> dat <- read.table(text = "A B C D E F G + 1 480 780 431 295 670 360 190 + 2 720 350 377 255 340 615 345 + 3 460 480 179 560 60 735 1260 + 4 220 240 876 789 820 100 75", header = TRUE) > > library(reshape2) > > dat$row <- seq_len(nrow(dat)) > dat2 <- melt(dat, id.vars = "row") > > library(ggplot2) Attaching package: ‘ggplot2’ The following objects are masked _by_ ‘.GlobalEnv’: is.facet, midwest > > ggplot(dat2, aes(x=variable, y=value, fill=row)) + + geom_bar(stat="identity") + + xlab("\nType") + + ylab("Time\n") + + guides(fill=FALSE) + + theme_bw()

set.seed(1); A <- sample(0:10, 100, replace = TRUE) stripchart(A, method = "stack", offset = .5, at = .15, pch = 19, main = "Dotplot of Random Values", xlab = "Random Values")

- ref:Graphing Data with R.

# Create FFTrees of the heart disease data heart.fft <- FFTrees(formula = diagnosis ~., data = heartdisease) # Visualise the tree plot(heart.fft, main = "Heart Disease Diagnosis", decision.labels = c("Absent", "Present"))

> heart.fft <- FFTrees(formula = diagnosis ~., data = heartdisease) heart.fft # Plot the best tree plot(heart.fft)

> library(hrbrthemes) > library(ggalt) > library(tidyverse) > sports <- read_tsv("https://github.com/halhen/viz-pub/raw/master/sports-time-of-day/activity.tsv") Parsed with column specification: cols( activity = col_character(), time = col_double(), p = col_double() ) > > sports %>% + group_by(activity) %>% + filter(max(p) > 3e-04, + !grepl('n\\.e\\.c', activity)) %>% + arrange(time) %>% + mutate(p_peak = p / max(p), + p_smooth = (lag(p_peak) + p_peak + lead(p_peak)) / 3, + p_smooth = coalesce(p_smooth, p_peak)) %>% + ungroup() %>% + do({ + rbind(., + filter(., time == 0) %>% + mutate(time = 24*60)) + }) %>% + mutate(time = ifelse(time < 3 * 60, time + 24 * 60, time)) %>% + mutate(activity = reorder(activity, p_peak, FUN=which.max)) %>% + arrange(activity) %>% + mutate(activity.f = reorder(as.character(activity), desc(activity))) -> sports > > sports <- mutate(sports, time2 = time/60) > > ggplot(sports, aes(time2, p_smooth)) + + geom_horizon(bandwidth=0.1) + + facet_grid(activity.f~.) + + scale_x_continuous(expand=c(0,0), breaks=seq(from = 3, to = 27, by = 3), labels = function(x) {sprintf("%02d:00", as.integer(x %% 24))}) + + viridis::scale_fill_viridis(name = "Activity relative to peak", discrete=TRUE, + labels=scales::percent(seq(0, 1, 0.1)+0.1)) + + labs(x=NULL, y=NULL, title="by Volkan OBAN using R - ggalt and hrbrthemes \n \n Peak time of day for sports and leisure", + subtitle="Number of participants throughout the day compared to peak popularity.") + + theme_ipsum_rc(grid="") + + theme(panel.spacing.y=unit(-0.05, "lines")) + + theme(strip.text.y = element_text(hjust=0, angle=360)) + + theme(axis.text.y=element_blank())

library(ggplot2) # devtools::install_github("hadley/ggplot2") library(ggalt) # devtools::install_github("hrbrmstr/ggalt") library(dplyr) # for data_frame() & arrange() # I'm not crazy enough to input all the data; this will have to do for the example df <- data_frame(country=c("Germany", "France", "Vietnam", "Japan", "Poland", "Lebanon", "Australia", "SouthnKorea", "Canada", "Spain", "Italy", "Peru", "U.S.", "UK", "Mexico", "Chile", "China", "India"), ages_35=c(0.39, 0.42, 0.49, 0.43, 0.51, 0.57, 0.60, 0.45, 0.65, 0.57, 0.57, 0.65, 0.63, 0.59, 0.67, 0.75, 0.52, 0.48), ages_18_to_34=c(0.81, 0.83, 0.86, 0.78, 0.86, 0.90, 0.91, 0.75, 0.93, 0.85, 0.83, 0.91, 0.89, 0.84, 0.90, 0.96, 0.73, 0.69), diff=sprintf("+%d", as.integer((ages_18_to_34-ages_35)*100))) # we want to keep the order in the plot, so we use a factor for country df <- arrange(df, desc(diff)) df$country <- factor(df$country, levels=rev(df$country)) # we only want the first line values with "%" symbols (to avoid chart junk) # quick hack; there is a more efficient way to do this percent_first <- function(x) { x <- sprintf("%d%%", round(x*100)) x[2:length(x)] <- sub("%$", "", x[2:length(x)]) x } gg <- ggplot() # doing this vs y axis major grid line gg <- gg + geom_segment(data=df, aes(y=country, yend=country, x=0, xend=1), color="#b2b2b2", size=0.15) # dum…dum…dum!bell gg <- gg + geom_dumbbell(data=df, aes(y=country, x=ages_35, xend=ages_18_to_34), size=1.5, color="#b2b2b2", point.size.l=3, point.size.r=3, point.colour.l="#9fb059", point.colour.r="#edae52") # text below points gg <- gg + geom_text(data=filter(df, country=="Germany"), aes(x=ages_35, y=country, label="Ages 35+"), color="#9fb059", size=3, vjust=-2, fontface="bold", family="Calibri") gg <- gg + geom_text(data=filter(df, country=="Germany"), aes(x=ages_18_to_34, y=country, label="Ages 18-34"), color="#edae52", size=3, vjust=-2, fontface="bold", family="Calibri") # text above points gg <- gg + geom_text(data=df, aes(x=ages_35, y=country, label=percent_first(ages_35)), color="#9fb059", size=2.75, vjust=2.5, family="Calibri") gg <- gg + geom_text(data=df, color="#edae52", size=2.75, vjust=2.5, family="Calibri", aes(x=ages_18_to_34, y=country, label=percent_first(ages_18_to_34))) # difference column gg <- gg + geom_rect(data=df, aes(xmin=1.05, xmax=1.175, ymin=-Inf, ymax=Inf), fill="#efefe3") gg <- gg + geom_text(data=df, aes(label=diff, y=country, x=1.1125), fontface="bold", size=3, family="Calibri") gg <- gg + geom_text(data=filter(df, country=="Germany"), aes(x=1.1125, y=country, label="DIFF"), color="#7a7d7e", size=3.1, vjust=-2, fontface="bold", family="Calibri") gg <- gg + scale_x_continuous(expand=c(0,0), limits=c(0, 1.175)) gg <- gg + scale_y_discrete(expand=c(0.075,0)) gg <- gg + labs(x=NULL, y=NULL, title="The social media age gap", subtitle="Adult internet users or reported smartphone owners whonuse social networking sites", caption="Source: Pew Research Center, Spring 2015 Global Attitudes Survey. Q74") gg <- gg + theme_bw(base_family="Calibri") gg <- gg + theme(panel.grid.major=element_blank()) gg <- gg + theme(panel.grid.minor=element_blank()) gg <- gg + theme(panel.border=element_blank()) gg <- gg + theme(axis.ticks=element_blank()) gg <- gg + theme(axis.text.x=element_blank()) gg <- gg + theme(plot.title=element_text(face="bold")) gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12))) gg <- gg + theme(plot.caption=element_text(size=7, margin=margin(t=12), color="#7a7d7e")) gg

ref :http://lenkiefer.com/2017/08/03/joyswarm

set.seed(123) dt<- data.frame('label'=rep(letters[1:10], each=100), 'value'=as.vector(mapply(rnorm, rep(100, 10), rnorm(10), SIMPLIFY=TRUE)), 'rank'=rep(1:5, each=100, times=20)) ggplot(dt, aes(x=value, y=label, fill=label)) + + geom_joy(scale=3, rel_min_height=0.01) + + scale_fill_manual(values=rep(c('pink4', 'darkviolet'), length(unique(joy$label))/2)) + + scale_y_discrete(expand = c(0.01, 0)) + + xlab('Value') + + theme_joy() + + theme(axis.title.y = element_blank(), + legend.position='none')

> p1 = ggtree(tr) %<+% d1 + + geom_tippoint(aes(color=location), size=5) + + geom_tiplab(offset=-0.01, hjust=0.5, colour="white", size=3, fontface="bold") + ggtitle("by Volkan OBAN using R - ggjoy") + + scale_colour_manual(values = c("purple", "pink", "yellow")) + + scale_fill_manual(values = c("purple", "pink", "yellow")) > > facet_plot(p1, panel="Joy Plot", data=d4, geom_joy, + mapping = aes(x=val, group=label, fill=location), colour="grey40", lwd=0.3) ref:https://stackoverflow.com/questions/45384281/ggjoy-facet-with-ggtree

