color = "transparent", as.Tufte=TRUE)

library(metricsgraphics) library(RColorBrewer) tmp <- data.frame(year=seq(1790, 1970, 10), uspop=as.numeric(uspop)) tmp %>% mjs_plot(x=year, y=uspop) %>% mjs_line() %>% mjs_add_marker(1850, "Something Wonderful") %>% mjs_add_baseline(150, "Something Awful") tmp %>% mjs_plot(x=year, y=uspop, width=600) %>% mjs_line(area=TRUE) tmp %>% mjs_plot(x=uspop, y=year, width=500, height=400) %>% mjs_bar() %>% mjs_axis_x(xax_format = 'plain') mtcars %>% mjs_plot(x=wt, y=mpg, width=600, height=500) %>% mjs_point(color_accessor=carb, size_accessor=carb) %>% mjs_labs(x="Weight of Car", y="Miles per Gallon") mtcars %>% mjs_plot(x=wt, y=mpg, width=600, height=500) %>% mjs_point(color_accessor=cyl, x_rug=TRUE, y_rug=TRUE, size_accessor=carb, size_range=c(5, 10), color_type="category", color_range=brewer.pal(n=11, name="RdBu")[c(1, 5, 11)]) %>% mjs_labs(x="Weight of Car", y="Miles per Gallon") mtcars %>% mjs_plot(x=wt, y=mpg, width=400, height=300) %>% mjs_point(least_squares=TRUE) %>% mjs_labs(x="Weight of Car", y="Miles per Gallon") set.seed(1492) dat <- data.frame(date=seq(as.Date("2014-01-01"), as.Date("2014-01-31"), by="1 day"), value=rnorm(n=31, mean=0, sd=2)) dat %>% mjs_plot(x=date, y=value) %>% mjs_line() %>% mjs_axis_x(xax_format = "date") # Custom rollovers dat %>% mjs_plot(x=date, y=value) %>% mjs_line() %>% mjs_axis_x(xax_format = "date") %>% mjs_add_mouseover("function(d, i) { $('{{ID}} svg .mg-active-datapoint') .text('custom text : ' + d.date + ' ' + i); }") # also works for scatterplots with a slight mod set.seed(1492) dat <- data.frame(value=rnorm(n=30, mean=5, sd=1), value2=rnorm(n=30, mean=4, sd=1), test = c(rep(c('test', 'test2'), 15))) dat %>% mjs_plot(x = value, y = value2) %>% mjs_point() %>% mjs_add_mouseover("function(d, i) { $('{{ID}} svg .mg-active-datapoint') .text('custom text : ' + d.point.test + ' ' + i); }") set.seed(1492) stocks <- data.frame( time = as.Date('2009-01-01') + 0:9, X = rnorm(10, 0, 1), Y = rnorm(10, 0, 2), Z = rnorm(10, 0, 4)) stocks %>% mjs_plot(x=time, y=X) %>% mjs_line() %>% mjs_axis_x(show=FALSE) %>% mjs_axis_y(show=FALSE) stocks %>% mjs_plot(x=time, y=X) %>% mjs_line() %>% mjs_add_line(Y) %>% mjs_add_line(Z) %>% mjs_axis_x(xax_format="date") mjs_plot(rnorm(10000)) %>% mjs_histogram(bins=30, bar_margin=1) movies <- ggplot2movies::movies[sample(nrow(ggplot2movies::movies), 1000), ] mjs_plot(movies$rating) %>% mjs_histogram() mjs_plot(movies, rating) %>% mjs_histogram() %>% mjs_labs(x_label="Histogram of movie ratings", y_label="Frequency") mjs_plot(movies$rating) %>% mjs_histogram(bins=30) mjs_plot(runif(10000)) %>% mjs_labs(x_label="runif(10000)") %>% mjs_histogram() mjs_plot(rbeta(10000, 2, 5)) %>% mjs_labs(x_label="rbeta(10000, 2, 3)") %>% mjs_histogram(bins=100) %>% mjs_axis_y(extended_ticks=TRUE) bimod <- c(rnorm(1000, 0, 1), rnorm(1000, 3, 1)) mjs_plot(bimod) %>% mjs_histogram() mjs_plot(bimod) %>% mjs_histogram(bins=30) bimod %>% mjs_hist(30) library(shiny) library(metricsgraphics) ui = shinyUI(fluidPage( h3("MetricsGraphics Example", style="text-align:center"), metricsgraphicsOutput('mjs1'), br(), metricsgraphicsOutput('mjs2') )) server = function(input, output) { mtcars %>% mjs_plot(x=wt, y=mpg, width=400, height=300) %>% mjs_point(color_accessor=carb, size_accessor=carb) %>% mjs_labs(x="Weight of Car", y="Miles per Gallon") -> m1 set.seed(1492) stocks <- data.frame( time = as.Date('2009-01-01') + 0:9, X = rnorm(10, 0, 1), Y = rnorm(10, 0, 2), Z = rnorm(10, 0, 4)) stocks %>% mjs_plot(x=time, y=X) %>% mjs_line() %>% mjs_add_line(Y) %>% mjs_add_line(Z) %>% mjs_axis_x(xax_format="date") %>% mjs_add_legend(legend=c("X", "Y", "Z")) -> m2 output$mjs1 <- renderMetricsgraphics(m1) output$mjs2 <- renderMetricsgraphics(m2) } shinyApp(ui = ui, server = server)

library(maps) # Provides functions that let us plot the maps ) library('geosphere') library(mapdata) map('worldHires') map("worldHires","Turkey",col="white", border="gray10", fill=TRUE, bg="paleturquoise1")

> library(ggmap) > library(mapproj) > map <- get_map(location = 'Europe', zoom = 4) > ggmap(map)

df <- data.frame(group = rep(c("Above", "Below"), each=10), x = rep(1:10, 2), y = c(runif(10, 0, 1), runif(10, -1, 0))) > p <- ggplot(df, aes(x=x, y=y, fill=group)) + + geom_bar(stat="identity", position="identity") > print(p)

library(PerformanceAnalytics) chart.Correlation(iris[-5], bg=iris$Species, pch=21)

3 4 5 6 7 8 9 10 11 12 #Library library(leaflet) # Background 1: NASA m=leaflet() %>% addTiles() %>% setView( lng = 2.34, lat = 48.85, zoom = 5 ) %>% addProviderTiles("NASAGIBS.ViirsEarthAtNight2012") m # Background 2: World Imagery m=leaflet() %>% addTiles() %>% setView( lng = 2.34, lat = 48.85, zoom = 3 ) %>% addProviderTiles("Esri.WorldImagery") m

Anadolu-Anatolia.

> library(jpeg) > my_image=readJPEG("itu.jpg") > library(ggplot2) Attaching package: ‘ggplot2’ The following object is masked _by_ ‘.GlobalEnv’: midwest > # Set up a plot area with no plot > plot(1:2, type='n', main="", xlab="x", ylab="y") > > # Get the plot information so the image will fill the plot box, and draw it > lim <- par() > rasterImage(my_image, lim$usr[1], lim$usr[3], lim$usr[2], lim$usr[4]) > grid() > > #Add your plot ! > lines(c(1, 1.2, 1.4, 1.6, 1.8, 2.0), c(1, 1.3, 1.7, 1.6, 1.7, 1.0), type="b", lwd=5, col="white") > # Set up a plot area with no plot > plot(1:2, type='n', main="prepared by Volkan OBAN", xlab="x", ylab="y") > > # Get the plot information so the image will fill the plot box, and draw it > lim <- par() > rasterImage(my_image, lim$usr[1], lim$usr[3], lim$usr[2], lim$usr[4]) > grid() > > #Add your plot ! > lines(c(1, 1.2, 1.4, 1.6, 1.8, 2.0), c(1, 1.3, 1.7, 1.6, 1.7, 1.0), type="b", lwd=5, col="black")

> plotwidgetGallery() > ## automatically set black bg > plotwidgetGallery(theme="neon") > ## yuck, ugly: > plotwidgetGallery(pal=c("red", "#FF9900", "blue", "green", "cyan", "yellow")) > ## much better: > plotwidgetGallery(pal=plotPals("pastel", alpha=0.8))

plot.new() ## Loop over a few saturation / lightess values par(usr=c(-0.5, 0.5, -0.5, 0.5)) v <- c(10, 9, 19, 9, 15, 5) pal <- plotPals("zeileis") for(sat in seq.int(-0.4, 0.4, length.out=5)) { for(lgh in seq.int(-0.4, 0.4, length.out=5)) { cols <- saturateCol(darkenCol(pal, by=sat), by=lgh) wgPlanets(x=sat, y=lgh, w=0.16, h=0.16, v=v, col=cols) } } axis(1) axis(2) title(xlab="Darkness (L) by=", ylab="Saturation (S) by=") ## Now loop over hues a2xy <- function(a, r=1, full=FALSE) { t <- pi/2 - 2 * pi * a / 360 list( x=r * cos(t), y=r * sin(t) ) } plot.new() par(usr=c(-1,1,-1,1)) hues <- seq(0, 360, by=30) pos <- a2xy(hues, r=0.75) for(i in 1:length(hues)) { cols <- modhueCol(pal, by=hues[i]) wgPlanets(x=pos$x[i], y=pos$y[i], w=0.5, h=0.5, v=v, col=cols) } pos <- a2xy(hues[-1], r=0.4) text(pos$x, pos$y, hues[-1])

> rpivotTable( + Titanic, + rows = "Survived", + cols = c("Class","Sex"), + aggregatorName = "Sum as Fraction of Columns", + vals = "Freq", + rendererName = "Table Barchart" + ) > > # An example with inclusions and exclusions filters: > > rpivotTable( + Titanic, + rows = "Survived", + cols = c("Class","Sex"), + aggregatorName = "Sum as Fraction of Columns", + inclusions = list( Survived = list("Yes")), + exclusions= list( Class = list( "Crew")), + vals = "Freq", + rendererName = "Table Barchart" + )

daata(Titanic)

> library(rpivotTable) > data(mtcars) > ## One line to create pivot table > rpivotTable(mtcars, rows="gear", col="cyl", aggregatorName="Average", vals="mpg", rendererName="Treemap")

> library(rpivotTable) > data(mtcars) > ## One line to create pivot table > rpivotTable(mtcars, rows="gear", col="cyl", aggregatorName="Average", vals="mpg", rendererName="Treemap")

> bwplot(~weight|factor(Time),data=ChickWeight,col="blue", main="prepared by Volkan OBAN \n Weight by Days Since Birth",xlab="Weight in grams")

library(lattice) > bwplot(height~voice.part, singer) > at <- seq(60, 75, 2.5) > bwplot(height~voice.part, singer, scales=list(y=list(at=at))) > bwplot(height~voice.part, singer, scales=list(y=list(at=at)), + panel=function(...) { + panel.abline(h=at, col="gray") + panel.bwplot(...) + }) > lvls <- levels(singer$voice.part) > fill <- rep("white", length(lvls)) > fill[lvls %in% c("Tenor 1", "Tenor 2")] <- "gray" > bwplot(height~voice.part, singer, scales=list(y=list(at=at)), + fill=fill, panel=function(...) { + panel.abline(h=at, col="gray") + panel.bwplot(...) + })

library(genasis) genhistogram(rnorm(60)) ## Use of example data from the package: data(kosetice.pas.openair) genhistogram(kosetice.pas.openair[,1:8],col="orange",emboss=3) data(kosetice.pas.genasis) genhistogram(kosetice.pas.genasis[1:208,],input="genasis", distr="lnorm",col="orange",emboss=2)

highchart() %>% + hc_chart(backgroundColor = "#") %>% + hc_title(text = "Chart color gradient it's on fire-prepared by Volkan OBAN", style = list(color = "#CCC")) %>% + # hc_xAxis(categories = month.abb) %>% + hc_yAxis(labels = list(style = list(color = "#CCC")), + gridLineColor = "#111111") %>% + hc_series( + list( + data = abs(rnorm(100)) + 1, + type = "areaspline", + marker = list(enabled = FALSE), + color = list( + linearGradient = list(x1 = 0, y1 = 1, x2 = 0, y2 = 0), + stops = list( + list(0, "transparent"), + list(0.33, "yellow"), + list(0.66, "red"), + list(1, "#ccc") + ) + ), + fillColor = list( + linearGradient = list(x1 = 0, y1 = 1, x2 = 0, y2 = 0), + stops = list( + list(0, "transparent"), + list(0.1, "yellow"), + list(0.5, "red"), + list(1, "black") + ) + ) + ) + ) >

data(worldgeojson) data(GNI2014, package = "treemap") dshmstops <- data.frame(q = c(0, exp(1:5)/exp(5)), c = substring(viridis(5 + 1), 0, 7)) %>% list.parse2() highchart() %>% hc_title(text = "Charting GNI data") %>% hc_add_series_map(worldgeojson, GNI2014, value = "GNI", joinBy = "iso3") %>% hc_colorAxis(stops = dshmstops)

> highchart() %>% + hc_title(text = "prepared by Volkan OBAN-highcharter package") %>% + hc_xAxis(categories = month.abb) %>% + hc_defs(patterns = list( + list(id = 'custom-pattern', + path = list(d = 'M 0 0 L 10 10 M 9 -1 L 11 1 M -1 9 L 1 11', + stroke = "black", + strokeWidth = 1 + ) + ) + )) %>% + hc_add_series(data = c(7.0, 6.9, 9.5, 14.5, 18.2, 21.5, 25.2, + 26.5, 23.3, 18.3, 13.9, 9.6), + type = "area", + fillColor = 'url(#custom-pattern)') %>% + hc_add_theme(hc_theme_handdrawn())

library("MASS") dscars <- round(mvrnorm(n = 20, mu = c(1, 1), Sigma = matrix(c(1,0,0,1),2)), 2) dsplan <- round(mvrnorm(n = 10, mu = c(3, 4), Sigma = matrix(c(2,.5,2,2),2)), 2) dstrck <- round(mvrnorm(n = 15, mu = c(5, 1), Sigma = matrix(c(1,.5,.5,1),2)), 2) highchart() %>% hc_chart(type = "scatter", zoomType = "xy") %>% hc_tooltip( useHTML = TRUE, pointFormat = paste0("<span style=\"color:{series.color};\">{series.options.icon}</span>", "{series.name}: <b>[{point.x}, {point.y}]</b><br/>") ) %>% hc_add_series(data = list_parse2(as.data.frame(dscars)), marker = list(symbol = fa_icon_mark("car")), icon = fa_icon("car"), name = "car") %>% hc_add_series(data = list_parse2(as.data.frame(dsplan)), marker = list(symbol = fa_icon_mark("plane")), icon = fa_icon("plane"), name = "plane") %>% hc_add_series(data = list_parse2(as.data.frame(dstrck)), marker = list(symbol = fa_icon_mark("truck")), icon = fa_icon("truck"), name = "truck")

SPY <- getSymbols("SPY", from="2015-01-01", auto.assign=FALSE) SPY <- adjustOHLC(SPY) SPY.SMA.10 <- SMA(Cl(SPY), n=10) SPY.SMA.200 <- SMA(Cl(SPY), n=200) SPY.RSI.14 <- RSI(Cl(SPY), n=14) SPY.RSI.SellLevel <- xts(rep(70, NROW(SPY)), index(SPY)) SPY.RSI.BuyLevel <- xts(rep(30, NROW(SPY)), index(SPY)) highchart() %>% # create axis :) hc_yAxis_multiples( list(title = list(text = NULL), height = "45%", top = "0%"), list(title = list(text = NULL), height = "25%", top = "47.5%", opposite = TRUE), list(title = list(text = NULL), height = "25%", top = "75%") ) %>% # series :D hc_add_series_ohlc(SPY, yAxis = 0, name = "SPY") %>% hc_add_series_xts(SPY.SMA.10, yAxis = 0, name = "Fast MA") %>% hc_add_series_xts(SPY.SMA.200, yAxis = 0, name = "Slow MA") %>% hc_add_series_xts(SPY$SPY.Volume, color = "gray", yAxis = 1, name = "Volume", type = "column") %>% hc_add_series_xts(SPY.RSI.14, yAxis = 2, name = "Osciallator") %>% hc_add_series_xts(SPY.RSI.SellLevel, color = "red", yAxis = 2, name = "Sell level", enableMouseTracking = FALSE) %>% hc_add_series_xts(SPY.RSI.BuyLevel, color = "blue", yAxis = 2, name = "Buy level", enableMouseTracking = FALSE) %>% # I <3 themes hc_add_theme(hc_theme_smpl())

SPY <- getSymbols("SPY", from="2015-01-01", auto.assign=FALSE) SPY <- adjustOHLC(SPY) SPY.SMA.10 <- SMA(Cl(SPY), n=10) SPY.SMA.200 <- SMA(Cl(SPY), n=200) SPY.RSI.14 <- RSI(Cl(SPY), n=14) SPY.RSI.SellLevel <- xts(rep(70, NROW(SPY)), index(SPY)) SPY.RSI.BuyLevel <- xts(rep(30, NROW(SPY)), index(SPY)) highchart() %>% # create axis :) hc_yAxis_multiples( list(title = list(text = NULL), height = "45%", top = "0%"), list(title = list(text = NULL), height = "25%", top = "47.5%", opposite = TRUE), list(title = list(text = NULL), height = "25%", top = "75%") ) %>% # series :D hc_add_series_ohlc(SPY, yAxis = 0, name = "SPY") %>% hc_add_series_xts(SPY.SMA.10, yAxis = 0, name = "Fast MA") %>% hc_add_series_xts(SPY.SMA.200, yAxis = 0, name = "Slow MA") %>% hc_add_series_xts(SPY$SPY.Volume, color = "gray", yAxis = 1, name = "Volume", type = "column") %>% hc_add_series_xts(SPY.RSI.14, yAxis = 2, name = "Osciallator") %>% hc_add_series_xts(SPY.RSI.SellLevel, color = "red", yAxis = 2, name = "Sell level", enableMouseTracking = FALSE) %>% hc_add_series_xts(SPY.RSI.BuyLevel, color = "blue", yAxis = 2, name = "Buy level", enableMouseTracking = FALSE) %>% # I <3 themes hc_add_theme(hc_theme_smpl())

data(mpg) library(dplyr) mpgman3 <- group_by(mpg, manufacturer) %>% + summarise(n = n(), unique = length(unique(model))) %>% + arrange(-n, -unique) hchart(mpgman3, "treemap", x = manufacturer, value = n, color = unique)

> nuts2.df$cagr <- (((nuts2.df$pop2008 / nuts2.df$pop1999)^(1/9)) - 1) * 100 > summary(nuts2.df$cagr) Min. 1st Qu. Median Mean 3rd Qu. Max. -2.42900 -0.08116 0.27750 0.31550 0.65960 3.02800 > # Plot the compound annual growth rate > cols <- carto.pal(pal1 = "blue.pal", n1 = 2, pal2 = "red.pal", n2 = 4) > choroLayer(spdf = nuts2.spdf, + df = nuts2.df, + var = "cagr", breaks = c(-2.43,-1,0,0.5,1,2,3.1), + col = cols, + border = "grey40", + add = FALSE, + legend.pos = "topright",legend.title.txt = "Compound annual\ngrowth rate", + legend.values.rnd = 2) > # Layout plot > layoutLayer(title = " CARTOGRAPHY package in R.prepared by Volkan OBAN \n Demographic Trends", + sources = "Eurostat, 2008", + scale = NULL, + frame = TRUE, + col = "black", + coltitle = "white")

> dp.uspe <- dotplot(t(USPersonalExpenditure), groups = FALSE, layout = c(1, 5), xlab = "Expenditure (billion dollars)") > dp.uspe.log <- dotplot(t(USPersonalExpenditure), groups = FALSE, layout = c(1, 5), scales = list(x = list(log = 2)), xlab = "Expenditure (billion dollars)") > plot(dp.uspe, split = c(1, 1, 2, 1)) > plot(dp.uspe.log, split = c(2, 1, 2, 1), newpage = FALSE)

library(lattice) > VADeaths Rural Male Rural Female Urban Male Urban Female 50-54 11.7 8.7 15.4 8.4 55-59 18.1 11.7 24.3 13.6 60-64 26.9 20.3 37.0 19.3 65-69 41.0 30.9 54.6 35.1 70-74 66.0 54.3 71.1 50.0 > VADeathsDF <- as.data.frame.table(VADeaths, responseName = "Rate") > VADeathsDF Var1 Var2 Rate 1 50-54 Rural Male 11.7 2 55-59 Rural Male 18.1 3 60-64 Rural Male 26.9 4 65-69 Rural Male 41.0 5 70-74 Rural Male 66.0 6 50-54 Rural Female 8.7 7 55-59 Rural Female 11.7 8 60-64 Rural Female 20.3 9 65-69 Rural Female 30.9 10 70-74 Rural Female 54.3 11 50-54 Urban Male 15.4 12 55-59 Urban Male 24.3 13 60-64 Urban Male 37.0 14 65-69 Urban Male 54.6 15 70-74 Urban Male 71.1 16 50-54 Urban Female 8.4 17 55-59 Urban Female 13.6 18 60-64 Urban Female 19.3 19 65-69 Urban Female 35.1 20 70-74 Urban Female 50.0 > barchart(Var1 ~ Rate | Var2, VADeathsDF, layout = c(4, 1)) >

bwplot(gcsescore ~ gender | factor(score), Chem97, layout = c(6, 1))

> data(Chem97, package = "mlmRev") > qqmath(~ gcsescore | factor(score), Chem97, groups = gender, + f.value = ppoints(100), auto.key = TRUE, + type = c("p", "g"), aspect = "xy") > > bwplot(factor(score) ~ gcsescore | gender, Chem97)

depth.ord <- rev(order(quakes$depth)) quakes$Magnitude <- equal.count(quakes$mag, 4) quakes.ordered <- quakes[depth.ord, ] levelplot(depth ~ long + lat | Magnitude, data = quakes.ordered, panel = panel.levelplot.points, type = c("p", "g"), aspect = "iso", prepanel = prepanel.default.xyplot) ## a levelplot with jittered cells xyz <- expand.grid(x = 0:9, y = 0:9) xyz[] <- jitter(as.matrix(xyz)) xyz$z <- with(xyz, sqrt((x - 5)^2 + (y - 5)^2)) levelplot(z ~ x * y, xyz, panel = panel.voronoi, points = FALSE) ## hexagonal cells xyz$y <- xyz$y + c(0, 0.5) levelplot(z ~ x * y, xyz, panel = panel.voronoi, points = FALSE)

# load required libraries library(spatstat) library(lattice) library(grid) library(CircStats) # read in our data (see attached file) x <- read.csv('beer_battle.csv') # plot the data, as stratified by person xyplot(y ~ x | person, groups=beer, data=x, panel=panel.bulls_eye, key=list(points=list(col=c(1,2,3), pch=c(3,3,3)), text=list(c('0 beers', '1 beer', '3 beers')), columns=3), main='Beer Battle 1' )

library(gstat) library(latticeExtra) library(grid) # load example data data(meuse.grid) data(meuse) data(meuse.alt) coordinates(meuse.grid) <- ~ x + y coordinates(meuse) <- ~ x + y coordinates(meuse.alt) <- ~ x + y # converto SpatialPixelsDataFram gridded(meuse.grid) <- TRUE # convert 'soil' to factor and re-label meuse.grid$soil <- factor(meuse.grid$soil, labels=c('A','B','C')) meuse$soil <- factor(meuse$soil, levels=c('1','2','3'), labels=c('A','B','C')) # setup color scheme cols <- brewer.pal(n=3, 'Set1') p.pch <- c(2,3,4) # generate list of trellis settings tps <- list(regions=list(col=cols), superpose.polygon=list(col=cols), superpose.symbol=list(col='black', pch=p.pch)) # init list of overlays spl <- list('sp.points', meuse, cex=0.75, pch=p.pch[meuse$soil], col='black') # setup trellis options trellis.par.set(tps) # initial plot, missing key p1 <- spplot(meuse.grid, 'soil', sp.layout=spl, colorkey=FALSE, col.regions=cols, cuts=length(cols)-1) # add a key at the top + space for key p1 <- update(p1, key=simpleKey(levels(meuse.grid$soil), points=FALSE, lines=FALSE, rect=TRUE, regions=TRUE, columns=3, title='Class', cex=0.75)) # add a key on the right + space for key p1 <- update(p1, key=simpleKey(levels(meuse$soil), points=TRUE, columns=1, title='Class', cex=0.75, space='right', )) p1 ......... .....

