An introduction to R for modeling (lab 1)

Stephen Ellner*, modified by Ben Bolker

September 13, 2010

*Ecology and Evolutionary Biology, Cornell University
Department of Mathematics & Statistics and Biology, McMaster University


Licensed under the Creative Commons attribution-noncommercial license ( Please share & remix noncommercially, mentioning its origin.

Version: 2010-09-13 10:33:09

0 How to use this document

These notes are based in part on course materials by former TAs Colleen Webb, Jonathan Rowell and Daniel Fink at Cornell, Professors Lou Gross (University of Tennessee) and Paul Fackler (NC State University), and on the book Getting Started with Matlab by Rudra Pratap (Oxford University Press). They also draw on the documentation supplied with R. They parallel, but go into more depth than, the chapter supplement for the book Ecological Models and Data in R by Ben Bolker (Princeton University Press).

You can find many other similar introductions scattered around the web, or in the “contributed documentation” section on the R web site ( This particular version is limited (it has similar coverage to Sections 1 and 2 of the Introduction to R that comes with R) and targets biologists who are neither programmers nor statisticians.

1 What is R?

R is an object-oriented scripting language that combines

R is an open source project, available for free download via the Web. Originally a research project in statistical computing (?), it is now managed by a development team that includes a number of well-regarded statisticians, and is widely used by statistical researchers (and a growing number of theoretical ecologists and ecological modelers) as a platform for making new methods available to users. The commercial implementation of S (called S-PLUS) offers an Office-style “point and click” interface that R lacks. For our purposes, however, the advantage of this front-end is outweighed by the fact that R is built on a faster and much less memory-hungry implementation of S and is easier to interface with other languages (and is free!). A standard installation of R also includes extensive documentation, including an introductory manual (100 pages) and a comprehensive reference manual (over 1000 pages). (There is a graphical front-end for parts of R, called “R commander” (Rcmdr for short), available at the R site, but we will not be using it in this class.)

1.1 Installing R on your computer: basics

If R is already installed on your computer, you can skip this section.

The main source for R is the CRAN home page You can get the source code, but most users will prefer a precompiled version. To get one of these from CRAN:

R should work well on any reasonably modern computer. Version 2.5.1 requires MacOS X 10.4.4 (or higher) or Windows 95 (or higher), or just about any version of Linux; it can also be compiled on other versions of Unix. Windows XP or higher is recommended. R moves quickly: if possible, you should make sure you have upgraded to the most recent version available, or at least that your version isn’t more than about a year old.

The standard distributions of R include several packages, user-contributed suites of add-on functions (unfortunately, the command to load a package into R is library!). These Notes use some packages that are not part of the standard distribution. In general, you can install additional packages from within R using the Packages menu, or the install.packages command. (See below.)

For Windows or MacOS, install R by launching the downloaded file and following the on-screen instructions. At the end you’ll have an R icon on your desktop that you can use to launch the program. Installing versions for Linux or Unix is slightly more complicated, which will not bother the corresponding users. For doing anything more than very simple examples, we recommend the Tinn-R ( or Notepad++ ( editors as better Windows interfaces than the built-in console: in particular, these editors have syntax highlighting, which colors R commands and allows you to identify missing parentheses, quotation marks, etc..

○w If you are using R on a machine where you have sufficient permissions, you may want to edit some of your graphical user interface (GUI) options.

1.2 Starting R

○w Just click on the icon on your desktop, or in the Start menu (if you allowed the Setup program to make either or both of these). If you lose these shortcuts for some reason, you can search for the executable file Rgui.exe on your hard drive, which will probably be somewhere like Program Files\R\R-x.y.z\bin\Rgui.exe. If you are using Tinn-R, first start Tinn-R, then go to …

1.3 Stopping R

When entering, always look for the exit.
Lebanese proverb

You can stop R from the File menu (○w), or you can stop it by typing q() at the command prompt (if you type q by itself, you will get some confusing output which is actually R trying to tell you the definition of the q function; more on this later).

When you quit, R will ask you if you want to save the workspace (that is, all of the variables you have defined in this session); for now (and in general), say “no” in order to avoid clutter.

Should an R command seem to be stuck or take longer than you’re willing to wait, click on the stop sign on the menu bar or hit the Escape key (in Unix, type Control-C).

2 Interactive calculations

○w When you start R it opens the console window. The console has a few basic menus at the top; check them out on your own. The console is also where you enter commands for R to execute interactively, meaning that the command is executed and the result is displayed as soon as you hit the Enter key. For example, at the command prompt >, type in 2+2 and hit Enter; you will see

  > 2+2

  [1] 4

(When cutting and pasting from this document to R, don’t include the text for the command prompt (>).)

