Data visualization with multiple groups using ggplot2

Data Visualization - III
By using geom_histogram(), facet(), geom_density(),
geom_boxplot(), geom_bin2d()
Rupak Roy

>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()

#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()

#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()

#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

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

Data visualization with multiple groups using ggplot2

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