R (and S-Plus) for Ecologists

R is exceptional statistical software for ecological analysis as it includes a broad range of analyses employed in ecological analysis, as well as numerous routines for exploratory data analysis (EDA). Technically, the language is called S, and R is the open source implementation available for many systems for free; S-Plus is a commercial implementation of the S language. R and S-Plus are extremely similar, although not identical. I will specify explicitly when any of the following information differs between systems, and a brief summary of the significant differences is given here. I will generally use S to mean information common to both packages.

Unfortunately, the syntax of S is moderately quirky unless you are a C programmer. The Windoze versions of S-Plus (and the most recent unix/linux versions) have a graphical user interface (GUI) avaliable, but to fully employ the power of S you really want to know the syntax of the command line or command window. The following is a general guide to S with hints for performing the exercises in in the accompanying labs.

Data Structures

S is a 4th generation language, meaning that it includes high-level routines for working with data structures, rather than requiring extensive programming by the analyst. In S there are 4 primary data structures we will use repeatedly.
  1. vectors --- vectors are one-dimensional ordered sets composed of a single data type. Data types include integers, real numbers, and strings (character variables)
  2. matrices --- matrices are two dimensional ordered sets composed of a single data type, equivalent to the concept of matrix in linear algebra.
  3. data frames --- data frames are one to multi-dimensional sets, and can be composed of different data types (although all data in a single column must be of the same type). In addition, each column and row in a data frame may be given a label or name to identify it. Data frames are equivalent to a flat file database, and similar to spreadsheets. Accordingly, we often refer to specific columns in a data frame as "fields."
  4. lists --- lists are compound objects of associated data. Like data frames, they need not contain only a single data type, but can include strings (character variables), numeric variables, and even such things as matrices and data frames. In contrast to data frames, lists items do not have a row-column structure, and items need not be the same length; some can be a single values, and others a matrix. It's a little hard to imagine how lists operate in the abstract, but you will see that many of the results of analyses in S are returned as lists, so we'll introduce them as necessary that way.

Vectors and Matrices

Vectors, matrices, data frames and lists are identified by a name given the data structure at the time it is created. Names should be unique, and long enough to clearly identify the contents of the structure. Names can consist of letters, numbers, and the character ".". They may not start with a number, or include the characters "$" or "_" or any arithmetic symbols as these have special meaning in S.

Individual items within a vector or matrix can be identified by subscript (numbered 1-n), which is indicated by a number (or numeric variable) within square brackets. For example, if the number of species per plot is stored in a vector spcplt, then

spcplt[37] = the number of species in plot 37

Matrices are specified in the order "row, column", so that

veg[23,48] = row 23, column 48 in matrix veg

Individual rows or columns within a matrix can be referred to by implied subscript, where the the value of the desired row or column is specified, but other values are omitted. For example,

veg[,3] = third column of matrix veg

represents the third column of matrix veg, as the row number before the comma was omitted. Similarly,

veg[5,] = row 5 of matrix veg

represents row 5, as the column after the comma was omitted. In addition, a number of specialized subscripts can be used.

veg[] = all rows and columns of matrix veg
spcplt[a:b] = spcplt[a] through spcplt[b]
spcplt[-a] = all of vector spcplt except spcplt[a]
veg[a:b,c:d] = a submatrix of veg from row a to b and column c to d

Data Frames

Data frames can be accessed exactly as can matrices, but can also be accessed by data frame and column or field name, without knowing the column number for a specific data item. For example, in the Bryce dataset, there is a column labeled "elev" that holds the elevation of each sample plot. This column can be accessed as bryce$elev, where "bryce" is the name of the data frame, "elev" is the name of the field or column of interest, and the "$" is a separator to distinguish data frame from field. If you are routinely working with one or a few data frames, S can be told the name(s) of the data frames in an "attach " statement, and the data frame name and separator can be omitted. For example, if we give the command


we can specify the field "elev" simply as "elev" rather than "bryce$elev." This is more concise notation, but means that we cannot have any variables with the same name as a field in a data frame that is attached. Data frames are extraordinarily useful in command line S, and critical in GUI S.