To do anything complicated, you have to store the results from calculations by assigning them to variables, using = or <-. For example:

  > a <- 2+2

R automatically creates the variable a and stores the result (4) in it, but it doesn’t print anything. This may seem strange, but you’ll often be creating and manipulating huge sets of data that would fill many screens, so the default is to skip printing the results. To ask R to print the value, just type the variable name by itself at the command prompt:

  > a

  [1] 4

(the [1] at the beginning of the line is just R printing an index of element numbers; if you print a result that displays on multiple lines, R will put an index at the beginning of each line. print(a) also works to print the value of a variable.) By default, a variable created this way is a vector, and it is numeric because we gave R a number rather than some other type of data (e.g. a character string like "pxqr"). In this case a is a numeric vector of length 1, which acts just like a number.

You could also type a <- 2+2; a, using a semicolon to put two or more commands on a single line. Conversely, you can break lines anywhere that R can tell you haven’t finished your command and R will give you a “continuation” prompt (+) to let you know that it doesn’t think you’re finished yet: try typing

a=3*(4+ [Enter]  

to see what happens (you will sometimes see the continuation prompt when you don’t expect it, e.g. if you forget to close parentheses). If you get stuck continuing a command you don’t want—e.g. you opened the wrong parentheses—just hit the Escape key or the stop icon in the menu bar to get out.

Variable names in R must begin with a letter, followed by letters or numbers. You can break up long names with a period, as in very.long.variable.number.3, or an underscore (_), but you can’t use blank spaces in variable names (or at least it’s not worth the trouble). Variable names in R are case sensitive, so Abc and abc are different variables. Make variable names long enough to remember, short enough to type. N.per.ha or pop.density are better than x and y (too short) or available.nitrogen.per.hectare (too long). Avoid c, l, q, t, C, D, F, I, O, and T, which are either built-in R functions or hard to tell apart from other symbols (e.g. l (lower-case L) vs. 1 (numeral 1)).

R does calculations with variables as if they were numbers. It uses +, -, *, /, and ^ for addition, subtraction, multiplication, division and exponentiation, respectively. For example:

  > x <- 5
  > y <- 2
  > z1 <- x*y  ## no output
  > z2 <- x/y  ## no output
  > z3 <- x^y  ## no output
  > z2

  [1] 2.5

  > z3

  [1] 25

Even though R did not display the values of x and y, it “remembers” that it assigned values to them. Type x; y to display the values.

You can retrieve and edit previous commands. The up-arrow ( ) key (or Control-P) recalls previous commands to the prompt. For example, you can bring back the second-to-last command and edit it to

  > z3 <- 2*x^y

(experiment with the other arrow keys (, , ), Home and End keys too). This will save you many hours in the long run.

You can combine several operations in one calculation:

  > A <- 3
  > C <- (A+2*sqrt(A))/(A+5*sqrt(A)); C

  [1] 0.5543706

Parentheses specify the order of operations. The command

  > C <- A+2*sqrt(A)/A+5*sqrt(A)

is not the same as the one above; rather, it is equivalent to C <- A + 2*(sqrt(A)/A) + 5*sqrt(A).

The default order of operations is: (1) parentheses; (2) exponentiation, or powers, (3) multiplication and division, (4) addition and subtraction (“pretty please excuse my dear Aunt Sally”).

b  <-  12-4/2^3 gives 12 - 4/8 = 12 - 0.5 = 11.5
b  <-  (12-4)/2^3 gives 8/8 = 1
b  <-  -1^2 gives -(1^2) = -1
b  <-  (-1)^2 gives 1

In complicated expressions you might start off by using parentheses to specify explicitly what you want, such as  b  <-  12 - (4/(2^3))  or at least  b  <-  12 - 4/(2^3) ; a few extra sets of parentheses never hurt anything, although when you get confused it’s better to think through the order of operations rather than flailing around adding parentheses at random.

R also has many built-in mathematical functions that operate on variables (Table 1 shows a few).


absolute value

cos, sin, tan

cosine, sine, tangent of angle x in radians


exponential function, ex


natural (base-e) logarithm


common (base-10) logarithm


square root

Table 1: Some of the built-in mathematical functions in R. You can get a more complete list from the Help system: ?Arithmetic for simple, ?log for logarithmic, ?sin for trigonometric, and ?Special for special functions.

Exercise 2.1 : Using editing shortcuts wherever you can, have R compute the values of

  1. -277-
2- 1 and compare it with (1 -17
2)-1 (If any square brackets [] show up in your web browser’s rendition of these equations, replace them with regular parentheses ().)
  2. the standard normal probability density, √1--
 2πe-x22 , for values of x = 1 and x = 2 (R knows π as pi.) (You can check your answers against the built-in function for the normal distribution; dnorm(1) and dnorm(2) should give you the values for the standard normal for x = 1 and x = 2.)

3 The help system

R has a help system, although it is generally better for providing detail or reminding you how to do things than for basic “how do I …?” questions.

By default, R’s help system only provides information about functions that are in the base system and packages that you have loaded with library (see below).

Try out one or more of these aspects of the help system.

