Visualization Class 3

Introduction 

In the previous Blog post I showed the basic features of ggplot. In this blogpost I will show some of the advance features that are present in ggplot2 library of R. 

We will see the 7 charts that can be used for visualisation

1. Scatter Plot
2. Histogram
3. Bar & Stack Bar Chart
4. Box Plot
5. Area Chart
6. Heat Map
7. Correlogram 

Loading Dataset and Library in R

train = read.csv("/home/sanket/praxis/DVL/Big Mart Dataset - Sheet1.csv")

library(ggplot2)        

#Basic gqplot as we seen in the previous post

ggplot(train, aes(Item_Visibility, Item_MRP)) + geom_point() + 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() 

Scatter Plot : 

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() + labs(title="Scatterplot")

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() + labs(title="Scatterplot") + facet_wrap( ~ Item_Type)

Histogram :

ggplot(train, aes(Item_MRP)) + geom_histogram(binwidth = 2)+
  scale_x_continuous("Item MRP", breaks = seq(0,270,by = 30))+
  scale_y_continuous("Count", breaks = seq(0,200,by = 20))+
  labs(title = "Histogram")

ggplot(train, aes(Outlet_Establishment_Year)) + geom_bar(fill = "red")+theme_bw()+
  scale_x_continuous("Establishment Year", breaks = seq(1985,2010)) + 
  scale_y_continuous("Count", breaks = seq(0,1500,150)) +
  coord_flip()+ labs(title = "Bar Chart") + theme_gray()

Bar Graph :

ggplot(train, aes(Item_Type, Item_Weight)) + geom_bar(stat = "identity", fill = "darkblue") + scale_x_discrete("Outlet Type")+ scale_y_continuous("Item Weight", breaks = seq(0,15000, by = 500))+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) + labs(title = "Bar Chart")

# Stacked Bar Graph

ggplot(train, aes(Outlet_Location_Type, fill = Outlet_Type)) + geom_bar()+
  labs(title = "Stacked Bar Chart", x = "Outlet Location Type", y = "Count of Outlets")

Box Plot:

ggplot(train, aes(Outlet_Identifier, Item_Outlet_Sales)) + geom_boxplot(fill = "red")+
  scale_y_continuous("Item Outlet Sales", breaks= seq(0,15000, by=500))+
  labs(title = "Box Plot", x = "Outlet Identifier")

Area Chart :

ggplot(train, aes(Item_Outlet_Sales)) + geom_area(stat = "bin", bins = 30, fill = "steelblue") + scale_x_continuous(breaks = seq(0,11000,1000))+ labs(title = "Area Chart", x = "Item Outlet Sales", y = "Count")

Correlogram :

#install.packages("corrgram")
library(corrgram)
corrgram(train, order=NULL, panel=panel.shade, text.panel=panel.txt,
         main="Correlogram")