> set.seed(654) > week <- sample(0:9, 3000, rep=TRUE, prob = rchisq(10, df = 3)) > status <- factor(rbinom(3000, 1, 0.15), labels = c("Shipped", "Not-Shipped")) > data.df <- data.frame(Week = week, Status = status) > library("plyr") > plot.df <- ddply(data.df, .(Week, Status), nrow) > plot.df$V1 <- ifelse(plot.df$Status == "Shipped", + plot.df$V1, -plot.df$V1) > library("ggplot2") > ggplot(plot.df) + + aes(x = as.factor(Week), y = V1, fill = Status) + + geom_bar(stat = "identity", position = "identity") + + scale_y_continuous(breaks = 100 * -1:5, + labels = 100 * c(1, 0:5)) + + geom_text(aes(y = sign(V1) * max(V1) / 30, label = abs(V1))) > r<-ggtitle("prepared by Volkan OBAN") > library("ggplot2") > ggplot(plot.df) + + aes(x = as.factor(Week), y = V1, fill = Status) + + geom_bar(stat = "identity", position = "identity") + + scale_y_continuous(breaks = 100 * -1:5, + labels = 100 * c(1, 0:5)) + + geom_text(aes(y = sign(V1) * max(V1) / 30, label = abs(V1)))

library(XML) > library(reshape2) > library(plyr) > library(ggplot2) > source('http://klein.uk/R/Viz/pyramids.R') > popGHcens <- getAgeTable(country = "QA", year = 2015) > > pyramidGH <- ggplot(popGHcens, aes(x = Age, y = Population, fill = Gender)) + + geom_bar(data = subset(popGHcens, Gender == "Female"), stat = "identity") + + geom_bar(data = subset(popGHcens, Gender == "Male"), stat = "identity") + + scale_y_continuous(labels = paste0(as.character(c(seq(2, 0, -1), seq(1, 2, 1))), "m")) + + coord_flip() > pyramidGH

> test <- (data.frame(v=rnorm(1000), g=c('M','F'))) > require(ggplot2) > ggplot(data=test, aes(x=v)) + + geom_histogram() + + coord_flip() + + facet_grid(. ~ g)

library(plotrix) xy.males.overweight<-c(23.2,33.5,43.6,33.6,43.5,43.5,43.9,33.7,53.9,43.5,43.2,42.8,22.2,51.8, 41.5,31.3,60.7,50.4) xx.females.overweight<-c(13.2,9.4,13.5,13.5,13.5,23.7,8,3.18,3.9,3.16,23.2,22.5,22,12.7,12.5, 12.3,10,0.8) agelabels<-c("uk","scotland","france","ireland","germany","sweden","norway", "iceland","portugal","austria","switzerland","australia","new zealand","dubai","south africa", "finland","italy","morocco") par(mar=pyramid.plot(xy.males.overweight,xx.females.overweight,labels=agelabels, gap=9))

plot.iris <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point() + facet_grid(. ~ Species) + stat_smooth(method = "lm") + background_grid(major = 'y', minor = "none") + # add thin horizontal lines panel_border() # and a border around each panel # plot.mpg and plot.diamonds were defined earlier ggdraw() + draw_plot(plot.iris, 0, .5, 1, .5) + draw_plot(plot.mpg, 0, 0, .5, .5) + draw_plot(plot.diamonds, .5, 0, .5, .5) + draw_plot_label(c("A", "B", "C"), c(0, 0, 0.5), c(1, 0.5, 0.5), size = 15)

boxes <- data.frame( x = sample((0:36)/40, 40, replace = TRUE), y = sample((0:32)/40, 40, replace = TRUE) ) # plot on top of annotations ggdraw() + geom_rect(data = boxes, aes(xmin = x, xmax = x + .15, ymin = y, ymax = y + .15), colour = "gray60", fill = "gray80") + draw_plot(plot.mpg) + draw_label("Plot is on top of the grey boxes", x = 1, y = 1, vjust = 1, hjust = 1, size = 10, fontface = 'bold') # plot below annotations ggdraw(plot.mpg) + geom_rect(data = boxes, aes(xmin = x, xmax = x + .15, ymin = y, ymax = y + .15), colour = "gray60", fill = "gray80") + draw_label("Plot is underneath the grey boxes", x = 1, y = 1, vjust = 1, hjust = 1, size = 10, fontface = 'bold')

> require(cowplot) > plot.mpg <- ggplot(mpg, aes(x = cty, y = hwy, colour = factor(cyl))) + + geom_point(size=2.5) >plot.diamonds <- ggplot(diamonds, aes(clarity, fill = cut)) + geom_bar() + + theme(axis.text.x = element_text(angle=70, vjust=0.5)) >plot_grid(plot.mpg, plot.diamonds, labels = c("A", "B"))

> library(caret) Zorunlu paket yükleniyor: lattice > # load the iris dataset > data(iris) > x <- iris[,1:4] > y <- iris[,5] > featurePlot(x=x, y=y, plot="box")

> library(ggplot2) > data(iris) > par(mfrow=c(1,4)) > for(i in 1:4) { boxplot(iris[,i], main=names(iris)[i]) }

>library(GGally) >data(twitter_spambots) > ggnetworkmap(net = twitter_spambots, + arrow.size = 0.5, + node.group = followers, + ring.group = friends, + size = 4, + weight = indegree, + label.nodes = TRUE, vjust = -1.5) + + scale_fill_continuous("Followers", high = "red", low = "yellow") + + labs(color = "Friends") + + scale_color_continuous(low = "lightgreen", high = "darkgreen")

library(caroline)

> library(latticeExtra) > data(postdoc) > library(lattice) > barchart(prop.table(postdoc, margin = 1), + auto.key = TRUE, xlab = "Proportion")

ggplot2 data(airquality) > stripchart(Temp~Month, + data=airquality, + main="Different strip chart for each month", + xlab="Months", + ylab="Temperature", + col="brown3", + group.names=c("May","June","July","August","September"), + vertical=TRUE, + pch=16 + )

> # There are 12 cities > n.cities <- 12 > > # The area of those cities (more reasonably, the logarithm > # of their areas) are gaussian, independant variables. > area.moyenne <- 5 > area.sd <- 1 > area <- rnorm(n.cities, area.moyenne, area.sd) > > a <- rnorm(n.cities) > b <- rnorm(n.cities) > > # 200 inhabitants sampled in each city > n.inhabitants <- 20 > city <- rep(1:n.cities, each=n.inhabitants) > > # The age are independant gaussian variables, mean=40, sd=10 > # We could have chosen a different distribution for each city. > # (either randomly, or depending on their area or population). > > age <- rnorm(n.cities*n.inhabitants, 40, 10) > > # The income (the variable we try to explain) is a function of the > # age, but the coefficients depend on the city > # Here, the coefficients are taken at random, but they could > # depend on the city area or population. > # Here, the coefficients are independant -- this is rarely the case > a <- rnorm(n.cities, 20000, sd=2000) > b <- rnorm(n.cities, sd=20) > income <- 200*area[city] + a[city] + b[city]*age + + rnorm(n.cities*n.inhabitants, sd=200) > > plot(income ~ age, col=rainbow(n.cities)[city], pch=16) library(nlme) d <- data.frame(income, age, city, area=area[city]) r <- lmList(income ~ age | city, data=d) plot(intervals(r))

# There are 12 cities n.cities <- 12 # The area of those cities (more reasonably, the logarithm # of their areas) are gaussian, independant variables. area.moyenne <- 5 area.sd <- 1 area <- rnorm(n.cities, area.moyenne, area.sd) a <- rnorm(n.cities) b <- rnorm(n.cities) # 200 inhabitants sampled in each city n.inhabitants <- 20 city <- rep(1:n.cities, each=n.inhabitants) # The age are independant gaussian variables, mean=40, sd=10 # We could have chosen a different distribution for each city. # (either randomly, or depending on their area or population). age <- rnorm(n.cities*n.inhabitants, 40, 10) # The income (the variable we try to explain) is a function of the # age, but the coefficients depend on the city # Here, the coefficients are taken at random, but they could # depend on the city area or population. # Here, the coefficients are independant -- this is rarely the case a <- rnorm(n.cities, 20000, sd=2000) b <- rnorm(n.cities, sd=20) income <- 200*area[city] + a[city] + b[city]*age + rnorm(n.cities*n.inhabitants, sd=200) plot(income ~ age, col=rainbow(n.cities)[city], pch=16)

> n <- 2000 # Number of experiments > k <- 20 # Number of subjects > l <- 4 # Number of groups > kl <- sample(1:l, k, replace=T) # Group of each subject > x1 <- sample(1:k, n, replace=T) > x2 <- kl[x1] > A <- rnorm(1,sd=4) > B <- rnorm(k,sd=4) > C <- rnorm(l,sd=4) > y <- A + B[x1] + C[x2] + rnorm(n) > x1 <- factor(x1) > x2 <- factor(x2) > op <- par(mfrow=c(1,2)) > plot(y~x1, col='pink') > plot(y~x2, col='pink') > par(op) > mtext("Hierarchical anova", line=1.5, font=2, cex=1.2) > # If the data were real, we wouldn't know kl. > # One may recover it that way. > kl <- tapply(x2, + x1, + function (x) { + a <- table(x) + names(a)[which(a==max(a))[1]] + }) > kl <- factor(kl, levels=levels(x2)) > plot( y ~ x1, col = rainbow(l)[kl], + main = "Hierarchical anova")

> n <- 2000 # Number of experiments > k <- 20 # Number of subjects > l <- 4 # Number of groups > kl <- sample(1:l, k, replace=T) # Group of each subject > x1 <- sample(1:k, n, replace=T) > x2 <- kl[x1] > A <- rnorm(1,sd=4) > B <- rnorm(k,sd=4) > C <- rnorm(l,sd=4) > y <- A + B[x1] + C[x2] + rnorm(n) > x1 <- factor(x1) > x2 <- factor(x2) > op <- par(mfrow=c(1,2)) > plot(y~x1, col='pink') > plot(y~x2, col='pink') > par(op) > mtext("Hierarchical anova", line=1.5, font=2, cex=1.2)

> library(ggplot2) > > this_base <- "playfair-s-population-of-cities" > > my_data <- data.frame( + population = c(60, 63, 75, 80, 80, 80, 90, 120, + 130, 140, 145, 160, 180, 200, 210, + 220, 250, 255, 380, 690, 900, 1100), + city = c('Istanbul', 'Izmir', 'Erzurum', 'Kars', 'Manisa', + 'Muğla', 'Bursa', 'Antalya', 'Mersin', 'Rize', + 'Van', 'Adıyaman', 'Ankara', 'Kayseri', 'Trabzon', + 'Muş', 'Balıkesir', 'Sinop', 'Kastamonu', 'Aydın', + 'Isparta', 'İzmit')) > > # include helper variables > my_data$row <- c(rep("row1", 11), rep("row2", 11)) # to organize into 2 rows > my_data$pos <- rep(11:1, 2) # specify x coord > > p <- ggplot(my_data, aes(x = pos, y = row, size = population)) + + geom_point(aes(size = population), shape = 21, fill = "white", + show_guide = FALSE) + + geom_text(aes(label = city), vjust = 2.7, hjust = 0.95, angle = 40, + size = 4) + + scale_size_continuous(range = c(3, 12)) + + ggtitle("Fig 2.18 Playfair's Population of Cities") + + theme_bw() + + theme(panel.grid.major = element_blank(), + plot.title = element_text(size = rel(1.5), face = "bold", vjust = 1.5), + axis.title = element_blank(), + axis.text = element_blank(), + axis.ticks = element_blank()) Warning message: `show_guide` has been deprecated. Please use `show.legend` instead. > > p > > ggsave(paste0(this_base, ".png"), + p, width = 7, height = 5)

library(ggplot2) library(reshape2) data <- textConnection("Month,Series 1,Series 2,Series 3,Series 4 Jan,7.41,9.38,5.52,6.25 Feb,5.74,8.27,7.29,3.39 Mar,6.52,5.42,7.51,6.20 Apr,2.02,0.70,0.24,1.88 May,7.90,0.35,9.99,6.84 Jun,3.22,8.01,0.91,1.61 Jul,1.43,8.54,8.08,7.62 Aug,9.80,7.79,8.71,8.21 Sep,2.36,8.17,5.70,4.48 Oct,4.39,9.71,7.19,4.96 Nov,3.24,0.26,7.65,1.37 Dec,8.44,7.78,9.44,3.65 ") data <- read.csv(data, h=T) data$Month <- factor(data$Month, data$Month) data.lng <- melt(data, id=c("Month")) p <- ggplot(aes(x=Month, weight=value, fill=variable), data=data.lng) p + geom_bar() + coord_flip() + scale_x_discrete("Legend Title") + labs(x="X Label", y="Y Label", title="An Example- Stacked Bar Chart prepared by Volkan OBAN") # full output: http://www.yaksis.com/static/img/03/large/StackedBar.png

n <- 60 m <- 50 x <- seq(-4,4, len=m) # Make up some fake y data y <- matrix(NA, n, m) for (i in 1:n) y[i,] <- dnorm(x)*runif(m, 0.5,1) par(bg="black") yrange <- range(c(y, y+n/20)) plot(x, x, type="n", axes=FALSE, bg="black", ylim=yrange) for (i in n:1) { y1 <- c(y[i,] + i/20, 0, 0) x1 <- c(x, x[m], x[1]) polygon(x1,y1,col="black") lines(x, y[i,] + i/20, col="white") }

set.seed(1) # for reproducibility Day <- c(rep(1:10,each=24)) Hour <- rep(1:24) data <- data.frame(Day,Hour) data$Sunlight <- with(data,-10*cos(2*pi*(Hour-1+abs(rnorm(240)))/24)) data$Sunlight[data$Sunlight<0] <- 0 library(ggplot2) ggplot(data,aes(x=Hour,y=10+24*Day+Hour-1))+ geom_tile(aes(color=Sunlight),size=2)+ scale_color_gradient(low="black",high="yellow")+ ylim(0,250)+ labs(y="",x="")+ coord_polar(theta="x")+ theme(panel.background=element_rect(fill="black"),panel.grid=element_blank(), axis.text.y=element_blank(), axis.text.x=element_text(color="white"), axis.ticks.y=element_blank())

> library(DiagrammeR) > library(magrittr) > graph <- + create_graph() %>% + set_graph_name("DAG") %>% + set_global_graph_attrs("graph", "overlap", "true") %>% + set_global_graph_attrs("graph", "fixedsize", "true") %>% + set_global_graph_attrs("node", "color", "blue") %>% + set_global_graph_attrs("node", "fontname", "Helvetica") %>% + add_n_nodes(11) %>% + select_nodes_by_id(c(1:4, 8:11)) %>% + set_node_attrs_ws("shape", "box") %>% + clear_selection %>% + select_nodes_by_id(5:7) %>% + set_node_attrs_ws("shape", "circle") %>% + clear_selection %>% + add_edges_w_string( + "1->5 2->6 3->9 4->7 5->8 5->10 7->11", "green") %>% + add_edges_w_string( + "1->8 3->6 3->11 3->7 5->9 6->10", "red") %>% + select_edges("rel", "green") %>% + set_edge_attrs_ws("color", "green") %>% + invert_selection %>% + set_edge_attrs_ws("color", "red") > > render_graph(graph) >

library(vcd) a<- c (0.1, 0.5, 0.5, 0.6, 0.2, 0, 0, 0.004166667, 0.45) b<- c (0.75,0.5,0,0.1,0.2,0.951612903,0.918103448,0.7875,0.45) c<- c (0.15,0,0.5,0.3,0.6,0.048387097,0.081896552,0.208333333,0.1) d<- c (500,2324.90,2551.44,1244.50, 551.22,-644.20,-377.17,-100, 2493.04) df<- data.frame (a, b, c) # First create the limit of the ternary plot: plot(NA,NA,xlim=c(0,1),ylim=c(0,sqrt(3)/2),asp=1,bty="n",axes=F,xlab="",ylab="") segments(0,0,0.5,sqrt(3)/2) segments(0.5,sqrt(3)/2,1,0) segments(1,0,0,0) text(0.5,(sqrt(3)/2),"c", pos=3) text(0,0,"a", pos=1) text(1,0,"b", pos=1) # The biggest difficulty in the making of a ternary plot is to transform triangular coordinates into cartesian coordinates, here is a small function to do so: tern2cart <- function(coord){ coord[1]->x coord[2]->y coord[3]->z x+y+z -> tot x/tot -> x # First normalize the values of x, y and z y/tot -> y z/tot -> z (2*y + z)/(2*(x+y+z)) -> x1 # Then transform into cartesian coordinates sqrt(3)*z/(2*(x+y+z)) -> y1 return(c(x1,y1)) } # Apply this equation to each set of coordinates t(apply(df,1,tern2cart)) -> tern # Intrapolate the value to create the contour plot resolution <- 0.001 require(akima) interp(tern[,1],tern[,2],z=d, xo=seq(0,1,by=resolution), yo=seq(0,1,by=resolution)) -> tern.grid # And then plot: image(tern.grid,breaks=c(-1000,0,500,1000,1500,2000,3000),col=rev(heat.colors(6)),add=T) contour(tern.grid,levels=c(-1000,0,500,1000,1500,2000,3000),add=T) points(tern,pch=19)

library(dplyr) library(ggplot2) library(reshape2) # creating a data samples # content df.content <- data.frame(content = c('main', 'ad landing', 'product 1', 'product 2', 'product 3', 'product 4', 'shopping cart', 'thank you page'), step = c('awareness', 'awareness', 'interest', 'interest', 'interest', 'interest', 'desire', 'action'), number = c(150000, 80000, 80000, 40000, 35000, 25000, 130000, 120000)) # customers df.customers <- data.frame(content = c('new', 'engaged', 'loyal'), step = c('new', 'engaged', 'loyal'), number = c(25000, 40000, 55000)) # combining two data sets df.all <- rbind(df.content, df.customers) # calculating dummies, max and min values of X for plotting df.all <- df.all %>% group_by(step) %>% mutate(totnum = sum(number)) %>% ungroup() %>% mutate(dum = (max(totnum) - totnum)/2, maxx = totnum + dum, minx = dum) # data frame for plotting funnel lines df.lines <- df.all %>% select(step, maxx, minx) %>% group_by(step) %>% unique() %>% ungroup() # data frame with dummies df.dum <- df.all %>% select(step, dum) %>% unique() %>% mutate(content = 'dummy', number = dum) %>% select(content, step, number) # data frame with rates conv <- df.all$totnum[df.all$step == 'action'] df.rates <- df.all %>% select(step, totnum) %>% group_by(step) %>% unique() %>% ungroup() %>% mutate(prevnum = lag(totnum), rate = ifelse(step == 'new' | step == 'engaged' | step == 'loyal', round(totnum / conv, 3), round(totnum / prevnum, 3))) %>% select(step, rate) df.rates <- na.omit(df.rates) # creting final data frame df.all <- df.all %>% select(content, step, number) df.all <- rbind(df.all, df.dum) df.all <- df.all %>% group_by(step) %>% arrange(desc(content)) %>% ungroup() # calculating position of labels df.all <- df.all %>% group_by(step) %>% mutate(pos = cumsum(number) - 0.5*number) %>% ungroup() # defining order of steps df.all$step <- factor(df.all$step, levels = c('loyal', 'engaged', 'new', 'action', 'desire', 'interest', 'awareness')) list <- c(unique(as.character(df.all$content))) df.all$content <- factor(df.all$content, levels = c('dummy', c(list))) # creating custom palette with 'white' color for dummies cols <- c("#ffffff", "#fec44f", "#fc9272", "#a1d99b", "#fee0d2", "#2ca25f", "#8856a7", "#43a2ca", "#fdbb84", "#e34a33", "#a6bddb", "#dd1c77", "#ffeda0", "#756bb1") # plotting chart ggplot() + theme_minimal() + coord_flip() + scale_fill_manual(values=cols) + geom_bar(data=df.all, aes(x=step, y=number, fill=content), stat="identity", width=1) + geom_text(data=df.all[df.all$content!='dummy', ], aes(x=step, y=pos, label=paste0(content, '-', number/1000, 'K')), size=4, color='white', fontface="bold") + geom_ribbon(data=df.lines, aes(x=step, ymax=max(maxx), ymin=maxx, group=1), fill='white') + geom_line(data=df.lines, aes(x=step, y=maxx, group=1), color='darkred', size=4) + geom_ribbon(data=df.lines, aes(x=step, ymax=minx, ymin=min(minx), group=1), fill='white') + geom_line(data=df.lines, aes(x=step, y=minx, group=1), color='darkred', size=4) + geom_text(data=df.rates, aes(x=step, y=(df.lines$minx[-1]), label=paste0(rate*100, '%')), hjust=1.2, color='darkblue', fontface="bold") + theme(legend.position='none', axis.ticks=element_blank(), axis.text.x=element_blank(), axis.title.x=element_blank()) ref: analyzecore.com/2015/06/23/sales-funnel-visualization-with-r/

install package and reference: https://bioconductor.org/packages/release/bioc/html/Gviz.html

> library(circlize) > > par(mar = c(1, 1, 1, 1)) > bed1 = generateRandomBed(nr = 100) > bed1 = bed1[sample(nrow(bed1), 20), ] > bed2 = generateRandomBed(nr = 100) > bed2 = bed2[sample(nrow(bed2), 20), ] > circos.par("track.height" = 0.1, cell.padding = c(0, 0, 0, 0)) > circos.initializeWithIdeogram() > > circos.genomicLink(bed1, bed2, col = sample(1:5, 20, replace = TRUE), border = NA) > circos.clear()

#Evaluates lower tail. visualize.nbinom(stat = 1, size = 5, prob = 0.5, section = "lower", strict = 0) #Evaluates bounded region. visualize.nbinom(stat = c(1,3), size = 10, prob = 0.35, section = "bounded", strict = c(TRUE, FALSE)) #Evaluates upper tail. visualize.nbinom(stat = 1, size = 5, prob = 0.5, section = "upper", strict = 1)

#Evaluates lower tail. visualize.logis(stat = 1, location = 4, scale = 2, section = "lower") #Evaluates bounded region. visualize.logis(stat = c(3,5), location = 4, scale = 2, section = "bounded") #Evaluates upper tail. visualize.logis(stat = 1, location = 4, scale = 2, section = "upper")

