Data visualization with multiple groups using ggplot2
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This document provides instructions on data visualization techniques using R's ggplot2 package, including creating histograms, boxplots, density plots, and bin2d plots. It demonstrates how to differentiate data distributions based on factors such as 'cyl', 'gear', and 'carb' in the mt_cars dataset. The document concludes with a mention of an upcoming case study to apply these visualization methods.
Data visualization with multiple groups using ggplot2
- 1. Data Visualization - III By using geom_histogram(), facet(), geom_density(), geom_boxplot(), geom_bin2d() Rupak Roy
- 2. >h<-ggplot(mt_cars,aes(x=mpg)) #differentiate the distribution ‘mpg’ based on ‘cyl’ >h+geom_histogram(aes(fill=cyl), position = "dodge") #position =‘dodge’ is optional #plotting multiple groups using facet >h+geom_histogram(aes(fill=cyl))+facet_grid(cyl~.) >h+geom_histogram(aes(fill=cyl))+facet_grid(.~gear) >h+geom_histogram(aes(fill=cyl))+facet_grid(cyl~gear) ggplot::geom_histogram()
- 3. #boxplots >mt_cars$carb<-as.factor(mt_cars$carb) >b<-ggplot(mt_cars,aes(x=carb,y=disp)) >b+geom_boxplot(aes(colour=carb)) #boxplots with multiple groups >mt_cars$cyl<-as.factor(mt_cars$cyl) >b<-ggplot(mt_cars,aes(x=carb,y=disp)) >b+geom_boxplot(aes(colour=cyl)) ggplot:geom_boxplot()
- 4. #density plots >d<-ggplot(mt_cars,aes(x=mpg)) >d+geom_density() #improving the density plot >d+geom_density(aes(fill=carb,color=carb),alpha=0.4) ggplot:: geom_density()
- 5. #geom_bin2d() >d2<-ggplot(iris,aes(x=Sepal.Length,y=Petal.Length)) >d2+geom_bin2d(aes(colour=Species)) #we can also create heatmaps with geom_title() and offcourse with geom_bin2d() ggplot:: geom_bin2d() Rupak Roy
- 6. Next: We will use a small case study to understand the data by using the visualization methods that we have learned so far. Data Visualization - III Rupak Roy