> require(ggtree) > require(ggstance) > # generate tree > tr <- rtree(30) > > # create simple ggtree object with tip labels > p <- ggtree(tr) + geom_tiplab(offset = 0.02) > > # Generate categorical data for each "species" > d1 <- data.frame(id=tr$tip.label, location=sample(c("GZ", "HK", "CZ"), 30, replace=TRUE)) > > #Plot the categorical data as colored points on the tree tips > p1 <- p %<+% d1 + geom_tippoint(aes(color=location)) > > # Generate distribution of points for each species > d4 = data.frame(id=rep(tr$tip.label, each=20), + val=as.vector(sapply(1:30, function(i) + rnorm(20, mean=i))) + ) > > require(ggjoy) > > ggplot(d4, aes(x = val, y = id)) + + geom_joy(scale = 2, rel_min_height=0.03) + + scale_y_discrete(expand = c(0.01, 0)) + theme_joy() + ggtitle("by Volkan OBAN using R - ggjoy") Picking joint bandwidth of 0.439 > p <- ggtree(tr) + geom_tiplab(offset = 0.02);p1 <- p %<+% d1 + geom_tippoint(aes(color=location));facet_plot(p1, panel="Joy Plot", data=d4, geom_joy, + mapping = aes(x=val, group=label, fill=location), colour="grey50", lwd=0.3)

> set.seed(1234) > pois_data <- data.frame(mean = rep(1:5, each = 10)) > pois_data$group <- factor(pois_data$mean, levels=5:1) > pois_data$value <- rpois(nrow(pois_data), pois_data$mean) > > # make plot > ggplot(pois_data, aes(x = value, y = group, group = group)) + + geom_joy2(aes(fill = group), stat = "binline", binwidth = 1, scale = 0.95) + + geom_text(stat = "bin", + aes(y = group + 0.95*(..count../max(..count..)), + label = ifelse(..count..>0, ..count.., "")), + vjust = 1.4, size = 3, color = "white", binwidth = 1) + + scale_x_continuous(breaks = c(0:12), limits = c(-.5, 13), expand = c(0, 0), + name = "random value") + + scale_y_discrete(expand = c(0.01, 0), name = "Poisson mean", + labels = c("5.0", "4.0", "3.0", "2.0", "1.0")) + + scale_fill_cyclical(values = c("#0000B0", "#7070D0")) + + labs(title = " Poisson random samples with different means", + subtitle = "sample size n=10") + + guides(y = "none") + + theme_joy(grid = FALSE) + + theme(axis.title.x = element_text(hjust = 0.5), + axis.title.y = element_text(hjust = 0.5)) ref: https://cran.r-project.org/web/packages/ggjoy/vignettes/gallery.html

> library(ggplot2movies) > ggplot(movies[movies$year>1912,], aes(x = length, y = year, group = year)) + + geom_joy(scale = 10, size = 0.25, rel_min_height = 0.03) + + theme_joy() + + scale_x_continuous(limits=c(1, 200), expand = c(0.01, 0)) + + scale_y_reverse(breaks=c(2000, 1980, 1960, 1940, 1920, 1900), expand = c(0.01, 0))

ggplot(diamonds, aes(x = price, y = cut, fill = cut)) + + geom_joy(scale = 4) + + scale_fill_cyclical(values = c("purple", "pink"))

> library(ggjoy) Warning message: package ‘ggjoy’ was built under R version 3.4.1 > > ggplot(diamonds, aes(x = price, y = cut)) + + geom_joy(scale = 4) + theme_joy() + + scale_y_discrete(expand = c(0.01, 0)) + # will generally have to set the `expand` option + scale_x_continuous(expand = c(0, 0))

a<- qplot(color, price/carat, data = diamonds, geom = "jitter", alpha = I(1/15)) ggdraw(a) + + draw_plot_label("R - Data Visualization-data(diamonds)", size = 12) + + draw_label("", angle = 25, size = 50, alpha = .7)

a<-ggplot(data=diamonds,aes(x=price, group=cut, fill=cut)) + geom_density(adjust=1.5, position="fill") ggdraw(a) + + draw_plot_label("Data Science & Analytics", size = 8) + + draw_label("", angle = 45, size = 40, alpha = .6)

ggdraw

df <- data.frame(expand.grid(1:100,1:100),rep(10,1000)) ;colnames(df) <- c("x","y","z"); wireframe(z~x*y,df,colorkey=TRUE,drape=TRUE);wireframe(z~x*y,df,main="",color="",drape=TRUE, zlim=c(0,24))

> require(ggplot2movies) > require(viridis) > ggplot(movies[movies$year>1989,], aes(x = length, y = year, fill = factor(year))) + + stat_binline(scale = 1.9, bins = 40) + + theme_joy() + theme(legend.position = "none") + + scale_x_continuous(limits = c(1, 180), expand = c(0.01, 0)) + + scale_y_reverse(expand = c(0.01, 0)) + + scale_fill_viridis(begin = 0.3, discrete = TRUE, option = "B") + + labs(title = " Movie lengths 1990 - 2005")

ggplot(iris, aes(x = Sepal.Length, y = Species, group = Species)) + + geom_joy(rel_min_height = 0.005) + + scale_y_discrete(expand = c(0.01, 0)) + + scale_x_continuous(expand = c(0.01, 0)) + + theme_joy()

cone <- function(x, y){ sqrt(x*cos(x^2)+sin(y)) } ;x <- y <- seq(-1, 1, length= 50); z <- outer(x, y, cone); persp(x, y, z, main="" ,col="pink")

y <- x <- seq(-10, 10, length=60) f <- function(x,y) { r <- sqrt(x^2+y^2); 10 * sin(r)/r } z <- outer(x, y, f) persp3D(x, y, z, color.palette = heat.colors, phi = 30, theta = 225, box = TRUE, border = NA, shade = .4)

df <- data.frame(expand.grid(1:10,1:10),rep(10,100)) ;colnames(df) <- c("x","y","z"); wireframe(z~x*y,df,colorkey=TRUE,drape=TRUE);wireframe(z~x*y,df,main="",colorkey=TRUE,drape=TRUE, zlim=c(0,24))

gplot(mtcars, aes(wt, mpg)) + + geom_point(shape = 21, colour = "purple", fill = "slateblue1", size = 5, stroke = 5) + theme_solarized() + + scale_colour_solarized("blue")

library("tidyverse") library("forcats") library(ggthemes) rincome_plot <- gss_cat %>% ggplot(aes(rincome)) + geom_bar() rincome_plot gss_cat %>% filter(!denom %in% c("No answer", "Other", "Don't know", "Not applicable", "No denomination")) %>% count(relig) gss_cat %>% count(relig, denom) %>% ggplot(aes(x = relig, y = denom, size = n)) + geom_point() + theme(axis.text.x = element_text(angle = 90) + theme_igray() ref:https://jrnold.github.io/e4qf/factors.html

> dataframe <- tibble( + x = rnorm(10000), + y = rnorm(10000) ) ggplot(dataframe , aes(x, y)) + + geom_hex() + + scale_fill_gradient(low = "thistle2", high = "purple") + + coord_fixed()

> ggplot(mpg, aes(displ, hwy, colour = class)) + + geom_point(aes(colour = class)) + + geom_smooth(method = "lm", se = FALSE) + + labs( + title = "Fuel efficiency generally decreases with engine size", + subtitle = "Subcompact cars show the greatest sensitivity to engine size", + caption = "Data from fueleconomy.gov" + )

> library(gapminder) Warning message: package ‘gapminder’ was built under R version 3.4.1 > lifeExp ~ poly(year, 2) lifeExp ~ poly(year, 2) > country_model <- function(df) { + lm(lifeExp ~ poly(year - median(year), 2), data = df) + } > > by_country <- gapminder %>% + group_by(country, continent) %>% + nest() > > by_country <- by_country %>% + mutate(model = map(data, country_model)) > by_country <- by_country %>% + mutate( + resids = map2(data, model, add_residuals) + ) > by_country unnest(by_country, resids) %>% + ggplot(aes(year, resid)) + + geom_line(aes(group = country), alpha = 1 / 3) + + geom_smooth(se = FALSE)

World population 2014

> params.grid.length <- 20 > params.alpha.list <- seq(0.3, 0.6, length = params.grid.length) > params.beta.list <- seq(1,9, length = params.grid.length) > z <- matrix(ncol = params.grid.length, nrow = params.grid.length) > > # Loop through and calculate negative log likelihood at defined values in grid > for (i in 1:length(params.alpha.list )){ + for (ii in 1:length(params.beta.list)){ + alpha <- params.alpha.list[i] + beta <- params.beta.list[ii] + y <- 0.5 + (1 - 0.5 - 0.025)* pweibull(resp.frame$x, beta, alpha) + negLog <- -sum(resp.frame$ny * log(y) + (resp.frame$num.tr - resp.frame$ny) * log(1 - y) ) # Negative log likelihood + z[i,ii] <- negLog + } + } > > # Need to generate stacked list of values to pass to wireframe in lattice > params.alpha.wireList <- rep(unique(params.alpha.list),params.grid.length) > params.beta.wireList <- rep(unique(params.beta.list),params.grid.length) > > temp <- stack(data.frame(z)) > negLog <- subset(temp, select=c(values)) > df.wireFrame <- data.frame(params.alpha.wireList,params.beta.wireList,negLog) > > # Plot parameter space > wirePlot <- wireframe(values ~ params.alpha.wireList*params.beta.wireList, data=df.wireFrame, drape = TRUE, + col="purple",main="by Volkan OBAN using R - lattice", + col.regions = rainbow(100, s = 1, v = 1, start = 0, end = max(1,100-1)/100, alpha = 0.5), + xlab="Alpha", ylab="Beta", zlab="NLL", + screen = list (z = -140, x = -70, y = 3), + scales = list(arrows=FALSE,cex=.5,tick.number = 10)) > wirePlot >

ref:http://rgraphgallery.blogspot.com.tr/2013/04/rg28-contour-plot.html > require(ggplot2) Zorunlu paket yükleniyor: ggplot2 > plt <- ggplot(gdr, aes(xvar, yvar, z= zvar)) + ggtitle("by Volkan OBAN using R - lattice \n contourplot - data:gdr ") > plt + stat_contour() + theme_bw() > plt + geom_tile(aes(fill = zvar)) + stat_contour() + theme_bw() > plt + stat_contour(geom="polygon", aes(fill=..level..)) + theme_bw() > require(ggplot2) > plt <- ggplot(gdr, aes(xvar, yvar, z= zvar)) > plt + stat_contour() + theme_bw() > plt + geom_tile(aes(fill = zvar)) + stat_contour() + theme_bw() > plt + stat_contour(geom="polygon", aes(fill=..level..)) + theme_bw() >