Exercise 3.1 : Do an Apropos on sin via the Help menu, to see what it does. Now enter the command


and see what that does (answer: pulls up all help pages that include ‘sin’ anywhere in their title or text. Apropos just looks at the name of the function).

4 A first interactive session: a “leaky bucket” model

To get a feel for working in R we’ll construct a simple discrete-time dynamical system, sometimes known as the “leaky bucket” model.

Suppose that in a queue (a group of people or things waiting for service — e.g. people waiting at a bank, or jobs waiting for processing on a computer system, or cars in line at a toll booth), 25% (rounded) of the people waiting are served every hour and 10 new people arrive every hour. (For precision, we will assume that the 10 new people arrive at the end of the hour and are not counted in the fraction served.)

Suppose the queue is initially empty. We could run this model by brute force by typing

  > N <- 0

to set the initial state and then typing (or cutting and pasting)

  > N <- N-round(0.25*N)
  > N <- N+10

over and over again.

Of course, this would be extremely tedious (we also wouldn’t see the results unless we typed N by itself from time to time to see where we’d gotten). This approach doesn’t save the results over time; we can only see the current state of the system.

Suppose that we decide we want to run the system for 20 steps. We can set aside space for a vector of 20 numbers, set the first to zero, and assign a variable i as a counter (if i is an integer in the correct range, then N[i] refers to the ith value from the vector N — more on this below):

  > N <- numeric(20)
  > N[1] <- 0
  > i <- 1

Then we can do the following steps repeatedly:

  > i <- i+1
  > N[i] <- N[i-1]-round(0.25*N[i-1])+10  ## condense

Of course, this doesn’t help much in the tedium department. We should instead use a while statement:

  > i <- 1
  > while (i<20) {
     i <- i+1
     N[i] <- N[i-1]-round(0.25*N[i-1])+10
  > N

   [1]  0 10 18 24 28 31 33 35 36 37 38 38 38 38 38 38 38 38 38 38

R repeatedly tests the condition i<20 given in parentheses after while; if it is true, it runs all of the code inside the curly brackets ({}), then starts again.

Exercise 4.1 : Why did we use the condition i<20 rather than the condition i<=20?

Alternatively, since we know in advance how many times we want to run the model, we can instead use a “for loop”:

  > for (i in 2:20) {
     N[i] <- N[i-1]-round(0.25*N[i-1])+10
  > N

   [1]  0 10 18 24 28 31 33 35 36 37 38 38 38 38 38 38 38 38 38 38

The colon (:) operator creates a sequence of integers starting at 2 and ending at 20 (notice that we start from 1, not 2, because we have already set the value for N[1]). For each value in this vector, R sets the loop variable i to that value and runs the code inside the curly brackets.

Let’s plot the results. plot(N) by itself will plot the values in N on the y-axis against their “index” (position in the vector) on the x-axis, but it might be better to specify the x values explicitly, as in plot(1:20,N). Perhaps even better, we can assign the x values to a variable: at the same time, we can (1) specify that R should use both lines and points to plot the values by specifying type="b" and (2) specify more informative labels for the axes (see Figure 1).

  > tvec <- 1:20
  > plot(tvec,N,type="b",xlab="Time (hours)",ylab="Queue length")


Figure 1: Queue length “simulation”: N(t) = N(t- 1) - 0.25 round(N(t- 1) + 10.


Figure 2: Some of R’s graphics parameters. Color specification, col, also applies in many other contexts: colors are set to a rainbow scale here. See ?par for (many more) details on graphics parameters, and one or more of ?rgb, ?palette, or apropos("color") for more on colors.

R’s default plotting character is an open circle. Open symbols are good for plotting large data sets because it is easier to see where they overlap, but you could include pch=16 in the plot command if you wanted closed circles instead. Figure 2 shows several more ways to adjust the appearance of lines and points in R.

5 R interfaces

5.1 Editors and GUIs

While R works perfectly well out of the box, there are interfaces that can make your R experience easier. Editors such as Tinn-R (Windows:, Kate (Linux:, or Emacs/ESS (cross-platform: will color R commands and quoted material, allow you to submit lines or blocks of R code to an R session, and give hints about function arguments: the standard MacOS interface has all of these features built in. Graphical interfaces such as JGR (cross-platform: or SciViews (Windows: include similar editors and have extra functions such as a workspace browser for looking at all the variables you have defined. (All of these interfaces, which are designed to facilitate R programming, are in a different category from Rcmdr, which tries to simplify basic statistics in R.) If you are using Windows or Linux I would strongly recommend that, once you have tried R a little bit, you download at least an R-aware editor and possibly a GUI to make your life easier. Links to all of these systems, and more, can be found at

5.2 Script files and data files

Modeling and complicated data analysis are much easier if you use scripts, which are a series of commands stored in a text file. The Windows and MacOS versions of R both have basic script editors: you can also use Windows Notepad or Wordpad, or a more featureful editor like PFE, Xemacs, or Tinn-R: you should not use MS Word.

