Creating box plots_R Graphs Cookbook Second Edition-QQ阅读男生轻小说网

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Creating box plots

In this recipe, you will learn how to make box plots that are useful in comparing the spread of values in different measurements.

Getting ready

First, we need to load the metals.csv example data file that contains measurements of metal concentrations in London's air (you can download this file from the code download section of the book's companion website):

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

How to do it...

We can make a box plot to summarize the metal concentration data using the boxplot() command as follows:

boxplot(metals,
xlab="Metals",
ylab="Atmospheric Concentration in ng per cubic metre",
main="Atmospheric Metal Concentrations in London")

How it works...

The main argument, the boxplot() function, takes a set of numeric values (in the form of a vector or data frame). In our first example, we used a dataset containing numerical values of air pollution data from London. The dark line inside the box for each metal represents the median of values for that metal. The bottom and top edges of the box represent the first and third quartiles, respectively. Thus, the length of the box is equal to the interquartile range (IQR, which is the difference between first and third quartiles). The maximum length of a whisker is a multiple of the IQR, (the default multiplier is approximately 1.5). The ends of the whiskers are at data points closest to the maximum length of the whisker.

All the points lying beyond these whiskers are considered outliers.

As with most other plot types, the common arguments such as xlab, ylab, and main can be used to set the titles for the x and y axes and the graph itself, respectively.

There's more...

We can also make another type of box plot where we can group the observations by categories. For example, if we want to study the spread of copper concentrations by the source of the measurements, we can use a formula to include the source. First, we need to read the copper_site.csv example data file, as follows:

copper<-read.csv("copper_site.csv",header=TRUE)

Then, we can add the following code:

boxplot(copper$Cu~copper$Source, 
xlab="Measurement Site",
ylab="Atmospheric Concentration of Copper in ng per cubic metre",
main="Atmospheric Copper Concentrations in London")

In this example, the boxplot() function takes a formula as an argument. This formula, which is in the value~group form (Cu~source), specifies a column of values and the group of categories it should be summarized over.