> source("https://www.r-statistics.com/wp-content/uploads/2010/07/coefplot.r.txt") > > data("Mroz", package = "car") > fm <- glm(lfp ~ ., data = Mroz, family = binomial) > coefplot(fm, parm = -1)

library(mapmate) > library(dplyr) > library(purrr) data(annualtemps) library(RColorBrewer) pal <- rev(brewer.pal(11, "RdYlBu")) temps <- mutate(annualtemps, frameID = Year - min(Year) + 1) frame1 <- filter(temps, frameID == 1) # subset to first frame save_map(frame1, ortho = FALSE, col = pal, type = "maptiles", save.plot = FALSE, return.plot = TRUE) save_map(frame1, col = pal, type = "maptiles", save.plot = FALSE, return.plot = TRUE)

library(numDeriv) library(RColorBrewer) library(gridExtra) ## Polynom: choose only one or try yourself f <- function (z) {z^3-1} #Blurry 1 #f <- function (z) {z^4+z-1} #Blurry 2 #f <- function (z) {z^5+z^3+z-1} #Blurry 3 z <- outer(seq(-2, 2, by = 0.01),1i*seq(-2, 2, by = 0.01),'+') for (k in 1:5) z <- z-f(z)/matrix(grad(f, z), nrow=nrow(z)) ## Supressing texts, titles, ticks, background and legend. opt <- theme(legend.position="none", panel.background = element_blank(), axis.ticks=element_blank(), axis.title=element_blank(), axis.text =element_blank()) z <- data.frame(expand.grid(x=seq(ncol(z)), y=seq(nrow(z))), z=as.vector(exp(-Mod(f(z))))) # Create plots. Choose a palette with display.brewer.all() p1 <- ggplot(z, aes(x=x, y=y, color=z)) + geom_tile() + scale_colour_gradientn(colours=brewer.pal(8, "Paired")) + opt p2 <- ggplot(z, aes(x=x, y=y, color=z)) + geom_tile() + scale_colour_gradientn(colours=brewer.pal(7, "Paired")) + opt p3 <- ggplot(z, aes(x=x, y=y, color=z)) + geom_tile() + scale_colour_gradientn(colours=brewer.pal(6, "Paired")) + opt p4 <- ggplot(z, aes(x=x, y=y, color=z)) + geom_tile() + scale_colour_gradientn(colours=brewer.pal(5, "Paired")) + opt # Arrange four plots in a 2x2 grid grid.arrange(p1, p2, p3, p4, ncol=2)

ref:https://github.com/jokergoo/rGREAT

library(gtrellis) bed = circlize::generateRandomBed() gtrellis_layout(track_ylim = range(bed[[4]])) add_track(bed, panel.fun = function(bed) { x = (bed[[2]] + bed[[3]]) / 2 y = bed[[4]] grid.points(x, y, pch = 16, size = unit(1, "mm")) })

code and example: https://www.r-bloggers.com/the-yarrr-package-0-0-8-is-finally-on-cran/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+RBloggers+%28R+bloggers%29

library(yarrr) pirateplot(formula = budget ~ creative.type, data = subset(movies, budget > 0 & creative.type %in% c("Multiple Creative Types", "Factual") == FALSE), point.o = .02, xlab = "Movie Creative Type", main = "Movie budgets (in millions) by rating", gl.col = "gray", pal = "black") mtext("-Prepared by Volkan OBAN", side = 3, font = 3) mtext("*Superhero movies tend to have the highest budgets\n...by far!", side = 1, adj = 1, line = 3, cex = .8, font = 3)

> maxmin <- data.frame( + total=c(5, 1), + phys=c(15, 3), + psycho=c(3, 0), + social=c(5, 1), + env=c(5, 1)) > # data for radarchart function version 1 series, minimum value must be omitted from above. > RNGkind("Mersenne-Twister") > set.seed(123) > dat <- data.frame( + total=runif(3, 1, 5), + phys=rnorm(3, 10, 2), + psycho=c(0.5, NA, 3), + social=runif(3, 1, 5), + env=c(5, 2.5, 4)) > dat <- rbind(maxmin,dat) > op <- par(mar=c(1, 2, 2, 1),mfrow=c(2, 2)) > radarchart(dat, axistype=1, seg=5, plty=1, vlabels=c("Total\nQOL", "Physical\naspects", + "Phychological\naspects", "Social\naspects", "Environmental\naspects"), + title="(axis=1, 5 segments, with specified vlabels)", vlcex=0.5) > radarchart(dat, axistype=2, pcol=topo.colors(3), plty=1, pdensity=c(5, 10, 30), + pangle=c(10, 45, 120), pfcol=topo.colors(3), + title="(topo.colors, fill, axis=2)") > radarchart(dat, axistype=3, pty=32, plty=1, axislabcol="grey", na.itp=FALSE, + title="(no points, axis=3, na.itp=FALSE)") > radarchart(dat, axistype=1, plwd=1:5, pcol=1, centerzero=TRUE, + seg=4, caxislabels=c("worst", "", "", "", "best"), + title="(use lty and lwd but b/w, axis=1,\n centerzero=TRUE, with centerlabels)") > par(op) >

>libraryfmsb) > library(zoo) > > dat<-as.data.frame(sunspot.month) > dat$TS<-seq(as.yearmon("1749-01-01"), as.yearmon("2013-09-01"), by = 1/12) > colnames(dat)[1] <- "sunspot" > dat$decade <- floor(as.numeric(format(dat$TS, "%Y"))/10)*10 > dat$century <- floor(as.numeric(format(dat$TS, "%Y"))/100)*100 > dat$month <- format(dat$TS, "%b") > dat$month <- factor(dat$month, levels = unique(dat$month)) > library(reshape2) > > agg <- recast(data = dat,century~month, measure.var = "sunspot", mean) > MX <- c(NA, rep(max(agg[,-1]), ncol(agg)-1)) > MN <- c(NA, rep(min(agg[,-1]), ncol(agg)-1)) > > agg <- rbind(MX, MN, agg) > radarchart(agg[,-1])

library(circlize) factors = sample(letters[1:6], 100, replace = TRUE) x = rnorm(100) y = rnorm(100) par(mar = c(1, 1, 1, 1)) circos.initialize(factors = factors, x = x) circos.trackPlotRegion(factors = factors, x = x, y = y, bg.col = "#EEEEEE", bg.border = NA, track.height = 0.4, panel.fun = function(x, y) { cell.xlim = get.cell.meta.data("cell.xlim") cell.ylim = get.cell.meta.data("cell.ylim") # reference lines for(xi in seq(cell.xlim[1], cell.xlim[2], length.out = 10)) { circos.lines(c(xi, xi), cell.ylim, lty = 2, col = "white") } for(yi in seq(cell.ylim[1], cell.ylim[2], length.out = 5)) { circos.lines(cell.xlim, c(yi, yi), lty = 2, col = "white") } xlim = get.cell.meta.data("xlim") ylim = get.cell.meta.data("ylim") circos.rect(xlim[1], 1, xlim[2], ylim[2], col = "#FF000020", border = NA) circos.rect(xlim[1], ylim[1], xlim[2], -1, col = "#00FF0020", border = NA) circos.points(x[y >= 1], y[y >= 1], pch = 16, cex = 0.8, col = "red") circos.points(x[y <= -1], y[y <= -1], pch = 16, cex = 0.8, col = "green") circos.points(x[y > -1 & y < 1], y[y > -1 & y < 1], pch = 16, cex = 0.5) }) circos.clear()

library(circlize) df = read.table(textConnection(" brand_from model_from brand_to model_to VOLVO s80 BMW 5series BMW 3series BMW 3series VOLVO s60 VOLVO s60 VOLVO s60 VOLVO s80 BMW 3series AUDI s4 AUDI a4 BMW 3series AUDI a5 AUDI a5 "), header = TRUE, stringsAsFactors = FALSE) brand = c(structure(df$brand_from, names=df$model_from), structure(df$brand_to,names= df$model_to)) brand = brand[!duplicated(names(brand))] brand = brand[order(brand, names(brand))] brand_color = structure(2:4, names = unique(brand)) model_color = structure(2:8, names = names(brand)) library(circlize) gap.degree = do.call("c", lapply(table(brand), function(i) c(rep(2, i-1), 8))) circos.par(gap.degree = gap.degree) chordDiagram(df[, c(2, 4)], order = names(brand), grid.col = model_color, directional = 1, annotationTrack = "grid", preAllocateTracks = list( list(track.height = 0.02)) ) circos.trackPlotRegion(track.index = 2, panel.fun = function(x, y) { xlim = get.cell.meta.data("xlim") ylim = get.cell.meta.data("ylim") sector.index = get.cell.meta.data("sector.index") circos.text(mean(xlim), mean(ylim), sector.index, col = "white", cex = 0.6, facing = "inside", niceFacing = TRUE) }, bg.border = NA) for(b in unique(brand)) { model = names(brand[brand == b]) highlight.sector(sector.index = model, track.index = 1, col = brand_color[b], text = b, text.vjust = -1, niceFacing = TRUE) } circos.clear()

circlize package in R

library(pheatmap) > test = matrix(rnorm(200), 20, 10) > test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3 > test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2 > test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4 > colnames(test) = paste("Test", 1:10, sep = "") > rownames(test) = paste("Gene", 1:20, sep = "") > > # Generate column annotations > annotation = data.frame(Var1 = factor(1:10 %% 2 == 0, labels = c("Class1", "Class2")), Var2 = 1:10) > annotation$Var1 = factor(annotation$Var1, levels = c("Class1", "Class2", "Class3")) > rownames(annotation) = paste("Test", 1:10, sep = "") > > pheatmap(test, annotation = annotation) > pheatmap(test, annotation = annotation, annotation_legend = FALSE) > pheatmap(test, annotation = annotation, annotation_legend = FALSE, drop_levels = FALSE) > > # Specify colors > Var1 = c("navy", "darkgreen") > names(Var1) = c("Class1", "Class2") > Var2 = c("lightgreen", "navy") > > ann_colors = list(Var1 = Var1, Var2 = Var2) > > pheatmap(test, annotation = annotation, annotation_colors = ann_colors, main = "Example with all the features")

> edges = data.frame(N1 = paste0(rep(LETTERS[1:4], each = 4), rep(1:5, each = 16)), + N2 = paste0(rep(LETTERS[1:4], 4), rep(2:6, each = 16)), + Value = runif(80, min = 2, max = 5) * rep(c(1, 0.8, 0.6, 0.4, 0.3), each = 16), + stringsAsFactors = F) > > edges = edges[sample(c(TRUE, FALSE), nrow(edges), replace = TRUE, prob = c(0.8, 0.2)),] > nodes = data.frame(ID = unique(c(edges$N1, edges$N2)), stringsAsFactors = FALSE) > # > nodes$x = as.integer(substr(nodes$ID, 2, 2)) > nodes$y = as.integer(sapply(substr(nodes$ID, 1, 1), charToRaw)) - 65 > rownames(nodes) = nodes$ID >library(RColorBrewer) > palette = paste0(brewer.pal(4, "Set1"), "60") > styles = lapply(nodes$y, function(n) { + list(col = palette[n+1], lty = 0, textcol = "black") + }) > names(styles) = nodes$ID > library(riverplot) > > rp <- list(nodes = nodes, edges = edges, styles = styles) > # > class(rp) <- c(class(rp), "riverplot") > plot(rp, plot_area = 0.95, yscale=0.06) ref:http://www.exegetic.biz/blog/2014/08/plotting-flows-with-riverplot/?utm_source=rss&utm_medium=rss&utm_campaign=plotting-flows-with-riverplot

cloud(prop.table(Titanic, margin = 1:3), type = c("p", "h"), strip = strip.custom(strip.names = TRUE), scales = list(arrows = FALSE, distance = 2), panel.aspect = 0.7, zlab = "Proportion")[, 1]

hist3D_fancy<- function(x, y, break.func = c("Sturges", "scott", "FD"), breaks = NULL, colvar = NULL, col="white", clab=NULL, phi = 5, theta = 25, ...){ # Compute the number of classes for a histogram break.func <- break.func [1] if(is.null(breaks)){ x.breaks <- switch(break.func, Sturges = nclass.Sturges(x), scott = nclass.scott(x), FD = nclass.FD(x)) y.breaks <- switch(break.func, Sturges = nclass.Sturges(y), scott = nclass.scott(y), FD = nclass.FD(y)) } else x.breaks <- y.breaks <- breaks # Cut x and y variables in bins for counting x.bin <- seq(min(x), max(x), length.out = x.breaks) y.bin <- seq(min(y), max(y), length.out = y.breaks) xy <- table(cut(x, x.bin), cut(y, y.bin)) z <- xy xmid <- 0.5*(x.bin[-1] + x.bin[-length(x.bin)]) ymid <- 0.5*(y.bin[-1] + y.bin[-length(y.bin)]) oldmar <- par("mar") par (mar = par("mar") + c(0, 0, 0, 2)) hist3D(x = xmid, y = ymid, z = xy, ..., zlim = c(-max(z)/2, max(z)), zlab = "counts", bty= "g", phi = phi, theta = theta, shade = 0.2, col = col, border = "black",d = 1, ticktype = "detailed") scatter3D(x, y,z = rep(-max(z)/2, length.out = length(x)), colvar = colvar, col = gg.col(100), add = TRUE, pch = 18, clab = clab, colkey = list(length = 0.5, width = 0.5, dist = 0.05, cex.axis = 0.8, cex.clab = 0.8)) par(mar = oldmar)} hist3D_fancy(quakes$long, quakes$lat, colvar=quakes$depth,breaks =30)

df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3), dose=rep(c("D0.5", "D1", "D2"),2), len=c(6.8, 15, 33, 4.2, 10, 29.5)) > head(df2) supp dose len 1 VC D0.5 6.8 2 VC D1 15.0 3 VC D2 33.0 4 OJ D0.5 4.2 5 OJ D1 10.0 6 OJ D2 29.5 > library(plyr) > > df_sorted <- arrange(df2, dose, supp) > head(df_sorted) > df_cumsum <- ddply(df_sorted, "dose", transform, label_ypos=cumsum(len)) > ggplot(data=df_cumsum, aes(x=dose, y=len, fill=supp)) + geom_bar(stat="identity")+ geom_text(aes(y=label_ypos, label=len), vjust=1.6, color="white", size=3.5)+ scale_fill_brewer(palette="Paired")+ theme_minimal()

> df <- data.frame(supp=rep(c("VC", "OJ"), each=3), dose=rep(c("D0.5", "D1", "D2"),2), len=c(6.8, 15, 33, 4.2, 10, 29.5)) > ggplot(data=df, aes(x=dose, y=len, fill=supp)) + geom_bar(stat="identity", position=position_dodge())+ geom_text(aes(label=len), vjust=1.6, color="white", position = position_dodge(0.9), size=3.5)+ scale_fill_brewer(palette="Paired")+ theme_minimal()

> library(rLiDAR) > data(chm)

>library(treemapify) > library(ggplot2) > country <- c("Ireland","England","France","Germany","USA","Spain") > job <- c("IT","SOCIAL","Project Manager","Director","Vice-President") > > mydf <- data.frame(countries = sample(country,100,replace = TRUE), + career = sample(job,100,replace=TRUE), + participent = sample(1:100, replace = TRUE) + ) > > # Set Up the coords > treemap_coords <- treemapify(mydf, + area="participent", + fill="countries", + label="career", + group="countries") > > # Plot the results using the Green Pallete > ggplotify(treemap_coords, + group.label.size.factor = 2, + group.label.colour = "white", + label.colour = "black", + label.size.factor = 1) + + labs(title="Work Breakdown") + + scale_colour_brewer(palette = "Greens")

treemap(business, index=c("NACE1", "NACE2", "NACE3"), vSize="turnover", type="index")

library(treemap) > data(GNI2014) > treemap(GNI2014, index=c("continent", "iso3"), vSize="population", vColor="GNI", type="value"

countries <- map_data("world") #Plot everything on a flat map p<- ggplot() + geom_polygon(data=countries, aes(x=long, y=lat, group=group), fill=NA, color="black") + geom_polygon(data=grid, aes(x=long, y=lat, group=group), fill="green", alpha=0.4) + geom_path (data=grid, aes(x=long, y=lat, group=group), alpha=0.4, color="white") p

library(dggridR) library(dplyr) data(dgquakes) p<- ggplot() + geom_polygon(data=countries, aes(x=long, y=lat, group=group), fill=NA, color="black") + geom_polygon(data=grid, aes(x=long, y=lat, group=group, fill=count), alpha=0.4) + geom_path (data=grid, aes(x=long, y=lat, group=group), alpha=0.4, color="white") + scale_fill_gradient(low="blue", high="red") p+coord_map("ortho", orientation = c(-38.49831, -179.9223, 0))+ xlab('')+ylab('')+ theme(axis.ticks.x=element_blank())+ theme(axis.ticks.y=element_blank())+ theme(axis.text.x=element_blank())+ theme(axis.text.y=element_blank())+ ggtitle('Your data could look like this')

library(dggridR) d> library(dplyr) d> dggs <- dgconstruct(spacing=1000, metric=FALSE, resround='down') d> data(dgquakes) d> dgquakes$cell <- dgtransform(dggs,dgquakes$lat,dgquakes$lon) d> quakecounts <- dgquakes %>% group_by(cell) %>% summarise(count=n()) grid <- dgcellstogrid(dggs,quakecounts$cell,frame=TRUE,wrapcells=TRUE) grid <- merge(grid,quakecounts,by.x="Name",by.y="cell") d> grid$count <- log(grid$count) d> cutoff <- quantile(grid$count,0.9) d> grid <- grid %>% mutate(count=ifelse(count>cutoff,cutoff,count)) d> d> #Get polygons for each country of the world d> countries <- map_data("world") d> p<- ggplot() + + geom_polygon(data=countries, aes(x=long, y=lat, group=group), fill=NA, color="black") + + geom_polygon(data=grid, aes(x=long, y=lat, group=group, fill=count), alpha=0.4) + + geom_path (data=grid, aes(x=long, y=lat, group=group), alpha=0.4, color="white") + + scale_fill_gradient(low="blue", high="red") d> p

>library(treemapify) > data(G20) > treeMapCoordinates <- treemapify(G20, + area = "Nom.GDP.mil.USD", + fill = "HDI", + label = "Country", + group = "Region") > treeMapPlot <- ggplotify(treeMapCoordinates) > print(treeMapPlot)

> library(vcd) > mosaic(HairEyeColor, shade=TRUE, legend=TRUE)

> library(wordcloud) > library(tm) wordcloud(c(" TURKEY","OBAN","DATA SCIENCE","ANALYTICS","MATHEMATICS","Machine Learnings","Istanbul","Researcher","Philosophy","Mathematician","VOLKAN","Data"),freq = c(25,22,10,24,30,25,7,9,12,18,62,14),min.freq = 0,col="purple")

> library(pheatmap) > data=as.matrix(scale(USArrests)) > clst=hclust(dist(data)) > pheatmap(data)

library(lattice) > data("quakes") > quakes$Magnitude<-equal.count(quakes$mag,4) > cloud(depth~lat*long | Magnitude, data=quakes, zlim=rev(range(quakes$depth)),screen=list(z=105,x=-70),panel.aspect =0.75,lab="Longitude",ylab = "Latitude",zlab="Depth" )

> library(lattice) > parallel(~mtcars[c(1,3,4,5,6,7)] | factor(cyl),mtcars,groups = carb, layout=c(3,1),auto.key = list(space="top",columns=3))

> library(RColorBrewer) > par(mar = c(0, 4, 0, 0)) > display.brewer.all()

library(plotly) x <- rnorm(200) y <- rnorm(200) s <- subplot( plot_ly(x = x, type = "histogram"), plotly_empty(), plot_ly(x = x, y = y, type = "histogram2dcontour"), plot_ly(y = y, type = "histogram"), nrows = 2, heights = c(0.2, 0.8), widths = c(0.8, 0.2), margin = 0, shareX = TRUE, shareY = TRUE, titleX = FALSE, titleY = FALSE ) layout(s, showlegend = FALSE)

res = contourLines(volcano) contour(volcano, col = "darkgreen", lwd = 2)

> d <- read.csv("sociogram-employees-un.csv", header=FALSE) > g <- graph.adjacency(as.matrix(d), mode="directed") > V(g)$name <- LETTERS[1:NCOL(d)] > V(g)$color <- "yellow" > V(g)$shape <- "sphere" > E(g)$color <- "gray" > E(g)$arrow.size <- 0.2 > plot(g) > diameter.nodes <- get.diameter(g) > diameter.nodes + 4/25 vertices, named: [1] S B A T > ## + 4/25 vertices, named: > ## [1] S B A T > V(g)$size <- 20 > V(g)[diameter.nodes]$color <- "red" > V(g)[diameter.nodes]$size <- V(g)[diameter.nodes]$size+10 > E(g)$width <- 1 > E(g, path=diameter.nodes)$color <- "red" > E(g, path=diameter.nodes)$width <- 2 > plot.igraph(g, layout=layout.fruchterman.reingold)

> drawScene(surfaceTriangles(seq(-1,1,len=30), seq(-1,1,len=30),function(x, y) (x^2 + y^2), color2 = "white")) > drawScene.rgl(surfaceTriangles(seq(-1,1,len=30), seq(-1,1,len=30), function(x, y) (x^2 + y^2), color2 = "white"))

library(xkcd) datascaled <- data.frame(x=c(-3,3),y=c(-30,30)) p <- ggplot(data=datascaled, aes(x=x,y=y)) + geom_point() xrange <- range(datascaled$x) yrange <- range(datascaled$y) ratioxy <- diff(xrange) / diff(yrange) mapping <- aes(x=x, y=y, scale=scale, ratioxy=ratioxy, angleofspine = angleofspine, anglerighthumerus = anglerighthumerus, anglelefthumerus = anglelefthumerus, anglerightradius = anglerightradius, angleleftradius = angleleftradius, anglerightleg = anglerightleg, angleleftleg = angleleftleg, angleofneck = angleofneck, color = color ) dataman <- data.frame( x= c(-1,0,1), y=c(-10,0,10), scale = c(10,7,5), ratioxy = ratioxy, angleofspine = seq(- pi / 2, -pi/2 + pi/8, l=3) , anglerighthumerus = -pi/6, anglelefthumerus = pi + pi/6, anglerightradius = 0, angleleftradius = runif(3,- pi/4, pi/4), angleleftleg = 3*pi/2 + pi / 12 , anglerightleg = 3*pi/2 - pi / 12, angleofneck = runif(3, min = 3 * pi / 2 - pi/10 , max = 3 * pi / 2 + pi/10), color=c("A","B","C")) p + xkcdman(mapping,dataman)

library(d3heatmap) d3heatmap(mtcars, scale="column", colors="Blues")

tableplot(diamonds, select = 1:7, fontsize = 14, legend.lines = 8, title = "Shine on you crazy Diamond", fontsize.title = 18)

tableplot(diamonds, pals = list(cut="Set1(6)", color="Set5", clarity=rainbow(8))) diamonds$carat_class <- num2fac(diamonds$carat, n=20) diamonds$price_class <- num2fac(diamonds$price, n=100) tableplot(diamonds, select=c(carat, price, carat_class, price_class))

#I'll use the example in the R cookbook data(Cars93, package="MASS") coplot(Horsepower ~ MPG.city | Origin, data=Cars93)

library(ggplot2) data(diamonds) #run ?diamonds for more information on the dataset tableplot(diamonds) #sort by depth tableplot(diamonds, sortCol=depth)

library(tabplot) #how the iris dataset looks row.sample <- function(dta, rep) { dta[sample(1:nrow(dta), rep, replace=FALSE), ] } head(row.sample(iris)) Sepal.Length Sepal.Width Petal.Length Petal.Width Species 18 5.1 3.5 1.4 0.3 setosa 71 5.9 3.2 4.8 1.8 versicolor 83 5.8 2.7 3.9 1.2 versicolor 133 6.4 2.8 5.6 2.2 virginica 21 5.4 3.4 1.7 0.2 setosa 144 6.8 3.2 5.9 2.3 virginica tableplot(iris, sortCol="Species")

require(ggplot2) data(diamonds) ## add some NA's is.na(diamonds$price) <- diamonds$cut == "Ideal" is.na(diamonds$cut) <- (runif(nrow(diamonds)) > 0.8)

3 4 5 6 7 8 9 # load the library library(caret) # load the data data(iris) # density plots for each attribute by class value x <- iris[,1:4] y <- iris[,5] scales <- list(x=list(relation="free"), y=list(relation="free")) featurePlot(x=x, y=y, plot="density", scales=scales)