Most programs for working with models or analyzing data follow a simple pattern of program parts:

  1. “Setup” statements.
  2. (Possibly) input some data from a file or the keyboard.
  3. Carry out the calculations that you want.
  4. Print the results, graph them, or save them to a file.

For example, a script file might

  1. Load some packages, or run another script file that creates some functions (more on functions later).
  2. Read in data from a text file.
  3. Fit several statistical models to the data and compare them.
  4. Graph the results, and save the graph to disk for including in your term project.

Even for relatively simple tasks, script files are useful for building up a calculation step-by-step, making sure that each part works before adding on to it.

Tips for working with data and script files (sounding slightly scary but just trying to help you avoid common pitfalls):

As a first example, the file Intro1_2e3.R has the commands from the leaky bucket model. Important: before working with an example file, create a personal copy in some location on your own computer. We will refer to this location as your temp folder. At the end of a lab session you must move files onto your personal disk (or email them to yourself).

Now open your copy of Intro1_2e3.R. In your editor, select and Copy the entire text of the file, and then Paste the text into the R console window (Ctrl-C and Ctrl-V [Apple-C and Apple-V on the Mac] are useful shortcuts). This has the same effect as entering the commands by hand into the console: they will be executed and so a graph is displayed with the results. Cut-and-Paste allows you to execute script files one piece at a time (which is useful for finding and fixing errors). The source function allows you to run an entire script file, e.g.

  > source("c:/temp/Intro1.R")

You can also source files by pointing and clicking via the File menu on the console window.

Exercise 5.1 Make a copy of Intro1_2e3.R under a new name, and modify the copy so that it picks the number of new arrivals from a Poisson distribution with mean 10 (rpois(1,lambda=10)) and plots the data appropriately. (We will learn about the Poisson distribution later.) In the “setup” phase of your code, use the command set.seed(101) to initialize the random number generator so that you get the same answer as I did. You should end up with a graph that resembles Figure 3.


Figure 3: Queue simulation with random arrivals.

Exercise 5.2 The axes in plots are scaled automatically, but the outcome is not always ideal (e.g. if you want several graphs with exactly the same axis limits). You can use the xlim and ylim arguments in plot to control the limits:

plot(x,y,xlim=c(x1,x2), [other stuff])

will draw the graph with the x-axis running from x1 to x2, and using ylim=c(y1,y2) within the plot command will do the same for the y-axis.

Create a plot of queue length versus time with the x-axis running from 0 to 50 and the y-axis running from 0 to 50.

Exercise 5.3 You can place several graphs within a single figure by using the par function (short for “parameter”) to adjust the layout of the plot. For example, the command

  > par(mfrow=c(2,3))

divides the plotting area into 2 rows and 3 columns. As R draws a series of graphs, it places them along the top row from left to right, then along the next row, and so on. mfcol=c(2,3) has the same effect except that R draws successive graphs down the first column, then down the second column, and so on.

Save Intro1_2e3.R with a new name and modify the program as follows. Use mfrow=c(1,2) to create side-by-side graphs of queue length vs. time for the deterministic (exactly 10 new people per hour) and stochastic (X ~ Poisson(λ = 10) new people per hour). Use the ylim argument to make sure the y-axis scales are the same in each plot.

Exercise 5.4 Use ?par to read about other plot control parameters that you use par to set (you should definitely skim — this is one of the longest help files in the whole R system!). Then draw a 2 × 2 set of plots, each showing the line y = 5x + 3 with x running from 3 to 8, but with 4 different line styles and 4 different line colors.

Exercise 5.5 Modify one of your scripts so that at the very end it saves the plot to disk. Use either savePlot or dev.print. Use ?savePlot, ?dev.print to read about these functions. Note that the argument filename can include the path to a folder; for example, in Windows you can use filename="c:/temp/Intro2Figure".

(These are really exercises in using the help system, with the bonus that you learn some things about plot. (Let’s see, we know plot can graph a simulation (N versus tvec and all that). Maybe it can also draw a line to connect the points, or just draw the line and leave out the points. That would be useful. So let’s try ?plot and see if it says anything about lines …Hey, it also says that graphical parameters can be given as arguments to plot, so maybe I can set line colors inside the plot command instead of using par all the time …) The help system can be quite helpful (amazingly enough) once you get used to it and get in the habit of using it often.)

The main point is not to be afraid of experimenting; if you have saved your previous commands in a script file, there’s almost nothing you can break by trying out commands and inspecting the results.

Exercise 5.6 * Read about lines and legend. Using everything you have learned so far, recreate versions of Figures 1.5, 1.6, and 1.7 on pp. 15 and 16 of Mooney and Swift’s book.