> require(ggplot2) > plt <- ggplot(gdr, aes(xvar, yvar, z= zvar)) > plt + stat_contour() + theme_bw()

> xvr <- seq(-5, 5, len = 50) > yvr <- seq(-5, 5, len = 50) > gdr <- expand.grid(xvar = xvr, yvar = yvr) > gdr$zvar <- rnorm (nrow(gdr), 4, 1) > > > #plot > require(lattice) > contourplot(zvar ~ xvar * yvar, data = gdr,main="by Volkan OBAN using R - lattice", cuts = 10) > xvr <- seq(-5, 5, len = 50) > yvr <- seq(-5, 5, len = 50) > gdr <- expand.grid(xvar = xvr, yvar = yvr) > gdr$zvar <- rnorm (nrow(gdr), 4, 1)

> x <- seq(1,2,0.2); > y <- seq(0.5,1.5,0.1); > > data1 <- matrix(0,nrow=length(x)*length(y),ncol=3); > data2 <- matrix(0,nrow=length(x)*length(y),ncol=3); > > n <- 0; > j <- 1; > while(j<=length(x)){ + for (k in 1:length(y)){ + data1[k+n,1] <- x[j]; + data1[k+n,2] <- y[k]; + data1[k+n,3] <- x[j]^4 + y[k]; + + data2[k+n,1] <- x[j]; + data2[k+n,2] <- y[k]; + data2[k+n,3] <- x[j]^4 + y[k]^4 + 3; + } + n <- n+length(y); + j <- j+1; + } > rm(x,y,j,n,k) > > # Arranging data into a data frame > data1_2 <-as.data.frame(rbind(data1,data2)); > colnames(data1_2) <- c("x","y","z"); > data1_2$group <- gl(2, nrow(data1_2)/2, labels=c("data1", "data2")) > rm(data1,data2) > > # Plotting data as a surface > wireframe(z~x*y,data=data1_2,groups=group, + + # Naming labels and Axis + main =list(label="by Volkan OBAN using R - lattice - wireframe ",cex=2,distance=5), + zlab=list(rot=90,label = "Z",cex=2), + xlab=list(label = "X",cex=2), + ylab=list(label = "Y",cex=2), + + # Coloring the groups + col.groups=c(rgb(red=200,green=100,blue=80, + alpha=200,maxColorValue=255), # Orange + rgb(red=150,green=200,blue=205, + alpha=200,maxColorValue=255)), # Blue + + # Coloring the grids + col=c(rgb(red=0,green=0,blue=0,alpha=50,maxColorValue=255), + rgb(red=0,green=0,blue=0,alpha=50,maxColorValue=255)), + + aspect=c(1,1), # y-size/x-size and z-size/x-size + screen = list(z=40,y=0,x=-80)); # axis rotation >

> df <- data.frame(expand.grid(1:10,1:10),rep(10,100)) > colnames(df) <- c("x","y","z") > wireframe(z~x*y,df,colorkey=TRUE,drape=TRUE) >wireframe(z~x*y,df,main="",colorkey=TRUE,drape=TRUE, zlim=c(0,10))

ref:http://zoonek.free.fr/blosxom/R/2006-08-10_R_Graphics.html # Minimum Spanning Tree (MST) panel.mst <- function (x, y, ...) { require(ape) # For mst() d <- dist(cbind(x,y)) m <- mst(d) i <- which(m == 1) panel.segments(x[row(m)[i]], y[row(m)[i]], x[col(m)[i]], y[col(m)[i]], ...) } # 2-dimensional Kernel Density Estimation panel.kde <- function (x, y, ...) { require(grid) # for convertX() and unit() require(MASS) # For kde2d() k <- kde2d( x, y, n = 500, # The limits of the current plot lims = c(as.numeric(convertX(unit(0,"npc"),"native")), as.numeric(convertX(unit(1,"npc"),"native")), as.numeric(convertY(unit(0,"npc"),"native")), as.numeric(convertY(unit(1,"npc"),"native")))) panel.levelplot(rep(k$x, length(k$y)), rep(k$y, each = length(k$x)), sqrt(k$z), subscripts = 1:length(k$z), ...) } # The same example as above library(RColorBrewer) xyplot(lat ~ long | Depth, data = quakes, panel = function (x, y, ...) { panel.kde(x, y, col.regions = brewer.pal(9, "YlOrRd")) panel.mst(x, y, col = "black", lwd = 2) }, strip = strip.custom(strip.names = TRUE, strip.levels = TRUE), par.strip.text = list(cex = 0.75), aspect = "iso")

X <- seq(0, pi, length.out = 50) > > Y <- seq(0, 2*pi, length.out = 50) > > M <- mesh(X, Y) > > phi <- M$x > > theta <- M$y > > # x, y and z grids > x <- sin(phi) * cos(theta) > > y <- cos(phi) > > z <- sin(phi) * sin(theta) > > # these are the defaults > p <- list(ambient = 0.3, diffuse = 0.6, specular = 1.,exponent = 20, sr = 0, alpha = 1) > > par(mfrow = c(3, 3), mar = c(0, 0, 0, 0)) > > Col <- "magenta4" > > surf3D(x, y, z, box = FALSE, col = Col, lighting = TRUE) > surf3D(x, y, z, box = FALSE, col = Col, lighting = list(exponent = 5)) > surf3D(x, y, z, box = FALSE, col = Col, lighting = list(exponent = 50)) > surf3D(x, y, z, box = FALSE, col = Col, shade = 0.9) > surf3D(x, y, z, box = FALSE, col = Col, lighting = list(sr = 1)) > surf3D(x, y, z, box = FALSE, col = Col, lighting = list(diffuse = 0)) > surf3D(x, y, z, box = FALSE, col = Col, lighting = list(exponent = 50)) > surf3D(x, y, z, box = FALSE, col = Col, lighting = list(exponent = 20)) > surf3D(x, y, z, box = FALSE, col = Col, lighting = list(exponent = 1)) >

image2D

box3D(x0 = runif(4), y0 = runif(4), z0 = runif(4), + x1 = runif(4), y1 = runif(4), z1 = runif(4), + col = c("purple", "pink", "lightpink4"), alpha = 0.5, + border = "black", lwd = 2)

z <- seq(0, 10, 0.2) > x <- cos(z) > y <- sin(z)*z > scatter3D(x, y, z, phi = 0, bty = "g", type = "h", ticktype = "detailed")

x <- y <- z <- seq(-1, 1, by = 0.1) > grid <- mesh(x, y, z) > colvar <- with(grid, x*exp(-x^2 - y^2 - z^2)) slice3D (x, y, z, colvar = colvar, theta = 60) > slicecont3D (x, y, z, ys = seq(-1, 1, by = 0.5), colvar = colvar, theta = 60, border = "purple")

a <- volcano[seq(1, 87, 15), seq(1, 61, 15)] hist3D(z = a, scale = FALSE, expand = 0.01, bty = "g", phi = 20, + col = "#9932CC", border = "white", shade = 0.2, ltheta = 90, space = 0.3, ticktype = "detailed", d = 2)

rect3D(x0 = seq(-0.8, -0.1, by = 0.1), + y0 = seq(-0.8, -0.1, by = 0.1), + z0 = seq(-0.8, -0.1, by = 0.1), + x1 = seq(0.8, 0.1, by = -0.1), + y1 = seq(0.8, 0.1, by = -0.1), + col = rainbow(8), border = "pink", + bty = "g", lwd = 2, phi = 20, main = " rect3D")

box3D(x0 = seq(-0.8, -0.1, by = 0.1), + y0 = seq(-0.8, -0.1, by = 0.1), + z0 = seq(-0.8, -0.1, by = 0.1), + x1 = seq(0.8, 0.1, by = -0.1), + y1 = seq(0.8, 0.1, by = -0.1), + z1 = seq(0.8, 0.1, by = -0.1), + col = rainbow(n = 8, alpha = 0.1), + border = "purple", lwd = 2, phi = 20) ref: https://rpubs.com/yoshio/95844

