B. Plotting Systems

1. The Base Plotting System

The base plotting system is the original plotting system for R. The basic model is sometimes referred to as the “artist’s palette” model. The idea is you start with blank canvas and build up from there.

In more R-specific terms, you typically start with plot function (or similar plot creating function) to initiate a plot and then annotate the plot with various annotation functions (text, lines, points, axis)

The base plotting system is often the most convenient plotting system to use because it mirrors how we sometimes think of building plots and analyzing data. If we don’t have a completely well-formed idea of how we want to look at some data, often we’ll start by “throwing some data on the page” and then slowly add more information to it as our thought process evolves.

The core plotting and graphics engine in R is encapsulated in the following packages:

graphics: contains plotting functions for the “base” graphing systems, including plot, hist, boxplot and many others.
grDevices: contains all the code implementing the various graphics devices, including X11, PDF, PostScript, PNG, etc.

The grDevices package contains the functionality for sending plots to various output devices. The graphics package contains the code for actually constructing and annotating plots.

## Create the plot / draw canvas
with(cars, plot(speed, dist))

The downside of the base plotting system is that it’s difficult to describe or translate a plot to others because there’s no clear graphical language or grammar that can be used to communicate what you’ve done. The only real way to describe what you’ve done in a base plot is to just list the series of commands/functions that you’ve executed, which is not a particularly compact way of communicating things. This is one problem that the ggplot2 package attempts to address.

2. Base Graphics

Base graphics are used most commonly and are a very powerful system for creating data graphics. There are two phases to creating a base plot:

Initializing a new plot
Annotating (adding to) an existing plot

Calling plot(x, y) or hist(x) will launch a graphics device (if one is not already open) and draw a new plot on the device. If the arguments to plot are not of some special class, then the default method for plot is called; this function has many arguments, letting you set the title, x axis label, y axis label, etc.

The base graphics system has many global parameters that can set and tweaked. These parameters are documented in ?par and are used to control the global behavior of plots, such as the margins, axis orientation, and other details. It wouldn’t hurt to try to memorize at least part of this help page!

Another typical base plot is constructed with the following code.

data(cars)

## Create the plot / draw canvas
with(cars, plot(speed, dist))

## Add annotation
title("Speed vs. Stopping distance")

Base plot with title

3. Simple Base Graphics

Histogram

Here is an example of a simple histogram made using the hist() function in the graphics package. If you run this code and your graphics window is not already open, it should open once you call the hist() function.

library(datasets)

## Draw a new plot on the screen device
hist(airquality$Ozone)

Ozone levels in New York City

Boxplot

Boxplots can be made in R using the boxplot() function, which takes as its first argument a formula. The formula has form of y-axis ~ x-axis. Anytime you see a ~ in R, it’s a formula. Here, we are plotting ozone levels in New York by month, and the right hand side of the ~ indicate the month variable. However, we first have to transform the month variable in to a factor before we can pass it to boxplot(), or else boxplot() will treat the month variable as continuous.

airquality <- transform(airquality, Month = factor(Month))
boxplot(Ozone ~ Month, airquality, xlab = "Month", ylab = "Ozone (ppb)")

Ozone levels by month in New York City

Each boxplot shows the median, 25th and 75th percentiles of the data (the “box”), as well as +/- 1.5 times the interquartile range (IQR) of the data (the “whiskers”). Any data points beyond 1.5 times the IQR of the data are indicated separately with circles.

In this case the monthly boxplots show some interesting features. First, the levels of ozone tend to be highest in July and August. Second, the variability of ozone is also highest in July and August. This phenomenon is common with environmental data where the mean and the variance are often related to each other.

Scatterplot

Here is a simple scatterplot made with the plot() function.

with(airquality, plot(Wind, Ozone))

Scatterplot of wind and ozone in New York City

Generally, the plot() function takes two vectors of numbers: one for the x-axis coordinates and one for the y-axis coordinates. However, plot() is what’s called a generic function in R, which means its behavior can change depending on what kinds of data are passed to the function.

One thing to note here is that although we did not provide labels for the x- and the y-axis, labels were automatically created from the names of the variables (i.e. “Wind” and “Ozone”). This can be useful when you are making plots quickly, but it demands that you have useful descriptive names for the your variables and R objects.

4. Some Important Base Graphics Parameters

Many base plotting functions share a set of global parameters. Here are a few key ones:

pch: the plotting symbol (default is open circle)
lty: the line type (default is solid line), can be dashed, dotted, etc.
lwd: the line width, specified as an integer multiple
col: the plotting color, specified as a number, string, or hex code; the colors() function gives you a vector of colors by name
xlab: character string for the x-axis label
ylab: character string for the y-axis label

The par() function is used to specify the global graphics parameters that affect all plots in an R session. These parameters can be overridden when they are specified as arguments to specific plotting functions.

las: the orientation of the axis labels on the plot
bg: the background color
mar: the margin size
oma: the outer margin size (default is 0 for all sides)
mfrow: number of plots per row, column (plots are filled row-wise)
mfcol: number of plots per row, column (plots are filled column-wise)

You can see the default values for global graphics parameters by calling the par() function and passing the name of the parameter in quotes.

par("lty")

## [1] "solid"

par("col")

## [1] "black"

par("pch")

## [1] 1

Here are some more default values for global graphics parameters.

par("bg")

## [1] "white"

par("mar")

## [1] 5.1 4.1 4.1 2.1

par("mfrow")

## [1] 1 1

For the most part, you usually don’t have to modify these when making quick plots. However, you might need to tweak them for finalizing finished plots.

