/data_vis

Basics of data visualization in R with ggplot

GNU General Public License v3.0GPL-3.0

Introduction to data Visualization in R

data structure

**Nilanjan Chatterjee** February, 2020

Topics

  • What is data visualization?
  • Why is data visualization important?
  • How to do data visualization?
  • Possible options and pitfalls

What is data visualization?

Technique to communicate insights from data through visual representation.
Allow easy understanding of large dataset.
Provides basic knowledge about variables.
Most efficient way to identify, locate, manipulate, format, and present data.

Why data visualization is important?

  • Ever increasing amount of data.
  • Humanly impossible to see distinct patterns.
  • Improved insight.
  • Faster Decision making.

How to do data visualization?

  • Plot in Base R
  • ggplot2 package and associates
data(mtcars)
plot(mpg~wt, mtcars, pch=19, col="blue")

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plot vs ggplot

plot vs ggplot

Pros Cons
In-built Additional package
Easy to learn Steep learning curve
Indepenedent of data-structures Works only with data-frame
Easy for simple plots Verbose for complex plots
Low level of abstraction High abstraction level
Visually less appealing Visually more appealing

ggplot

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ggplot

Based on Grammer of graphics (Wilkinson, 2005).
Consists of several building blocks like a sentence.

  • data
  • aesthetic mapping
  • geometric object
  • scales
  • coordination system
  • position adjustmnets
  • faceting

lost or very-lost

ggplot

#install.packages("ggplot2", dependencies = T)
library(ggplot2)
ggplot(mtcars, aes(x= wt, y= mpg))+ 
  geom_point(colour="blue", size=3)

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yay

How to plot in ggplot

ggplot(mtcars) #data

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How to plot in ggplot

ggplot(mtcars, aes(x= wt, y= mpg)) #data+aesthetic map

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How to plot in ggplot

ggplot(mtcars, aes(x= wt, y= mpg))+ #data+aesthetic map
  geom_point() #geometric obj

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How to plot in ggplot

ggplot(mtcars, aes(x= wt, y= mpg))+ #data+aesthetic map
  geom_point(colour="blue", size=3) #geometric obj

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How to plot in ggplot

ggplot(mtcars, aes(x= wt, y= mpg))+ #data+aesthetic map
  geom_point(colour="blue", size=3)+ #geometric obj
  ggtitle("Scatterplot") #Plot title

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Different sections of ggplot

  • DATA only data-frame is allowed
  • AES takes into account the aesthetics
  • GEOM stands for the different geometrices
    • geom_point for point plot
    • geom_bar for barplot
    • geom_line for line plot
    • geom_histogram for histogram
    • geom_boxplot for boxplot
      and so on

Some more examples

ggplot(mtcars, aes(x=mpg))+
  geom_bar()

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Some more examples

ggplot(mtcars, aes(x=cyl, y=mpg, fill= cyl))+ 
  geom_bar(stat="identity")

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Some more examples

ggplot(mtcars, aes(x=cyl, y=mpg))+ 
  geom_point(stat="identity", size=4)

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Export graphs from R/Rstudio

You can export any plots using the plot window from R/RStudio.
To save files in high-resolution these commands are helpful

sct <-ggplot(mtcars, aes(x= wt, y= mpg))+ 
  geom_point(colour="blue", size=3)+   ggtitle("Scatterplot")

ggsave(sct, "Scatterplot_with_R.jpeg", dpi=100, device = "jpeg")

Exercise

  • Use your own data and make a basic plot (scatterplot, barplot, histogram) in ggplot
  • change the color of the plot
  • What is the difference if you put colour or shape in data part rather than geometric object part?

Thanks

thank-you