6 The R package system

R has many extra packages that provide extra functions. You may be able to install new packages from a menu within R. You can always type

  > install.packages("plotrix")

(for example — this installs the plotrix package). You can install more than one package at a time:

  > install.packages(c("ellipse","plotrix"))

(c stands for “combine”, and is the command for combining multiple things into a single object.) If the machine on which you use R is not connected to the Internet, you can download the packages to some other medium (such as a flash drive or CD) and install them later, using Install from local zip file in the menu or

  > install.packages("plotrix",repos=NULL)

If you do not have permission to install packages in R’s central directory, R will may ask whether you want to install the packages in a user-specific directory. Go ahead and say yes.

You will frequently get a warning message something like Warning message: In file.create( : cannot create file ’.../packages.html’, reason ’Permission denied’. Don’t worry about this; it means the package has been installed successfully, but the main help system index files couldn’t be updated because of file permissions problems.

7 Statistics in R

Some of the important functions and packages (collections of functions) for statistical modeling and data analysis are summarized in Table 2. ? give a good practical (although somewhat advanced) overview, and you can find a list of available packages and their contents at CRAN, the main R website ( — select a mirror site near you and click on Package sources). For the most part, we will not be concerned here with this side of R.

aov, anova

Analysis of variance or deviance


Linear models (regression, ANOVA, ANCOVA)


Generalized linear models (e.g. logistic, Poisson regression)


Generalized additive models (in package mgcv)


Fit nonlinear models by least-squares

lme, nlme, lmer, glmer

Linear, generalized linear, and nonlinear mixed-effects models (repeated measures, block effects, spatial models): in packages nlme and lme4


Package: bootstrapping functions


Package: nonparametric regression (more in packages fields, KernSmooth, logspline, sm and others)

princomp, manova, lda, cancor

Multivariate analysis (some in package MASS; also see packages vegan, ade4)


Package: survival analysis

tree, rpart

Packages: tree-based regression

Table 2: A few of the functions and packages in R for statistical modeling and data analysis. There are many more, but you will have to learn about them somewhere else.

8 Vectors

Vectors and matrices (1- and 2-dimensional rectangular arrays of numbers) are pre-defined data types in R. Operations with vectors and matrices may seem a bit abstract now, but we need them to do useful things later. Vectors’ only properties are their type (or class) and length, although they can also have an associated list of names.

We’ve already seen two ways to create vectors in R:

  1. A command in the console window or a script file listing the values, such as
      > initialsize=c(1,3,5,7,9,11)
  2. Using read.table:
      > initialsize=read.table("c:/temp/initialdata.txt")

    (assuming of course that the file exists in the right place).

You can then use a vector in calculations as if it were a number (more or less)

  > (finalsize=initialsize+1)

  [1]  2  4  6  8 10 12

  > (newsize=sqrt(initialsize))

  [1] 1.000000 1.732051 2.236068 2.645751 3.000000 3.316625

(The parentheses are an R trick that tell it to print out the results of the calculation even though we assigned them to a variable.)

Notice that R applied each operation to every entry in the vector. Similarly, commands like initialsize-5, 2*initialsize, initialsize/10 apply subtraction, multiplication, and division to each element of the vector. The same is true for

  > initialsize^2

  [1]   1   9  25  49  81 121

In R the default is to apply functions and operations to vectors in an element by element (or “vectorized”) manner.

8.1 Functions for creating vectors

You can use the seq function to create a set of regularly spaced values. seq’s syntax is

  > x <- seq(from,to,by)


  > x <- seq(from,to)


  > x <- seq(from,to,length.out) ## can abbreviate length.out to 'length'

The first form generates a vector (from,from+by,from+2*by,...) with the last entry not extending further than than to. In the second form the value of by is assumed to be 1 or -1, depending on whether from or to is larger. The third form creates a vector with the desired endpoints and length. As we saw above, the syntax from:to is a shortcut for seq(from,to):

  > 1:8

  [1] 1 2 3 4 5 6 7 8

Exercise 8.1 * Use seq to create the vector v=(1 5 9 13), and to create a vector going from 1 to 5 in increments of 0.2.

You can use rep to create a constant vector such as (1 1 1 1); the basic syntax is rep(values,lengths). For example,

  > rep(3,5)

  [1] 3 3 3 3 3

creates a vector in which the value 3 is repeated 5 times. rep will repeat a whole vector multiple times

  > rep(1:3,3)

  [1] 1 2 3 1 2 3 1 2 3

or will repeat each of the elements in a vector a given number of times:

  > rep(1:3,each=3)

  [1] 1 1 1 2 2 2 3 3 3

Even more flexibly, you can repeat each element in the vector a different number of times:

  > rep( c(3,4),c(2,5) )

  [1] 3 3 4 4 4 4 4

The value 3 was repeated 2 times, followed by the value 4 repeated 5 times. rep can be a little bit mind-blowing as you get started, but it will turn out to be useful.

Table 3 lists some of the main functions for creating and working with vectors.