> border3D(x0 = seq(-0.8, -0.1, by = 0.1), + y0 = seq(-0.8, -0.1, by = 0.1), + z0 = seq(-0.8, -0.1, by = 0.1), + x1 = seq(0.8, 0.1, by = -0.1), + y1 = seq(0.8, 0.1, by = -0.1), + z1 = seq(0.8, 0.1, by = -0.1), + col = rainbow(8), lty = 2, + lwd = c(1, 4), phi = 20, main = "")

with (mtcars, { # linear regression fit <- lm(mpg ~ wt + disp) # predict values on regular xy grid wt.pred <- seq(1.5, 5.5, length.out = 30) disp.pred <- seq(71, 472, length.out = 30) xy <- expand.grid(wt = wt.pred, disp = disp.pred) mpg.pred <- matrix (nrow = 30, ncol = 30, data = predict(fit, newdata = data.frame(xy), interval = "prediction")) # fitted points for droplines to surface fitpoints <- predict(fit) scatter3D(z = mpg, x = wt, y = disp, pch = 18, cex = 2, theta = 20, phi = 20, ticktype = "detailed", xlab = "wt", ylab = "disp", zlab = "mpg", surf = list(x = wt.pred, y = disp.pred, z = mpg.pred, facets = NA, fit = fitpoints), main = "") }) ref:https://rpubs.com/yoshio/95844

reference:https://rpubs.com/yoshio/95844 > X <- seq(0, pi, length.out = 50) > Y <- seq(0, 2*pi, length.out = 50) > M <- mesh(X, Y) > phi <- M$x > theta <- M$y > r <- sin(4*phi)^3 + cos(2*phi)^3 + sin(6*theta)^2 + cos(6*theta)^4 > x <- r * sin(phi) * cos(theta) > y <- r * cos(phi) > z <- r * sin(phi) * sin(theta) > surf3D(x, y, z, colvar = y, colkey = FALSE, shade = 0.5,box = FALSE, theta = 60) > surf3D(x, y, z, colvar = y, colkey = FALSE, box = FALSE, theta = 60, facets = FALSE

> x <- rchisq(1000, df = 5) > hs <- hist(x, breaks = 20) hist3D(x = hs$mids, y = 1, z = matrix(ncol = 1, data = hs$density), bty = "g", ylim = c(0., 2.0), scale = FALSE, expand = 20, border = "pink", col = "red", shade = 0.4, space = 0.1, theta = 20, phi = 20, main = "")

volkan <- volcano[seq(1, 87, 15), seq(1, 61, 15)] ribbon3D(z = volkan, scale = FALSE, expand = 0.01, bty = "g", phi = 20, col = "pink", border = "purple", shade = 0.2, ltheta = 90,space = 0.3, ticktype = "detailed", d = 2, curtain = TRUE)

hist3D

> x <- y <- z <- seq(-4, 4, by = 0.2) > M <- mesh(x, y, z) > R <- with (M, sqrt(x^2 + y^2 + z^2)) > p <- sin(2*R) /(R+1e-3) > slice3D(x, y, z, colvar = p, d = 2, theta = 60, border = "black", xs = c(-4, 0), ys = c(-4, 0, 4), zs = c(-4, 0))

library(ggplot2) library(geofacet) ggplot(eu_imm, aes(year, persons)) + + geom_line() + + facet_geo(~ name, grid = "eu_grid1") + + scale_x_continuous(labels = function(x) paste0("'", substr(x, 3, 4))) + + scale_y_sqrt(minor_breaks = NULL) + + ylab("# Resettled Persons") + + theme_bw()