3 4 5 6 7 8 9 10 11 # load the library library(mlbench) # load the dataset data(BreastCancer) # create a bar plot of each categorical attribute par(mfrow=c(2,4)) for(i in 2:9) { counts <- table(BreastCancer[,i]) name <- names(BreastCancer)[i] barplot(counts, main=name) }

library(timevis) timevis() timevis( data.frame(id = 1:2, content = c("one", "two"), start = c("2016-01-10", "2016-01-12")) ) #----------------------- Hide the zoom buttons, allow items to be editable ----------------- timevis( data.frame(id = 1:2, content = c("one", "two"), start = c("2016-01-10", "2016-01-12")), showZoom = FALSE, options = list(editable = TRUE, height = "200px") ) #----------------------- You can use %>% pipes to create timevis pipelines ----------------- timevis() %>% setItems(data.frame( id = 1:2, content = c("one", "two"), start = c("2016-01-10", "2016-01-12") )) %>% setOptions(list(editable = TRUE)) %>% addItem(list(id = 3, content = "three", start = "2016-01-11")) %>% setSelection("3") %>% fitWindow(list(animation = FALSE)) #------- Items can be a single point or a range, and can contain HTML ------- timevis( data.frame(id = 1:2, content = c("one", "two<br><h3>HTML is supported</h3>"), start = c("2016-01-10", "2016-01-18"), end = c("2016-01-14", NA), style = c(NA, "color: red;") ) ) #----------------------- Alternative look for each item ----------------- timevis( data.frame(id = 1:2, content = c("one", "two"), start = c("2016-01-10", "2016-01-14"), end = c(NA, "2016-01-18"), type = c("point", "background")) ) #----------------------- Using a function in the configuration options ----------------- timevis( data.frame(id = 1, content = "double click anywhere<br>in the timeline<br>to add an item", start = "2016-01-01"), options = list( editable = TRUE, onAdd = htmlwidgets::JS('function(item, callback) { item.content = "Hello!<br/>" + item.content; callback(item); }') ) ) #----------------------- Using groups ----------------- timevis(data = data.frame( start = c(Sys.Date(), Sys.Date(), Sys.Date() + 1, Sys.Date() + 2), content = c("one", "two", "three", "four"), group = c(1, 2, 1, 2)), groups = data.frame(id = 1:2, content = c("G1", "G2")) ) #----------------------- Getting data out of the timeline into Shiny ----------------- if (interactive()) { library(shiny) data <- data.frame( id = 1:3, start = c("2015-04-04", "2015-04-05 11:00:00", "2015-04-06 15:00:00"), end = c("2015-04-08", NA, NA), content = c("<h2>Vacation!!!</h2>", "Acupuncture", "Massage"), style = c("color: red;", NA, NA) ) ui <- fluidPage( timevisOutput("appts"), div("Selected items:", textOutput("selected", inline = TRUE)), div("Visible window:", textOutput("window", inline = TRUE)), tableOutput("table") ) server <- function(input, output) { output$appts <- renderTimevis( timevis( data, options = list(editable = TRUE, multiselect = TRUE, align = "center") ) ) output$selected <- renderText( paste(input$appts_selected, collapse = " ") ) output$window <- renderText( paste(input$appts_window[1], "to", input$appts_window[2]) ) output$table <- renderTable( input$appts_data ) } shinyApp(ui, server) }

Cahit Sıtkı Tarancı-Otuz beş Yaş Şiiri

> res<-rquery.wordcloud(filePath, type ="file",colorPalette = "red", lang = "english")

res<-rquery.wordcloud(filePath, type ="file",colorPalette = "blue", lang = "english")

library(wordcloud) library("tm") library("SnowballC") library("wordcloud") library("RColorBrewer") filePath <- "C:/Users/lenovo/Documents/text/corpus.txt" res<-rquery.wordcloud(filePath, type ="file",colorPalette = "black", lang = "english")

> library(qcc) > x <- rep(10, 100) + rnorm(100) > new.x <- rep(11, 15) + rnorm(15) > qcc(x, newdata=new.x, type="xbar.one")

> library(ggplot2) > data("iris") > > boxplot(Sepal.Width ~ Species, data=iris, ylim=c(2, 4.5), xaxt='n', yaxt='n',notch=TRUE, boxwex=0.5, boxcol="blue", medcol="red", medlwd=1,outcol="red", outpch=3, outcex=0.8) > boxplot(Sepal.Width ~ Species, data=iris, ylim=c(2, 4.5), xaxt='n', yaxt='n',notch=TRUE, boxwex=0.5, boxcol="blue", medcol="red", medlwd=1,outcol="red", outpch=3, outcex=0.8) > mtext("Comparison of three species in the Fisher iris data", 3, cex=0.9) > mtext("Sepal width in mm", 2, cex=0.9, line=2) > boxplot(Sepal.Width ~ Species, data=iris, ylim=c(2, 4.5), xaxt='n', yaxt='n',notch=TRUE, boxwex=0.5, boxcol="blue", medcol="red", medlwd=1, outcol="red", outpch=3, outcex=0.8) > mtext("Comparison of three species in the Fisher iris data", 3, cex=0.9) > mtext("Sepal width in mm", 2, cex=0.9, line=2) > > lab <- format(as.character(pretty(c(2,4.5))), drop0trailing=TRUE, justify="right") > axis(2, tck=0.02, at=pretty(c(2,4.5)), labels=lab, las=1, hadj=0.3) > axis(4, tck=0.02, labels=FALSE) > axis(1, at=1:3, labels=unique(iris$Species), tck=0, padj=-1)

> data(scallops) > Y.gpa<-gpagen(A=scallops$coorddata, curves=scallops$curvslide, + surfaces=scallops$surfslide) |=======================================================| 100% > ref<-mshape(Y.gpa$coords) > plotRefToTarget(ref,Y.gpa$coords[,,1],method="TPS", mag=3)> data(scallops) > Y.gpa<-gpagen(A=scallops$coorddata, curves=scallops$curvslide,surfaces=scallops$surfslide) > ref<-mshape(Y.gpa$coords) > plotRefToTarget(ref,Y.gpa$coords[,,1],method="TPS", mag=3)

> data(lalonde, package = "MatchIt") > table(lalonde$treat) > lalonde.formu <- treat ~ age + I(age^2) + educ + I(educ^2) + black + hispan + married + nodegree + + re74 + I(re74^2) + re75 + I(re75^2) + re74 + re75 > boot.lalonde <- PSAboot(Tr = lalonde$treat, Y = lalonde$re78, X = lalonde, formu = lalonde.formu, + M = 100, seed = 2112) boxplot(boot.lalonde)

data(lalonde, package = "MatchIt") > lalonde.formu <- treat ~ age + I(age^2) + educ + I(educ^2) + black + hispan + married + nodegree + + re74 + I(re74^2) + re75 + I(re75^2) + re74 + re75 > boot.lalonde <- PSAboot(Tr = lalonde$treat, Y = lalonde$re78, X = lalonde, formu = lalonde.formu, + M = 100, seed = 2112) plot(boot.lalonde)

> library(plotly) > p <- subplot( + plot_ly(economics, x = date, y = uempmed), + plot_ly(economics, x = date, y = unemploy), + margin = 0.05, + nrows=2 + ) %>% layout(showlegend = FALSE) > p

> library(plotly) > p <- subplot( + plot_ly(economics, x = date, y = uempmed), + plot_ly(economics, x = date, y = unemploy), + margin = 0.05 + ) %>% layout(showlegend = FALSE) > p

>library(ggthemes) > ggplot(nmmaps, aes(date, temp, color=factor(season)))+ + geom_point()+ggtitle("This plot looks a lot different from the default")+ + theme_economist()+scale_colour_economist()

>library("ggtree") >data(chiroptera) > groupInfo <- split(chiroptera$tip.label, gsub("_\\w+", "", chiroptera$tip.label)) > chiroptera <- groupOTU(chiroptera, groupInfo) > ggtree(chiroptera, aes(color=group), layout='circular') + geom_tiplab(size=1, aes(angle=angle)) >

library("ggtree") nwk <- system.file("extdata", "sample.nwk", package="ggtree") tree <- read.tree(nwk) ggplot(tree, aes(x, y)) + geom_tree() + theme_tree()

ggtree https://bioconductor.org/packages/release/bioc/html/ggtree.html library(ggplot2) library(ape) library(ggtree) file <- system.file("extdata/BEAST", "beast_mcc.tree", package="ggtree") beast <- read.beast(file) ggtree(beast, ndigits=2, branch.length = 'none') + geom_text(aes(x=branch, label=length_0.95_HPD), vjust=-.5, color='firebrick')

tiles <- c(One=80, Two=30, Three=20, Four=10) Waffleplot(tiles, rows=8) Senate <- c(`Male (44%)`=44, `Female (56%)`=56) Waffleplot(Senate, rows=10, size=0.5, colors=c("#af9139", "#544616"))

library(sciencepro) parts <- c(80, 30, 20, 10) > w1 <- Waffleplot(parts, rows=8) > w2 <- Waffleplot(parts, rows=8) > w3 <- Waffleplot(parts, rows=8) > chart <- Forge(w1, w2, w3) > print(chart)

> world <- map_data("world") > > world <- world[world$region != "Antarctica",] > > gg <- ggplot() > gg <- gg + geom_map(data=world, map=world, + aes(x=long, y=lat, map_id=region)) > gg <- gg + coord_proj("+proj=wintri") > gg

> library(tidyr) > library(scales) > library(ggplot2) > library(ggalt) # devtools::install_github("hrbrmstr/ggalt") > > health <- read.csv("https://gist.githubusercontent.com/hrbrmstr/0d206070cea01bcb0118/raw/0ea32190a8b2f54b5a9770cb6582007132571c98/zhealth.csv", stringsAsFactors=FALSE, + header=FALSE, col.names=c("pct", "area_id")) > > areas <- read.csv("https://gist.githubusercontent.com/hrbrmstr/0d206070cea01bcb0118/raw/0ea32190a8b2f54b5a9770cb6582007132571c98/zarea_trans.csv", stringsAsFactors=FALSE, header=TRUE) > > health %>% + mutate(area_id=trunc(area_id)) %>% + arrange(area_id, pct) %>% + mutate(year=rep(c("2014", "2013"), 26), + pct=pct/100) %>% + left_join(areas, "area_id") %>% + mutate(area_name=factor(area_name, levels=unique(area_name))) -> health > > setNames(bind_cols(filter(health, year==2014), filter(health, year==2013))[,c(4,1,5)], + c("area_name", "pct_2014", "pct_2013")) -> health > > > gg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=area_name, group=area_name)) > gg <- gg + geom_dumbbell(color="#a3c4dc", size=0.75, point.colour.l="#0e668b") > gg <- gg + scale_x_continuous(label=percent) > gg <- gg + labs(x=NULL, y=NULL) > gg <- gg + theme_bw() > gg <- gg + theme(plot.background=element_rect(fill="#f7f7f7")) > gg <- gg + theme(panel.background=element_rect(fill="#f7f7f7")) > gg <- gg + theme(panel.grid.minor=element_blank()) > gg <- gg + theme(panel.grid.major.y=element_blank()) > gg <- gg + theme(panel.grid.major.x=element_line()) > gg <- gg + theme(axis.ticks=element_blank()) > gg <- gg + theme(legend.position="top") > gg <- gg + theme(panel.border=element_blank()) > gg

df <- read.csv(text="category,pct Other,0.09 South Asian/South Asian Americans,0.12 Interngenerational/Generational,0.21 S Asian/Asian Americans,0.25 Muslim Observance,0.29 Africa/Pan Africa/African Americans,0.34 Gender Equity,0.34 Disability Advocacy,0.49 European/European Americans,0.52 Veteran,0.54 Pacific Islander/Pacific Islander Americans,0.59 Non-Traditional Students,0.61 Religious Equity,0.64 Caribbean/Caribbean Americans,0.67 Latino/Latina,0.69 Middle Eastern Heritages and Traditions,0.73 Trans-racial Adoptee/Parent,0.76 LBGTQ/Ally,0.79 Mixed Race,0.80 Jewish Heritage/Observance,0.85 International Students,0.87", stringsAsFactors=FALSE, sep=",", header=TRUE) library(ggplot2) library(ggalt) library(scales) gg <- ggplot(df, aes(y=reorder(category, pct), x=pct)) gg <- gg + geom_lollipop(point.colour="steelblue", point.size=3, horizontal=TRUE) gg <- gg + scale_x_continuous(expand=c(0,0), labels=percent, breaks=seq(0, 1, by=0.2), limits=c(0, 1)) gg <- gg + labs(x=NULL, y=NULL, title="SUNY Cortland Multicultural Alumni survey results", subtitle="Ranked by race, ethnicity, home land and orientation\namong the top areas of concern", caption="Data from http://stephanieevergreen.com/lollipop/") gg <- gg + theme_minimal(base_family="Arial Narrow") gg <- gg + theme(panel.grid.major.y=element_blank()) gg <- gg + theme(panel.grid.minor=element_blank()) gg <- gg + theme(axis.line.y=element_line(color="#2b2b2b", size=0.15)) gg <- gg + theme(axis.text.y=element_text(margin=margin(r=-5, l=0))) gg <- gg + theme(plot.margin=unit(rep(30, 4), "pt")) gg <- gg + theme(plot.title=element_text(face="bold")) gg <- gg + theme(plot.subtitle=element_text(margin=margin(b=10))) gg <- gg + theme(plot.caption=element_text(size=8, margin=margin(t=10))) gg

m + stat_bkde2d(bandwidth=c(0.5, 4), aes(fill = ..level..), geom = "polygon")

m <- ggplot(faithful, aes(x = eruptions, y = waiting)) + + geom_point() + + xlim(0.5, 6) + + ylim(40, 110) There were 12 warnings (use warnings() to see them) > > m + geom_bkde2d(bandwidth=c(0.5, 4))

map('world',proj='orth',orient=c(41,-74,0))

p <- p + theme_tufte() + theme( axis.title.y = element_text(vjust=1, angle=0, hjust=1), legend.position='none') p

> p <- p + scale_colour_manual(values = hiva.contrast) + + scale_fill_manual(values = hiva.contrast) > > # add titles and annotation for the median lines > p <- p + labs( + title='Labor in the transportation sector is on average some of the most\n strained work: high intensity, low autonomy (Eurofound, EWCS 2010)', + x='Work intensity', y='Job\nautonomy') > > p <- p + annotate('text', + label='EU median', + x=34.5,y=43, color=hiva.grijs) > > p <- p + annotate('text', + label='EU median', + x=26,y=59, color=hiva.grijs) > > p

> library(ggplot2) > library(ggrepel) > library(ggthemes) > strain.sector <- read.csv2('https://git.io/v2lGL') > dim(strain.sector) [1] 32 3 > strain.sector$highlight <- ifelse( + strain.sector$sector_label == 'Transport and storage', + TRUE, FALSE) > hiva.oranje.donker <- '#F67504' > hiva.oranje.licht <- "#fc9e49" # officieel > hiva.grijs <- 'grey60' > hiva.groen.licht <- "#bad80a" > hiva.groen.donker <- "#8EA608" > hiva.contrast <- c(hiva.grijs, hiva.oranje.donker) > p <- ggplot(strain.sector, aes( + x=work_intensity, + y=job_autonomy, + group=highlight, + color=highlight)) > p <- p + geom_hline(yintercept=60, colour=hiva.grijs) + + geom_vline(xintercept=37, colour=hiva.grijs) > > # Add points > p <- p + geom_point(size = 5, color='grey80') > p > set.seed(42) # set a reed to get the same label-placement > p <- p + geom_label_repel( + aes( + fill = highlight, + label = sector_label), + fontface = 'bold', color = 'white', + size = 2, + box.padding = unit(0.25, "lines"), + point.padding = unit(0.5, "lines") + ) > p

library(ggrepel) set.seed(42) > ggplot(mtcars) + + geom_point(aes(wt, mpg), color = 'grey', size = 4, shape = 15) + + geom_text_repel( + aes( + wt, mpg, + color = factor(cyl), + label = rownames(mtcars) + ), + size = 5, + fontface = 'bold', + box.padding = unit(0.5, 'lines'), + point.padding = unit(1.6, 'lines'), + segment.color = '#555555', + segment.size = 0.5, + arrow = arrow(length = unit(0.01, 'npc')), + force = 1, + max.iter = 2e3, + nudge_x = ifelse(mtcars$cyl == 6, 1, 0), + nudge_y = ifelse(mtcars$cyl == 6, 8, 0) + ) + + scale_color_discrete(name = 'cyl') + + theme_classic(base_size = 16) >

> library(ggplot2) > library(ggrepel) > ggplot(mtcars, aes(wt, mpg)) + + geom_point(color = 'red') + + geom_text_repel(aes(label = rownames(mtcars))) + + theme_classic(base_size = 16)

g <- ggplot(mpg, aes( x = displ, y = cty, color = drv) ) + theme_minimal() my_gg <- g + geom_point_interactive(aes(tooltip = model), size = 2) ggiraph(code = print(my_gg), width = .7)

library(ggplot2) data(blood) p <- ggplot(data = blood$edges, aes(from_id = from, to_id = to)) p + geom_net(vertices=blood$vertices, aes(colour=..type..)) + theme_net() bloodnet <- merge(blood$edges, blood$vertices, by.x="from", by.y="label", all=TRUE) p <- ggplot(data = bloodnet, aes(from_id = from, to_id = to)) p + geom_net() p + geom_net(aes(colour=rho)) + theme_net() p + geom_net(aes(colour=rho), label=TRUE, vjust = -0.5) p + geom_net(aes(colour=rho), label=TRUE, vjust=-0.5, labelcolour="black", directed=TRUE, curvature=0.2) + theme_net() p + geom_net(colour = "orange", layout = 'circle', size = 6) p + geom_net(colour = "orange", layout = 'circle', size = 6, linewidth=.75) p + geom_net(colour = "orange", layout = 'circle', size = 0, linewidth=.75, directed = TRUE) p + geom_net(aes(size=Predominance, colour=rho, shape=rho, linetype=group_to), linewidth=0.75, label =TRUE, labelcolour="black") + facet_wrap(~Ethnicity) + scale_colour_brewer(palette="Set2") gg <- ggplot(data = blood$edges, aes(from_id = from, to_id = to)) + geom_net(colour = "darkred", layout = "circle", label = TRUE, size = 15, directed = TRUE, vjust = 0.5, labelcolour = "grey80", arrowsize = 1.5, linewidth = 0.5, arrowgap = 0.05, selfies = TRUE, ecolour = "grey40") + theme_net() gg dframe <- ggplot_build(gg)$data[[1]] # contains calculated node and edge values 10 geom_net #Madmen Relationships data(madmen) MMnet <- merge(madmen$edges, madmen$vertices, by.x="Name1", by.y="label", all=TRUE) p <- ggplot(data = MMnet, aes(from_id = Name1, to_id = Name2)) p + geom_net(label=TRUE) p + geom_net(aes(colour=Gender), size=6, linewidth=1, label=TRUE, fontsize=3, labelcolour="black") p + geom_net(aes(colour=Gender), size=6, linewidth=1, label=TRUE, labelcolour="black") + scale_colour_manual(values=c("#FF69B4", "#0099ff")) + xlim(c(-.05,1.05)) p + geom_net(aes(colour=Gender), size=6, linewidth=1, directed=TRUE, label=TRUE, arrowgap=0.01, labelcolour="black") + scale_colour_manual(values=c("#FF69B4", "#0099ff")) + xlim(c(-.05,1.05)) p <- ggplot(data = MMnet, aes(from_id = Name1, to_id = Name2)) # alternative labelling: specify label variable. p + geom_net(aes(colour=Gender, label=Gender), size=6, linewidth=1, fontsize=3, labelcolour="black") ## visualizing ggplot2 theme elements data(theme_elements) TEnet <- merge(theme_elements$edges, theme_elements$vertices, by.x="parent", by.y="name", all=TRUE) ggplot(data = TEnet, aes(from_id = parent, to_id = child)) + geom_net(label=TRUE, vjust=-0.5) ## emails example from VastChallenge 2014 # care has to be taken to make sure that for each panel all nodes are included with # the necessary information. # Otherwise line segments show on the plot without nodes. data(email) employee <- data.frame(expand.grid( label=unique(email$nodes$label), day=unique(email$edges$day))) employee <- merge(employee, email$nodes, by="label") emailnet <- merge(subset(email$edges, nrecipients < 54), employee, by.x=c("From", "day"), by.y=c("label", "day"), all=TRUE) #no facets ggplot(data = emailnet, aes(from_id = From, to_id = to)) + geom_net(aes(colour= CurrentEmploymentType), linewidth=0.5) + scale_colour_brewer(palette="Set2") #facet by day ggplot(data = emailnet, aes(from_id = From, to_id = to)) + geom_net(aes(colour= CurrentEmploymentType), linewidth=0.5, fiteach=TRUE) + scale_colour_brewer(palette="Set2") + facet_wrap(~day, nrow=2) + theme(legend.position="bottom") ggplot(data = emailnet, aes(from_id = From, to_id = to)) + geom_net(aes(colour= CitizenshipCountry), linewidth=0.5, fiteach=TRUE) + scale_colour_brewer(palette="Set2") + facet_wrap(~day, nrow=2) + theme(legend.position="bottom") ggplot(data = emailnet, aes(from_id = From, to_id = to)) + geom_net(aes(colour= CurrentEmploymentType), linewidth=0.5, fiteach=FALSE) + jtt 11 scale_colour_brewer(palette="Set2") + facet_wrap(~day, nrow=2) + theme(legend.position="bottom") ## Les Miserables example data(lesmis) lesmisnet <- merge(lesmis$edges, lesmis$vertices, by.x="from", by.y="label", all=TRUE) p <- ggplot(data=lesmisnet, aes(from_id=from, to_id=to)) p + geom_net(layout="fruchtermanreingold") p + geom_net(layout="fruchtermanreingold", label=TRUE, vjust=-0.5) p + geom_net(layout="fruchtermanreingold", label=TRUE, vjust=-0.5, aes(linewidth=degree/5)) ## College Football Games in the Fall 2000 regular season # Hello world! # Source: http://www-personal.umich.edu/~mejn/netdata/ data(football) ftnet <- merge(football$edges, football$vertices, by.x="from", by.y="label", all=TRUE) p <- ggplot(data=ftnet, aes(from_id=from, to_id=to)) p + geom_net(aes(colour=value), linewidth=0.75, size=4.5, ecolour="grey80") + scale_colour_brewer("Conference", palette="Paired") + theme_net() + theme(legend.position="bottom") ## End(Not run)

library(ggplot2) data(mpg) ggplot(mpg, aes(displ, hwy)) + geom_circle(radius=0.1) + geom_point() ggplot(mpg, aes(displ, hwy)) + geom_circle(linetype=2, radius=0.05, alpha=0.5) ggplot(mpg, aes(displ, hwy)) + geom_circle(aes(linetype=factor(cyl)), radius=0.05, alpha=0.5)