Vector of evenly spaced values, default increment = 1)

seq(from, to, length.out)

Vector of evenly spaced values, specified length)


Combine a set of numbers and/or vectors into a single vector


Create vector by repeating elements of a by amounts in b


Convert an object of some other type to a vector


Histogram plot of value in v


Estimate of population mean, variance, standard deviation based on data values in v


Correlation between two vectors

Table 3: Some important R functions for creating and working with vectors. Many of these have other optional arguments; use the help system (e.g. ?cor) for more information. The statistical functions such as var regard the values as samples from a population and compute an estimate of the population statistic; for example sd(1:3)=1.

8.2 Vector indexing

You will often want to extract a specific entry or other part of a vector. This procedure is called vector indexing, and uses square brackets ([]):

  >  z=c(1,3,5,7,9,11)
  > z[3]

  [1] 5

(how would you use seq to construct z?) z[3] extracts the third item, or element, in the vector z. You can also access a block of elements using the functions for vector construction, e.g.

  > z[2:5]

  [1] 3 5 7 9

extracts the second through fifth elements.

What happens if you enter v=z[seq(1,5,2)] ? Try it and see, and make sure you understand what happened.

You can extracted irregularly spaced elements of a vector. For example

  > z[c(1,2,5)]

  [1] 1 3 9

You can also use indexing to set specific values within a vector. For example,

  > z[1]=12

changes the value of the first entry in z while leaving all the rest alone, and

  > z[c(1,3,5)]=c(22,33,44)

changes the first, third, and fifth values (note that we had to use c to create the vector — can you interpret the error message you get if you try z[1,3,5] ?)

Exercise 8.2 * Write a one-line command to extract a vector consisting of the second, first, and third elements of z in that order.

Exercise 8.3 Write a script file that computes values of y = (x-1)
(x+1) for x = 1,2,⋅⋅⋅,10, and plots y versus x with the points plotted and connected by a line (hint: in ?plot, search for type).

Exercise 8.4 * The sum of the geometric series 1 + r + r2 + r3 + ... + rn approaches the limit 1(1 - r) for r < 1 as n →∞. Set the values r = 0.5 and n = 10, and then write a one-line command that creates the vector G = (r0,r1,r2,...,rn). Compare the sum (using sum) of this vector to the limiting value 1(1 - r). Repeat for n = 50. (Note that comparing very similar numeric values can be tricky: rounding can happen, and some numbers cannot be represented exactly in binary (computer) notation. By default R displays 7 significant digits (options("digits")). For example:

  > x = 1.999999
  > x

  [1] 1.999999

  > x-2

  [1] -1e-06

  > x=1.9999999999999
  > x

  [1] 2

  > x-2

  [1] -9.992007e-14

All the digits are still there, in the second case, but they are not shown. Also note that x-2 is not exactly -1 × 10-13; this is unavoidable.)

8.3 Logical operators

Logical operators return a value of TRUE or FALSE. For example, try:

  > a=1
  > b=3
  > c=a<b
  > d=(a>b)
  > c

  [1] TRUE

  > d

  [1] FALSE

The parentheses around (a>b) are optional but make the code easier to read. One special case where you do need parentheses (or spaces) is when you make comparisons with negative values; a<-1 will surprise you by setting a=1, because <- (representing a left-pointing arrow) is equivalent to = in R. Use a< -1, or more safely a<(-1), to make this comparison.


less than


greater than


less than or equal to


greater than or equal to


equal to


not equal to

Table 4: Some comparison operators in R. Use ?Comparison to learn more.

When we compare two vectors or matrices of the same size, or compare a number with a vector or matrix, comparisons are done element-by-element. For example,

  > x=1:5
  > (b=(x<=3))

So if x and y are vectors, then (x==y) will return a vector of values giving the element-by-element comparisons. If you want to know whether x and y are identical vectors, use identical(x,y) which returns a single value: TRUE if each entry in x equals the corresponding entry in y, otherwise FALSE. You can use ?Logical to read more about logical operators. Note the difference between = and ==: can you figure out what happened in the following cautionary tale?

  > a = 1:3
  > b = 2:4
  > a==b
  > a=b
  > a==b

Exclamation points ! are used in R to mean “not”; != (not !==) means “not equal to”.

R also does arithmetic on logical values, treating TRUE as 1 and FALSE as 0. So  sum(b)  returns the value 3, telling us that three entries of x satisfied the condition (x<=3). This is useful for (e.g.) seeing how many of the elements of a vector are larger than a cutoff value.

Build more complicated conditions by using logical operators to combine comparisons:

! Negation
&, && AND
|, || OR

OR is non-exclusive, meaning that x|y is true if either x or y or both are true (a ham-and-cheese sandwich would satisfy the condition “ham OR cheese”). For example, try

  > a=c(1,2,3,4)
  > b=c(1,1,5,5)
  > (a<b)& (a>3)
  > (a<b) | (a>3)

and make sure you understand what happened (if it’s confusing, try breaking up the expression and looking at the results of a<b and a>3 separately first). The two forms of AND and OR differ in how they handle vectors. The shorter one does element-by-element comparisons; the longer one only looks at the first element in each vector.