# Load packages library(forecast) # Most popular forecasting pkg library(sweep) # Broom tidiers for forecast pkg library(timekit) # Working with time series in R library(tidyquant) # Get's data from FRED, loads tidyverse behind the scenes library(geofacet) > ne_gdp <- tq_get("NENGSP", get = "economic.data", from = "2007-01-01", to = "2017-06-01") %>% + rename(gdp = price) > states <- tibble(abbreviation = state.abb) %>% + mutate(fred_code = paste0(abbreviation, "NGSP")) %>% + select(2:1) > states_gdp <- states %>% + tq_get(get = "economic.data", from = "2007-01-01", to = "2017-06-01") > > # Group and rename > states_gdp <- states_gdp %>% + select(-fred_code) %>% + group_by(abbreviation) %>% + rename(gdp = price) > ne_gdp_ts <- ne_gdp %>% + tk_ts(start = 2017, freq = 1, silent = TRUE) > ne_fit_arima <- auto.arima(ne_gdp_ts) > sw_glance(ne_fit_arima) # A tibble: 1 x 12 model.desc sigma logLik AIC BIC <chr> <dbl> <dbl> <dbl> <dbl> 1 ARIMA(0,1,0) with drift 2149.529 -81.29672 166.5934 166.9879 # ... with 7 more variables: ME <dbl>, RMSE <dbl>, MAE <dbl>, # MPE <dbl>, MAPE <dbl>, MASE <dbl>, ACF1 <dbl> > ne_fcast <- forecast(ne_fit_arima, h = 3) > ne_sweep <- sw_sweep(ne_fcast, timekit_idx = TRUE, rename_index = "date") > ne_sweep # A tibble: 13 x 7 date key gdp lo.80 lo.95 hi.80 hi.95 <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> 1 2007-01-01 actual 81926.0 NA NA NA NA 2 2008-01-01 actual 84873.0 NA NA NA NA 3 2009-01-01 actual 86961.0 NA NA NA NA 4 2010-01-01 actual 92231.0 NA NA NA NA 5 2011-01-01 actual 99935.0 NA NA NA NA 6 2012-01-01 actual 101973.0 NA NA NA NA 7 2013-01-01 actual 106765.0 NA NA NA NA 8 2014-01-01 actual 112087.0 NA NA NA NA 9 2015-01-01 actual 113458.0 NA NA NA NA 10 2016-01-01 actual 115345.0 NA NA NA NA 11 2017-01-01 forecast 119058.2 116303.5 114845.2 121813.0 123271.2 12 2018-01-01 forecast 122771.4 118875.7 116813.4 126667.2 128729.5 13 2019-01-01 forecast 126484.7 121713.3 119187.5 131256.0 133781.8 > ne_sweep %>% + ggplot(aes(x = date, y = gdp, color = key)) + + # Prediction intervals + geom_ribbon(aes(ymin = lo.95, ymax = hi.95), + fill = "#D5DBFF", color = NA, size = 0) + + geom_ribbon(aes(ymin = lo.80, ymax = hi.80, fill = key), + fill = "#596DD5", color = NA, size = 0, alpha = 0.8) + + # Actual & Forecast + geom_line(size = 1) + + geom_point(size = 2) + + # Aesthetics + theme_tq(base_size = 16) + + scale_color_tq() + + labs(title = " by Volkan OBAN using R : forecast-sweep-geofacet-timelit-tidyquant packages \n Nebraska GDP, 3-Year Forecast", x = "", y = "GDP, USD Millions") > states_gdp <- states_gdp %>% + nest() > states_gdp # A tibble: 50 x 2 abbreviation data <chr> <list> 1 AL <tibble [10 x 2]> 2 AK <tibble [10 x 2]> 3 AZ <tibble [10 x 2]> 4 AR <tibble [10 x 2]> 5 CA <tibble [10 x 2]> 6 CO <tibble [10 x 2]> 7 CT <tibble [10 x 2]> 8 DE <tibble [10 x 2]> 9 FL <tibble [10 x 2]> 10 GA <tibble [10 x 2]> # ... with 40 more rows > states_gdp <- states_gdp %>% + mutate(data_ts = map(data, tk_ts, freq = 1, start = 2007, silent = TRUE)) > states_gdp # A tibble: 50 x 3 abbreviation data data_ts <chr> <list> <list> 1 AL <tibble [10 x 2]> <S3: ts> 2 AK <tibble [10 x 2]> <S3: ts> 3 AZ <tibble [10 x 2]> <S3: ts> 4 AR <tibble [10 x 2]> <S3: ts> 5 CA <tibble [10 x 2]> <S3: ts> 6 CO <tibble [10 x 2]> <S3: ts> 7 CT <tibble [10 x 2]> <S3: ts> 8 DE <tibble [10 x 2]> <S3: ts> 9 FL <tibble [10 x 2]> <S3: ts> 10 GA <tibble [10 x 2]> <S3: ts> # ... with 40 more rows > states_gdp <- states_gdp %>% + mutate(fit = map(data_ts, auto.arima)) > states_gdp # A tibble: 50 x 4 abbreviation data data_ts fit <chr> <list> <list> <list> 1 AL <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 2 AK <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 3 AZ <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 4 AR <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 5 CA <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 6 CO <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 7 CT <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 8 DE <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 9 FL <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 10 GA <tibble [10 x 2]> <S3: ts> <S3: ARIMA> # ... with 40 more rows > states_gdp %>% + mutate(glance = map(fit, sw_glance)) %>% + unnest(glance, .drop = T) # A tibble: 50 x 13 abbreviation model.desc sigma logLik <chr> <chr> <dbl> <dbl> 1 AL ARIMA(0,1,0) with drift 3267.828 -85.06590 2 AK ARIMA(0,0,0) with non-zero mean 4199.313 -97.08934 3 AZ ARIMA(0,2,0) 7559.654 -82.79488 4 AR ARIMA(0,1,0) with drift 2231.839 -81.63464 5 CA ARIMA(0,2,0) 60035.965 -99.37208 6 CO ARIMA(0,1,0) with drift 7064.218 -92.00497 7 CT ARIMA(0,2,0) 5009.932 -79.50274 8 DE ARIMA(0,1,0) with drift 1865.871 -80.02328 9 FL ARIMA(0,2,0) 17001.163 -89.27758 10 GA ARIMA(0,2,0) 6369.686 -81.42147 # ... with 40 more rows, and 9 more variables: AIC <dbl>, # BIC <dbl>, ME <dbl>, RMSE <dbl>, MAE <dbl>, MPE <dbl>, # MAPE <dbl>, MASE <dbl>, ACF1 <dbl> > states_gdp <- states_gdp %>% + mutate(forecast = map(fit, forecast, h = 3)) > states_gdp # A tibble: 50 x 5 abbreviation data data_ts fit <chr> <list> <list> <list> 1 AL <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 2 AK <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 3 AZ <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 4 AR <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 5 CA <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 6 CO <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 7 CT <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 8 DE <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 9 FL <tibble [10 x 2]> <S3: ts> <S3: ARIMA> 10 GA <tibble [10 x 2]> <S3: ts> <S3: ARIMA> # ... with 40 more rows, and 1 more variables: forecast <list> > states_gdp_sweep <- states_gdp %>% + mutate(sweep = map(forecast, sw_sweep, timekit_idx = T, rename_index = "date")) %>% + select(abbreviation, sweep) %>% + unnest() > states_gdp_sweep # A tibble: 650 x 8 abbreviation date key gdp lo.80 lo.95 hi.80 hi.95 <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> 1 AL 2007-01-01 actual 169923 NA NA NA NA 2 AL 2008-01-01 actual 172646 NA NA NA NA 3 AL 2009-01-01 actual 168315 NA NA NA NA 4 AL 2010-01-01 actual 174710 NA NA NA NA 5 AL 2011-01-01 actual 180665 NA NA NA NA 6 AL 2012-01-01 actual 185878 NA NA NA NA 7 AL 2013-01-01 actual 190319 NA NA NA NA 8 AL 2014-01-01 actual 194404 NA NA NA NA 9 AL 2015-01-01 actual 199980 NA NA NA NA 10 AL 2016-01-01 actual 204861 NA NA NA NA # ... with 640 more rows > states_gdp_sweep %>% + ggplot(aes(x = date, y = gdp, color = key)) + + # Prediction intervals + geom_ribbon(aes(ymin = lo.95, ymax = hi.95), + fill = "#D5DBFF", color = NA, size = 0) + + geom_ribbon(aes(ymin = lo.80, ymax = hi.80, fill = key), + fill = "#596DD5", color = NA, size = 0, alpha = 0.8) + + # Actual & Forecast + geom_line() + + # Aesthetics + scale_y_continuous(label = function(x) x*1e-6) + + scale_x_date(date_breaks = "5 years", labels = scales::date_format("%Y")) + + facet_geo(~ abbreviation, scale = "free_y") + + theme_tq() + + scale_color_tq() + + theme(legend.position = "none", + axis.text.x = element_text(angle = 45, hjust = 1), + axis.text.y = element_blank() + ) + + ggtitle(" State GDP, 3-Year Forecast") + + xlab("") + + ylab("GDP, Free Scale") > states_gdp_sweep %>% + ggplot(aes(x = date, y = gdp, color = key)) + + # Prediction intervals + geom_ribbon(aes(ymin = lo.95, ymax = hi.95), + fill = "#D5DBFF", color = NA, size = 0) + + geom_ribbon(aes(ymin = lo.80, ymax = hi.80, fill = key), + fill = "#596DD5", color = NA, size = 0, alpha = 0.8) + + # Actual & Forecast + geom_line() + + # Aesthetics + scale_y_continuous(label = function(x) x*1e-6) + + scale_x_date(date_breaks = "5 years", labels = scales::date_format("%Y")) + + facet_geo(~ abbreviation, scale = "free_y") + + theme_tq() + + scale_color_tq() + + theme(legend.position = "none", + axis.text.x = element_text(angle = 45, hjust = 1), + axis.text.y = element_blank() + ) + + ggtitle(" by Volkan OBAN using R :TIME SERIES FORECASTING - forecast-sweep-geofacet-timelit-tidyquant packages \n State GDP, 3-Year Forecast") + + xlab("") + + ylab("GDP, Free Scale") > states_gdp_sweep %>% + ggplot(aes(x = date, y = gdp, color = key)) + + # Prediction intervals + geom_ribbon(aes(ymin = lo.95, ymax = hi.95), + fill = "#D5DBFF", color = NA, size = 0) + + geom_ribbon(aes(ymin = lo.80, ymax = hi.80, fill = key), + fill = "#596DD5", color = NA, size = 0, alpha = 0.8) + + # Actual & Forecast + geom_line() + + # Aesthetics + scale_y_continuous(label = function(x) x*1e-6) + + scale_x_date(date_breaks = "5 years", labels = scales::date_format("%Y")) + + facet_geo(~ abbreviation, scale = "free_y") + + theme_tq() + + scale_color_tq() + + theme(legend.position = "none", + axis.text.x = element_text(angle = 45, hjust = 1), + axis.text.y = element_blank() + ) + + ggtitle(" State GDP, 3-Year Forecast") + + xlab("") + + ylab("GDP, Free Scale") > states_gdp_sweep %>% + ggplot(aes(x = date, y = gdp, color = key)) + + # Prediction intervals + geom_ribbon(aes(ymin = lo.95, ymax = hi.95), + fill = "#D5DBFF", color = NA, size = 0) + + geom_ribbon(aes(ymin = lo.80, ymax = hi.80, fill = key), + fill = "#596DD5", color = NA, size = 0, alpha = 0.8) + + # Actual & Forecast + geom_line() + + # Aesthetics + scale_y_continuous(label = function(x) x*1e-6) + + scale_x_date(date_breaks = "5 years", labels = scales::date_format("%Y")) + + facet_geo(~ abbreviation, scale = "free_y") + + theme_tq() + + scale_color_tq() + + theme(legend.position = "none", + axis.text.x = element_text(angle = 45, hjust = 1), + axis.text.y = element_blank() + ) + + ggtitle(" State GDP, 3-Year Forecast") + + xlab("") + + ylab("GDP, Free Scale")

plot.xmean.ordinaly

w <- upData(d, cens = 15 * runif(n), h = .02 * exp(.04 * (age - 50) + .8 * (sex == 'Female')), d.time = -log(runif(n)) / h, death = ifelse(d.time <= cens, 1, 0), d.time = pmin(d.time, cens)) f <- psm(Surv(d.time,death) ~ sex * age, data=w, dist='lognormal') med <- Quantile(f) surv <- Survival(f) # This would also work if f was from cph plot(nomogram(f, fun=function(x) med(lp=x), funlabel="Median Survival Time"))

> n <- 1000 # define sample size > set.seed(17) # so can reproduce the results > age <- rnorm(n, 50, 10) > blood.pressure <- rnorm(n, 120, 15) > cholesterol <- rnorm(n, 200, 25) > sex <- factor(sample(c('female','male'), n,TRUE)) > label(age) <- 'Age' # label is in Hmisc > label(cholesterol) <- 'Total Cholesterol' > label(blood.pressure) <- 'Systolic Blood Pressure' > label(sex) <- 'Sex' > units(cholesterol) <- 'mg/dl' # uses units.default in Hmisc > units(blood.pressure) <- 'mmHg' > # Specify population model for log odds that Y=1 > L <- .4*(sex=='male') + .045*(age-50) + + (log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male')) > # Simulate binary y to have Prob(y=1) = 1/[1+exp(-L)] > y <- ifelse(runif(n) < plogis(L), 1, 0) > ddist <- datadist(age, blood.pressure, cholesterol, sex) > options(datadist='ddist') > fit <- lrm(y ~ blood.pressure + sex * (age + rcs(cholesterol,4)), + x=TRUE, y=TRUE) > p <- Predict(fit, age, cholesterol, sex, np=50) # vary sex last > bplot(p, main="by Volkan OBAN using R - rms package") > bplot(p,, main="by Volkan OBAN using R - rms package", lfun=wireframe)

plot(stl(Quandl("WIKI/GOOG",type="ts",collapse="monthly")[,11],s.window="per"))

doodle <- function( start=c(0,0), targets = rbind(c(0,10),c(10,10), c(10,0), c(0,0)) , tdist = .25, speed = c(0,0), accel = .1, resis = .005, jitter = .0005, chncStp = 0) { # start - We start with the starting position # targ - Points that will be pursued (initially just a square) # tdist - How close we need to get to each point before moving on # speed - Initial speed # accel - How fast does the drawer accelerate towards that point # resis - What percentage of speed is lost each round # jitter - A normal draw random jitter that moves the writing tool in an unexpected direction. # chncStp - There is some chance that the drawing tool will kill all momentum and stop. # First off I define a function uvect to convert any two sets of points # into a unit vector and measure the distance between the two points. uvect <- function(p1,p2=NULL) { if (is.null(p2)) { p2 <- p1[[2]] p1 <- p1[[1]] } list(vect=(p2-p1)/sqrt(sum((p1-p2)^2)), dist=sqrt(sum((p1-p2)^2))) } # Starup parameters i <- 1 plist <- position <- start # plist saves all of the points that the drawing tool has passed through vect <- uvect(position,targets[i,]) while(i<=nrow(targets)) { # Calculate the appropriate unit vector and distance from end point vect <- uvect(position,targets[i,]) # Remove some amount of speed from previous velocity speed <- speed*(1-resis) # IF drawer randomly stops remove all speed if (rbinom(1,1,chncStp)) speed<-0 # speed <- speed + accel*vect[[1]] + rnorm(2)*jitter position <- position + speed plist <- rbind(plist,position) vect <- uvect(position,targets[i,]) if (vect[[2]]<tdist) i <- i+1 } plist } plist <- doodle() shape <- doodle(cbind(c(0,-2,10,15,5,0),c(5,12,10,9,2,0)),resis=.2) > > plot(shape, type="l",lwd=1) >

plot(play_forestfire(40, 0.8))

