Dr. Mark Gardener
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Introduction to graphingUsing R for statistical analyses - Graphs 2
This page is intended to be a help in getting to grips with the powerful statistical program called R. It is not intended as a course in statistics. If you have an analysis to perform I hope that you will be able to find the commands you need here and copy/paste them into R to get going.
On this page you can find out information on producing a range of graphs to illustrate your analyses. Specifically on this page find out about scatter plots, stem-leaf plots and pie charts. To find out about bar charts, histograms and box-whisker plots go to the graphs1 page.
R is Open Source
R is Free
What is R?
R is an open-source (GPL) statistical environment modeled after S and S-Plus. The S language was developed in the late 1980s at AT&T labs. The R project was started by Robert Gentleman and Ross Ihaka (hence the name, R) of the Statistics Department of the University of Auckland in 1995. It has quickly gained a widespread audience. It is currently maintained by the R core-development team, a hard-working, international team of volunteer developers. The R project web page is the main site for information on R. At this site are directions for obtaining the software, accompanying packages and other sources of documentation.
R is a powerful statistical program but it is first and foremost a programming language. Many routines have been written for R by people all over the world and made freely available from the R project website as "packages". However, the basic installation (for Linux, Windows or Mac) contains a powerful set of tools for most purposes.
Because R is a programming language it can seem a bit daunting; you have to type in commands to get it to work. However, it does have a Graphical User Interface (GUI) to make things easier. You can also copy and paste text from other applications into it (e.g. word processors). So, if you have a library of these commands it is easy to pop in the ones you need for the task at hand. That is the purpose of this web page; to provide a library of basic commands that the user can copy and paste into R to perform a variety of statistical analyses.
Navigation index
Introduction to Graphing
R has great graphical power but it is not a point and click interface. This means that you must use typed commands to get it to produce the graphs you desire. This can be a bit tedious at first but once you have the hang of it you can save a list of useful commands as text that you can copy and paste into the R command line.
Scatter Plots
A scatter plot is used when you have two variables to plot against one another. R has a basic command to perform this task. The command is plot(). As usual with R there are many additional parameters that you can add to customise your plots.
The basic command is:
plot(x, y)
Where x is the name of your x-variable and y is the name of your y-variable. This is fine if you have two variables but if they are part of a bigger data set then you have to remember to attach(data.file) your data set. A more powerful command is:
plot(y ~ x, data= your.data)
Note the use of the model syntax. This model syntax is used widely in R for setting-up ANOVA and regression analyses for example (see also it's use in the box-whisker plot).
R comes with a number of data sets built-in; these are used in the examples and can be useful to 'play with'. For example the data set cars contains two variables speed and dist.
To see a basic scatter plot try the following:
plot(dist ~ speed, data= cars)
This basic scatter takes the axes labels from the variables and uses open circles as the plotting symbol. As usual with R we have a wealth of additional commands at our disposal to beef up the display. A useful additional command is to add a line of best-fit. This is a command that adds to the current plot (like the title() command). For the above example we'd type:
abline(lm(dist ~ speed, data= cars))
The basic command uses abline(a, b), where a= slope and b= intercept. Here we use a linear model command to calculate the best-fit equation for us (try typing the lm() command separately, you get the intercept and slope).
If we combine this with a couple of extra lines we can produce a better looking plot:
plot(dist
~ speed, data= cars, xlab="Speed",
ylab="Distance", col= "blue")
title(main="Scatter plot with best-fit line", font.main= 4)
abline(lm(dist ~ speed, data= cars), col= "red")
This illustrates several of the additional commands. We have set the axis labels and the colour of the plotting symbols. Next we added a main title and set the font to bold italic (try other values). Finally we set the best-fit line and made it red.
We can alter the plotting symbol using the command pch= n, where n is a simple number. We can also alter the range of the x and y axes using xlim= c(lower, upper) and ylim= c(lower, upper). The size of the plotted points is manipulated using the cex= n command, where n = the 'magnification' factor. Here are some commands that illustrate these parameters:
plot(dist
~ speed, data= cars, pch= 19, xlim= c(0,25), ylim= c(-20, 120), cex=
2)
abline(lm(dist ~ speed, data= cars))
title(main="Scatter plot with altered y-axis")
Here the plotting symbol is set to 19 (a solid circle) and expanded by a factor of 2. Both x and y axes have been rescaled. The labels on the axes have been left blank and default to the name of the variable (which is taken from the data set).
Stem and leaf plots
A very basic yet useful plot is a stem and leaf plot. It is a quick way to represent the distribution of a single sample. The basic command is:
stem(variable)
Here is a vector of numbers saved as the variable test.data:
[1] 2.1 2.6 2.7 3.2 4.1 4.3 5.2 5.1 4.8 1.8 1.4 2.5 2.7 3.1 2.6 2.8
To see the stem plot of these data we type:
stem(test.data)
The decimal point is at the |
1 | 48
2 | 1566778
3 | 12
4 | 138
5 | 12
We can now see quite clearly that the data are not normally distributed. This is a useful command for moderately small samples as you can easily re-construct the original data from the plot. For other samples the barplot function may be used to create a frequency plot. Alternatively a histogram may be more useful.
Pie charts
Pie charts are not necessarily the most useful way of displaying data but they remain popular. We can produce pie charts easily in R using the basic command pie()
To start with get your data organised into a .CSV file. Make a file with multiple columns then each column can have a title and a single value (to plot). Here is a simple example file:
First |
Second |
Third |
Fourth |
Fifth |
Sixth |
12 |
16 |
25 |
11 |
6 |
4 |
To produce a simple pie chart we type the following:
pie(pie.data)
This is a basic chart; we can see that the names of the columns have been appended to each slice. We can add a title in the usual way using the title() command.
By default the slices are presented in anti-clockwise order, we can alter this by adding a simple command clockwise= TRUE
The colours are set to pastel shades by default, to alter them you can add a list of colours to the command line in the form col= c("col1", col2", col3"). Here is the finished article:
pie(pie.data,
clockwise=TRUE, col= c("red", "orange", "yellow", "green", "blue", "purple"))
title(main="Clockwise Pie Chart with custom colours", font.main=
4)
Now we have clockwise slices with our own selection of colours. The title was set with a separate command and the font set to bold italic (try other values).