8.4 Vector indexing II

We can also use logical vectors (lists of TRUE and FALSE values) to pick elements out of vectors. This is important, e.g., for subsetting data (getting rid of those pesky outliers!)

As a simple example, we might want to focus on just the values N near the maximum in the leaky bucket example:

  > (largeN <- N[N>0.9*max(N)])

  [1] 38 38 39 42 40

  > (largeN_tvec = tvec[N>0.9*max(N)])

  [1]  9 13 15 18 19

What is really happening here (think about it for a minute) is that N>0.9*max(N) generates a logical vector the same length as N (FALSE FALSE FALSE ) which is then used to select the appropriate values.

If you want the positions at which N is greater than 90% of its maximum, you could say (1:length(N))[N>0.9*max(N)], but you can also use a built-in function: which(N>0.9*max(N)). If you wanted the position at which the maximum value of N occurs, you could say which(N==max(N)). (This normally results in a vector of length 1; when could it give a longer vector?) There is even a built-in command for this specific function, which.max (although which.max always returns just the first position at which the maximum occurs).

Exercise 8.5 : What would happen if instead of setting largeN you replaced N by saying N <- N[N>0.9*max(N)], and then tvec <- tvec[N>0.9*max(N)]? Why would that be wrong? Try it with some temporary variables — set N2 <- N and tvec2 <- tvec and then play with N2 and tvec2 so you don’t mess up your working variables — and work out what happened …

We can also combine logical operators (making sure to use the element-by-element & and | versions of AND and OR):

  > N[N>0.9*max(N) & tvec >= 15]

  [1] 39 42 40

  > tvec[N>0.9*max(N) & tvec >= 15]

  [1] 15 18 19

If we were going to do this a lot, we could save typing by assigning a logical vector

  > bigN <- N>0.9*max(N) & tvec>= 15

and using it to do the indexing.

Exercise 8.6 runif(n) is a function (more on it soon) that generates a vector of n random, uniformly distributed numbers between 0 and 1. Create a vector of 20 numbers, then select the subset of those numbers that is less than the mean. (If you want your answers to match mine exactly, use set.seed(273) to set the random-number generator to a particular starting point before you use runif; [273 is an arbitrary number I chose].)

Exercise 8.7 * Find the positions of the elements that are less than the mean of the vector you just created (e.g. if your vector were (0.1 0.9. 0.7 0.3) the answer would be (1 4)).

As I mentioned in passing above, vectors can have names associated with their elements: if they do, you can also extract elements by name (use names to find out the names).

  > x = c(first=7,second=5,third=2)
  > names(x)

  [1] "first"  "second" "third"

  > x["first"]


  > x[c("third","first")]

  third first
      2     7

Finally, it is sometimes handy to be able to drop a particular set of elements, rather than taking a particular set: you can do this with negative indices. For example, x[-1] extracts all but the first element of a vector.

Exercise 8.8 *: Specify two ways to take only the elements in the odd positions (first, third, …) of a vector of arbitrary length.

9 Matrices

9.1 Creating matrices

Like vectors, you can create matrices by using read.table to read in values from a data file. (Actually, this creates a data frame, which is almost the same as a matrix — see section 10.2.) You can also create matrices of numbers by creating a vector of the matrix entries, and then reshaping them to the desired number of rows and columns using the function matrix. For example

  > (X=matrix(1:6,nrow=2,ncol=3))

       [,1] [,2] [,3]
  [1,]    1    3    5
  [2,]    2    4    6

takes the values 1 to 6 and reshapes them into a 2 by 3 matrix.

By default R loads the values into the matrix column-wise (this is probably counter-intuitive since we’re used to reading tables row-wise). Use the optional parameter byrow to change this behavior. For example :

  > (A=matrix(1:9,nrow=3,ncol=3,byrow=TRUE))

       [,1] [,2] [,3]
  [1,]    1    2    3
  [2,]    4    5    6
  [3,]    7    8    9

R will re-cycle through entries in the data vector, if necessary to fill a matrix of the specified size. So for example

  > matrix(1,nrow=10,ncol=10)

creates a 10×10 matrix of ones.

Exercise 9.1 Use a command of the form X=matrix(v,nrow=2,ncol=4) where v is a data vector, to create the following matrix X:

       [,1] [,2] [,3] [,4]
  [1,]    1    1    1    1
  [2,]    2    2    2    2

If you can, try to use R commands to construct the vector rather than typing out all of the individual values.

R will also collapse a matrix to behave like a vector whenever it makes sense: for example sum(X) above is 12.