> library(geomnet) > # data step: merge vertices and edges > ftnet <- merge( + football$edges, football$vertices, + by.x = "from", by.y = "label", all = TRUE + ) > # label independent schools > ftnet$schools <- ifelse(ftnet$value == "Independents", ftnet$from, "") > # create data plot > ggplot(data = ftnet, + aes(from_id = from, to_id = to)) + + geom_net( + aes( + colour = value, group = value, + linetype = factor(same.conf != 1), + label = schools + ), + linewidth = 0.5, + size = 5, vjust = -0.75, alpha = 0.3, + layout = 'fruchtermanreingold' + ) + + theme_net() + + theme(legend.position = "bottom") + + scale_colour_brewer("Conference", palette = "Paired") + + guides(linetype = FALSE)

ibrary("ggstance") > ggplot(mpg, aes(hwy, class, fill = factor(cyl))) + + geom_boxploth() > ------------------------------------------------------- library("ggplot2") # Vertical ggplot(mpg, aes(class, hwy, fill = factor(cyl))) + geom_boxplot() # Horizontal with coord_flip() ggplot(mpg, aes(class, hwy, fill = factor(cyl))) + geom_boxplot() + coord_flip() -----------------------------------

library(ggradar) suppressPackageStartupMessages(library(dplyr)) > library(scales) > mtcars %>% + add_rownames( var = "group" ) %>% + mutate_each(funs(rescale), -group) %>% + tail(4) %>% select(1:10) -> mtcars_radar > ggradar(mtcars_radar

library("ggplot2") data("diamonds") ggplot(subset(diamonds, carat >= 2.2), aes(x = table, y = price, colour = cut)) + geom_point(alpha = 0.7) + geom_smooth(alpha = 0.1, size = 1, span = 1) + theme_bw() + scale_color_rickandmorty() ggplot(subset(diamonds, carat > 2.2 & depth > 55 & depth < 70), aes(x = depth, fill = cut)) + geom_histogram(colour = "black", binwidth = 1, position = "dodge") + theme_bw() + scale_fill_rickandmorty()

library(ggtech) > > d <- qplot(carat, data = diamonds[diamonds$color %in%LETTERS[4:7], ], geom = "histogram", bins=30, fill = color) > d + theme_tech(theme="airbnb") + + scale_fill_tech(theme="airbnb") + + labs(title="Airbnb theme", + subtitle="now with subtitles for ggplot2 >= 2.1.0")

data(emon) ggplot(emon[[1]], aes(x = x, y = y, xend = xend, yend = yend)) + + geom_edges(arrow = arrow(length = unit(6, "pt"), type = "closed")) + + geom_nodes(color = "tomato", size = 4) + + theme_blank() > ggplot(ggnetwork(emon[[1]], arrow.gap = 0.04, by = "Frequency"), + aes(x = x, y = y, xend = xend, yend = yend)) + + geom_edges(arrow = arrow(length = unit(6, "pt"), type = "closed"), + aes(color = Sponsorship)) + + geom_nodes(aes(color = Sponsorship), size = 4) + + facet_wrap(~ Frequency) + + theme_facet()

data(emon) ggplot(emon[[1]], aes(x = x, y = y, xend = xend, yend = yend)) + geom_edges(arrow = arrow(length = unit(6, "pt"), type = "closed")) + geom_nodes(color = "tomato", size = 4) + theme_blank()

p2 <- ggplot(mtcars, aes(x = wt, y = mpg, colour = factor(gear))) + + geom_point() + + ggtitle("Cars") p2 + theme_solarized(light = FALSE) + scale_colour_solarized("red")

ggplot(diamonds, aes(x = clarity, fill = cut)) + geom_bar() + theme_pander() + scale_fill_pander()

library(rbokeh) url <- c("http://bokeh.pydata.org/en/latest/_static/images/logo.png", "http://developer.r-project.org/Logo/Rlogo-4.png") ss <- seq(0, 2*pi, length = 13)[-1] ws <- runif(12, 2.5, 5) * rep(c(1, 0.8), 6) imgdat <- data.frame( x = sin(ss) * 10, y = cos(ss) * 10, w = ws, h = ws * rep(c(1, 0.76), 6), url = rep(url, 6) ) p <- figure(xlab = "x", ylab = "y") %>% ly_image_url(x, y, w = w, h = h, image_url = url, data = imgdat, anchor = "center") %>% ly_lines(sin(c(ss, ss[1])) * 10, cos(c(ss, ss[1])) * 10, width = 15, alpha = 0.1) p

library(rbokeh) > > aapl <- read.csv('table.csv') > aapl$Date <- as.Date(aapl$Date) > p <- figure(title = 'Apple Stock Data') %>% ly_points(Date, Volume / (10 ^ 6), data = aapl, hover = c(Date, High, Open, Close)) %>% ly_abline(v = with(aapl, Date[which.max(Volume)])) %>% y_axis(label = 'Volume in millions', number_formatter = 'numeral', format = '0.00') > p

library(plotly) z <- c( c(8.83,8.89,8.81,8.87,8.9,8.87), c(8.89,8.94,8.85,8.94,8.96,8.92), c(8.84,8.9,8.82,8.92,8.93,8.91), c(8.79,8.85,8.79,8.9,8.94,8.92), c(8.79,8.88,8.81,8.9,8.95,8.92), c(8.8,8.82,8.78,8.91,8.94,8.92), c(8.75,8.78,8.77,8.91,8.95,8.92), c(8.8,8.8,8.77,8.91,8.95,8.94), c(8.74,8.81,8.76,8.93,8.98,8.99), c(8.89,8.99,8.92,9.1,9.13,9.11), c(8.97,8.97,8.91,9.09,9.11,9.11), c(9.04,9.08,9.05,9.25,9.28,9.27), c(9,9.01,9,9.2,9.23,9.2), c(8.99,8.99,8.98,9.18,9.2,9.19), c(8.93,8.97,8.97,9.18,9.2,9.18) ) dim(z) <- c(15,6) z2 <- z + 1 z3 <- z - 1 p <- plot_ly(z=z, type="surface",showscale=FALSE) %>% add_trace(z=z2, type="surface", showscale=FALSE, opacity=0.98) %>% add_trace(z=z3, type="surface", showscale=FALSE, opacity=0.98) p

library(plotly) kd <- with(MASS::geyser, MASS::kde2d(duration, waiting, n = 50)) with(kd, plot_ly(x = x, y = y, z = z, type = "surface"))

co2dat <- data.frame( y = co2, x = floor(time(co2)), m = rep(month.abb, 39)) figure() %>% ly_lines(x, y, group = m, data = co2dat)

point_types()

wa_cancer <- droplevels(subset(latticeExtra::USCancerRates, state == "Washington")) ## y axis sorted by male rate ylim <- levels(with(wa_cancer, reorder(county, rate.male))) figure(ylim = ylim, width = 700, height = 600, tools = "") %>% ly_segments(LCL95.male, county, UCL95.male, county, data = wa_cancer, color = NULL, width = 2) %>% ly_points(rate.male, county, glyph = 16, data = wa_cancer)

figure(ylab = "Height (inches)", width = 600) %>% ly_boxplot(voice.part, height, data = lattice::singer)

ly_baseball <- function(x) { base_x <- c(90 * cos(pi/4), 0, 90 * cos(3 * pi/4), 0) base_y <- c(90 * cos(pi/4), sqrt(90^2 + 90^2), 90 * sin(pi/4), 0) distarc_x <- lapply(c(2:4) * 100, function(a) seq(a * cos(3 * pi/4), a * cos(pi/4), length = 200)) distarc_y <- lapply(distarc_x, function(x) sqrt((x[1]/cos(3 * pi/4))^2 - x^2)) x %>% ## boundary ly_segments(c(0, 0), c(0, 0), c(-300, 300), c(300, 300), alpha = 0.4) %>% ## bases ly_crect(base_x, base_y, width = 10, height = 10, angle = 45*pi/180, color = "black", alpha = 0.4) %>% ## infield/outfield boundary ly_curve(60.5 + sqrt(95^2 - x^2), from = base_x[3] - 26, to = base_x[1] + 26, alpha = 0.4) %>% ## distance arcs (ly_arc should work here and would be much simpler but doesn't) ly_multi_line(distarc_x, distarc_y, alpha = 0.4) } figure(xgrid = FALSE, ygrid = FALSE, width = 630, height = 540, xlab = "Horizontal distance from home plate (ft.)", ylab = "Vertical distance from home plate (ft.)") %>% ly_baseball() %>% ly_hexbin(doubles, xbins = 50, shape = 0.77, alpha = 0.75, palette = "Spectral10")

figure() %>% ly_hexbin(rnorm(10000), rnorm(10000))

tools <- c("pan", "wheel_zoom", "box_zoom", "box_select", "reset") nms <- expand.grid(names(iris)[1:4], rev(names(iris)[1:4]), stringsAsFactors = FALSE) splom_list <- vector("list", 16) for(ii in seq_len(nrow(nms))) { splom_list[[ii]] <- figure(width = 200, height = 200, tools = tools, xlab = nms$Var1[ii], ylab = nms$Var2[ii]) %>% ly_points(nms$Var1[ii], nms$Var2[ii], data = iris, color = Species, size = 5, legend = FALSE) } grid_plot(splom_list, ncol = 4, same_axes = TRUE, link_data = TRUE)

> library(rbokeh) > p <- figure(width = 800, height = 400) %>% + ly_lines(date, Freq, data = flightfreq, alpha = 0.3) %>% + ly_points(date, Freq, data = flightfreq, + hover = list(date, Freq, dow), size = 5) %>% + ly_abline(v = as.Date("2001-09-11")) > p

> library(maps) > data(world.cities) > caps <- subset(world.cities, capital == 1) > caps$population <- prettyNum(caps$pop, big.mark = ",") > figure(width = 800, padding_factor = 0) %>% + ly_map("world", col = "gray") %>% + ly_points(long, lat, data = caps, size = 5, + hover = c(name, country.etc, population))

>library(dplyr) > library(plotly) > p <- ggplot2::mpg %>% group_by(class) %>% + summarise(mn = mean(hwy), sd = 1.96 * sd(hwy)) %>% + arrange(desc(mn)) %>% + plot_ly(x = class, y = mn, error_y = list(array = sd), + mode = "markers", name = "Highway") %>% + layout(yaxis = list(title = "Miles Per Gallon")) > p

> diamonds <- ggplot2:: diamonds > l <- levels(diamonds$color) > plot_list <- vector(mode = 'list', 7) > > for (i in 1:length(l)) { + data <- subset(diamonds, color == l[i]) + plot_list[[i]] <- figure(width = 350, height = 350) %>% + ly_points(carat, price, data = data, legend = l[i], hover = c(cut, clarity)) + } > > grid_plot(plot_list, nrow = 2)

library(rbokeh) > elements <- subset(elements, !is.na(group)) > elements$group <- as.character(elements$group) > elements$period <- as.character(elements$period) > > # add colors for groups > metals <- c("alkali metal", "alkaline earth metal", "halogen", + "metal", "metalloid", "noble gas", "nonmetal", "transition metal") > colors <- c("#a6cee3", "#1f78b4", "#fdbf6f", "#b2df8a", "#33a02c", + "#bbbb88", "#baa2a6", "#e08e79") > elements$color <- colors[match(elements$metal, metals)] > elements$type <- elements$metal > > # make coordinates for labels > elements$symx <- paste(elements$group, ":0.1", sep = "") > elements$numbery <- paste(elements$period, ":0.8", sep = "") > elements$massy <- paste(elements$period, ":0.15", sep = "") > elements$namey <- paste(elements$period, ":0.3", sep = "") > > # create figure > p <- figure(title = "Periodic Table", tools = c("resize", "hover"), + ylim = as.character(c(7:1)), xlim = as.character(1:18), + xgrid = FALSE, ygrid = FALSE, xlab = "", ylab = "", + height = 445, width = 800) %>% + + # plot rectangles + ly_crect(group, period, data = elements, 0.9, 0.9, + fill_color = color, line_color = color, fill_alpha = 0.6, + hover = list(name, atomic.number, type, atomic.mass, + electronic.configuration)) %>% + + # add symbol text + ly_text(symx, period, text = symbol, data = elements, + font_style = "bold", font_size = "10pt", + align = "left", baseline = "middle") %>% + + # add atomic number text + ly_text(symx, numbery, text = atomic.number, data = elements, + font_size = "6pt", align = "left", baseline = "middle") %>% + + # add name text + ly_text(symx, namey, text = name, data = elements, + font_size = "4pt", align = "left", baseline = "middle") %>% + + # add atomic mass text + ly_text(symx, massy, text = atomic.mass, data = elements, + font_size = "4pt", align = "left", baseline = "middle") > > p

> library(rbokeh) > clusters <- hclust(dist(iris[, 3:4]), method = 'average') > clusterCut <- cutree(clusters, 3) > p <- figure(title = 'Hierarchical Clustering of Iris Data') %>% + ly_points(Petal.Length, Petal.Width, data = iris, color = Species, hover = c(Sepal.Length, Sepal.Width)) %>% + ly_points(iris$Petal.Length, iris$Petal.Width, glyph = clusterCut, size = 13) > p

irisDen <- as.dendrogram(hclust(dist(iris[1:4], method='euclidean'), method='ward.D2')) ## Add the species information to the leafs irisDen <- dendrapply(irisDen, function(d) { if(is.leaf(d)) attr(d, 'nodePar') <- list(species=iris[as.integer(attr(d, 'label')),5]) d }) # Plotting this looks very much like ggplot2 except for the new geoms ggraph(graph = irisDen, layout = 'dendrogram', repel = TRUE, circular = TRUE, ratio = 0.5) + geom_edge_elbow() + geom_node_text(aes(x = x*1.05, y=y*1.05, filter=leaf, angle = nAngle(x, y), label = label), size=3, hjust='outward') + geom_node_point(aes(filter=leaf, color=species)) + coord_fixed() + ggforce::theme_no_axes()

>library(igraph) >library(ggforce) > library(ggraph) > friendGraph <- graph_from_data_frame(highschool) > V(friendGraph)$degree <- degree(friendGraph, mode = 'in') > graph1957 <- subgraph.edges(friendGraph, which(E(friendGraph)$year ==1957), F) > graph1958 <- subgraph.edges(friendGraph, which(E(friendGraph)$year ==1958), F) > V(friendGraph)$pop.increase <- degree(graph1958, mode = 'in') > + degree(graph1957, mode = 'in') > > ggraph(friendGraph, 'igraph', algorithm = 'kk') + + geom_edge_fan(aes(alpha = ..index..)) + + geom_node_point(aes(size = degree, colour = pop.increase)) + + scale_edge_alpha('Friends with', guide = 'edge_direction') + + scale_colour_manual('Improved', values = c('firebrick', 'forestgreen')) + + scale_size('# Friends') + + facet_wrap(~year) + + ggforce::theme_no_axes() >

ibrary(GERGM) ########################### 1. No Covariates ############################# # Preparing an unbounded network without covariates for gergm estimation # set.seed(12345) net <- matrix(rnorm(100,0,20),10,10) colnames(net) <- rownames(net) <- letters[1:10] formula <- net ~ edges(method = "endogenous") + mutual + ttriads test <- gergm(formula, normalization_type = "division", network_is_directed = TRUE, number_of_networks_to_simulate = 40000, thin = 1/10, proposal_variance = 0.2, MCMC_burnin = 10000, seed = 456, convergence_tolerance = 0.01, force_x_theta_update = 4) ########################### 2. Covariates ############################# # Preparing an unbounded network with covariates for gergm estimation # set.seed(12345) net <- matrix(runif(100,0,1),10,10) colnames(net) <- rownames(net) <- letters[1:10] node_level_covariates <- data.frame(Age = c(25,30,34,27,36,39,27,28,35,40), Height = c(70,70,67,58,65,67,64,74,76,80), Type = c("A","B","B","A","A","A","B","B","C","C")) rownames(node_level_covariates) <- letters[1:10] network_covariate <- net + matrix(rnorm(100,0,.5),10,10) formula <- net ~ edges(method = "regression") + mutual + ttriads + sender("Age") + netcov("network_covariate") + nodemix("Type",base = "A") test <- gergm(formula, covariate_data = node_level_covariates, number_of_networks_to_simulate = 100000, thin = 1/10, proposal_variance = 0.2, MCMC_burnin = 50000, seed = 456, convergence_tolerance = 0.01, force_x_theta_update = 2) # Generate Estimate Plot Estimate_Plot(test) # Generate GOF Plot GOF(test) # Generate Trace Plot Trace_Plot(test) # Generate Hysteresis plots for all structural parameter estimates hysteresis_results <- hysteresis(test, networks_to_simulate = 1000, burnin = 500, range = 2, steps = 20, simulation_method = "Metropolis", proposal_variance = 0.2)

> set.seed(12345) > net <- matrix(runif(100,0,1),10,10) > colnames(net) <- rownames(net) <- letters[1:10] > node_level_covariates <- data.frame(Age = c(25,30,34,27,36,39,27,28,35,40), + Height = c(70,70,67,58,65,67,64,74,76,80), + Type = c("A","B","B","A","A","A","B","B","C","C")) > rownames(node_level_covariates) <- letters[1:10] > network_covariate <- net + matrix(rnorm(100,0,.5),10,10) > formula <- net ~ edges(method = "regression") + mutual + ttriads + sender("Age") + + netcov("network_covariate") + nodemix("Type",base = "A") > > test <- gergm(formula, + covariate_data = node_level_covariates, + number_of_networks_to_simulate = 100000, + thin = 1/10, + proposal_variance = 0.2, + MCMC_burnin = 50000, + seed = 456, + convergence_tolerance = 0.01, + force_x_theta_update = 2)

library(cluster) > autoplot(clara(iris[-5], 3)) > autoplot(fanny(iris[-5], 3), frame = TRUE) >

set.seed(1) > autoplot(kmeans(USArrests, 3), data = USArrests) > autoplot(kmeans(USArrests, 3), data = USArrests, label = TRUE, label.size = 3)

res <- lapply(c(3, 4, 5), function(x) kmeans(iris[-5], x)) autoplot(res, data = iris[-5], ncol = 3)

library(ggfortify) > res <- lm(Volume ~ Girth, data = trees) > mp <- autoplot(res, ncol = 4) > mp

library(ggtern) > data(Feldspar) > data(FeldsparRaster) > ggtern(Feldspar,aes(Ab,An,Or)) + + theme_rgbw() + + annotation_raster_tern(FeldsparRaster,xmin=0,xmax=1,ymin=0,ymax=1) + + geom_mask() + + geom_point(size=5,aes(shape=Feldspar,fill=Feldspar),color='black') + + scale_shape_manual(values=c(21,24)) + + labs(title="Demonstration of Raster Annotation") >

> world <- map_data("world") > #> > #> # maps v3.1: updated 'world': all lakes moved to separate new # > #> # 'lakes' database. Type '?world' or 'news(package="maps")'. # > world <- world[world$region != "Antarctica",] > > gg <- ggplot() > gg <- gg + geom_map(data=world, map=world, + aes(x=long, y=lat, map_id=region)) > gg <- gg + coord_proj("+proj=wintri") > gg

library(ggplot2) > library(gridExtra) > library(ggalt) > > # current verison > packageVersion("ggalt") [1] ‘0.1.1’ > #> [1] '0.3.0.9000' > > set.seed(1492) > dat <- data.frame(x=c(1:10, 1:10, 1:10), + y=c(sample(15:30, 10), 2*sample(15:30, 10), 3*sample(15:30, 10)), + group=factor(c(rep(1, 10), rep(2, 10), rep(3, 10)))) > ggplot(dat, aes(x, y, group=group, color=group)) + + geom_point() + + geom_line() > m <- ggplot(faithful, aes(x = eruptions, y = waiting)) + + geom_point() + + xlim(0.5, 6) + + ylim(40, 110) > > m + geom_bkde2d(bandwidth=c(0.5, 4)) > m + stat_bkde2d(bandwidth=c(0.5, 4), aes(fill = ..level..), geom = "polygon") >

library(ggplot2) > library(gridExtra) > library(ggalt) > > # current verison > packageVersion("ggalt") [1] ‘0.1.1’ > #> [1] '0.3.0.9000' > > set.seed(1492) > dat <- data.frame(x=c(1:10, 1:10, 1:10), + y=c(sample(15:30, 10), 2*sample(15:30, 10), 3*sample(15:30, 10)), + group=factor(c(rep(1, 10), rep(2, 10), rep(3, 10)))) > ggplot(dat, aes(x, y, group=group, color=group)) + + geom_point() + + geom_line() > m <- ggplot(faithful, aes(x = eruptions, y = waiting)) + + geom_point() + + xlim(0.5, 6) + + ylim(40, 110) > > m + geom_bkde2d(bandwidth=c(0.5, 4)) >

http://www.cbcb.umd.edu/~hcorrada/PracticalML/src/classification.R http://www.cbcb.umd.edu/~hcorrada/PracticalML/

palette(rainbow(10)); palette() palette(rainbow(10)); palette() > barplot(rep(1,20), yaxt="n", col=palette()); > palette(gray(1:10/10)); palette()0FF" "#FF0099" > barplot(rep(1,20), yaxt="n", col=palette()); > palette(gray(1:10/10)); palette()

plot(1:10,type="n",xlab="",ylab="",main="plot math&numbers") > theta<-1.23;mtext(bquote(hat(theta)==.(theta)),line=.25) > for(i in 2:9) + text(i,i+1,substitute(list(xi,eta)==group("(",list(x,y),")"),list(x=i, y=i+1))) > text(1,10, "Derivatives:"adj=0) text(8,5, expression(paste(frac(1,sigma*sqrt(2*pi)),"",plain(e)^{frac(-(x-mu)^2, 2*sigma^2)})),cex=1.2)

library(ggmap) qmap(location = "Istanbul", zoom = 14)

library(rpart) # grow tree fit <- rpart(Kyphosis ~ Age + Number + Start, method="class", data=kyphosis) printcp(fit) # display the results plotcp(fit) # visualize cross-validation results summary(fit) # detailed summary of splits # plot tree plot(fit, uniform=TRUE, main="Classification Tree for Kyphosis") text(fit, use.n=TRUE, all=TRUE, cex=.8) # create attractive postscript plot of tree post(fit, file = "c:/tree.ps", title = "Classification Tree for Kyphosis")

## Plot all Starbucks locations using OpenStreetMap ## Credit: http://www.computerworld.com/article/2893271/business-intelligence/5-data-visualizations-in-5-minutes-each-in-5-lines-or-less-of-r.html library(checkpoint) checkpoint("2016-08-22") file<- "https://opendata.socrata.com/api/views/ddym-zvjk/rows.csv" starbucks <- read.csv(file) library(leaflet); library(magrittr) leaflet() %>% addTiles() %>% setView(-84.3847, 33.7613, zoom = 16) %>% addMarkers(data = starbucks, lat = ~ Latitude, lng = ~ Longitude, popup = starbucks$Name)

library(rworldmap) > mapCountryData(mapRegion = "Turkey")

> library(rworldmap) > newmap <- getMap(resolution = "coarse") # different resolutions available > plot(newmap)

mymap <- gmap("Turkey", type = "satellite") plot(mymap)

> library(sp) # classes for spatial data > library(raster) # grids, rasters > library(rasterVis) # raster visualisation > library(maptools) > library(rgeos) > # and their dependencies > > library(dismo) > > mymap <- gmap("Turkey") # choose whatever country > plot(mymap) >

country.list <- c("USA", "MEX", "CAN", "BLZ") country.dat <- get_historical_temp(country.list, "year") ggplot(country.dat, aes(x = year, y = data, group = locator)) + geom_point() + geom_path() + ylab("Average annual temperature of Canada") + theme_bw() + xlab("Year") + stat_smooth(se = F, colour = "black") + facet_wrap(~locator, scale = "free")

country.list <- c("ISL", "FIN", "NOR", "SWE") country.dat <- get_ensemble_stats(country.list, "mavg", "tmin_means") ####### Subset data Exclude A2 scenario country.dat.b1 <- subset(country.dat, country.dat$scenario == "b1") # choose just one percentile country.dat.b1 <- subset(country.dat.b1, country.dat.b1$percentile == 50) # get just one year period country.dat.b1 <- subset(country.dat.b1, country.dat.b1$fromYear == 2081) ggplot(country.dat.b1, aes(x = month, y = data, group = locator, colour = locator)) + geom_point() + geom_path() + ylab("Average daily minimum temperature") + theme_bw() + xlab("Month")

idn.dat <- get_ensemble_precip("IDN", "mavg", 2080, 2100) # Set line types ltype <- rep(1, dim(idn.dat)[1]) ltype[idn.dat$percentile != 50] <- 2 idn.dat$ltype <- ltype # Create uniqueIDs idn.dat$uid <- paste(idn.dat$scenario, idn.dat$percentile, sep = "-") ggplot(idn.dat, aes(x = as.factor(month), y = data, group = uid, colour = scenario, linetype = as.factor(ltype))) + geom_point() + geom_path() + xlab("Month") + ylab("Rain in mm") + theme_bw()

ggplot(usa.dat[usa.dat$scenario == "a2", ], aes(x = month, y = data, group = gcm, colour = gcm)) + geom_point() + geom_path() + ylab("Average temperature in degrees C \n between 2080 and 2100") + xlab("Month") + theme_bw()

usa.dat <- get_model_temp("USA", "mavg", 2080, 2100) usa.dat.bcc <- usa.dat[usa.dat$gcm == "bccr_bcm2_0", ] usa.dat.had <- usa.dat[usa.dat$gcm == "ukmo_hadcm3", ] ## Add a unique ID to each for easier plotting usa.dat.bcc$ID <- paste(usa.dat.bcc$scenario, usa.dat.bcc$gcm, sep = "-") usa.dat.had$ID <- paste(usa.dat.had$scenario, usa.dat.had$gcm, sep = "-") plot.df <- rbind(usa.dat.bcc, usa.dat.had) ggplot(plot.df, aes(x = as.factor(month), y = data, group = ID, colour = gcm, linetype = scenario)) + geom_point() + geom_path() + ylab("Average temperature in degrees C \n between 2080 and 2100") + xlab("Month") + theme_bw()

data(VADeaths) > # hist3D and ribbon3D with greyish background, rotated, rescaled,... > hist3D(z = VADeaths, scale = FALSE, expand = 0.01, bty = "g", phi = 20, + col = "#0072B2", border = "black", shade = 0.2, ltheta = 90, + space = 0.3, ticktype = "detailed", d = 2)

ref:http://www.sthda.com/english/wiki/impressive-package-for-3d-and-4d-graph-r-software-and-data-visualization

library(Amelia) missmap(training.data.raw, main = "Missing values vs observed")