As noted above, a list is a compound object composed of associated data. Items within a lists are generally referred to as components. Similar to data frames, components in a list can be given a name, and the component can be specified by name at any time. In addition, components can be specified by their position in the list, similar to a subscript in a vector. However, in contrast to a vector, lists components are specified in double [[ ]] delimiters. We will ultimately find it quite handy to create our own lists, but for the first few labs we will just see them as results from analyses, so we'll take them as they come and demonstrate their properties by example.

For the time being, I'll give a very simple example. Using the spcplt vector above, and the names of the veg data frame.

list.demo <- list(spcplt,names(veg))
names(list.demo) <- c('species per plot','species names')
$"species per plot":
 50001 50002 50003 50004 50005 50006 50007 50008 50009 50010 50011 50012 50013 
     9    14    12     8    16    11    12     8     8    16    19    18     9

 50014 50015 50016 50017 50018 50019 50020 50021 50022 50023 50024 50025 50026 
    14    19     8    10    12    13     9    15     6    13    18    16    12

 50027 50028 50029 50030 50031 50032 50033 50034 50035 50036 50037 50038 50039 
    19    13     6    13    19    10    15    16    13    16    15     9    27
     .     .     .     .     .     .     .     .     .     .     .     .     .
     .     .     .     .     .     .     .     .     .     .     .     .     .
     .     .     .     .     .     .     .     .     .     .     .     .     .
 50156 50157 50158 50159 50160 50161 50162 50163 50164 50165 50166 50167 50168 
     6     5     6     6     7     4    10    13     3    12     4     5    16

 50169 50170 50171 50172 
    10    10     8    12
$"species names":
        .         .        .        .        .        .        .        .
        .         .        .        .        .        .        .        .
        .         .        .        .        .        .        .        .
[153] "SPC475" "SPC476" "SPC477" "SPC478" "SPC479" "SPC.70" "SPHCOC" "STAPIN"
In this case, the first component "species per plot" has 160 numbers (each with the plot identifier attached), and the second item has 174 strings.

S Vector and Matrix Operators

Because S is a 4th generation language, it is often possible to perform fairly sophisticated routines with little programming. The key is to recognize that S operates best on vectors, matrices, or data fames, and to capitalize on that. A large number of functions exists for manipulating vectors, and by extension, matrices. For example, if veg is a vegetation matrix of 100 sample plots and 200 species (plots as rows and species as columns), we can perform the following, where "<-" is the S assignment operator: In addition, S supports logical subscripts, where the subscript is applied whenever the logical function is true. Logical operators include:

For example

A final special case is of special note. Missing values in a vector or matrix are always a problem in ecological data sets. Sometimes it is best simply to remove samples with missing data, but often only one or a few values are missing, and it's best to keep the sample in the matrix with a suitable missing value code. We'll discuss missing value codes in more detail in the next section, but for now lets assume that we have missing values in a vector. To use all of the vector EXCEPT the missing value, use


That's complicated enough to merit some discussion. The S function to identify a missing value is

is.na( )

so that to say all of a vector except missing values, we set a logical test to be true when values are not missing. Since the S operator for "not" is !, the correct test is

!is.na( )

and to specify which vector we're testing for missing value, we put the vector in parentheses as follows:


Accordingly, the full expression is


While the symbol for a missing value in a vector or matrix is NA, using


will NOT work.

We can use the missing value test on any vector as necessary. For example, the vector of elevations, except where the number of species per plot is missing, is


This use of missing values is critical to S because all operations on vectors or matrices must have the same number of elements. So, if there are missing values in any field we're using in a calculation, the same record (row) must be omitted from all the other fields as well. In a later lab I'll demonstrate how to create a "mask" that we can use to simplify working with vectors or matrices with missing values.