Base Plotting Functions

The most basic base plotting function is plot(). The plot() function makes a scatterplot, or other type of plot depending on the class of the object being plotted. Calling plot() will draw a plot on the screen device (and open the screen device if not already open). After that, annotation functions can be called to add to the already-made plot.

Some key annotation functions are

lines: add lines to a plot, given a vector of x values and a corresponding vector of y values (or a 2-column matrix); this function just connects the dots
points: add points to a plot
text: add text labels to a plot using specified x, y coordinates
title: add annotations to x, y axis labels, title, subtitle, outer margin
mtext: add arbitrary text to the margins (inner or outer) of the plot
axis: adding axis ticks/labels

Here’s an example of creating a base plot and the adding some annotation. First we make the plot with the plot() function and then add a title to the top of the plot with the title() function.

library(datasets)

## Make the initial plot
with(airquality, plot(Wind, Ozone))

## Add a title
title(main = "Ozone and Wind in New York City")

Base plot with annotation

Here, I start with the same plot as above (although I add the title right away using the main argument to plot()) and then annotate it by coloring blue the data points corresponding to the month of May.

with(airquality, plot(Wind, Ozone, main = "Ozone and Wind in New York City"))
with(subset(airquality, Month == 5), points(Wind, Ozone, col = "blue"))

Base plot with annotation

The following plot colors the data points for the month of May blue and colors all of the other points red.

Notice that when constructing the initial plot, we use the option type = "n" in the call to plot(). This is a common paradigm as plot() will draw everything in the plot except for the data points inside the plot window. Then you can use annotation functions like points() to add data points. So here, we create the plot without drawing the data points, then add the blue points and then add the red points. Finally, we add a legend with the legend() function explaining the meaning of the different colors in the plot.

with(airquality, plot(Wind, Ozone, main = "Ozone and Wind in New York City", type = "n"))
with(subset(airquality, Month == 5), points(Wind, Ozone, col = "blue"))
with(subset(airquality, Month != 5), points(Wind, Ozone, col = "red"))
legend("topright", pch = 1, col = c("blue", "red"), legend = c("May", "Other Months"))

Base plot with multiple annotations

Add and customize titles
Add and customize legends
Add texts
Add straight lines: vertical, horizontal and regression lines
Add an axis
Change axis scale: minimum, maximum and log scale
Customize tick mark labels
Change point shapes
Change line types
Change colors

Plot Titles Plot titles can be specified either directly to the plotting functions during the plot creation or by using the title() function (to add titles on an existing plot).

# Add titles
barplot(c(2,5), main="Main title",
        xlab="X axis title",
        ylab="Y axis title",
        sub="Sub-title",
        col.main="red", col.lab="blue", col.sub="black")

# Increase the size of titles
barplot(c(2,5), main="Main title",
        xlab="X axis title",
        ylab="Y axis title",
        sub="Sub-title",
        cex.main=2, cex.lab=1.7, cex.sub=1.2)

1. Add Legends

The legend() function can be used. A simplified format is :

x and y : the co-ordinates to be used for the legend. Keywords can also be used for x : “bottomright”, “bottom”, “bottomleft”, “left”, “topleft”, “top”, “topright”, “right” and “center”. legend : the text of the legend col : colors of lines and points beside the text for legends

legend(x, y=NULL, legend, col)

# Generate some data
x<-1:10; y1=x*x; y2=2*y1
# First line plot
plot(x, y1, type="b", pch=19, col="red", xlab="x", ylab="y")
# Add a second line
lines(x, y2, pch=18, col="blue", type="b", lty=2)
# Add legends
legend("topleft", legend=c("Line 1", "Line 2"),
       col=c("red", "blue"), lty=1:2, cex=0.8)

2.Add Texts

Add texts within the graph Add text in the margins of the graph Add mathematical annotation to a plot

To add a text to a plot in R, the text() function [to draw a text inside the plotting area] and mtext()[to put a text in one of the four margins of the plot] function can be used.

A simplified format for text() is : text(x, y, labels)

x and y are the coordinates of the texts labels : vector of texts to be drawn

plot(cars[1:10,], pch=19)
text(cars[1:10,],  row.names(cars[1:10,]), 
     cex=0.65, pos=1,col="red")

3. Add straight lines

Add a vertical line Add an horizontal line Add regression line

The R function abline() can be used to add straight lines (vertical, horizontal or regression lines) to a graph.

A simplified format is : abline(a=NULL, b=NULL, h=NULL, v=NULL, ...)

a, b : single values specifying the intercept and the slope of the line h : the y-value(s) for horizontal line(s) v : the x-value(s) for vertical line(s)

# Add horizontal and vertical lines
#++++++++++++++++++++++++++++++++++
plot(cars, pch=19)
abline(v=15, col="blue") # Add vertical line
# Add horizontal line, change line color, size and type
abline(h=60, col="red", lty=2, lwd=3)

# Fit regression line
#++++++++++++++++++++++++++++++++++
require(stats)
reg<-lm(dist ~ speed, data = cars)
coeff=coefficients(reg)
# equation of the regression line : 
eq = paste0("y = ", round(coeff[2],1), "*x ", round(coeff[1],1))
plot(cars, main=eq, pch=18)
abline(reg, col="blue", lwd=2)

References

Paul Murrell (2011). R Graphics, CRC Press.

Hadley Wickham (2009). ggplot2, Springer.

Deepayan Sarkar (2008). Lattice: Multivariate Data Visualization with R, Springer.

Session1

NoviyantiSagala

2022-03-09

A. R ENVIRONMENT

1. Working Directory

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