> plate_pnpp <- new_plate(dir, type = plate_types$fam_positive_pnpp) Reading data files into plate... Parsed with column specification: cols( `Assay1 Amplitude` = col_double(), `Assay2 Amplitude` = col_double(), Cluster = col_integer() ) Parsed with column specification: cols( `Assay1 Amplitude` = col_double(), `Assay2 Amplitude` = col_double(), Cluster = col_integer() ) Parsed with column specification: cols( `Assay1 Amplitude` = col_double(), `Assay2 Amplitude` = col_double(), Cluster = col_integer() ) Parsed with column specification: cols( `Assay1 Amplitude` = col_double(), `Assay2 Amplitude` = col_double(), Cluster = col_integer() ) Parsed with column specification: cols( `Assay1 Amplitude` = col_double(), `Assay2 Amplitude` = col_double(), Cluster = col_integer() ) DONE (0 seconds) Initializing plate of type `fam_positive_pnpp`... DONE (0 seconds) > clusters(plate_pnpp) [1] "UNDEFINED" "FAILED" "OUTLIER" "EMPTY" "RAIN" "POSITIVE" "NEGATIVE" > plate_pnpp <- analyze(plate_pnpp) Identifying failed wells... DONE (0 seconds) Identifying outlier droplets... DONE (0 seconds) Identifying empty droplets... DONE (1 seconds) Classifying droplets... DONE (1 seconds) Reclassifying droplets... skipped (not enough wells with significant mutant clusters) Analysis complete > plate_pnpp %>% plate_meta(only_used = TRUE) well sample row col used drops success drops_outlier drops_empty drops_non_empty 1 A01 Dean A 1 TRUE 15820 TRUE 2 13690 2130 2 A05 Dave A 5 TRUE 13165 TRUE 1 11283 1882 3 C01 Mike C 1 TRUE 14256 TRUE 0 12879 1377 4 F05 Mary F 5 TRUE 15377 TRUE 0 14126 1251 5 C05 Emily C 5 TRUE 14109 FALSE 0 NA NA drops_empty_fraction concentration mutant_border filled_border 1 0.865 170 4194 8286 2 0.857 181 3789 8136 3 0.903 120 4356 8445 4 0.919 99 3926 8294 5 NA NA NA NA significant_mutant_cluster mutant_num wildtype_num mutant_freq 1 FALSE 4 1827 0.218 2 TRUE 368 1224 23.100 3 FALSE 3 1248 0.240 4 TRUE 211 855 19.800 5 NA NA NA NA > plate_pnpp %>% plot(text_size_mutant_freq = 8)

> plate %>% plate_meta(only_used = TRUE) well sample row col used drops 1 A01 Dean A 1 TRUE 15820 2 A05 Dave A 5 TRUE 13165 3 C01 Mike C 1 TRUE 14256 4 C05 Emily C 5 TRUE 14109 5 F05 Mary F 5 TRUE 15377 > plate <- plate %>% subset("A01:C05") > # could have also used subset("A01, A05, C01, C05") > plate %>% wells_used() [1] "A01" "A05" "C01" "C05" > plate %>% plate_data() # A tibble: 57,350 x 4 well HEX FAM cluster <chr> <int> <int> <int> 1 A01 577 494 1 2 A01 515 495 1 3 A01 690 645 1 4 A01 929 860 1 5 A01 844 868 1 6 A01 942 907 1 7 A01 985 923 1 8 A01 1058 966 1 9 A01 1058 979 1 10 A01 1095 1002 1 # ... with 57,340 more rows > plate %>% plate_meta(only_used = TRUE) well sample row col used drops 1 A01 Dean A 1 TRUE 15820 2 A05 Dave A 5 TRUE 13165 3 C01 Mike C 1 TRUE 14256 4 C05 Emily C 5 TRUE 14109 > well_info(plate, "A05", "drops_empty") NULL > plate %>% plot() > well_info(plate, "A05", "drops_empty") NULL > plate %>% plot(wells = "A01,A05", show_full_plate = TRUE, + show_drops_empty = TRUE, col_drops_empty = "red", + title = "Show full plate") > plate %>% plot(wells = "A01,A05", superimpose = TRUE, + show_grid = TRUE, show_grid_labels = TRUE, title = "Superimpose")

library(ddpcr) dir <- sample_data_dir() plate <- new_plate(dir) plot(plate)

library(ddpcr) dir <- sample_data_dir() # example 1: manually set thresholds plate1 <- new_plate(dir, type = plate_types$custom_thresholds) %>% subset("A01,A05") %>% set_thresholds(c(5000, 7500)) %>% analyze() plot(plate1, show_grid_labels = TRUE, alpha_drops = 0.3, title = "Manually set gating thresholds\nworks with any data") # example 2: automatic gating new_plate(dir, type = plate_types$fam_positive_pnpp) %>% subset("A01:A05") %>% analyze() %>% plot(show_mutant_freq = FALSE, show_grid_labels = TRUE, alpha_drops = 0.3, title = "Automatic gating\nworks with PNPP experiments")

library(ddpcr) dir <- sample_data_dir() # example 1: manually set thresholds plate1 <- new_plate(dir, type = plate_types$custom_thresholds) %>% subset("A01,A05") %>% set_thresholds(c(5000, 7500)) %>% analyze() plot(plate1, show_grid_labels = TRUE, alpha_drops = 0.3, title = "Manually set gating thresholds\nworks with any data") # example 2: automatic gating new_plate(dir, type = plate_types$fam_positive_pnpp) %>% subset("A01:A05") %>% analyze() %>% plot(show_mutant_freq = FALSE, show_grid_labels = TRUE, alpha_drops = 0.3, title = "Automatic gating\nworks with PNPP experiments")

ggMarginal(p1, type = "histogram", xparams = list(binwidth = 1, fill = "orange"))

.... ggMarginal(p1, type = "histogram")

> suppressPackageStartupMessages({ + library("ggExtra") + library("ggplot2") + }) set.seed(30) > df1 <- data.frame(x = rnorm(500, 50, 10), y = runif(500, 0, 50)) > (p1 <- ggplot(df1, aes(x, y)) + geom_point() + theme_bw()) > ggMarginal(p1) > ggMarginal(p1 + theme_bw(30) + ylab("Two\nlines")) >

plotar(data=heart_disease, str=c('resting_blood_pressure', 'max_heart_rate'), str_target="has_heart_disease", plot_type = "histdens")

plotar(data=heart_disease, str=c('resting_blood_pressure', 'max_heart_rate'), str_target="has_heart_disease", plot_type = "histdens")

suppressMessages(library(funModeling)) data(heart_disease) plotar(data=heart_disease, str_input="age", str_target="has_heart_disease", plot_type = "histdens")

library(expm) library(markovchain) library(diagram) library(pracma) stateNames <- c("Rain","Nice","Snow") Oz <- matrix(c(.5,.25,.25,.5,0,.5,.25,.25,.5), nrow=3, byrow=TRUE) row.names(Oz) <- stateNames; colnames(Oz) <- stateNames Oz # Rain Nice Snow # Rain 0.50 0.25 0.25 # Nice 0.50 0.00 0.50 # Snow 0.25 0.25 0.50 plotmat(Oz,pos = c(1,2), lwd = 1, box.lwd = 2, cex.txt = 0.8, box.size = 0.1, box.type = "circle", box.prop = 0.5, box.col = "light yellow", arr.length=.1, arr.width=.1, self.cex = .4, self.shifty = -.01, self.shiftx = .13, main = "") Oz3 <- Oz %^% 3 round(Oz3,3) # Rain Nice Snow # Rain 0.406 0.203 0.391 # Nice 0.406 0.188 0.406 # Snow 0.391 0.203 0.406 u <- c(1/3, 1/3, 1/3) round(u %*% Oz3,3) #0.401 0.198 0.401

> library(lattice) > data("iris") > bwplot(Sepal.Length~Sepal.Width, data=iris)

library(raster) alt<-getData('alt',country="turkey") plot(alt)

p <- ggplot(iris, aes(Petal.Length, Petal.Width, group=Species,color=Species)) + geom_line() p

> df <- read.csv('https://cdn.rawgit.com/plotly/documentation/source/_posts/r/scattergl/weather-data.csv') > df$Date <- zoo::as.Date(df$Date, format = "%m/%d/%Y") > p <- plot_ly(df, x = Date, y = Mean_TemperatureC, name = "Mean Temp.", type = "scattergl", marker = list(color = "#3b3b9e")) %>% layout(title = "Mean Temparature in Seattle (1948 - 2015)", yaxis = list(title = "Temperature (^oC)")) > p

source("http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/overLapper.R") # Imports required functions. setlist <- list(A=sample(letters, 18), B=sample(letters, 16), C=sample(letters, 20), D=sample(letters, 22), E=sample(letters, 18), F=sample(letters, 22, replace=T)) # To work with the overLapper function, the sample sets (here six) need to be stored in a list object where the different # compontents are named by unique identifiers, here 'A to F'. These names are used as sample labels in all subsequent data # sets and plots. sets <- read.delim("http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/Samples/sets.txt") setlistImp <- lapply(colnames(sets), function(x) as.character(sets[sets[,x]!="", x])) names(setlistImp) <- colnames(sets) # Example how a list of test sets can be imported from an external table file stored in tab delimited format. Such # a file can be easily created from a spreadsheet program, such as Excel. As a reminder, copy & paste from external # programs into R is also possible (see read.delim function). OLlist <- overLapper(setlist=setlist, sep="_", type="vennsets"); OLlist; names(OLlist) # With the setting type="vennsets", the overLapper function computes all Venn Intersects for the six test samples in # setlist and stores the results in the Venn_List component of the returned OLlist object. By default, duplicates are # removed from the test sets. The setting keepdups=TRUE will retain duplicates by appending a counter to each entry. When # assigning the value "intersects" to the type argument then the function will compute Regular # Intersects instead of Venn Intersects. The Regular Intersect approach (not compatible with Venn diagrams!) is described # in the next section. Both analyses return a present-absent matrix in the Intersect_Matrix component of OLlist. Each overlap # set in the Venn_List data set is labeled according to the sample names provided in setlist. For instance, the composite # name 'ABC' indicates that the entries are restricted to A, B and C. The seperator used for naming the intersect samples # can be specified under the sep argument. By adding the argument cleanup=TRUE, one can minimize formatting issues in the # sample sets. This setting will convert all characters in the sample sets to upper case and remove leading/trailing spaces. ############################# ## Bar plot of Venn counts ## ############################# olBarplot(OLlist=OLlist, horiz=T, las=1, cex.names=0.6, main="Venn Bar Plot") # Generates a bar plot for the Venn counts of the six test sample sets. In contrast to Venn diagrams, bar plots scale # to larger numbers of sample sets. The layout of the plot can be adjusted by changing the default values of the argument: # margins=c(4,10,3,1). The minimum number of counts to consider in the plot can be set with the mincount argument # (default is 0). The bars themselves are colored by complexity levels using the default setting: mycol=OLlist$Complexity_Levels. ######################### ## 2-way Venn diagrams ## ######################### setlist2 <- setlist[1:2]; OLlist2 <- overLapper(setlist=setlist2, sep="_", type="vennsets") OLlist2$Venn_List; counts <- sapply(OLlist2$Venn_List, length); vennPlot(counts=counts) # Plots a non-proportional 2-way Venn diagram. The main graphics features of the vennPlot function can be controlled by # the following arguments (here with 2-way defaults): mymain="Venn Diagram": main title; mysub="default": subtitle; # ccol=c("black","black","red"): color of counts; lcol=c("red","green"): label color; lines=c("red","green"): # line color; mylwd=3: line width; ccex=1.0: font size of counts; lcex=1.0: font size of labels. Note: the vector # lengths provided for the arguments ccol, lcol and lines should match the number of their corresponding features # in the plot, e.g. 3 ccol values for a 2-way Venn diagram and 7 for a 3-way Venn diagram. The argument setlabels # allows to provide a vector of custom sample labels. However, assigning the proper names in the original test set list # is much more effective for tracking purposes. ######################### ## 3-way Venn diagrams ## ######################### setlist3 <- setlist[1:3]; OLlist3 <- overLapper(setlist=setlist3, sep="_", type="vennsets") counts <- list(sapply(OLlist3$Venn_List, length), sapply(OLlist3$Venn_List, length)) vennPlot(counts=counts, mysub="Top: var1; Bottom: var2", yoffset=c(0.3, -0.2)) # Plots a non-proportional 3-way Venn diagram. The results from several Venn comparisons can be combined in a # single Venn diagram by assigning to the count argument a list with several count vectors. The positonal offset # of the count sets in the plot can be controlled with the yoffset argument. The argument setting colmode=2 allows # to assign different colors to each count set. For instance, with colmode=2 one can assign to ccol a color vector # or a list, such as ccol=c("blue", "red") or ccol=list(1:8, 8:1). ######################### ## 4-way Venn diagrams ## ######################### setlist4 <- setlist[1:4] OLlist4 <- overLapper(setlist=setlist4, sep="_", type="vennsets") counts <- list(sapply(OLlist4$Venn_List, length), sapply(OLlist4$Venn_List, length)) vennPlot(counts=counts, mysub="Top: var1; Bottom: var2", yoffset=c(0.3, -0.2)) # Plots a non-proportional 4-way Venn diagram. The setting type="circle" returns an incomplete 4-way Venn diagram as # circles. This representation misses two overlap sectors, but is sometimes easier to navigate than the default # ellipse version. ######################### ## 5-way Venn diagrams ## ######################### setlist5 <- setlist[1:5]; OLlist5 <- overLapper(setlist=setlist5, sep="_", type="vennsets") counts <- sapply(OLlist5$Venn_List, length) vennPlot(counts=counts, ccol=c(rep(1,30),2), lcex=1.5, ccex=c(rep(1.5,5), rep(0.6,25),1.5)) # Plots a non-proportional 5-way Venn diagram. ################################ ## Export and other utilities ## ################################ OLexport <- as.matrix(unlist(sapply(OLlist5[[4]], paste, collapse=" "))) write.table(OLexport, file="test.xls", col.names=F, quote=F, sep="\t") # Exports intersect data in tabular format to a file. OLexport <- data.frame(Venn_Comp=rep(names(OLlist5[[4]]), sapply(OLlist5[[4]], length)), IDs=unlist(OLlist5[[4]])) write.table(OLexport, file="test.xls", row.names=F, quote=F, sep="\t") # Same as above, but exports to an alternative tabular format. tapply(counts, OLlist5[[3]], function(x) rev(sort(x))) # Sorts the overlap results within each complexity level by their size. This allows to identify the sample set # combinations with the largest intersect within each complexity level. sapply(names(setlist), function(x) table(setlist[[x]])[table(setlist[[x]])!=1]) # Command to identify and count duplicated objects in the original sample set object 'setlist'. In the given example, # only set 'F' contains duplications. Their frequency is provided in the result. vennPlot(counts, mymain="", mysub="", ccol="white", lcol="white") # Returns an empty Venn diagram without counts or labels. ## Typical analysis routine for sets of differentially expressed genes (DEGs) ratio <- matrix(sample(seq(-5, 5, by=0.1), 100, replace=T), 100, 4, dimnames=list(paste("g", 1:100, sep=""), paste("DEG", 1:4, sep="")), byrow=T) # Creates a sample matrix of gene expression log2 ratios. This could be any data type! setlistup <- sapply(colnames(ratio), function(x) rownames(ratio[ratio[,x]>=1,])) setlistdown <- sapply(colnames(ratio), function(x) rownames(ratio[ratio[,x]<=-1,])) # Identifies all genes with at least a two fold up or down regulation and stores the corresponding gene identifiers # in setlistup and setlistdown, respectively. OLlistup <- overLapper(setlist=setlistup, sep="_", type="vennsets") OLlistdown <- overLapper(setlist=setlistdown, sep="_", type="vennsets") counts <- list(sapply(OLlistup$Venn_List, length), sapply(OLlistdown$Venn_List, length)) vennPlot(counts=counts, ccol=c("red", "blue"), colmode=2, mysub="Top: DEG UP; Bottom: DEG Down", yoffset=c(0.3, -0.2)) # Performs Venn analysis for the four sets stored in setlistup and setlistdown. The argument setting colmode=2 allows # to assign different colors to each count set. # For instance, with colmode=2 one can assign to ccol a color vector or a list, such as ccol=c("blue", "red") or ccol=list(1:8, 8:1).

library(lattice); library(gplots) > y <- lapply(1:4, function(x) matrix(rnorm(50), 10, 5, dimnames=list(paste("g", 1:10, sep=""), paste("t", 1:5, sep="")))) > > ## Plot single heatmap: > levelplot(y[[1]]) > > ## Arrange several heatmaps in one plot > x1 <- levelplot(y[[1]], col.regions=colorpanel(40, "darkblue", "yellow", "white"), main="colorpanel") > x2 <- levelplot(y[[2]], col.regions=heat.colors(75), main="heat.colors") > x3 <- levelplot(y[[3]], col.regions=rainbow(75), main="rainbow") > x4 <- levelplot(y[[4]], col.regions=redgreen(75), main="redgreen") > print(x1, split=c(1,1,2,2)) > print(x2, split=c(2,1,2,2), newpage=FALSE) > print(x3, split=c(1,2,2,2), newpage=FALSE) > print(x4, split=c(2,2,2,2), newpage=FALSE)

library(ggplot2) > ggplot(data = diamonds, aes(x = carat, y = price)) + geom_point(color = "red")

library(lattice) > xyplot(Sepal.Length ~ Sepal.Width | Species, data=iris, type="a", layout=c(1,3,1)) > parallel(~iris[1:4] | Species, iris) > parallel(~iris[1:4] | Species, iris, horizontal.axis = FALSE, layout = c(1, 3, 1))

library(ggplot2) > qplot(date, uempmed, data = economics, geom = "line")

> library(visNetwork) > library(igraph) > > graph.famous("Walther") %>% + get.data.frame( what = "both" ) %>% + { + visNetwork( + nodes = data.frame( + id = unique( c( .[["edges"]][,"from"], .[["edges"]][,"to"] ) ) + ) + ,edges = .[["edges"]] + ) + } %>% + visOptions(highlightNearest = TRUE)

> library(dygraphs) > lungDeaths <- cbind(mdeaths, fdeaths) > dygraph(lungDeaths)

source:https://briatte.github.io/ggnet/

library(igraph) par(mfrow=c(2,2),mar=c(0,0,0,0), oma=c(0,0,0,0)) g = watts.strogatz.game(1,20,3,0.4) layout.old = layout.fruchterman.reingold(g) for(i in 1:4){ layout.new = layout.fruchterman.reingold(g,params=list(niter=10,maxdelta=2,start=layout.old)) plot(g,layout=layout.new) layout.old = layout.new }

library(networkD3) ..... diagonalNetwork(List = Flare, fontSize = 10, opacity = 0.9)

library(networkD3) URL <- paste0( "https://cdn.rawgit.com/christophergandrud/networkD3/", "master/JSONdata//flare.json") ## Convert to list format Flare <- jsonlite::fromJSON(URL, simplifyDataFrame = FALSE) # Use subset of data for more readable diagram Flare$children = Flare$children[1:3] radialNetwork(List = Flare, fontSize = 10, opacity = 0.9)

