ggplot2

ggplot2 is one of the packages which comes with tidyverse(A package for Data Science) The purpose of visualization is getting insight about data. Data visualization have made simple using ggplot2.
ggplot2 works based on the idea called grammar of graphics, which means we have to consider following components while creating plots.

1. Data
   Dataset which we use
2. Aesthetics
   Customising look and feel
3. Geometry
   Scaling the plot in a way that it displays data better
4. Facet
   Grouping data based on various characteristics so that individual impacts are determined

We shall look at how to visualize data using examples. For this let us Consider broad data types

1. Metric data
2. Categorical data

We shall see how to make beautiful visualizations based on data

We need to install and load ggplot2 package

library(ggplot2)

Dataset used

ggplot2 package have a data called diamonds, let us use the data for visualization

data("diamonds", package = "ggplot2") 

The data provides prices of over 50,000 round cut diamonds

Metric variable

Initial ggplot
For creating a plot using ggplot2 we need to provide data in ggplot(data,aes(x=variable we wish to display by x axis,y=variable we wish to display by y axis))
Then we specify the geometry of plot

x= seq(1,length(diamonds$price))

ggplot(diamonds, aes(x=x,y=price)) 

ggplot(diamonds, aes(x=x,y=price))+geom_point() 

ggplot(diamonds, aes(x=x,y=price))+geom_point()  + 
  geom_point(size=4,shape=2,color='blue') 

#saving as a variable g
g=ggplot(diamonds, aes(x=depth,y=price))+geom_point()  + 
  geom_point(size=4,shape=4,color='blue') 

g1=g+ labs(title="Diamond data",
            subtitle = "cut depth vs price ",
            x="depth", 
            y="Price",caption="Scatter plot")
g1

g3=g1+theme(
    plot.title = element_text(color="red", size=14, face="bold.italic"),
    plot.subtitle = element_text(color="green4", size=14, face="italic"),
    axis.title.x = element_text(color="blue", size=14, face="bold"),
    axis.title.y = element_text(color="brown", size=14, face="bold"),
    plot.caption =  element_text(color="steelblue", size=14, face="italic"))
g3

g1+
  theme(plot.title=element_text(hjust=0.5,vjust=0.5,
    color="tomato",size=14, face="bold.italic"),
            plot.subtitle=element_text(size=15, face="bold",hjust=0.5),  
 
            plot.caption=element_text(size=15,face="bold.italic",color="blue3"),  
      
            axis.title.x=element_text(vjust=10, hjust=0.1,size=10),  
      
            axis.title.y=element_text(size=15,angle = 270),  
      
            axis.text.x=element_text(size=10, angle = 30,vjust=.5),  
            
            axis.text.y=element_text(size=10)) 

g3+
coord_cartesian(xlim=c(55, 80),ylim=c(400, 500))

g3+scale_x_continuous(breaks=seq(40,80,2))

g3+scale_y_continuous(breaks=seq(0,20000,1000))

facet_plot=ggplot(diamonds,aes(price,fill=color))+
  geom_histogram()+facet_grid(clarity~cut)

facet_plot

Categorical variable

If we wish to know about single categorical variable, counting the number of times particular category is present will be our action of interest. To Visualize this we use bar plot

ggplot(diamonds,aes(x=cut))+
  geom_bar()

Changing color and displaying the count on the box and giving title, subtitle and axis labels can be done using

ggplot(diamonds,aes(x=cut))+
  geom_bar(col="red2",fill="green3")+
geom_text(aes(label = ..count..), stat = "count",
          vjust=1, size=3.5,color='yellow')+
       labs(title="Distribution of cut quality",
            subtitle = "",
            x="Cut category", 
            y="Count")

Horizontal bar plot can also be obtained

ggplot(diamonds,aes(x=cut))+
  geom_bar(col="red2",fill="green3")+
  coord_flip()

Grouped bar chart can be obtained using following command, here we use ‘color’ for grouping

ggplot(diamonds,aes(x=cut,fill=as.factor(color)))+     
  geom_bar() +
       labs(title="Distribution of Cut quality",
            subtitle = "Group: Color",
            x="Cut category", 
            y="Count")

To compare data over two categories we can use stacked bar chart, it can be obtained using

ggplot(diamonds,aes(x=cut,fill=as.factor(color)))+     
       geom_bar(position=position_dodge()) +
       labs(title="Distribution of Cut quality",
            subtitle = "Group: Color",
            x="Cut category", 
            y="Count")

Pie diagram can be obrained using

g<-ggplot(diamonds,
       aes(x = factor(""), fill = cut) ) +
  geom_bar() +
  coord_polar(theta = "y") +
  scale_x_discrete("")
g

Colors can be customised using

g+scale_fill_brewer(palette = "Set2")

Removal of White circle and plot titles

ggplot(diamonds,
         aes(x = factor(""), fill = cut) ) +
    geom_bar() +
    coord_polar(theta = "y") +
    scale_x_discrete("")+
    scale_y_discrete("")+
  theme(axis.ticks=element_blank(),  
        axis.title=element_blank(),  
        axis.text.y=element_blank(),
        axis.text.x=element_blank(),
        panel.grid  = element_blank(),
        legend.position = "bottom")+
    labs(title = "Cut category",
         caption="Diamonds dataset")