Row or Column Operations on a Matrix

Vector operators can be applied to every row or column of a matrix to produce a vector with the apply command. For example:

spcmax <- apply(veg,2,max) creates a vector "spcmax" with the maximum value for each species in its respective position. The apply operator is employed as:

apply("matrix name",1(rowwise) or 2(columnwise),vector operator)

so that

pltsum <- apply(veg,1,sum)

creates a vector of total species abundance in each plot. The vector is as long as the number of rows in matrix veg. If the function to be applied doesn't exist, it can be created on the fly as follows:

pltspc <- apply(veg,2,function(x){sum(veg[,x]>0)})

where function(x){sum(veg[,x]>0)}) sums the number of plots where species x is greater than 0, and x is assigned to each column (species) in turn,

Triangular Matrices

Often in community ecology we work with symmetric matrices (e.g. similarity, dissimilarity, of distance matrices). These matrices take up extra space (since the value of the diagonal is known by definition, and since every other value is stored twice (matrix[x,y]=matrix[y,x]). We can save space by only storing one triangle of the matrix. In addition, some analyses require a vector argument, rather than a matrix, and it's convenient to convert the triangular matrix to a vector. This can be done as follows:

triang <- matrix[row(matrix) > col(matrix)]

Getting Data Into S

Getting data into any program is often the hardest part about using the program. For S, this is generally not true, as long as the data are reasonably formatted. The R Development Core Team has developed a special manual to cover the ins and outs of getting data into and out of R. It's available as a PDF or HTML at http://cran.r-project.org. Much of the material covered there is also applicable to S-Plus.

The easiest way is to format the data in columns, with column headings, and blanks or tabs between. For example:

plot elev aspect slope text
   1 1300   240   30   loam
   2 1640   170   20   clay.loam
   3 1840    NA   24   silty.clay.loam
   .  .      .     .     .
   .  .      .     .     .
   .  .      .     .     .
 100 1730    70   15    sandy.loam

The columns do not need to be straight, but multi-word variables like "clay loam" need to be connected or put in quotes. The S convention (but it is just a convention) is to connect with a period, as shown above. It CANNOT be connected with "$" or "_". The above file (if named "site.dat" for instance) could be read with the read.table command as follows:

site <- read.table('site.dat',header=TRUE)

The resulting data frame would be named "site", and the columns would be named exactly as in the data file. Note that the value for aspect in the third plot is NA. This is a missing value code, and will cause S to treat that value as missing, rather than as a code NA. It's possible to use other codes as missing values if you specify them in the read.table command. For example, suppose in your data set you used -999 as the missing value code. To tell S to set -999 to missing, add the na.strings= argument as follows:

site <- read.table('site.dat',header=TRUE,na.strings="-999")

Alternatively, data can be organized as in traditional spreadsheet "csv" comma delimited files, as follows:
. .  .  .  .
. .  .  .  .
. .  .  .  .

In which case it would be read:

site <- read.table('site.dat',header=TRUE,sep=",")

to tell S that the values were separated by commas. Alternatively, in R, you can use

site <- read.csv('site.dat',header=TRUE)

to read the file, as read.csv() calls read.table() witht he appropriate parameters as defaults.

Finally, if the data are in a formatted file with no delimiters (spaces or commas) it can be read by specifying the columns that start each field. For example:

  31840 9024silty.clay.loam
   .  .      .     .     .
   .  .      .     .     .
   .  .      .     .     .
1001730 7015sandy.loam
can be read

site <- read.table('site.dat',sep=c(1,4,8,11,13)).

In this case (or in any case where column headings are absent), they can be entered separately with the names command. For example:

names(site) <- c("plot","elev","aspect","slope","text")

where c is an S function meaning "combine." Row names (such as plot IDs) can also added if desired, using the row.names() function in a similar way.

There is one element of read.table() that sometimes causes problems. Ordinarily, read.table() will use the first column that contains all unique values as the row labels. Generally (but not universally) this is the first column. It is often best to explicitly specify which coulmn contains row identifiers (as opposed to data), using the row.labels= specifier. Going back to the original example,

site <- read.table('site.dat',header=TRUE, row.labels=1)

makes sure that S knows that the first column is identifiers, not data.

The beauty of the read.table() function is the way it handles variables. If any value in a column is alphabetic, it treats the column as composed of "factors," or categorical variables. There is NEVER a reason to convert categorical variable to numeric. However, if you already have categorical variables coded as integers, you can explain that to S with the factor() function after you read the data in. If all values in a column are numeric, it treats that variable as numeric.

