The Base Plotting System in R

When conducting data analysis plotting is critically important. In R, plots are crafted by calling successive functions to essentially build-up a plot. Just like a house you should start with the foundation and progress one step at a time until the home is complete. A best practice when dealing with charts in R is to think in two phases: (1) creating a plot and (2) annotating (adding lines, points, texts, etc) the plot. R is very robust in its plotting system and as such offers a high-degree of flexibility and control over charts which you will come to enjoy.

 

Plotting System

If you are trying to get to the core of the graphics engine with R remember the following two packages:

  1. graphics: this includes items such as plot, hist, and boxplot 
  2. grDevices: this includes the graphic devices such as PDF, PostScrip, and PNG

There is a very important package known as the lattice plotting system and it is uniquely implemented as such:

  1. lattice: this includes the code for creating Trellis graphics using functions like xyplotbwpot, and levelplot
  2. grid: lattice build on top of the grid, so you will not directly be calling packages from here
The Process of Making a Plot

During this phrase it is important to consider what it is you would like to accomplish by way of making a plot.

A few questions that you may want to think about before proceeding are:

  1. Where should I make the plot? (on the screen?, in a file?, etc)
  2. How is the plot going to be used?
  3. Is it just for me to conduct exploratory data analysis (temporary)
  4. Will this be going to a browser online?
  5. Will this end up in a publication of sorts?
  6. Is this going to be in a presentation?
  7. Is it going to just a few points of data or a large amount of data?
  8. Will I need to have a dynamic graphic?
  9. What graphic package should I aim to use (base, lattice, or ggplot2)?

It is important to note that graphics generally are constructed in a modular fashion. This means that each section are built in a one-by-one setup using a series of function calls. Many data scientist like this approach as it simulates the way we think.

Alternatively the lattice package requires that you define all parameters upfront which allows for lattice to calculate the appropriate spacing and font sizes.

ggplot2 is a fine package and plots using elements from both base and lattice, however it uses an independent implementation so we will not cover it in this post.

Base Graphics

If you are interested in creating 2-D graphics than you should use the base graphics system.

This is a two-step process:

  1. Initialize a new plot
  2. Add to an existing plot

You can call by plot(x, y) or hist(x). This will launch the graphics device and render a new plot. If you are not using the base graphics for some special use case then it will default to the system standard. Keep in mind though it is possible to change things like the title, x-axis label, y-axis label, etc. If you want to investigate further what can actually be changed key in ?par.This will generate the help page for you.

Simple Base Graphics: Histogram

 

library(datasets)
hist(warpbreaks$breaks) ## Draw a new plot

Base Graphics: Histogram

 

Simple Base Graphics: Scatterplot

 

library(datasets)
with(ChickWeight, plot(weight, chick) )
Scatterplot
Simple Base Graphics: Boxplot

 

library(datasets)
airquality <- transform(airquality, Month = factor(Month))
boxplot(Ozone ~ Month, airquality, xlab = "Month", ylab = "Ozone (ppb)")
Boxplot
Some Important Base Graphics Parameters

 

Function Name Definition
col: the plotting color (review the colors() function)
lty: the line type (solid line by default)
lwd: the line width
pch: the plotting symbol (open circle by default)
xlab: characters for x-axis label
ylab: characters for y-axis label
It is worthwhile to investigate the par() function. This function controls the global graphics parameters which affect all the plots in a single R session. You can override parameters by using the following:

 

Parameter Name Definition
las: how axis labels are oriented on the plot
bg: background color
mar: size of margin
oma: outer margin size
mfrow: how many plots per row (row-wise)
mfcol: how many plots per row (column-wise)

Default values for global graphic parameters:

par("lty")
[1] "solid"
par("col")
[1] "black"
par("pch")
[1] 1
par("bg")
[1] "white"
par("mar")
[1] 5.1 4.1 4.1 2.1
par("mfrow")
[1] 1 1

 

Base Plotting Functions

 

Function Name Definition
plot: makes a scatterplot
lines: adds a line to a plot
points: adds points to a plot
text: add text labels to a plot
title: title and subtitle labels
mtext: adds text to margins of the plot
axis: add axis ticks/labels
Base Plot with Annotation

 

library(datasets)
with(ChickWeight, plot(weight, Chick))
title(main="Chicks and Weight in Nashville") ## Add a title

Annotated Scatterplot

with(ChickWeight, plot(weight, Chick, main = "Chicks and Weight in Nashville"))
with(subset(ChickWeight, Diet == 4), points(weight, Chick, col = "blue"))
Annotated with Color
with(ChickWeight, plot(weight, Chick, main = "Chicks and Weight in Nashville", type = "n"))
with(subset(ChickWeight, Diet == 4), points(weight, Chick, col = "blue"))
with(subset(ChickWeight, Diet != 4), points(weight, Chick, col = "red"))
legend("topright", pch = 1, col = c("blue", "red"), legend = c("Not Normal","Normal"))
Multi-color Annotation
Base Plot with Regression Line
with(ChickWeight, plot(weight, Chick, main = "Chicks and Weight in Nashville", pch = 20))
model <- lm(Chick ~ weight, ChickWeight)
abline(model, lwd = 2)

Linear Model

Multiple Base Plots

 

with(ChickWeight, {plot(weight, Chick, main="Chicks and Weight") 
+ plot(Diet, weight, main ="Weight and Diet")})

Multiplot