> x.coord <- seq(-10, 10, length= 50) > y.coord <- x.coord > func <- function(x,y) { r <- sqrt(abs(x^3)+y^2); sin(r)/r } > z.coord <- outer(x.coord, y.coord, func) > persp(x.coord,y.coord,z.coord,theta=30,phi=30,expand=0.5,col="hotpink4", + ltheta=120,shade=0.75,ticktype="detailed",xlab="X",ylab="Y",zlab="Z")

> data("EuStockMarkets") > dax <- EuStockMarkets[, 1] > plot(dax, ylim = c(0, 6000), axes = FALSE) > axis(1) > axis(2, las = 1) > par(new = TRUE) > plot(diff(log(dax)), ylim = c(-0.1, 0.9), axes = FALSE, col = 2, ylab = "") > box() > axis(4, col = 2, col.axis = 2, las = 1)

> y <- x <- seq(-3,3,length=50) > f <- function(x,y){ + dnorm(x^4)*dnorm(y^2)} > z <- outer(x,y,f) > persp(x,y,z, theta = 60, phi = 30,col = "lightpink1",zlim=c(0,0.2))

> cone <- function(x, y){ + sqrt(x^4+y^4) + } > x <- y <- seq(-1, 1, length= 20) > z <- outer(x, y, cone) > persp(x, y, z)

library(treemap) library(d3treeR) # dataset group=c(rep("group-1",4),rep("group-2",2),rep("group-3",3)) subgroup=paste("subgroup" , c(1,2,3,4,1,2,1,2,3), sep="-") value=c(13,5,22,12,11,7,3,1,23) data=data.frame(group,subgroup,value) # basic treemap p=treemap(data, index=c("group","subgroup"), vSize="value", type="index" ) # make it interactive ("rootname" becomes the title of the plot): inter=d3tree2( p , rootname = "General" )

library(cartography) # Upload data attached with the package. data(nuts2006) # Now we have a spdf file (shape file) called nuts2.spdf with shape of european regions. We can plot it with the plot function. summary(nuts2.spdf) # We also have a dataframe with information concerning every region. head(nuts2.df) # Both object have a first column "id" that makes the link between them. # Create a grid mygrid <- getGridLayer(spdf = nuts2.spdf, cellsize = 2e+05) # You can plot the grid # plot(mygrid$spdf) # Adapt grid to a numerical variable datagrid.df <- getGridData(x = mygrid, df = nuts2.df, var = "pop2008") datagrid.df$densitykm <- datagrid.df$pop2008_density * 1000 * 1000 # Plot background plot(nuts0.spdf, border = NA, col = NA, bg = "#A6CAE0") plot(world.spdf, col = "#E3DEBF", border = NA, add = TRUE) # Plot density of population choroLayer(spdf = mygrid$spdf, df = datagrid.df, var = "densitykm", border = "grey80", col = carto.pal(pal1 = "wine.pal", n1 = 6), legend.pos = "topright", method = "q6", add = TRUE, legend.title.txt = "Population Density\n(inhabitant/km²)") # Title, legend.. layoutLayer(title = "Population Density", coltitle = "black", col = NA, sources = "Eurostat, 2011", scale = NULL, author = "cartography", frame = FALSE)

g <- barabasi.game(5000, power=1) > layout <- layout.fruchterman.reingold(g) > membership <- cut_at(eb, no = 10) > plot(g, + vertex.color= rainbow(10, .8, .8, alpha=.8)[membership], + vertex.size=5, layout=layout, vertex.label=NA, + edge.arrow.size=.2) > eb <- walktrap.community(g) > membership <- cut_at(eb, no = 10) > plot(g, + vertex.color= rainbow(10, .8, .8, alpha=.8)[membership], + vertex.size=5, layout=layout, vertex.label=NA, + edge.arrow.size=.2)

g <- barabasi.game(10000, power=1) > layout <- layout.fruchterman.reingold(g) > plot(g, layout=layout, vertex.size=2, vertex.label=NA, edge.arrow.size=.2)

wireframe(z ~ x * y, data = g, groups = gr, scales = list(arrows = FALSE, x = list(at = c(2, 5, 10)), y = list(at = c(6, 10, 14), lab = c('A', 'BBB', 'CCCCC')) ))

> g <- expand.grid(x = 1:50, y = 5:25, gr = 1:5) > g$z <- log((g$x^g$gr + g$y^2) * g$gr) > wireframe(z ~ x * y, data = g, groups = gr, + scales = list(arrows = FALSE), + drape = TRUE, colorkey = TRUE,main="by Volkan OBAN using R - lattice package", + screen = list(z = 30, x = -60))

> U = numeric(1000); > n = 100; > average = numeric(n); > for (i in 1 : n) + {U = runif(1000); + X = tan(pi ∗ (U − 0.5)); + average[i] = mean(X); } > plot(1 : n, average[1 : n], type = "l", lwd = 2, col = "red",main="by Volkan OBAN using R") + theme_solarized(light = FALSE) + + scale_colour_solarized("red")

> par(mfrow = c(1, 2), pty = "s", mar = rep(0, 4)) > plot(mb,col = cols, transform = "inverse") > plot(mb, col = cols, transform = "log") ref:https://github.com/blmoore/

> library(ggplot2) > > mb <- mandelbrot(xlim = c(-0.8335, -0.8325), + ylim = c(0.205, 0.206), + resolution = 1200L, + iterations = 1000) > > > cols <- c( + colorRampPalette(c("#e7f0fa", "#c9e2f6", "#95cbee", + "#0099dc", "#4ab04a", "#ffd73e"))(10), + colorRampPalette(c("#eec73a", "#e29421", "#e29421", + "#f05336","#ce472e"), bias=2)(90), + "black") > > df <- as.data.frame(mb) > ggplot(df, aes(x = x, y = y, fill = value)) + + geom_raster(interpolate = TRUE) + theme_void() + + scale_fill_gradientn(colours = cols, guide = "none") + ggtitle("by Volkan OBAN using R-mandelbrot package ") > library(ggplot2) > > mb <- mandelbrot(xlim = c(-0.8335, -0.8325), + ylim = c(0.205, 0.206), + resolution = 1200L, + iterations = 1000) > > > cols <- c( + colorRampPalette(c("#e7f0fa", "#c9e2f6", "#95cbee", + "#0099dc", "#4ab04a", "#ffd73e"))(10), + colorRampPalette(c("#eec73a", "#e29421", "#e29421", + "#f05336","#ce472e"), bias=2)(90), + "black") > > df <- as.data.frame(mb) > ggplot(df, aes(x = x, y = y, fill = value)) + + geom_raster(interpolate = TRUE) + theme_void() + + scale_fill_gradientn(colours = cols, guide = "none")

t <- 0:100 # time > sig2 <- 0.01 > nsim <- 1000 > ## we'll simulate the steps from a uniform distribution with limits set to > ## have the same variance (0.01) as before > X <- matrix(runif(n = nsim * (length(t) - 1), min = -sqrt(3 * sig2), max = sqrt(3 * + sig2)), nsim, length(t) - 1) > X <- cbind(rep(0, nsim), t(apply(X, 1, cumsum))) > plot(t, X[1, ], xlab = "time",ylab = "phenotype", ylim = c(-2, 2), type = "l") > apply(X[2:nsim, ], 1, function(x, t) lines(t, x), t = t)

library(photobiology) plot(sun.spct) + theme_solarized(light = FALSE) + + scale_colour_solarized("red")

library(photobiology) plot(yellow_gel.spct) plot(yellow_gel.spct, pc.out = TRUE)

graph <- graph_from_data_frame(flare$edges, vertices = flare$vertices) set.seed(1) ggraph(graph, 'circlepack', weight = 'size') + geom_node_circle(aes(fill = depth), size = 0.25, n = 50) + coord_fixed() > ggraph(graph, 'circlepack', weight = 'size') + + geom_node_circle(aes(fill = depth), size = 0.25, n = 50) + + coord_fixed() + ggtitle("by Volkan OBAN using R-ggraph ") + theme_economist() + scale_colour_economist() + + scale_y_continuous(position = "right")

ggsurvplot( + fit, # survfit object with calculated statistics. + data = lung, # data used to fit survival curves. + risk.table = TRUE, # show risk table. + pval = TRUE, # show p-value of log-rank test. + conf.int = TRUE, # show confidence intervals for + # point estimates of survival curves. + xlim = c(0,500), # present narrower X axis, but not affect + # survival estimates. + xlab = "Time in days", # customize X axis label. + break.time.by = 100, # break X axis in time intervals by 500. + ggtheme = theme_light(), # customize plot and risk table with a theme. + risk.table.y.text.col = T, # colour risk table text annotations. + risk.table.y.text = FALSE ,title="by Volkan OBAN using R - survminer" + ) >

calenda HeatMap

ggplot(data = happy) + + geom_mosaic(aes(weight = wtssall, x = product(health), fill = health)) + + facet_grid(happy~.)

