housetasks_tab = as.table(as.matrix(housetasks)) balloonplot(t(housetasks_tab) ,xlab = "" , ylab = "" , show.margins =F , label = F , main = "House Tasks" )

ggboxplot(data = ToothGrowth , x = "dose" ,y = "len" , color = "supp" ,palette =c( "#E69F00", "#56B4E9" ,"#CC79A7") , title = "ToothGrowth" ,xlab = "Dose" , ylab ="Tooth Length" )

PlantGrowth PlantGrowth%>% group_by(group)%>% summarise( count = n() , mean(weight) , sd(weight) ) palette.colors() ggboxplot(data = PlantGrowth , x = "group" ,y = "weight" , color = "group" ,palette =c( "#E69F00", "#56B4E9" ,"#CC79A7") , title = "Treatment Impact" ,xlab = "Group" , ylab ="Weight" ) with(PlantGrowth ,shapiro.test(weight[group == "ctrl"]) ) with(PlantGrowth ,shapiro.test(weight[group == "trt1"]) ) with(PlantGrowth ,shapiro.test(weight[group == "trt2"]) ) plant_avo = aov(weight~group , data = PlantGrowth) summary(plant_avo) TukeyHSD(plant_avo) Y = plot(plant_avo , 2)

PlantGrowth PlantGrowth%>% group_by(group)%>% summarise( count = n() , mean(weight) , sd(weight) ) palette.colors() ggboxplot(data = PlantGrowth , x = "group" ,y = "weight" , color = "group" ,palette =c( "#E69F00", "#56B4E9" ,"#CC79A7") , title = "Treatment Impact" ,xlab = "Group" , ylab ="Weight" ) with(PlantGrowth ,shapiro.test(weight[group == "ctrl"]) ) with(PlantGrowth ,shapiro.test(weight[group == "trt1"]) ) with(PlantGrowth ,shapiro.test(weight[group == "trt2"]) ) plant_avo = aov(weight~group , data = PlantGrowth) summary(plant_avo) TukeyHSD(plant_avo) X = plot(plant_avo , 1)

pr = paired(Before , After) plot(pr , type = "profile") + theme_light()

women_weight <- c(38.9, 61.2, 73.3, 21.8, 63.4, 64.6, 48.4, 48.8, 48.5) men_weight <- c(67.8, 60, 63.4, 76, 89.4, 73.3, 67.3, 61.3, 62.4) gender_weight = data.frame( gender =rep(c("men", "women") , each = 9) , weight = c(men_weight , women_weight) ) gender_weight%>% group_by(gender)%>% summarise( count = n() , mean(weight) , sd(weight) ) ggboxplot(data = gender_weight , x = "gender" ,y = "weight" , color = "gender" ,palette =c( "#E69F00", "#56B4E9") , title = "Is weight differance" ,xlab = "Gender" , ylab ="weight" ) with(gender_weight ,shapiro.test(weight[gender == "men"]) ) with(gender_weight ,shapiro.test(weight[gender == "women"]) ) t.test(weight ~ gender , data = gender_weight , var.equal = T )

H.H. = read.table("bionform/GSE46224_Yang_et_al_human_heart_RNASeq.txt/GSE46224_Yang_et_al_human_heart_RNASeq.txt") colnames(H.H.) = H.H.[1,] rownames(H.H.) = H.H.[ , 1] Human_H = H.H.[-1,-1] class(Human_H) # PREPARATION Human_H Human_Hr =as.matrix(Human_H) is.matrix(Human_Hr) Human_Hrt = apply(X = Human_Hr, MARGIN = 1 , FUN = var) # SELECT THE MOST 100 VARIANCE GENE Human_gene = names(Human_Hrt[order(Human_Hrt , decreasing = T)][1:100]) human_heart = Human_H[Human_gene , ] human_heart_fl = apply(human_heart , 2 ,FUN = as.numeric) pheatmap(mat = human_heart_fl , scale = "row" , cluster_rows = T , cluster_cols = T , cutree_cols = 5 , show_rownames = T , show_colnames = T, main = "Human_Heart data") str(human_heart_fl)

ggplot(data = mpg , mapping = aes(x= drv,y = hwy,color = displ < 5))+ geom_point()+ facet_wrap(~ class)+ theme_light()+ ggtitle("mpg Data")+ xlab("he type of drive train")+ ylab("highway miles per gallon")

ggplot(data = mpg , mapping = aes(x = drv , y = displ ))+ geom_violin( fill = "green" )+ geom_boxplot(width = 0.2, fill = "red")+ facet_grid(~ class)+ theme_light()+ ggtitle("mpg data")+ xlab("The type of drive train")+ ylab("Engine displacement, in litres")

ggplot(data = diamonds, mapping = aes(x= price,y= carat , color = cut ))+ geom_point()+ theme_light()+ facet_wrap(~cut)+ xlab("Diamonds Price")+ ylab("Diamond Carat") + ggtitle("Scatterplot")

ggplot(data = economics, mapping = aes(x= pop,y= psavert , color = uempmed ))+ geom_point()+ theme_light()+ xlab("Population")+ ylab("Save_money") + ggtitle("Negative_linear correlation")