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") 

Beginner's Guide to ggplot2:Painless Data Visualization

One of the most appealing things about R is its ability to create data visualizations with just a couple of lines of code.
For example, it takes just one line of code -- and a short one at that -- to plot two variables in a scatterplot. Let's use as an example the mtcars data set installed with R by default.

The ggplot2 package was created by Hadley Wicham. ggplot2 allows you to create graphs that represent both univariate and multivariate numerical and categorical data in a straightforward manner. Grouping can be represented by color, symbol, size, and transparency.

### install & load ggplot library

install.package("ggplot2")

library("ggplot2")

### show info about the data

head(iris)

head(iris)

qplot(Sepal.Length, Petal.Length, data = iris, color = Species)

qplot(Sepal.Length, Petal.Length, data = iris)

qplot(Sepal.Length, Petal.Length, data = iris, color = Species)

qplot(Sepal.Length, Petal.Length, data = iris, color = Species, size = Petal.Width)

qplot(Sepal.Length, Petal.Length, data = iris, geom = "line", color = Species)

qplot(age, circumference, data = Orange, geom = c("point", "line"), colour = Tree)

### show info about the data

head(diamonds)

head(mtcars)

### comparison qplot vs ggplot

##qplot histogram

qplot(clarity, data=diamonds, fill=cut, geom="bar")

# ggplot histogram -> same output

ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar()

ggplot also produces the same output.

### how to use qplot

# scatterplot

qplot(wt, mpg, data=mtcars)

  

# continuous scale vs. discrete scale

head(mtcars)

qplot(wt, mpg, data=mtcars,colour=cyl)

levels(mtcars$cyl)

qplot(wt, mpg, data=mtcars,colour=factor(cyl))

# use different aesthetic mappings

qplot(wt, mpg, data=mtcars,shape=factor(cyl))

qplot(wt, mpg, data=mtcars,size=qsec)

#combine mappings (hint: hollow points, geom-concept, legend combination)
qplot(wt, mpg, data=mtcars, size=qsec, color=factor(carb), shape=I(1))

# bar-plot
qplot(factor(cyl), data=mtcars, geom="bar")

# use different display of bars (stacked, dodged, identity)
head(diamonds)
qplot(clarity, data=diamonds, geom="bar", fill=cut, position="stack")

# use geom to combine plots (hint: order of layers)
qplot(wt, mpg, data=mtcars, color=factor(cyl), geom=c("point", "smooth"))

# illustrate flip versus changing of variable allocation
qplot(mpg, wt, data=mtcars, facets=cyl~., geom=c("point", "smooth"))

qplot(mpg, wt, data=mtcars, facets=cyl~., geom=c("point", "smooth")) + coord_flip()

qplot(wt, mpg, data=mtcars, facets=cyl~., geom=c("point", "smooth"))

# using ggplot-syntax with qplot (hint: qplot creates layers automatically)
qplot(mpg, wt, data=mtcars, color=factor(cyl), geom=c("point","line"))

# add an additional layer with different mapping
p.tmp + geom_point() + geom_point(aes(y=disp))

# setting aesthetics instead of mapping
p.tmp + geom_point(color="darkblue")      

# dealing with overplotting (hollow points, pixel points, alpha[0-1] )
t.df <- data.frame(x=rnorm(2000), y=rnorm(2000))
p.norm <- ggplot(t.df, aes(x,y))
p.norm + geom_point()  

 p.norm + geom_point(shape=1)

p.norm + geom_point(shape=".")

p.norm + geom_point(colour=alpha("black", 1/2))

p.norm + geom_point(colour=alpha("blue", 1/10))

# using scales (color palettes, manual colors, matching of colors to values)
p.tmp <- qplot(cut, data=diamonds, geom="bar", fill=cut)
p.tmp

RColorBrewer::display.brewer.all()

p.tmp + scale_fill_manual(values=c("#7fc6bc","#083642","#b1df01","#cdef9c","#466b5d"))

### create a pie-chart, radar-chart (hint: not recommended)
# map a barchart to a polar coordinate system
p.tmp <- ggplot(mtcars, aes(x=factor(1), fill=factor(cyl))) + geom_bar(width=1)
p.tmp

p.tmp + coord_polar(theta="y")

p.tmp + coord_polar()

ggplot(mtcars, aes(factor(cyl), fill=factor(cyl))) + geom_bar(width=1) + coord_polar()

### create survival/cumulative incidence plot
library(survival)
head(lung)
# create a kaplan-meier plot with survival package
t.Surv <- Surv(lung$time, lung$status)
t.survfit <- survfit(t.Surv~1, data=lung)
plot(t.survfit, mark.time=TRUE)