Reference: https://gist.github.com/rich-iannone/de0bb88d155a2c1c7e38 Richard Ioannone codes: grViz(" graph severalranks { node [shape = circle, fixedsize = true, fontcolor = '#555555', fontname = Helvetica, fontsize = 7, style = filled, fillcolor ='#AAAAAA', color='#555555', width = 0.12, height = 0.12, nodesep = 0.1] edge [color = '#AAAAAA'] graph [overlap = true, layout = neato] node [label='', fillcolor='#000000'] n1 node [label='', fillcolor='#AAAAAA'] n2 node [label='', fillcolor='#AAAAAA'] n3 node [label='', fillcolor='#AAAAAA'] n4 node [label='', fillcolor='#AAAAAA'] n5 node [label='', fillcolor='#FFA500'] n6 node [label='', fillcolor='#FFA500'] n7 node [label='', fillcolor='#FFA500'] n8 node [label='', fillcolor='#FF00FF'] n9 node [label='', fillcolor='#555555'] n10 node [label='', fillcolor='#FFA500'] n11 node [label='', fillcolor='#FFA500'] n12 node [label='', fillcolor='#FFA500'] n13 node [label='', fillcolor='#0000FF'] n14 node [label='', fillcolor='#FFA500'] n15 node [label='', fillcolor='#FFA500'] n16 node [label='', fillcolor='#0000FF'] n17 node [label='', fillcolor='#FFA500'] n18 node [label='', fillcolor='#FFA500'] n19 node [label='', fillcolor='#0000FF'] n20 node [label='', fillcolor='#FFA500'] n21 node [label='', fillcolor='#FFA500'] n22 node [label='', fillcolor='#0000FF'] n23 node [label='', fillcolor='#FFA500'] n24 node [label='', fillcolor='#FFA500'] n25 node [label='', fillcolor='#0000FF'] n26 node [label='', fillcolor='#FFA500'] n27 node [label='', fillcolor='#FFA500'] n28 node [label='', fillcolor='#0000FF'] n29 node [label='', fillcolor='#FFA500'] n30 node [label='', fillcolor='#FFA500'] n31 node [label='', fillcolor='#0000FF'] n32 node [label='', fillcolor='#FFA500'] n33 node [label='', fillcolor='#FFA500'] n34 node [label='', fillcolor='#0000FF'] n35 node [label='', fillcolor='#FFA500'] n36 node [label='', fillcolor='#FFA500'] n37 node [label='', fillcolor='#0000FF'] n38 node [label='', fillcolor='#FFA500'] n39 node [label='', 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fillcolor='#0000FF'] n88 node [label='', fillcolor='#F0E68C'] n89 node [label='', fillcolor='#0000FF'] n90 node [label='', fillcolor='#0000FF'] n91 node [label='', fillcolor='#F0E68C'] n92 node [label='', fillcolor='#0000FF'] n93 node [label='', fillcolor='#0000FF'] n94 node [label='', fillcolor='#F0E68C'] n95 node [label='', fillcolor='#0000FF'] n96 node [label='', fillcolor='#0000FF'] n97 node [label='', fillcolor='#00FFFF'] n98 node [label='', fillcolor='#0000FF'] n99 node [label='', fillcolor='#F0E68C'] n100 node [label='', fillcolor='#F0E68C'] n101 node [label='', fillcolor='#0000FF'] n102 node [label='', fillcolor='#0000FF'] n103 node [label='', fillcolor='#F0E68C'] n104 node [label='', fillcolor='#0000FF'] n105 node [label='', fillcolor='#0000FF'] n106 node [label='', fillcolor='#F0E68C'] n107 node [label='', fillcolor='#0000FF'] n108 node [label='', fillcolor='#F0E68C'] n109 node [label='', fillcolor='#0000FF'] n110 node [label='', fillcolor='#F0E68C'] n111 node [label='', fillcolor='#0000FF'] n112 node [label='', fillcolor='#F0E68C'] n113 node [label='', fillcolor='#0000FF'] n114 node [label='', fillcolor='#F0E68C'] n115 node [label='', fillcolor='#0000FF'] n116 node [label='', fillcolor='#F0E68C'] n117 node [label='', fillcolor='#0000FF'] n118 node [label='', fillcolor='#F0E68C'] n119 node [label='', fillcolor='#0000FF'] n120 node [label='', fillcolor='#F0E68C'] n121 node [label='', fillcolor='#0000FF'] n122 node [label='', fillcolor='#F0E68C'] n123 node [label='', fillcolor='#0000FF'] n124 node [label='', fillcolor='#F0E68C'] n125 node [label='', fillcolor='#0000FF'] n126 node [label='', fillcolor='#F0E68C'] n127 node [label='', fillcolor='#0000FF'] n128 node [label='', fillcolor='#F0E68C'] n129 node [label='', fillcolor='#0000FF'] n130 node [label='', fillcolor='#F0E68C'] n131 node [label='', fillcolor='#0000FF'] n132 node [label='', fillcolor='#F0E68C'] n133 node [label='', fillcolor='#0000FF'] n134 node [label='', fillcolor='#F0E68C'] n135 node [label='', fillcolor='#0000FF'] n136 node [label='', fillcolor='#F0E68C'] n137 node [label='', fillcolor='#0000FF'] n138 node [label='', fillcolor='#555555'] n139 node [label='', fillcolor='#0000FF'] n140 node [label='', fillcolor='#F0E68C'] n141 node [label='', fillcolor='#0000FF'] n142 node [label='', fillcolor='#F0E68C'] n143 node [label='', fillcolor='#0000FF'] n144 node [label='', fillcolor='#F0E68C'] n145 node [label='', fillcolor='#0000FF'] n146 node [label='', fillcolor='#F0E68C'] n147 node [label='', fillcolor='#0000FF'] n148 node [label='', fillcolor='#F0E68C'] n149 node [label='', fillcolor='#0000FF'] n150 node [label='', fillcolor='#555555'] n151 node [label='', fillcolor='#0000FF'] n152 node [label='', fillcolor='#555555'] n153 node [label='', fillcolor='#0000FF'] n154 node [label='', fillcolor='#00FFFF'] n155 node [label='', fillcolor='#0000FF'] n156 node [label='', fillcolor='#F0E68C'] n157 node [label='', fillcolor='#F0E68C'] n158 node [label='', fillcolor='#0000FF'] n159 node [label='', fillcolor='#F0E68C'] n160 node [label='', fillcolor='#0000FF'] n161 node [label='', fillcolor='#F0E68C'] n162 node [label='', fillcolor='#0000FF'] n163 node [label='', fillcolor='#0000FF'] n164 node [label='', fillcolor='#F0E68C'] n165 node [label='', fillcolor='#0000FF'] n166 node [label='', fillcolor='#F0E68C'] n167 node [label='', fillcolor='#0000FF'] n168 node [label='', fillcolor='#F0E68C'] n169 node [label='', fillcolor='#0000FF'] n170 node [label='', fillcolor='#F0E68C'] n171 node [label='', fillcolor='#0000FF'] n172 node [label='', fillcolor='#F0E68C'] n173 node [label='', fillcolor='#0000FF'] n174 node [label='', fillcolor='#F0E68C'] n175 node [label='', fillcolor='#0000FF'] n176 node [label='', fillcolor='#F0E68C'] n177 node [label='', fillcolor='#0000FF'] n178 node [label='', fillcolor='#F0E68C'] n179 node [label='', fillcolor='#0000FF'] n180 node [label='', fillcolor='#0000FF'] n181 node [label='', fillcolor='#0000FF'] n182 node [label='', fillcolor='#F0E68C'] n183 node [label='', fillcolor='#0000FF'] n184 node [label='', fillcolor='#00FFFF'] n185 node [label='', fillcolor='#0000FF'] n186 node [label='', fillcolor='#F0E68C'] n187 node [label='', fillcolor='#F0E68C'] n188 node [label='', fillcolor='#0000FF'] n189 node [label='', fillcolor='#F0E68C'] n190 node [label='', fillcolor='#0000FF'] n191 node [label='', fillcolor='#0000FF'] n192 node [label='', fillcolor='#F0E68C'] n193 node [label='', fillcolor='#0000FF'] n194 node [label='', fillcolor='#F0E68C'] n195 node [label='', fillcolor='#0000FF'] n196 node [label='', fillcolor='#F0E68C'] n197 node [label='', fillcolor='#F0E68C'] n198 node [label='', fillcolor='#0000FF'] n199 node [label='', fillcolor='#F0E68C'] n200 node [label='', fillcolor='#0000FF'] n201 node [label='', fillcolor='#F0E68C'] n202 node [label='', fillcolor='#0000FF'] n203 node [label='', fillcolor='#0000FF'] n204 node [label='', fillcolor='#F0E68C'] n205 node [label='', fillcolor='#0000FF'] n206 node [label='', fillcolor='#0000FF'] n207 node [label='', fillcolor='#F0E68C'] n208 node [label='', fillcolor='#0000FF'] n209 node [label='', fillcolor='#F0E68C'] n210 node [label='', fillcolor='#0000FF'] n211 node [label='', fillcolor='#F0E68C'] n212 node [label='', fillcolor='#0000FF'] n213 node [label='', fillcolor='#F0E68C'] n214 node [label='', fillcolor='#0000FF'] n215 node [label='', fillcolor='#0000FF'] n216 node [label='', fillcolor='#F0E68C'] n217 node [label='', fillcolor='#0000FF'] n218 node [label='', fillcolor='#0000FF'] n219 node [label='', fillcolor='#F0E68C'] n220 node [label='', fillcolor='#0000FF'] n221 node [label='', fillcolor='#F0E68C'] n222 node [label='', fillcolor='#0000FF'] n223 node [label='', fillcolor='#F0E68C'] n224 node [label='', fillcolor='#0000FF'] n225 node [label='', fillcolor='#F0E68C'] n226 node [label='', fillcolor='#0000FF'] n227 node [label='', fillcolor='#F0E68C'] n228 node [label='', fillcolor='#0000FF'] n229 node [label='', fillcolor='#00FFFF'] n230 node [label='', fillcolor='#0000FF'] n231 node [label='', fillcolor='#F0E68C'] n232 node [label='', fillcolor='#F0E68C'] n233 node [label='', fillcolor='#0000FF'] n234 node [label='', fillcolor='#F0E68C'] n235 node [label='', fillcolor='#0000FF'] n236 node [label='', fillcolor='#0000FF'] n237 node [label='', fillcolor='#F0E68C'] n238 node [label='', fillcolor='#0000FF'] n239 node [label='', fillcolor='#0000FF'] n240 node [label='', fillcolor='#555555'] n241 node [label='', fillcolor='#0000FF'] n242 node [label='', fillcolor='#555555'] n243 node [label='', fillcolor='#0000FF'] n244 node [label='', fillcolor='#555555'] n245 node [label='', fillcolor='#F0E68C'] n246 node [label='', fillcolor='#0000FF'] n247 node [label='', fillcolor='#F0E68C'] n248 node [label='', fillcolor='#0000FF'] n249 node [label='', fillcolor='#00FFFF'] n250 node [label='', fillcolor='#0000FF'] n251 node [label='', fillcolor='#F0E68C'] n252 node [label='', fillcolor='#F0E68C'] n253 node [label='', fillcolor='#0000FF'] n254 node [label='', fillcolor='#F0E68C'] n255 node [label='', fillcolor='#0000FF'] n256 node [label='', fillcolor='#F0E68C'] n257 node [label='', fillcolor='#0000FF'] n258 node [label='', fillcolor='#0000FF'] n259 node [label='', fillcolor='#F0E68C'] n260 node [label='', fillcolor='#0000FF'] n261 node [label='', fillcolor='#F0E68C'] n262 node [label='', fillcolor='#0000FF'] n263 node [label='', fillcolor='#F0E68C'] n264 node [label='', fillcolor='#0000FF'] n265 node [label='', fillcolor='#F0E68C'] n266 node [label='', fillcolor='#0000FF'] n267 node [label='', fillcolor='#F0E68C'] n268 node [label='', fillcolor='#0000FF'] n269 node [label='', fillcolor='#F0E68C'] n270 node [label='', fillcolor='#0000FF'] n271 node [label='', fillcolor='#F0E68C'] n272 node [label='', fillcolor='#0000FF'] n273 node [label='', fillcolor='#F0E68C'] n274 node [label='', fillcolor='#0000FF'] n275 node [label='', fillcolor='#F0E68C'] n276 node [label='', fillcolor='#0000FF'] n277 node [label='', fillcolor='#0000FF'] n278 node [label='', fillcolor='#F0E68C'] n279 node [label='', fillcolor='#0000FF'] n280 node [label='', fillcolor='#F0E68C'] n281 node [label='', fillcolor='#0000FF'] n282 node [label='', fillcolor='#0000FF'] n283 node [label='', fillcolor='#F0E68C'] n284 node [label='', fillcolor='#0000FF'] n285 node [label='', fillcolor='#0000FF'] n286 node [label='', fillcolor='#F0E68C'] n287 node [label='', fillcolor='#0000FF'] n288 node [label='', fillcolor='#F0E68C'] n289 node [label='', fillcolor='#0000FF'] n290 node [label='', fillcolor='#F0E68C'] n291 node [label='', fillcolor='#0000FF'] n292 node [label='', fillcolor='#F0E68C'] n293 node [label='', fillcolor='#0000FF'] n294 node [label='', fillcolor='#F0E68C'] n295 node [label='', fillcolor='#0000FF'] n296 node [label='', fillcolor='#F0E68C'] n297 node [label='', fillcolor='#0000FF'] n298 node [label='', fillcolor='#F0E68C'] n299 node [label='', fillcolor='#0000FF'] n300 node [label='', fillcolor='#F0E68C'] n301 node [label='', fillcolor='#0000FF'] n302 node [label='', fillcolor='#F0E68C'] n303 node [label='', fillcolor='#0000FF'] n304 node [label='', fillcolor='#F0E68C'] n305 node [label='', fillcolor='#0000FF'] n306 node [label='', fillcolor='#F0E68C'] n307 node [label='', fillcolor='#0000FF'] n308 node [label='', fillcolor='#F0E68C'] n309 node [label='', fillcolor='#0000FF'] n310 node [label='', fillcolor='#F0E68C'] n311 node [label='', fillcolor='#0000FF'] n312 node [label='', fillcolor='#F0E68C'] n313 node [label='', fillcolor='#0000FF'] n314 node [label='', fillcolor='#F0E68C'] n315 node [label='', fillcolor='#0000FF'] n316 node [label='', fillcolor='#F0E68C'] n317 node [label='', fillcolor='#0000FF'] n318 node [label='', fillcolor='#F0E68C'] n319 node [label='', fillcolor='#0000FF'] n320 node [label='', fillcolor='#F0E68C'] n321 node [label='', fillcolor='#0000FF'] n322 node [label='', fillcolor='#F0E68C'] n323 node [label='', fillcolor='#0000FF'] n324 node [label='', fillcolor='#00FFFF'] n325 node [label='', fillcolor='#0000FF'] n326 node [label='', fillcolor='#F0E68C'] n327 node [label='', fillcolor='#F0E68C'] n328 node [label='', fillcolor='#0000FF'] n329 node [label='', fillcolor='#0000FF'] n330 node [label='', fillcolor='#F0E68C'] n331 node [label='', fillcolor='#555555'] n332 node [label='', fillcolor='#0000FF'] n333 node [label='', fillcolor='#0000FF'] n334 node [label='', fillcolor='#0000FF'] n335 node [label='', fillcolor='#F0E68C'] n336 node [label='', fillcolor='#0000FF'] n337 node [label='', fillcolor='#0000FF'] n338 node [label='', fillcolor='#F0E68C'] n339 node [label='', fillcolor='#0000FF'] n340 node [label='', fillcolor='#0000FF'] n341 node [label='', fillcolor='#0000FF'] n342 node [label='', fillcolor='#F0E68C'] n343 node [label='', fillcolor='#0000FF'] n344 node [label='', fillcolor='#555555'] n345 node [label='', fillcolor='#00FFFF'] n346 node [label='', fillcolor='#0000FF'] n347 node [label='', fillcolor='#F0E68C'] n348 node [label='', fillcolor='#F0E68C'] n349 node [label='', fillcolor='#0000FF'] n350 node [label='', fillcolor='#F0E68C'] n351 node [label='', fillcolor='#0000FF'] n352 node [label='', fillcolor='#0000FF'] n353 node [label='', fillcolor='#F0E68C'] n354 node [label='', fillcolor='#0000FF'] n355 node [label='', fillcolor='#F0E68C'] n356 node [label='', fillcolor='#0000FF'] n357 node [label='', fillcolor='#F0E68C'] n358 node [label='', fillcolor='#0000FF'] n359 node [label='', fillcolor='#F0E68C'] n360 node [label='', fillcolor='#0000FF'] n361 node [label='', fillcolor='#F0E68C'] n362 node [label='', fillcolor='#0000FF'] n363 node [label='', fillcolor='#F0E68C'] n364 node [label='', fillcolor='#0000FF'] n365 node [label='', fillcolor='#F0E68C'] n366 node [label='', fillcolor='#0000FF'] n367 node [label='', fillcolor='#00FFFF'] n368 node [label='', fillcolor='#0000FF'] n369 node [label='', fillcolor='#F0E68C'] n370 node [label='', fillcolor='#F0E68C'] n371 node [label='', fillcolor='#0000FF'] n372 node [label='', fillcolor='#F0E68C'] n373 node [label='', fillcolor='#0000FF'] n374 node [label='', fillcolor='#F0E68C'] n375 node [label='', fillcolor='#0000FF'] n376 node [label='', fillcolor='#00FFFF'] n377 node [label='', fillcolor='#0000FF'] n378 node [label='', fillcolor='#F0E68C'] n379 node [label='', fillcolor='#F0E68C'] n380 node [label='', fillcolor='#0000FF'] n381 node [label='', fillcolor='#F0E68C'] n382 node [label='', fillcolor='#0000FF'] n383 node [label='', fillcolor='#FF00FF'] n384 node [label='', fillcolor='#F0E68C'] n385 node [label='', fillcolor='#0000FF'] n386 node [label='', fillcolor='#F0E68C'] n387 node [label='', fillcolor='#0000FF'] n388 node [label='', fillcolor='#FF00FF'] n389 node [label='', fillcolor='#F0E68C'] n390 node [label='', fillcolor='#0000FF'] n391 node [label='', fillcolor='#FF00FF'] n392 node [label='', fillcolor='#F0E68C'] n393 node [label='', fillcolor='#0000FF'] n394 node [label='', fillcolor='#FF00FF'] n395 node [label='', fillcolor='#F0E68C'] n396 node [label='', fillcolor='#0000FF'] n397 node [label='', fillcolor='#FF00FF'] n398 node [label='', fillcolor='#F0E68C'] n399 node [label='', fillcolor='#0000FF'] n400 node [label='', fillcolor='#FF00FF'] n401 node [label='', fillcolor='#F0E68C'] n402 node [label='', fillcolor='#0000FF'] n403 node [label='', fillcolor='#FF00FF'] n404 node [label='', fillcolor='#F0E68C'] n405 node [label='', fillcolor='#0000FF'] n406 node [label='', fillcolor='#FF00FF'] n407 n1 -- n2 [len=0.800] n1 -- n5 [len=0.800] n2 -- n3 [len=0.728] n2 -- n4 [len=0.728] n5 -- n6 [len=0.728] n6 -- n7 [len=0.656] n6 -- n46 [len=0.656] n7 -- n8 [len=0.583] n7 -- n44 [len=0.583] n8 -- n9 [len=0.511] n8 -- n10 [len=0.511] n10 -- n11 [len=0.439] n11 -- n12 [len=0.367] n11 -- n15 [len=0.367] n11 -- n18 [len=0.367] n11 -- n21 [len=0.367] n11 -- n24 [len=0.367] n11 -- n27 [len=0.367] n11 -- n30 [len=0.367] n11 -- n33 [len=0.367] n11 -- n36 [len=0.367] n11 -- n39 [len=0.367] n11 -- n41 [len=0.367] n12 -- n13 [len=0.294] n13 -- n14 [len=0.222] n15 -- n16 [len=0.294] n16 -- n17 [len=0.222] n18 -- n19 [len=0.294] n19 -- n20 [len=0.222] n21 -- n22 [len=0.294] n22 -- n23 [len=0.222] n24 -- n25 [len=0.294] n25 -- n26 [len=0.222] n27 -- n28 [len=0.294] n28 -- n29 [len=0.222] n30 -- n31 [len=0.294] n31 -- n32 [len=0.222] n33 -- n34 [len=0.294] n34 -- n35 [len=0.222] n36 -- n37 [len=0.294] n37 -- n38 [len=0.222] n39 -- n40 [len=0.294] n41 -- n42 [len=0.294] n42 -- n43 [len=0.222] n44 -- n45 [len=0.511] n46 -- n47 [len=0.583] n47 -- n48 [len=0.511] n47 -- n49 [len=0.511] n47 -- n50 [len=0.511] n47 -- n51 [len=0.511] n47 -- n52 [len=0.511] n47 -- n53 [len=0.511] n47 -- n54 [len=0.511] n47 -- n55 [len=0.511] n47 -- n56 [len=0.511] n47 -- n57 [len=0.511] n47 -- n58 [len=0.511] n47 -- n59 [len=0.511] n47 -- n61 [len=0.511] n47 -- n63 [len=0.511] n47 -- n98 [len=0.511] n47 -- n100 [len=0.511] n47 -- n155 [len=0.511] n47 -- n157 [len=0.511] n47 -- n185 [len=0.511] n47 -- n187 [len=0.511] n47 -- n230 [len=0.511] n47 -- n232 [len=0.511] n47 -- n250 [len=0.511] n47 -- n252 [len=0.511] n47 -- n325 [len=0.511] n47 -- n327 [len=0.511] n47 -- n346 [len=0.511] n47 -- n348 [len=0.511] n47 -- n368 [len=0.511] n47 -- n370 [len=0.511] n47 -- n377 [len=0.511] n47 -- n379 [len=0.511] n59 -- n60 [len=0.439] n61 -- n62 [len=0.439] n63 -- n64 [len=0.439] n63 -- n66 [len=0.439] n63 -- n69 [len=0.439] n63 -- n71 [len=0.439] n63 -- n73 [len=0.439] n63 -- n76 [len=0.439] n63 -- n78 [len=0.439] n63 -- n80 [len=0.439] n63 -- n82 [len=0.439] n63 -- n84 [len=0.439] n63 -- n87 [len=0.439] n63 -- n89 [len=0.439] n63 -- n92 [len=0.439] n63 -- n95 [len=0.439] n64 -- n65 [len=0.367] n66 -- n67 [len=0.367] n66 -- n68 [len=0.367] n69 -- n70 [len=0.367] n71 -- n72 [len=0.367] n73 -- n74 [len=0.367] n73 -- n75 [len=0.367] n76 -- n77 [len=0.367] n78 -- n79 [len=0.367] n80 -- n81 [len=0.367] n82 -- n83 [len=0.367] n84 -- n85 [len=0.367] n85 -- n86 [len=0.294] n87 -- n88 [len=0.367] n89 -- n90 [len=0.367] n90 -- n91 [len=0.294] n92 -- n93 [len=0.367] n93 -- n94 [len=0.294] n95 -- n96 [len=0.367] n96 -- n97 [len=0.294] n98 -- n99 [len=0.439] n100 -- n101 [len=0.439] n100 -- n104 [len=0.439] n100 -- n107 [len=0.439] n100 -- n109 [len=0.439] n100 -- n111 [len=0.439] n100 -- n113 [len=0.439] n100 -- n115 [len=0.439] n100 -- n117 [len=0.439] n100 -- n119 [len=0.439] n100 -- n121 [len=0.439] n100 -- n123 [len=0.439] n100 -- n125 [len=0.439] n100 -- n127 [len=0.439] n100 -- n129 [len=0.439] n100 -- n131 [len=0.439] n100 -- n133 [len=0.439] n100 -- n135 [len=0.439] n100 -- n137 [len=0.439] n100 -- n141 [len=0.439] n100 -- n143 [len=0.439] n100 -- n145 [len=0.439] n100 -- n147 [len=0.439] n100 -- n149 [len=0.439] n101 -- n102 [len=0.367] n101 -- n103 [len=0.367] n104 -- n105 [len=0.367] n104 -- n106 [len=0.367] n107 -- n108 [len=0.367] n109 -- n110 [len=0.367] n111 -- n112 [len=0.367] n113 -- n114 [len=0.367] n115 -- n116 [len=0.367] n117 -- n118 [len=0.367] n119 -- n120 [len=0.367] n121 -- n122 [len=0.367] n123 -- n124 [len=0.367] n125 -- n126 [len=0.367] n127 -- n128 [len=0.367] n129 -- n130 [len=0.367] n131 -- n132 [len=0.367] n133 -- n134 [len=0.367] n135 -- n136 [len=0.367] n137 -- n138 [len=0.367] n137 -- n139 [len=0.367] n137 -- n140 [len=0.367] n141 -- n142 [len=0.367] n143 -- n144 [len=0.367] n145 -- n146 [len=0.367] n147 -- n148 [len=0.367] n149 -- n150 [len=0.367] n149 -- n151 [len=0.367] n149 -- n152 [len=0.367] n149 -- n153 [len=0.367] n149 -- n154 [len=0.367] n155 -- n156 [len=0.439] n157 -- n158 [len=0.439] n157 -- n160 [len=0.439] n157 -- n162 [len=0.439] n157 -- n165 [len=0.439] n157 -- n167 [len=0.439] n157 -- n169 [len=0.439] n157 -- n171 [len=0.439] n157 -- n173 [len=0.439] n157 -- n175 [len=0.439] n157 -- n177 [len=0.439] n157 -- n179 [len=0.439] n157 -- n183 [len=0.439] n158 -- n159 [len=0.367] n160 -- n161 [len=0.367] n162 -- n163 [len=0.367] n162 -- n164 [len=0.367] n165 -- n166 [len=0.367] n167 -- n168 [len=0.367] n169 -- n170 [len=0.367] n171 -- n172 [len=0.367] n173 -- n174 [len=0.367] n175 -- n176 [len=0.367] n177 -- n178 [len=0.367] n179 -- n180 [len=0.367] n179 -- n181 [len=0.367] n179 -- n182 [len=0.367] n183 -- n184 [len=0.367] n185 -- n186 [len=0.439] n187 -- n188 [len=0.439] n187 -- n190 [len=0.439] n187 -- n193 [len=0.439] n187 -- n195 [len=0.439] n187 -- n197 [len=0.439] n187 -- n198 [len=0.439] n187 -- n200 [len=0.439] n187 -- n202 [len=0.439] n187 -- n205 [len=0.439] n187 -- n208 [len=0.439] n187 -- n210 [len=0.439] n187 -- n212 [len=0.439] n187 -- n214 [len=0.439] n187 -- n217 [len=0.439] n187 -- n220 [len=0.439] n187 -- n222 [len=0.439] n187 -- n224 [len=0.439] n187 -- n226 [len=0.439] n187 -- n228 [len=0.439] n188 -- n189 [len=0.367] n190 -- n191 [len=0.367] n190 -- n192 [len=0.367] n193 -- n194 [len=0.367] n195 -- n196 [len=0.367] n198 -- n199 [len=0.367] n200 -- n201 [len=0.367] n202 -- n203 [len=0.367] n202 -- n204 [len=0.367] n205 -- n206 [len=0.367] n205 -- n207 [len=0.367] n208 -- n209 [len=0.367] n210 -- n211 [len=0.367] n212 -- n213 [len=0.367] n214 -- n215 [len=0.367] n214 -- n216 [len=0.367] n217 -- n218 [len=0.367] n217 -- n219 [len=0.367] n220 -- n221 [len=0.367] n222 -- n223 [len=0.367] n224 -- n225 [len=0.367] n226 -- n227 [len=0.367] n228 -- n229 [len=0.367] n230 -- n231 [len=0.439] n232 -- n233 [len=0.439] n232 -- n235 [len=0.439] n232 -- n238 [len=0.439] n232 -- n246 [len=0.439] n232 -- n248 [len=0.439] n233 -- n234 [len=0.367] n235 -- n236 [len=0.367] n235 -- n237 [len=0.367] n238 -- n239 [len=0.367] n238 -- n240 [len=0.367] n238 -- n242 [len=0.367] n238 -- n244 [len=0.367] n240 -- n241 [len=0.294] n242 -- n243 [len=0.294] n244 -- n245 [len=0.294] n246 -- n247 [len=0.367] n248 -- n249 [len=0.367] n250 -- n251 [len=0.439] n252 -- n253 [len=0.439] n252 -- n255 [len=0.439] n252 -- n257 [len=0.439] n252 -- n260 [len=0.439] n252 -- n262 [len=0.439] n252 -- n264 [len=0.439] n252 -- n266 [len=0.439] n252 -- n268 [len=0.439] n252 -- n270 [len=0.439] n252 -- n272 [len=0.439] n252 -- n274 [len=0.439] n252 -- n276 [len=0.439] n252 -- n279 [len=0.439] n252 -- n281 [len=0.439] n252 -- n284 [len=0.439] n252 -- n287 [len=0.439] n252 -- n289 [len=0.439] n252 -- n291 [len=0.439] n252 -- n293 [len=0.439] n252 -- n295 [len=0.439] n252 -- n297 [len=0.439] n252 -- n299 [len=0.439] n252 -- n301 [len=0.439] n252 -- n303 [len=0.439] n252 -- n305 [len=0.439] n252 -- n307 [len=0.439] n252 -- n309 [len=0.439] n252 -- n311 [len=0.439] n252 -- n313 [len=0.439] n252 -- n315 [len=0.439] n252 -- n317 [len=0.439] n252 -- n319 [len=0.439] n252 -- n321 [len=0.439] n252 -- n323 [len=0.439] n253 -- n254 [len=0.367] n255 -- n256 [len=0.367] n257 -- n258 [len=0.367] n257 -- n259 [len=0.367] n260 -- n261 [len=0.367] n262 -- n263 [len=0.367] n264 -- n265 [len=0.367] n266 -- n267 [len=0.367] n268 -- n269 [len=0.367] n270 -- n271 [len=0.367] n272 -- n273 [len=0.367] n274 -- n275 [len=0.367] n276 -- n277 [len=0.367] n276 -- n278 [len=0.367] n279 -- n280 [len=0.367] n281 -- n282 [len=0.367] n281 -- n283 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[len=0.294] n346 -- n347 [len=0.439] n348 -- n349 [len=0.439] n348 -- n351 [len=0.439] n348 -- n354 [len=0.439] n348 -- n356 [len=0.439] n348 -- n358 [len=0.439] n348 -- n360 [len=0.439] n348 -- n362 [len=0.439] n348 -- n364 [len=0.439] n348 -- n366 [len=0.439] n349 -- n350 [len=0.367] n351 -- n352 [len=0.367] n351 -- n353 [len=0.367] n354 -- n355 [len=0.367] n356 -- n357 [len=0.367] n358 -- n359 [len=0.367] n360 -- n361 [len=0.367] n362 -- n363 [len=0.367] n364 -- n365 [len=0.367] n366 -- n367 [len=0.367] n368 -- n369 [len=0.439] n370 -- n371 [len=0.439] n370 -- n373 [len=0.439] n370 -- n375 [len=0.439] n371 -- n372 [len=0.367] n373 -- n374 [len=0.367] n375 -- n376 [len=0.367] n377 -- n378 [len=0.439] n379 -- n380 [len=0.439] n379 -- n382 [len=0.439] n379 -- n385 [len=0.439] n379 -- n387 [len=0.439] n379 -- n390 [len=0.439] n379 -- n393 [len=0.439] n379 -- n396 [len=0.439] n379 -- n399 [len=0.439] n379 -- n402 [len=0.439] n379 -- n405 [len=0.439] n380 -- n381 [len=0.367] n382 -- n383 [len=0.367] n383 -- n384 [len=0.294] n385 -- n386 [len=0.367] n387 -- n388 [len=0.367] n388 -- n389 [len=0.294] n390 -- n391 [len=0.367] n391 -- n392 [len=0.294] n393 -- n394 [len=0.367] n394 -- n395 [len=0.294] n396 -- n397 [len=0.367] n397 -- n398 [len=0.294] n399 -- n400 [len=0.367] n400 -- n401 [len=0.294] n402 -- n403 [len=0.367] n403 -- n404 [len=0.294] n405 -- n406 [len=0.367] n406 -- n407 [len=0.294] }")

