GeoMapping_STATES

India's Map Using Tableau

Plots Using Tableau

Plotting State,District,Taluk level maps Using R

Working with R, we will need two R packages :

# Load required libraries
library(sp)
library(RColorBrewer)

# load level 1 india data downloaded from http://gadm.org/country
load("IND_adm1.RData")
ind1=readRDS("IND_adm1.rds")
spplot(ind1, "NAME_1", scales=list(draw=T), colorkey=F, main="India")

# map of India with states coloured with an arbitrary fake data

ind1$NAME_1 = as.factor(ind1$NAME_1)

ind1$fake.data = runif(length(ind1$NAME_1))

spplot(ind1,"NAME_1",  col.regions=rgb(0,ind1$fake.data,0), colorkey=T, main="Indian States")

and then executing these commands :

# map of West Bengal ( or any other state )

wb1 = (ind1[ind1$NAME_1=="West Bengal",])

spplot(wb1,"NAME_1", col.regions=rgb(0,0,1), main = "West Bengal, India",scales=list(draw=T), colorkey =F)

# map of Karnataka ( or any other state )

kt1 = (ind1[ind1$NAME_1=="Karnataka",])

spplot(kt1,"NAME_1", col.regions=rgb(0,1,0), main = "Karnataka, India",scales=list(draw=T), colorkey =F)

# load level 2 india data downloaded from http://gadm.org/country
load("IND_adm2.RData")

ind2=readRDS("IND_adm2.rds")

and then plot the various districts as

# plotting districts of a State, in this case West Bengal

wb2 = (ind2[ind2$NAME_1=="West Bengal",])

spplot(wb2,"NAME_1", main = "West Bengal Districts", colorkey =F)

To identify each district with a beautiful colour we can use the following commands :

# colouring the districts with rainbow of colours

wb2$NAME_2 = as.factor(wb2$NAME_2)

col = rainbow(length(levels(wb2$NAME_2)))

spplot(wb2,"NAME_2",  col.regions=col, colorkey=T)

# colouring the districts with some simulated, fake data

wb2$NAME_2 = as.factor(wb2$NAME_2)

wb2$fake.data = runif(length(wb2$NAME_1)) 

spplot(wb2,"NAME_2",  col.regions=rgb(0,wb2$fake.data, 0), colorkey=T)

# colouring the districts with range of colours

col_no = as.factor(as.numeric(cut(wb2$fake.data, c(0,0.2,0.4,0.6,0.8,1))))

levels(col_no) = c("<20%", "20-40%", "40-60%","60-80%", ">80%")

wb2$col_no = col_no

myPalette = brewer.pal(5,"Greens")

spplot(wb2, "col_no", col=grey(.9), col.regions=myPalette, main="District Wise Data")

# load level 3 india data downloaded from http://gadm.org/country
load("IND_adm3.RData")

ind3=readRDS("IND_adm3.rds")

# extracting data for West Bengal

wb3 = (ind3[ind3$NAME_1=="West Bengal",])

and then plot the subdivision or taluk level map as follows :
  
#plotting districts and sub-divisions / taluk
wb3$NAME_3 = as.factor(wb3$NAME_3)
col = rainbow(length(levels(wb3$NAME_3)))

spplot(wb3,"NAME_3", main = "Taluk, District - West Bengal", colorkey=T,col.regions=col,scales=list(draw=T))

# get map for "North 24 Parganas District"

wb3 = (ind3[ind3$NAME_1=="West Bengal",])

n24pgns3 = (wb3[wb3$NAME_2=="North 24 Parganas",])

spplot(n24pgns3,"NAME_3", colorkey =F, scales=list(draw=T), main = "24 Pgns (N) West Bengal")

# now draw the map of Basirhat subdivision

# recreate North 24 Parganas data

n24pgns3 = (wb3[wb3$NAME_2=="North 24 Parganas",])

basirhat3 = (n24pgns3[n24pgns3$NAME_3=="Basirhat",])

spplot(basirhat3,"NAME_3", colorkey =F, scales=list(draw=T), main = "Basirhat,24 Pgns (N) West Bengal")

# zoomed in data

wb2 = (ind2[ind2$NAME_1=="West Bengal",])

wb2$NAME_2 = as.factor(wb2$NAME_2)

col = rainbow(length(levels(wb2$NAME_2)))

spplot(wb2,"NAME_2",  col.regions=col,scales=list(draw=T),ylim=c(23.5,25),xlim=c(87,89), colorkey=T)

Visualization Using Python

Anscombe’s quartet

Grouped violinplots with split violins

Add caption

Joint kernel density estimate

Plotting a three-way ANOVA

Timeseries from DataFrame

Scatterplot with categorical variables

Discovering structure in heatmap data

Barplot timeseries

FacetGrid with custom projection

GeoMapping_States

Rounded Rectangles Chart

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Cycling

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Address_GoogleMap

Geomapping

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GeoMapping_States1

Geomapping

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Bubble Chart

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GeoMapping_Cities

Geomapping

Pie Chart

Data Visualizations in R

In this article, we will be working on basic and advanced visualizations.

The data used here is "Big Mart Data". The link for the same is given below:

https://docs.google.com/spreadsheets/d/1PR5StHxg2jlMCb4IUilGSEwhylXn-3q3EJucSaVolCU/edit#gid=0

### show info about the data
data=Big.Mart.Dataset
train=data

library(ggplot2) 

Basic Visualizations

1. Scatter Plot

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)

2. Histogram

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")

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")

3. Bar & Stack 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")

4. 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")

5. 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") 

Advanced Visualizations

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)

6. Mosaic Plot

data(HairEyeColor)

mosaicplot(HairEyeColor)

7. 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") 

8. Map Visualization 

devtools::install_github("rstudio/leaflet")

library(magrittr)
library(leaflet)
m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=77.2310, lat=28.6560, popup="The delicious food of chandni chowk")
m  # Print the map

9. 3D Graphs 

library(Rcmdr)

data(iris, package="datasets")

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

attach(iris)# 3d scatterplot by factor level
library(lattice)
cloud(Sepal.Length~Sepal.Width*Petal.Length|Species, main="3D Scatterplot by Species")

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

10. Correlogram

install.packages("corrgram")
library(corrgram)


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