Creating multiple plot matrix layouts

In this recipe, you will learn how to present more than one graph in a single image. Pairs plots are one example, which we saw in the last recipe, but here, you will learn how to include different types of graphs in each cell of a graph matrix.

How to do it...

Let's say we want to make a 2 x 3 matrix of graphs, made of two rows and three columns of graphs. We use the par() command as follows:

par(mfrow=c(2,3))
plot(rnorm(100),col="blue",main="Plot No.1")
plot(rnorm(100),col="blue",main="Plot No.2")
plot(rnorm(100),col="green",main="Plot No.3")
plot(rnorm(100),col="black",main="Plot No.4")
plot(rnorm(100),col="green",main="Plot No.5")
plot(rnorm(100),col="orange",main="Plot No.6")

How it works...

The par() command is by far the most important function to customize graphs in R. It is used to set and query many graphical arguments (hence the name), which control the layout and appearance of graphs.

Note that we need to issue the par() command before the actual graph commands. When you first run the par() command, only a blank graphics window appears. The par() command sets the argument for any subsequent graphs made. The mfrow argument is used to specify how many rows and columns of graphs we wish to plot. The mfrow argument takes values in the form of a vector of length 2: c(nrow,ncol). The first number specifies the number of rows and the second specifies the number of columns. In our preceding example, we wanted a matrix of two rows and three columns, so we set mfrow to c(2,3).

Note that there is another argument, mfcol, similar to mfrow, that can also be used to create multiple plot layouts. The mfcol argument also takes a two-value vector that specifies the number of rows and columns in the matrix. The difference is that mfcol draws subsequent figures by columns, rather than by rows as mfrow does. So, if we used mfcol instead of mfrow in the earlier example, we will get the following plot:

There's more...

Let's look at a practical example where a multiple plot layout would be useful. Let's read the dailymarket.csv example file, which contains data on the daily revenue, profits, and number of customer visits for a shop:

market<-read.csv("dailymarket.csv",header=TRUE)

Now, let's plot all the three variables over time in a plot matrix with the graphs stacked over one another:

par(mfrow=c(3,1))

plot(market$revenue~as.Date(market$date,"%d/%m/%y"),
type="l", #Specify type of plot as l for line
main="Revenue",
xlab="Date",
ylab="US Dollars",
col="blue")

plot(market$profits~as.Date(market$date,"%d/%m/%y"),
type="l", #Specify type of plot as l for line
main="Profits",
xlab="Date",
ylab="US Dollars",
col="red")

plot(market$customers~as.Date(market$date,"%d/%m/%y"),
type="l", #Specify type of plot as l for line
main="Customer visits",
xlab="Date",
ylab="Number of people",
col="black")

The preceding graph is a good way to visualize variables with different value ranges over the same time period. It helps in identifying where the trends match each other and where they differ.

See also

We will explore more examples and uses of multiple plot layouts in later chapters of the book.