Creating Charts and Maps with ggplot2

Data Visualization with R

In this article, I will work on the following visualizations:

Rudimentary Visualizations:

1. Scatter plot
2. Histogram
3. Line Plot
4. Bar chart
5. Box plot

Advanced Visualizations:

1. Area Chart
2. Heat Map
3. Correlogram
4. Hexbin Plot
5. Mosaic Map
6. Map Visualization
7. 3D Graphs

I will work on the 'Big Mart data' data sets as well as some of the small data sets provided in the HistData package.

#load the Big Mart data set

data=read.csv("/Users/Srinka_MAC/Documents/Praxis/Term3/DVL/BigMartDataset.csv")

train = data

Rudimentary Visualizations:

Scatter Plot

library(ggplot2)          

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() 

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

library(HistData)

library(RColorBrewer)

data(VADeaths)

par(mfrow=c(2,3))

hist(VADeaths,breaks=10, col=brewer.pal(3,"Set3"),main="Set3 3 colors")

hist(VADeaths,breaks=3 ,col=brewer.pal(3,"Set2"),main="Set2 3 colors")

hist(VADeaths,breaks=7, col=brewer.pal(3,"Set1"),main="Set1 3 colors")

hist(VADeaths,,breaks= 2, col=brewer.pal(8,"Set3"),main="Set3 8 colors")

hist(VADeaths,col=brewer.pal(8,"Greys"),main="Greys 8 colors")

hist(VADeaths,col=brewer.pal(8,"Greens"),main="Greens 8 colors")

Line Plot

plot(AirPassengers,type="l")

Bar Chart

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()

Vertical Bar Chart

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 chart

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")

Advanced Visualizations:

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") 

Heat Map

ggplot(train, aes(Outlet_Identifier, Item_Type))+geom_raster(aes(fill = Item_MRP))+

 labs(title ="Heat Map", x = "Outlet Identifier", y = "Item Type")+

scale_fill_continuous(name = "Item MRP") 

Correlogram

install.packages("corrgram")

library(corrgram)

corrgram(train, order=NULL, panel=panel.shade, text.panel=panel.txt,

         main="Correlogram") 

Hexbin plot

library(hexbin)

a=hexbin(diamonds$price,diamonds$carat,xbins=40)

library(RColorBrewer)

plot(a)

library(RColorBrewer)
rf <- colorRampPalette(rev(brewer.pal(40,'Set3')))
hexbinplot(diamonds$price~diamonds$carat, data=diamonds, colramp=rf)

Mosaic Plot

data(HairEyeColor)

mosaicplot(HairEyeColor)

Map Visualization

devtools::install_github("rstudio/leaflet")

library(magrittr)

library(leaflet)

m <- leaflet() %>%

  addTiles() %>%  # Add default OpenStreetMap map tiles

  addMarkers(lng=88.3630400, lat=22.5626300)

m  # Print the map

3D Graph

library(Rcmdr)

data(iris, package="datasets")

scatter3d(Petal.Width~Petal.Length+Sepal.Length|Species, data=iris, fit="linear", residuals=TRUE, parallel=FALSE, bg="white", axis.scales=TRUE, grid=TRUE, ellipsoid=FALSE)

library(lattice)

attach(iris)# 3d scatterplot by factor level

cloud(Sepal.Length~Sepal.Width*Petal.Length|Species, main="3D Scatterplot by Species")

xyplot(Sepal.Width ~ Sepal.Length, iris, groups = iris$Species, pch= 20)