ggplot(data = happy) + + geom_mosaic(aes(weight=wtssall, x=product(health, sex, degree), fill=happy), na.rm=TRUE)

wm <- map("world",fill=TRUE,col=0,xlim=c(-10,40),ylim=c(30,60)) ggplot(wm, aes(long, lat, group = group)) + + geom_polygon(fill = "white", colour = "purple") + theme_economist() + scale_colour_economist() + + scale_y_continuous(position = "right")

> library(dplyr) > library(tidyr) > library(scales) > library(ggplot2) > library(ggalt) # devtools::install_github("hrbrmstr/ggalt") > > health <- read.csv("https://rud.is/dl/zhealth.csv", stringsAsFactors=FALSE, + header=FALSE, col.names=c("pct", "area_id")) > > areas <- read.csv("https://rud.is/dl/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_2014, xend=pct_2013, y=area_name, group=area_name)) + ggtitle("by Volkan OBAN using R ") > gg <- gg + geom_dumbbell(colour="#a3c4dc", size=1.5, colour_xend="#0e668b", + dot_guide=TRUE, dot_guide_size=0.15) > > gg > gg + theme_wsj() + scale_colour_wsj("colors6", "") > gg + theme_hc(bgcolor = "darkunica") + + scale_colour_hc("darkunica") >

> library("ggstance") > > # Horizontal with ggstance > ggplot(mpg, aes(hwy, class, fill = factor(cyl))) + + geom_boxploth()

ref: http://kbroman.org/datacarpentry_R_2016-06-01/04-ggplot2.html

ref: http://kbroman.org/datacarpentry_R_2016-06-01/04-ggplot2.html

ref: http://kbroman.org/datacarpentry_R_2016-06-01/04-ggplot2.html

ref: http://kbroman.org/datacarpentry_R_2016-06-01/04-ggplot2.html

ref: http://kbroman.org/datacarpentry_R_2016-06-01/04-ggplot2.html

ref: http://kbroman.org/datacarpentry_R_2016-06-01/04-ggplot2.html

ref: http://kbroman.org/datacarpentry_R_2016-06-01/04-ggplot2.html

ref http://kbroman.org/datacarpentry_R_2016-06-01/04-ggplot2.html

a<- ggplot(surveys_complete, aes(x = species_id, y = hindfoot_length)) + geom_boxplot() a + theme_economist() + scale_colour_economist() + + scale_y_continuous(position = "right"

> plotSparklineTable(Theoph, row.var = 'Subject', col.vars = 'conc')

> msr <- file.path( find.package("epanetReader"), "extdata","example.rpt") > #read the results into R > x <- read.msxrpt(msr) > names(x) [1] "Title" "nodeResults" "linkResults" > summary(x) plot(x)

> mydf <- data.frame(y = as.factor(y), + sex = to_factor(efc$c161sex), + dep = to_factor(efc$e42dep), + barthel = efc$barthtot, + education = to_factor(efc$c172code)) > # fit model > fit <- glm(y ~., data = mydf, family = binomial(link = "logit")) # plot probability curves (relationship between predictors and response) > sjp.glm(fit, title = " Negative impact with 7 items", type = "slope")

airgrp <- sjc.qclus(airquality) sjc.qclus(airquality, groupcount = 3, groups = airgrp$classification, title=" new k-means cluster analysis")

a<-ggplot(mtcars, aes(x = mpg^2, y = wt/cyl)) + geom_smooth(fill="purple",color="pink",size=2) + geom_jitter(color="darkgreen",shape=2) + geom_point(color="yellow") + ggtitle("by Volkan OBAN using R ") a

> day=as.Date("2017-06-14") - 0:364 > value=runif(365) + seq(-140, 224)^2 / 10000 > data=data.frame(day, value) > data %>% mutate(month = as.Date(cut(day, breaks = "month"))) %>% + ggplot(aes(x=day, y=value, fill=as.factor(month))) + + geom_line() + + geom_area() + + theme( + legend.position="none", + axis.text.x=element_blank(), + axis.ticks.x=element_blank(), + strip.background = element_rect(fill=alpha("slateblue",0.2)), + strip.placement="bottom" + ) + + xlab("by Volkan OBAN using R \n faceting for time series") + + facet_wrap(~as.Date(month), scales="free", ncol=3) + theme_tufte(ticks=FALSE) + + geom_tufteboxplot(median.type = "line", whisker.type = 'line', hoffset = 0, width = 3)

library(plotly) library(quantmod) # get data getSymbols("AAPL",src='yahoo') df <- data.frame(Date=index(AAPL),coredata(AAPL)) # create Bollinger Bands bbands <- BBands(AAPL[,c("AAPL.High","AAPL.Low","AAPL.Close")]) # join and subset data df <- subset(cbind(df, data.frame(bbands[,1:3])), Date >= "2015-02-14") # colors column for increasing and decreasing for (i in 1:length(df[,1])) { if (df$AAPL.Close[i] >= df$AAPL.Open[i]) { df$direction[i] = 'Increasing' } else { df$direction[i] = 'Decreasing' } } i <- list(line = list(color = '#17BECF')) d <- list(line = list(color = '#7F7F7F')) # plot candlestick chart p <- df %>% plot_ly(x = ~Date, type="candlestick", open = ~AAPL.Open, close = ~AAPL.Close, high = ~AAPL.High, low = ~AAPL.Low, name = "AAPL", increasing = i, decreasing = d) %>% add_lines(y = ~up , name = "B Bands", line = list(color = '#ccc', width = 0.5), legendgroup = "Bollinger Bands", hoverinfo = "none") %>% add_lines(y = ~dn, name = "B Bands", line = list(color = '#ccc', width = 0.5), legendgroup = "Bollinger Bands", showlegend = FALSE, hoverinfo = "none") %>% add_lines(y = ~mavg, name = "Mv Avg", line = list(color = '#E377C2', width = 0.5), hoverinfo = "none") %>% layout(yaxis = list(title = "Price")) # plot volume bar chart pp <- df %>% plot_ly(x=~Date, y=~AAPL.Volume, type='bar', name = "AAPL Volume", color = ~direction, colors = c('#17BECF','#7F7F7F')) %>% layout(yaxis = list(title = "Volume")) # create rangeselector buttons rs <- list(visible = TRUE, x = 0.5, y = -0.055, xanchor = 'center', yref = 'paper', font = list(size = 9), buttons = list( list(count=1, label='RESET', step='all'), list(count=1, label='1 YR', step='year', stepmode='backward'), list(count=3, label='3 MO', step='month', stepmode='backward'), list(count=1, label='1 MO', step='month', stepmode='backward') )) # subplot with shared x axis p <- subplot(p, pp, heights = c(0.7,0.2), nrows=2, shareX = TRUE, titleY = TRUE) %>% layout(title = paste("Apple: 2015-02-14 -",Sys.Date()), xaxis = list(rangeselector = rs), legend = list(orientation = 'h', x = 0.5, y = 1, xanchor = 'center', yref = 'paper', font = list(size = 10), bgcolor = 'transparent'))

library(plotly) library(quantmod) getSymbols("AAPL",src='yahoo') df <- data.frame(Date=index(AAPL),coredata(AAPL)) # annotation a <- list(text = "Stock Split", x = '2014-06-06', y = 1.02, xref = 'x', yref = 'paper', xanchor = 'left', showarrow = FALSE ) # use shapes to create a line l <- list(type = line, x0 = '2014-06-06', x1 = '2014-06-06', y0 = 0, y1 = 1, xref = 'x', yref = 'paper', line = list(color = 'black', width = 0.5) ) p <- df %>% plot_ly(x = ~Date, type="candlestick", open = ~AAPL.Open, close = ~AAPL.Close, high = ~AAPL.High, low = ~AAPL.Low) %>% layout(title = "Apple Stock", annotations = a, shapes = l)

getSymbols("AAPL") chartSeries(AAPL) title(" quantmod ", sub = "", cex.main = 1, font.main= 2, col.main= "green", cex.sub = 0.75, font.sub =1, col.sub = "red")

a<- ggpairs(iris) a

pairs.panels(iris[1:4],bg=c("red","purple","blue")[iris$Species],pch=21,main=" Fisher Iris data by Species",hist.col="purple")

