Using ggplot2 in R Language

Introduction to using ggplot2 in R Language

ggplot2 is a popular R package that provides flexible and elegant grammar of graphics for creating a wide range of dynamic and static graphics. It breaks down plots into fundamental components like data, aesthetics, geometric objects, and statistical transformations. In this post, we will learn about using ggplot2 in R Language.

There are three strategies for plotting in R language.

  1. base graphics using functions such as plot(), points(), and par()
  2. lattice graphics to create nice graphics, however, it is not easy to create high-dimensional data graphics.
  3. ggplot package, it is an implementation of “Grammar of Graphics”.

The ggplot2 is built on the principle of layering graphical elements, making it flexible and customizable.

To plot using ggplot2 in R Langauge, a data.frame object is required as an input, then one needs to define plot layers that stack on top of each other, and each layer has visual/text elements that are mapped to aesthetics (size, colors, and opacity). An extremely informative graph will be produced using the above-described simple set of commands.

Before drawing high-quality informative graphs, one needs to install the ggplot2 package. If ggplot2 is already installed, one does not need to reinstall it using the command below.

install.packages("ggplot2")

Scatter Plot using ggplot2 in R

Let us draw a dot plot (scatter points) graph between variables $hp$ (horsepower) and $disp$ (displacement) from mtcars dataset.

# first load the data set say mtcars
attach(mtcars)

# load the ggplot2 library
library(ggplot2)

# now specify the dataset and variables
p <- ggplot(mtcars, aes(x = disp, y = hp))

# Add a plot layer with points
p <- p + geom_point()
print(p) # display/ show the plot
using ggplot2 in R Language

Note that geom, aesthetics, and facets are three important concepts in drawing the graphs using ggplot2, where

  • geom is the type of the plot
  • aesthetics is the shape, color, size, and alpha values used in ggplot
  • facet are small multiples, displaying different subsets of data

When certain aesthetics are defined, an appropriate legend is chosen and displayed automatically.

p <- ggplot(mtcars, aes(x = disp, y = hp))
p <- p + geom_point(aes(color = mpg))
p
using ggplot2 in R with aesthetics

Updating Graphs using aesthetics (color, size, and shape)

Graphs can be updated by assigning variables to aesthetics color, size, and shape. For example

p <- ggplot(mtcars, aes(x = disp, y = hp))
p <- p + geom_point(aes(color = gear, size = wt))
p
Using ggplot2 in R scatter plot with more aesthetics

Consider the following example. Here, the $gear$ variable is taken as a factor (grouping variable).

p <- ggplot(mtcars, aes(x = disp, y = hp))
p <- p + geom_point(aes(color = as.factor(gear), size = wt))
p
ggplot2

Note that the behaviour of the aesthetics is predictable and customizable.

AestheticDiscrete VariableContinuous Variable
colorRainbow of colorsGradient from red to blue
sizeDiscrete size stepsLinear mapping between radius and value
shapeDifferent shapes for each groupShould not work

Faceting in ggplot2

A small multiple (sometimes called faceting, trellis chart, lattice chart, panel chart, or grid chart) is a series or grid of small similar graphics or charts for comparison purposes. Usually, these small multiples are used to display different subsets of the data and these multiples are useful for exploring some conditional relationship between variables (especially when data is large enough).

Let us examine the faceting of different types. The following are some examples of subsetting the scatterplot in facets

# Create a basic scatter plot
p <- ggplot(mtcars, aes(x = disp, y = hp))
p <- p + geom_point()

# columns are cyl categories
p1 <- p + facet_grid(. ~ cyl)

# rows are cyl categories
p2 <- p + facet_grid(cyl ~ .)

# columns and rows both
p3 <- p + facet_grid(carb ~.)

wrap plots by cyl
p4 <- p + facet_grid(~ am)

# plot all four in one 
library(gridExtra)
grid.arrange(grobs = list(p1, p2, p3, p4), ncol = 2, top = "Facet Examples")
using ggplot2 in R using facets

https://itfeature.com

https://gmstat.com

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