URL <- paste0( "https://cdn.rawgit.com/christophergandrud/networkD3/", "master/JSONdata/energy.json") Energy <- jsonlite::fromJSON(URL) # Plot sankeyNetwork(Links = Energy$links, Nodes = Energy$nodes, Source = "source", Target = "target", Value = "value", NodeID = "name", units = "TWh", fontSize = 12, nodeWidth = 30) Sankey diagrams source: http://christophergandrud.github.io/networkD3/

library(networkD3) data(MisLinks, MisNodes) forceNetwork(Links = MisLinks, Nodes = MisNodes, Source = "source", Target = "target", Value = "value", NodeID = "name", Group = "group", opacity = 0.4) source:http://christophergandrud.github.io/networkD3/

mermaid(" + sequenceDiagram + customer->>ticket seller: ask ticket + ticket seller->>database: seats + alt tickets available + database->>ticket seller: ok + ticket seller->>customer: confirm + customer->>ticket seller: ok + ticket seller->>database: book a seat + ticket seller->>printer: print ticket + else sold out + database->>ticket seller: none left + ticket seller->>customer: sorry + end + ")

load("C:/github/HtmlWidgetExamples/data/climate.RData") library(data.table) library(reshape2) library(dplyr) library(DT) library(sparkline) dat <- mutate(dat, Decade=paste0(Year - Year %% 10, "s")) r <- range(filter(dat, Var=="Temperature")$Val) # @knitr table datatable(dat, rownames=FALSE) # @knitr defs colDefs1 <- list(list(targets=c(1:12), render=JS("function(data, type, full){ return '<span class=spark>' + data + '</span>' }"))) colDefs2 <- list(list(targets=c(1:6), render=JS("function(data, type, full){ return '<span class=spark>' + data + '</span>' }"))) # @knitr callbacks bar_string <- "type: 'bar', barColor: 'orange', negBarColor: 'purple', highlightColor: 'black'" cb_bar = JS(paste0("function (oSettings, json) { $('.spark:not(:has(canvas))').sparkline('html', { ", bar_string, " }); }"), collapse="") line_string <- "type: 'line', lineColor: 'black', fillColor: '#ccc', highlightLineColor: 'orange', highlightSpotColor: 'orange'" cb_line = JS(paste0("function (oSettings, json) { $('.spark:not(:has(canvas))').sparkline('html', { ", line_string, ", chartRangeMin: ", r[1], ", chartRangeMax: ", r[2], " }); }"), collapse="") box_string <- "type: 'box', lineColor: 'black', whiskerColor: 'black', outlierFillColor: 'black', outlierLineColor: 'black', medianColor: 'black', boxFillColor: 'orange', boxLineColor: 'black'" cb_box1 = JS(paste0("function (oSettings, json) { $('.spark:not(:has(canvas))').sparkline('html', { ", box_string," }); }"), collapse="") cb_box2 = JS(paste0("function (oSettings, json) { $('.spark:not(:has(canvas))').sparkline('html', { ", box_string, ", chartRangeMin: ", r[1], ", chartRangeMax: ", r[2], " }); }"), collapse="") # @knitr sparklines dat.p <- filter(dat, Var=="Precipitation" & Decade=="2000s" & Month=="Aug")$Val dat.p # @knitr sparkline_dt_prep dat.t <- filter(dat, Var=="Temperature") %>% group_by(Decade, Month) %>% summarise(Temperature=paste(Val, collapse = ",")) dat.ta <- dcast(dat.t, Decade ~ Month) dat.tb <- dcast(dat.t, Month ~ Decade) # @knitr table_DxM_line d1 <- datatable(data.table(dat.ta), rownames=FALSE, options=list(columnDefs=colDefs1, fnDrawCallback=cb_line)) d1$dependencies <- append(d1$dependencies, htmlwidgets:::getDependency('sparkline')) d1 # @knitr table_MxD_bar d2 <- datatable(data.table(dat.tb), rownames=FALSE, options=list(columnDefs=colDefs2, fnDrawCallback=cb_bar)) d2$dependencies <- append(d2$dependencies, htmlwidgets:::getDependency('sparkline')) d2 # @knitr table_MxD_box1 d3 <- datatable(data.table(dat.tb), rownames=FALSE, options=list(columnDefs=colDefs2, fnDrawCallback=cb_box1)) d3$dependencies <- append(d3$dependencies, htmlwidgets:::getDependency('sparkline')) d3 # @knitr table_MxD_box2 d4 <- datatable(data.table(dat.tb), rownames=FALSE, options=list(columnDefs=colDefs2, fnDrawCallback=cb_box2)) d4$dependencies <- append(d4$dependencies, htmlwidgets:::getDependency('sparkline')) d4 # @knitr final_prep dat.t2 <- filter(dat, Var=="Temperature" & Month=="Aug") %>% group_by(Location, Month, Var, Decade) %>% summarise(Mean=round(mean(Val), 1), SD=round(sd(Val), 2), Min=min(Val), Max=max(Val), Samples=paste(Val, collapse = ",")) %>% mutate(Series=Samples) %>% data.table cd <- list(list(targets=8, render=JS("function(data, type, full){ return '<span class=sparkSamples>' + data + '</span>' }")), list(targets=9, render=JS("function(data, type, full){ return '<span class=sparkSeries>' + data + '</span>' }"))) cb = JS(paste0("function (oSettings, json) { $('.sparkSeries:not(:has(canvas))').sparkline('html', { ", line_string, " }); $('.sparkSamples:not(:has(canvas))').sparkline('html', { ", box_string, " }); }"), collapse="") # @knitr table_final d5 <- datatable(data.table(dat.t2), rownames=FALSE, options=list(columnDefs=cd, fnDrawCallback=cb)) d5$dependencies <- append(d5$dependencies, htmlwidgets:::getDependency('sparkline')) d5

> ggplot(all_mod_plot, aes(x = conv_type, y = value, group = channel_name)) + + theme_solarized(base_size = 18, base_family = "", light = TRUE) + + scale_color_manual(values = pal(10)) + + scale_fill_manual(values = pal(10)) + + geom_line(aes(color = channel_name), size = 2.5, alpha = 0.8) + + geom_point(aes(color = channel_name), size = 5) + + geom_label_repel(aes(label = paste0(channel_name, ': ', value), fill = factor(channel_name)), + alpha = 0.7, + fontface = 'bold', color = 'white', size = 5, + box.padding = unit(0.25, 'lines'), point.padding = unit(0.5, 'lines'), + max.iter = 100) + + theme(legend.position = 'none', + legend.title = element_text(size = 16, color = 'black'), + legend.text = element_text(size = 16, vjust = 2, color = 'black'), + plot.title = element_text(size = 20, face = "bold", vjust = 2, color = 'black', lineheight = 0.8), + axis.title.x = element_text(size = 24, face = "bold"), + axis.title.y = element_text(size = 16, face = "bold"), + axis.text.x = element_text(size = 16, face = "bold", color = 'black'), + axis.text.y = element_blank(), + axis.ticks.x = element_blank(), + axis.ticks.y = element_blank(), + panel.border = element_blank(), + panel.grid.major = element_line(colour = "grey", linetype = "dotted"), + panel.grid.minor = element_blank(), + strip.text = element_text(size = 16, hjust = 0.5, vjust = 0.5, face = "bold", color = 'black'), + strip.background = element_rect(fill = "#f0b35f")) + + labs(x = 'Model', y = 'Conversions') + + ggtitle('Models comparison') +

library(plotly) # Read some weather data df <- read.csv('https://cdn.rawgit.com/plotly/documentation/source/_posts/r/scattergl/weather-data.csv') # Convert to dates df$Date <- zoo::as.Date(df$Date, format = "%m/%d/%Y") p <- plot_ly(df, x = Date, y = Mean_TemperatureC, name = "Mean Temp.", type = "scattergl", marker = list(color = "#3b3b9e")) %>% layout(title = "Mean Temparature in Seattle (1948 - 2015)", yaxis = list(title = "Temperature (^oC)")) p

library(plotly) p <- plot_ly(plotly::mic, r = r, t = t, color = nms, mode = "lines") layout(p, title = "Mic Patterns", orientation = -90)

df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/globe_contours.csv') df$id <- seq_len(nrow(df)) library(tidyr) d <- df %>% gather(key, value, -id) %>% separate(key, c("l", "line"), "\\.") %>% spread(l, value) p <- plot_ly(type = 'scattergeo', mode = 'lines', line = list(width = 2, color = 'violet')) for (i in unique(d$line)) p <- add_trace(p, lat = lat, lon = lon, data = subset(d, line == i)) geo <- list( showland = TRUE, showlakes = TRUE, showcountries = TRUE, showocean = TRUE, countrywidth = 0.5, landcolor = toRGB("grey90"), lakecolor = toRGB("white"), oceancolor = toRGB("white"), projection = list( type = 'orthographic', rotation = list( lon = -100, lat = 40, roll = 0 ) ), lonaxis = list( showgrid = TRUE, gridcolor = toRGB("gray40"), gridwidth = 0.5 ), lataxis = list( showgrid = TRUE, gridcolor = toRGB("gray40"), gridwidth = 0.5 ) ) layout(p, showlegend = FALSE, geo = geo, title = 'Contour lines over globe<br>(Click and drag to rotate)')

library(plotly) df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_ebola.csv') # restrict from June to September df <- subset(df, Month %in% 6:9) # ordered factor variable with month abbreviations df$abbrev <- ordered(month.abb[df$Month], levels = month.abb[6:9]) # September totals df9 <- subset(df, Month == 9) # common plot options g <- list( scope = 'africa', showframe = F, showland = T, landcolor = toRGB("grey90") ) # styling for "zoomed in" map g1 <- c( g, resolution = 50, showcoastlines = T, countrycolor = toRGB("white"), coastlinecolor = toRGB("white"), projection = list(type = 'Mercator'), list(lonaxis = list(range = c(-15, -5))), list(lataxis = list(range = c(0, 12))), list(domain = list(x = c(0, 1), y = c(0, 1))) ) g2 <- c( g, showcountries = F, bgcolor = toRGB("white", alpha = 0), list(domain = list(x = c(0, .6), y = c(0, .6))) ) plot_ly(df, type = 'scattergeo', mode = 'markers', locations = Country, locationmode = 'country names', text = paste(Value, "cases"), color = as.ordered(abbrev), marker = list(size = Value/50), inherit = F) %>% add_trace(type = 'scattergeo', mode = 'text', geo = 'geo2', showlegend = F, # plotly should support "unboxed" constants lon = list(21.0936), lat = list(7.1881), text = list('Africa')) %>% add_trace(type = 'choropleth', locations = Country, locationmode = 'country names', z = Month, colors = "black", showscale = F, geo = 'geo2', data = df9) %>% layout(title = 'Ebola cases reported by month in West Africa 2014<br> Source: <a href="https://data.hdx.rwlabs.org/dataset/rowca-ebola-cases">HDX</a>', geo = g1, geo2 = g2)

df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv') # light grey boundaries l <- list( color = toRGB("grey"), width = 0.5 ) # specify map projection/options g <- list( showframe = FALSE, showcoastlines = FALSE, projection = list(type = 'Mercator') ) plot_ly(df, z = GDP..BILLIONS., text = COUNTRY, locations = CODE, type = 'choropleth', color = GDP..BILLIONS., colors = 'Blues', marker = list(line = l), colorbar = list(tickprefix = '$', title = 'GDP Billions US$')) %>% # TODO: how to add the hyperlink? (<a href=""> doesn't seem to work) layout(title = '2014 Global GDP<br>Source: CIA World Factbook', geo = g)

df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv") df$hover <- with(df, paste(state, '<br>', "Beef", beef, "Dairy", dairy, "<br>", "Fruits", total.fruits, "Veggies", total.veggies, "<br>", "Wheat", wheat, "Corn", corn)) # give state boundaries a white border l <- list( color = toRGB("white"), width = 2 ) # specify some map projection/options g <- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) plot_ly(df, z = total.exports, text = hover, locations = code, type = 'choropleth', locationmode = 'USA-states', color = total.exports, colors = 'Purples', marker = list(line = l)), colorbar = list(title = "Millions USD")) %>% layout(title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)', geo = g)

p <- plot_ly(plotly::mic, r = r, t = t, color = nms, mode = "lines") layout(p, title = "Mic Patterns", orientation = -90) p <- plot_ly(plotly::hobbs, r = r, t = t, color = nms, opacity = 0.7, mode = "markers") layout(p, title = "Hobbs-Pearson Trials", plot_bgcolor = toRGB("grey90")) p <- plot_ly(plotly::wind, r = r, t = t, color = nms, type = "area") layout(p, radialaxis = list(ticksuffix = "%"), orientation = 270)

library(plotly) plot_ly(z = volcano, type = "contour") #' Advanced x <- rnorm(200) y <- rnorm(200) p1 <- plot_ly(x = x, type = "histogram") p2 <- plot_ly(x = x, y = y, type = "histogram2dcontour") p3 <- plot_ly(y = y, type = "histogram") a1 <- list(domain = c(0, .85)) a2 <- list(domain = c(.85, 1)) subplot( layout(p1, xaxis = a1, yaxis = a2), layout(p2, xaxis = a1, yaxis = a1), layout(p3, xaxis = a2, yaxis = a1) )

library(plotly) #' basic boxplot plot_ly(y = rnorm(50), type = "box") %>% add_trace(y = rnorm(50, 1)) #' adding jittered points plot_ly(y = rnorm(50), type = "box", boxpoints = "all", jitter = 0.3, pointpos = -1.8) #' several box plots data(diamonds, package = "ggplot2") plot_ly(diamonds, y = price, color = cut, type = "box") #' grouped box plots plot_ly(diamonds, x = cut, y = price, color = clarity, type = "box") %>% layout(boxmode = "group")

library(dplyr) ggplot2::diamonds %>% count(cut) %>% plot_ly(x = cut, y = n, type = "bar", marker = list(color = toRGB("black"))) # mapping a color variable ggplot2::diamonds %>% count(cut, clarity) %>% plot_ly(x = cut, y = n, type = "bar", color = clarity)

s <- matrix(c(1, .5, .5, .5, 1, .5, .5, .5, 1), ncol = 3) # use the mvtnorm package to sample 200 observations obs <- mvtnorm::rmvnorm(200, sigma = s) # collect everything in a data-frame df <- setNames(data.frame(obs), c("x", "y", "z")) library(plotly) plot_ly(df, x = x, y = y, z = z, type = "scatter3d", mode = "markers")

library(plotly) p <- plot_ly(midwest, x = percollege, color = state, type = "box") p

grViz("digraph {layout = twopi + node [shape = circle] + V -> {O L K A N}}")

https://beta.rstudioconnect.com/jjallaire/htmlwidgets-highcharter/ library(highcharter) library(dplyr) library(viridisLite) library(forecast) library(treemap) library(flexdashboard) thm <- hc_theme( colors = c("#1a6ecc", "#434348", "#90ed7d"), chart = list( backgroundColor = "transparent", style = list(fontFamily = "Source Sans Pro") ), xAxis = list( gridLineWidth = 1 ) ) ``` Column {data-width=600} ----------------------------------------------------------------------- ### Sales Forecast ```{r} AirPassengers %>% forecast(level = 90) %>% hchart() %>% hc_add_theme(thm) ``` ### Sales by State ```{r} data("USArrests", package = "datasets") data("usgeojson") USArrests <- USArrests %>% mutate(state = rownames(.)) n <- 4 colstops <- data.frame( q = 0:n/n, c = substring(viridis(n + 1), 0, 7)) %>% list.parse2() highchart() %>% hc_add_series_map(usgeojson, USArrests, name = "Sales", value = "Murder", joinBy = c("woename", "state"), dataLabels = list(enabled = TRUE, format = '{point.properties.postalcode}')) %>% hc_colorAxis(stops = colstops) %>% hc_legend(valueDecimals = 0, valueSuffix = "%") %>% hc_mapNavigation(enabled = TRUE) %>% hc_add_theme(thm) ``` Column {.tabset data-width=400} ----------------------------------------------------------------------- ### Sales by Category ```{r, fig.keep='none'} data("Groceries", package = "arules") dfitems <- tbl_df(Groceries@itemInfo) set.seed(10) dfitemsg <- dfitems %>% mutate(category = gsub(" ", "-", level1), subcategory = gsub(" ", "-", level2)) %>% group_by(category, subcategory) %>% summarise(sales = n() ^ 3 ) %>% ungroup() %>% sample_n(31) tm <- treemap(dfitemsg, index = c("category", "subcategory"), vSize = "sales", vColor = "sales", type = "value", palette = rev(viridis(6))) highchart() %>% hc_add_series_treemap(tm, allowDrillToNode = TRUE, layoutAlgorithm = "squarified") %>% hc_add_theme(thm) ``` ### Best Sellers ```{r} set.seed(2) nprods <- 10 dfitems %>% sample_n(nprods) %>% .$labels %>% rep(times = sort(sample( 1e4:2e4, size = nprods), decreasing = TRUE)) %>% factor(levels = unique(.)) %>% hchart(showInLegend = FALSE, name = "Sales", pointWidth = 10) %>% hc_add_theme(thm) %>% hc_chart(type = "bar")

https://beta.rstudioconnect.com/jjallaire/htmlwidgets-highcharter/

library(raster) > alt<-getData('alt',country="TUR") plot(alt)

require(corrgram) > corrgram(iris)

all_sites<-pfSiteSel() > plot(all_sites)

> library(ggbiplot) > data(wine) > wine.pca <- prcomp(wine, scale. = TRUE) > g <- ggbiplot(wine.pca, obs.scale = 1, var.scale = 1, + groups = wine.class, ellipse = TRUE, circle = TRUE) > g <- g + scale_color_discrete(name = '') > g <- g + theme(legend.direction = 'horizontal', + legend.position = 'top') > print(g)

library(MASS) > parcoord(iris[1:4], col = iris$Species)

dist.dist.matrix <- as.matrix(dist(iris[, 1:4])) > heatmap(dist.dist.matrix)

library(scatterplot3d) > scatterplot3d(iris$Petal.Width, iris$Sepal.Length, iris$Sepal.Width) > scatterplot3d(iris$Petal.Width, iris$Sepal.Length, iris$Sepal.Width)