Exercise 9.2 Use rnorm (which is like runif, but generates Gaussian (normally distributed) numbers with a specified mean and standard deviation instead) and matrix to create a 5 × 7 matrix of Gaussian random numbers with mean 1 and standard deviation 2. (Use set.seed(273) again for consistency.)

Another useful function for creating matrices is diag. diag(v,n) creates an n × n matrix with data vector v on its diagonal. So for example diag(1,5) creates the 5 × 5 identity matrix, which has 1’s on the diagonal and 0 everywhere else. Try diag(1:5,5) and diag(1:2,5); why does the latter produce a warning?

Finally, you can use the data.entry function. This function can only edit existing matrices, but for example (try this now!!)

  > A=matrix(0,nrow=3,ncol=4)
  > data.entry(A)

will create A as a 3 × 4 matrix, and then call up a spreadsheet-like interface in which you can change the values to whatever you need.


m × n matrix using the values in v


transpose (exchange rows and columns) of matrix A


dimensions of matrix X. dim(X)[1]=# rows, dim(X)[2]=# columns


call up a spreadsheet-like interface to edit the values in A


diagonal n×n matrix with v on diagonal, 0 elsewhere (v is 1 by default, so diag(n) gives an n × n identity matrix)


combine compatible objects by attaching them along columns


combine compatible objects by attaching them along rows


convert an object of some other type to a matrix, if possible


“outer product” of vectors v, w: the matrix whose (i,j)th element is v[i]*w[j]

Table 5: Some important functions for creating and working with matrices. Many of these have additional optional arguments; use the help system for full details.

9.2 cbind and rbind

If their sizes match, you can combine vectors to form matrices, and stick matrices together with vectors or other matrices. cbind (“column bind”) and rbind (“row bind”) are the functions to use.

cbind binds together columns of two objects. One thing it can do is put vectors together to form a matrix:

  > (C=cbind(1:3,4:6,5:7))

       [,1] [,2] [,3]
  [1,]    1    4    5
  [2,]    2    5    6
  [3,]    3    6    7

R interprets vectors as row or column vectors according to what you’re doing with them. Here it treats them as column vectors so that columns exist to be bound together. On the other hand,

  > (D=rbind(1:3,4:6))

       [,1] [,2] [,3]
  [1,]    1    2    3
  [2,]    4    5    6

treats them as rows. Now we have two matrices that can be combined.

Exercise 9.3 Verify that rbind(C,D) works, cbind(C,C) works, but cbind(C,D) doesn’t. Why not?

9.3 Matrix indexing

Matrix indexing is like vector indexing except that you have to specify both the row and column, or range of rows and columns. For example z=A[2,3] sets z equal to 6, which is the (2nd row, 3rd column) entry of the matrix A that you recently created, and

  > A[2,2:3]

  [1] 5 6

  > (B=A[2:3,1:2])

       [,1] [,2]
  [1,]    4    5
  [2,]    7    8

There is an easy shortcut to extract entire rows or columns: leave out the limits, leaving a blank before or after the comma.

  > (first.row=A[1,])

  [1] 1 2 3

  > (second.column=A[,2])

  [1] 2 5 8

(What does A[,] do?)

As with vectors, indexing also works in reverse for assigning values to matrix entries. For example,

  > (A[1,1]=12)

  [1] 12

You can do the same with blocks, rows, or columns, for example

  > (A[1,]=c(2,4,5))

  [1] 2 4 5

If you use which on a matrix, R will normally treat the matrix as a vector — so for example which(A==8) will give the answer 6 (figure out why). However, which does have an option that will treat its argument as a matrix:

  > which(A==8,arr.ind=TRUE)

       row col
  [1,]   3   2

10 Other structures: Lists and data frames

10.1 Lists

While vectors and matrices may seem familiar, lists are probably new to you. Vectors and matrices have to contain elements that are all the same type: lists in R can contain anything — vectors, matrices, other lists …Indexing lists is a little different too: use double square brackets [[ ]] (rather than single square brackets as for a vector) to extract an element of a list by number or name, or $ to extract an element by name (only). Given a list like this:

  > L = list(A=x,B=y,C=c("a","b","c"))

Then L$A, L[["A"]], and L[[1]] will all grab the first element of the list.

You won’t use lists too much at the beginning, but many of R’s own results are structured in the form of lists.

10.2 Data frames

Data frames are the solution to the problem that most data sets have several different kinds of variables observed for each sample (e.g. categorical site location and continuous rainfall), but matrices can only contain a single type of data. Data frames are a hybrid of lists and vectors; internally, they are a list of vectors that may be of different types but must all be the same length, but you can do most of the same things with them (e.g., extracting a subset of rows) that you can do with matrices. You can index them either the way you would index a list, using [[ ]] or $ — where each variable is a different item in the list — or the way you would index a matrix. Use as.matrix if you have a data frame (where all variables are the same type) that you really want to be a matrix, e.g. if you need to transpose it (use to go the other way).