Plotting in S

S has a powerful graphics capability that is much of the appeal to using the system. Many of the analyses have special plotting capabilities that allow you to plot results without storing multiple intermediate products. (S likes to point out that it is "object oriented", and that this object orientation is what allows the generality of its plotting routines. While that is generally true, the SYNTAX of S is more appropriately viewed as functional, rather than object oriented, and we will concern ourselves largely with syntax, rather than implementation). S supports a fairly broad range of graphic devices in addition to excellent on-screen plotting. Reflecting its origins on unix computers, it is quite good at Postscript output, but also includes other formats. The devices available to you for plotting will depend to some extent on your operating system (Windows versus unix/linux) and whether you are using S-Plus or R.


In unix/linux, we will be mostly working with X11. If you give S a plotting command without first opening a device, an X11 window will pop up automatically to contain the plot. This plotting area is usually a convenient size for working, and can be resized with the mouse to almost any size. Normally, this is convenient and sufficient. Sometimes, however, we want absolute control over the aspect ratio of the plot, so that 100 units on the X axis is exactly the same size as 100 units on the Y axis. There is a small number of ways to ensure that the plotting is "square", but all of them assume that the plotting window has not been re-sized with the mouse. Accordingly. it is sometimes important to know how to create a plotting window of a specific size. This is one of the interesting areas of difference between S-Plus and R.

In S-Plus, the window is created by the motif() function, named after the window manager of the Open Software Foundation. This is true even if you are not running an OSF operating system or window manager. The S-Plus motif() function speaks directly to the X11 window manager, and can pass a large number of X11 options and specifiers. This is quite helpful if you are familiar with X11, but quite cryptic if you are not. I won't attempt to teach X11 here, but merely show how to create a plotting window of a specific size. The size is specified in PIXELS, not centimeters or inches, and includes a position indicator as well. It is all specified with the X11 geometry command as follows. Suppose we want a plotting window of 800 by 600 pixels in the upper left of our monitor. We would enter

motif("-geometry 800x600+10+10")

This means create a window 800 pixels wide by 600 pixels high down 10 pixels from the top and 10 pixels from the left edge. Note that the entire expression is enclosed in quotes, that the expression begins with a dash (to specifiy an option), and that the size is delimited with a x (to mean "by") while the offset is delimited with plus signs. This seems like a fairly complicated scheme, but is consisitent with X11 syntax in general.

in R, the X11 window is controlled by the x11() function. The size of the window is specified in inches as arguments to the function. For example, to get a window 8 inches wide by 6 inches tall


This is simpler, except that you can't control the location. You can, however, move the window with your mouse. As long as you don't resize it you are fine.

Other Devices

The list of other devices you can plot to also depends on operating system and S-Plus versus R. Recent versions of S-Plus include java.graph, pdf.graph, and wmf.graph for Java, portable document format, and Windows metafile respectively, as well as hpgl, hplj, and postscript for hardcopy output on Hewlett Packard compatible plotters, HP Laserjet compatible printers, and postscript devices respectively.

R includes postscript, pictex, png, jpeg, and xfig devices as well as x11.

On either system, type

?Devices or help(Devices)

to get a list of available devices and their names (note the capital D on Devices). Each of the devices has options that can be set to control plot size, orientation (landscape or portrait), font size, etc.

S Libraries

While S is an expansive language with a large number of routines already included, it doesn't include everything, and has several specific areas of omission with respect to vegetation ecology (e.g. no NMDS or CCA). Fortunately, the core routines are easily augmented with additional user-written routines which can be loaded into your copy of S. These routines are usually provided in what S calls a "library," and which R calls a "package," which is a package with the routine itself (which may be partially implemented in FORTRAN or C, as well as S), help files, often test data, and other items as necessary. Accordingly, it's necessary to know how to load libraries to make the most of S.

Fortunately, in recent releases (S-Plus 5+ or S-Plus 2000 or R > 1.2) many of the libraries we want are already included and installed in the correct locations. For example, we will frequently use functions from the MASS library by Venables and Ripley. Lucky for us, it is inclued in both S-Plus and R. Before going to a great effort to install needed libraries, find out which libraries are already installed on your machine. Depending on your operating system and R versus S-Plus, do the following:

If the library you want is not installed, you will have to install it yourself. Again, depending on operating system and program, the details are somewhat different.