Show in New WindowClear OutputExpand/Collapse Output shiny.tag shiny.tag shiny.tag shiny.tag shiny.tag shiny.tag shiny.tag shiny.tag Show in New WindowClear OutputExpand/Collapse Output Error: unexpected symbol in: " print(p)Show" Modify Chunk OptionsRun All Chunks AboveRun Current ChunkModify Chunk OptionsRun All Chunks AboveRun Current ChunkModify Chunk OptionsRun All Chunks AboveRun Current Chunk Console~/ > library(miniCRAN) > library(igraph) > > > pk <- c("igraph","agop","bc3net","BDgraph","c3net","camel", + "cccd", "CDVine", "CePa", "CINOEDV", "cooptrees","corclass", "cvxclustr", "dcGOR", + "ddepn","dils", "dnet", "dpa", "ebdbNet", "editrules", + "fanovaGraph", "fastclime", "FisHiCal", + "flare", "G1DBN", "gdistance", "GeneNet", "GeneReg", "genlasso", "ggm", "gRapfa", "hglasso", + "huge", "igraphtosonia", "InteractiveIGraph", "iRefR", "JGL", "lcd", "linkcomm", "locits", + "loe", "micropan", "mlDNA", "mRMRe", "nets", "netweavers", "optrees", "packdep", "PAGI", + "pathClass", "PBC", "phyloTop", "picasso", "PoMoS", "popgraph", "PROFANCY", "qtlnet", "RCA", + "ReliabilityTheory", "rEMM", "restlos", "rgexf", "RNetLogo", "ror", "RWBP", "sand", "SEMID", + "shp2graph", "SINGLE", "spacejam", "TDA", "timeordered", "tnet") > > > dg <- makeDepGraph(pk) > plot(dg,main=" Network of reverse depends for igraph",cex=.4,vertex.size=8)

> require(graphics) > > fr <- function(x) { ## Rosenbrock Banana function + x1 <- x[1] + x2 <- x[2] + 100 * (x2 - x1 * x1)^2 + (1 - x1)^2 + } > grr <- function(x) { ## Gradient of 'fr' + x1 <- x[1] + x2 <- x[2] + c(-400 * x1 * (x2 - x1 * x1) - 2 * (1 - x1), + 200 * (x2 - x1 * x1)) + } > optim(c(-1.2,1), fr) > (res <- optim(c(-1.2,1), fr, grr, method = "BFGS")) > optimHess(res$par, fr, grr) > optim(c(-1.2,1), fr, NULL, method = "BFGS", hessian = TRUE) > ## These do not converge in the default number of steps > optim(c(-1.2,1), fr, grr, method = "CG") > optim(c(-1.2,1), fr, grr, method = "CG", control = list(type = 2)) > optim(c(-1.2,1), fr, grr, method = "L-BFGS-B") > > flb <- function(x) + { p <- length(x); sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2) } > ## 25-dimensional box constrained > optim(rep(3, 25), flb, NULL, method = "L-BFGS-B", + lower = rep(2, 25), upper = rep(4, 25)) # par[24] is *not* at boundary > > ## "wild" function , global minimum at about -15.81515 > fw <- function (x) + 10*sin(0.3*x)*sin(1.3*x^2) + 0.00001*x^4 + 0.2*x+80 > plot(fw, -50, 50, n = 1000, main = "optim() minimising 'wild function'") > > res <- optim(50, fw, method = "SANN", + control = list(maxit = 20000, temp = 20, parscale = 20)) > res > ## Now improve locally {typically only by a small bit}: > (r2 <- optim(res$par, fw, method = "BFGS")) > points(r2$par, r2$value, pch = 8, col = "red", cex = 2) > > ## Combinatorial optimization: Traveling salesman problem > library(stats) # normally loaded > > eurodistmat <- as.matrix(eurodist) > > distance <- function(sq) { # Target function + sq2 <- embed(sq, 2) + sum(eurodistmat[cbind(sq2[,2], sq2[,1])]) + } > > genseq <- function(sq) { # Generate new candidate sequence + idx <- seq(2, NROW(eurodistmat)-1) + changepoints <- sample(idx, size = 2, replace = FALSE) + tmp <- sq[changepoints[1]] + sq[changepoints[1]] <- sq[changepoints[2]] + sq[changepoints[2]] <- tmp + sq + } > > sq <- c(1:nrow(eurodistmat), 1) # Initial sequence: alphabetic > distance(sq) [1] 29625 > # rotate for conventional orientation > loc <- -cmdscale(eurodist, add = TRUE)$points > x <- loc[,1]; y <- loc[,2] > s <- seq_len(nrow(eurodistmat)) > tspinit <- loc[sq,] > > plot(x, y, type = "n", asp = 1, xlab = "", ylab = "", + main = "prepared by Volkan OBAN using R stats package + optim \n initial solution of traveling salesman problem", axes = FALSE) > arrows(tspinit[s,1], tspinit[s,2], tspinit[s+1,1], tspinit[s+1,2], + angle = 10, col = "green") > text(x, y, labels(eurodist), cex = 0.8) > > set.seed(123) # chosen to get a good soln relatively quickly > res <- optim(sq, distance, genseq, method = "SANN", + control = list(maxit = 30000, temp = 2000, trace = TRUE, + REPORT = 500)) > tspres <- loc[res$par,] > plot(x, y, type = "n", asp = 1, xlab = "", ylab = "", + main = "prepared by Volkan OBAN using R stats package optim \n optim() 'solving' traveling salesman problem", axes = FALSE) > arrows(tspres[s,1], tspres[s,2], tspres[s+1,1], tspres[s+1,2], + angle = 10, col = "red") > text(x, y, labels(eurodist), cex = 0.8) >

http://www.sthda.com/english/wiki/ggfortify-extension-to-ggplot2-to-handle-some-popular-packages-r-software-and-data-visualization

http://www.sthda.com/english/wiki/ggfortify-extension-to-ggplot2-to-handle-some-popular-packages-r-software-and-data-visualization

> par(mar=c(0,0,0,0)) > pie(abs(rnorm(150)) , radius=10 , border="transparent" , xlim=c(0,5) )

> xyplot(stl(log(co2), s.window=21), + main = "STL decomposition of CO2 data")

library(ggplot2) # Here's some data I had lying around tb <- structure(list(region = c("Africa", "Asia", "Latin America", "Other", "US-born"), ncases = c(36L, 34L, 56L, 2L, 44L)), .Names = c("region", "ncases"), row.names = c(NA, -5L), class = "data.frame") # A bar chart of counts ggplot(tb, aes(x = region, weight = ncases, fill = region)) + geom_bar() # Bar chart of percentages ggplot(tb, aes(x = region, weight = ncases/sum(ncases), fill = region)) + geom_bar() + scale_y_continuous(formatter = 'percent') # Pie chart equivalents. Forgive me, Hadley, for I must sin. ggplot(tb, aes(x = factor(1), weight = ncases, fill = region)) + geom_bar(width = 1) + coord_polar(theta = "y") + labs(x = "", y = "") ggplot(tb, aes(x = factor(1), weight = ncases/sum(ncases), fill = region)) + geom_bar() + scale_y_continuous(formatter = 'percent') + coord_polar(theta = "y") + labs(x = "", y = "") # Waffles # How many rows do you want the y axis? ndeep <- 5 # I need to convert my data into a data.frame with a unique specified x # and y axis for each case # Note - it's actually important to specify y first for a # horizontally-accumulating waffle tb4waffles <- expand.grid(y = 1:ndeep, x = seq_len(ceiling(sum(tb$ncases) / ndeep))) # Expand the counts into a full vector of region labels - i.e., de-aggregate regionvec <- rep(tb$region, tb$ncases) # Depending on the value of ndeep, there might be more spots on the x-y grid # than there are cases - so fill those with NA tb4waffles$region <- c(regionvec, rep(NA, nrow(tb4waffles) - length(regionvec))) # Plot it ggplot(tb4waffles, aes(x = x, y = y, fill = region)) + geom_tile(color = "white") + # The color of the lines between tiles scale_fill_manual("Region of Birth", values = RColorBrewer::brewer.pal(5, "Dark2")) + opts(title = "TB Cases by Region of Birth")

http://harrycaufield.net/severalog/2016/7/29/8jt1lrt7hd2vqh4heu7mcuqrdcg0od

ref. and code: http://harrycaufield.net/severalog/2016/7/29/8jt1lrt7hd2vqh4heu7mcuqrdcg0od

> data(flo) > nflo<-network(flo) > #Display the network, indicating degree and flagging the Medicis > plot(nflo, vertex.cex=apply(flo,2,sum)+1, usearrows=FALSE,vertex.sides=3+apply(flo,2,sum),vertex.col=2+(network.vertex.names(nflo)=="Medici"))

> library(plotly) > library(igraph) > > data(karate, package="igraphdata") > G <- upgrade_graph(karate) > L <- layout.circle(G) > vs <- V(G) > es <- as.data.frame(get.edgelist(G)) > > Nv <- length(vs) > Ne <- length(es[1]$V1) > Xn <- L[,1] > Yn <- L[,2] > > network <- plot_ly(x = ~Xn, y = ~Yn, mode = "markers", text = vs$label, hoverinfo = "text") > edge_shapes <- list() > for(i in 1:Ne) { + v0 <- es[i,]$V1 + v1 <- es[i,]$V2 + + edge_shape = list( + type = "line", + line = list(color = "#030303", width = 0.3), + x0 = Xn[v0], + y0 = Yn[v0], + x1 = Xn[v1], + y1 = Yn[v1] + ) + + edge_shapes[[i]] <- edge_shape + } > axis <- list(title = "", showgrid = FALSE, showticklabels = FALSE, zeroline = FALSE) > > p <- layout( + network, + title = 'by Volkan OBAN using R - igraph \n Karate Network', + shapes = edge_shapes, + xaxis = axis, + yaxis = axis + ) > p

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)