Installing S-Plus libraries

First, you have to locate the libraries you want to install. One of the best repositories for S-Plus libraries is StatLib at the Department of Statistics at Carnegie Mellon University (http://lib.stat.cmu.edu). Look under S Archive or simply http://www.lib.stat.cmu.edu/S for unix/linux or http://lib.stat.cmu.edu/DOS/S for Windows. Depending on operating system, the files and conventions differ.

unix/linux formats

windows formats

Windows .zip files are likely to be pre-compiled and ready to load as specified above. Under unix/linux, it's unlikely the library is compiled (although linux binaries are not too uncommon), so you will need to compile the executables with the make command. After unpacking the library, move to the subdirectory that is the root of the library. Then, at the shell prompt, enter S-Plus CHAPTER (you might use a different name for S-Plus, such as S-Plus5 or splus). Then enter S-Plus make to compile the source code into objects which are suitable for dynamic linking with S-Plus. If your SHOME environment variable is not defined, or you have your libraries in an unusual location, you may have to edit the Makefile to get this to work.

Installing packages or libraries in R

The best respository for R packages is CRAN at http://cran.r-project.org/. R generally refers to "packages" rather than "libraries,", but packages are simply collections of libraries. The R site has separate areas for source code (S functions and FORTRAN or C code in uncompiled ASCII) and binaries (compiled code for a specific machine). If your machine and operating system are supported, it's usually simpler to use the pre-compiled binaries.

If your machine is on the internet, R has routines available to automatically install or update libraries or packages from CRAN. This is one of the areas where R really outshines S-Plus.

Libraries and Packages for Vegetation Ecology

At present, there are two libraries or packages available specifically for vegetation ecology: vegan from Jari Oksanen and labdsv from Dave Roberts. At present, vegan is only available for R, but many of the routines should work in S-Plus with a little work. vegan is available at CRAN http://cran.r-project.org/, and labdsv is available at http://labdsv.nr.usu.edu/. Between the two of them they provide improved PCA, PCO, NMDS, CA, CCA, FSO, DECORANA, and a number of other utilities. We will make extensive use of them in subsequent labs.

On With The Good Stuff

This has been a trivial introduction to an expansive statistical language, but my intention is to bring this power to vegetation ecologists, and this is more easily done by example than continued abstract presentation. Accordingly, further insights into S will be included in specific exercises as appropriate. Begin with lab1

Differences Between S-Plus and R

The first obvious difference is that S-Plus is a commercial program, while R is an open source package. The practical significance of that difference is that S-Plus is not free, while R is. In fact, you cannot buy S-Plus, but only lease it for a year at a time; when the year is up you must pay for the renewal of your license to keep your existing copy of S-Plus running. For commercial enterprises S-Plus is quite expensive, but for academic use it is much more affordable, and it is available to university students at a significant discount.

The second major difference is in maturity. S-Plus has been a successful commercial package for many years, and most elements of it work flawlessly due to extenisve debugging over time. Nonetheless, there have been problems with incompatibility between versions with upgrades, and the intial port to Windows was quite problematic (now sorted out). R, on the other hand, is more recent, and because it is an open source project, the development has been much more diffuse, with contributions from a great many people. Overall, the management of the project has been extraordinarily good for an open source project. As a consequence, R has changed dramatically in capability and stability over the last few years, and is now very stable and solid. Partly because R was designed more recently and has no legacy code to support, it is much simpler and cleaner in many aspects. Creating new libraries and adding libraries is much simpler, and there is a central repository for contributed libraries.

Practical Differences

As a user, what sort of differences will you observe? Several, but generally trivial. Hastie, T. and Tibshirani, R. 1990. Generalized Additive Models. Chapman and Hall

Wood, S.N. 2000. Modelling and smoothing parameter estimation with multiple quadratic penalties. JRSSB 62(2):413-428.

Wood, S.N. and Augustin, N.H. 2002. GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol. Model. 157(2-3):157-177.