Programming in R

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General Overview
One of the main attractions of using the R ( environment is the ease with which users can write their own programs and custom functions. The R programming syntax is extremely easy to learn, even for users with no previous programming experience. Once the basic R programming control structures are understood, users can use the R language as a powerful environment to perform complex custom analyses of almost any type of data.

Format of this Manual
In this manual all commands are given in code boxes, where the R code is printed in black, the comment text in blue and the output generated by R in green. All comments/explanations start with the standard comment sign '#' to prevent them from being interpreted by R as commands. This way the content in the code boxes can be pasted with their comment text into the R console to evaluate their utility. Occasionally, several commands are printed on one line and separated by a semicolon ';'. Commands starting with a '$' sign need to be executed from a Unix or Linux shell. Windows users can simply ignore them.

R Basics

The R & BioConductor manual provides a general introduction to the usage of the R environment and its basic command syntax.

Code Editors for R

Several excellent code editors are available that provide functionalities like R syntax highlighting, auto code indenting and utilities to send code/functions to the R console.

Programming in R using Vim or Emacs                                                              Programming in R using RStudio

Integrating R with Vim and Tmux

Users interested in integrating R with vim and tmux may want to consult the Vim-R-Tmux configuration page.

Finding Help

Reference list on R programming (selection)

Control Structures

Conditional Executions

Comparison Operators

  • equal: ==
  • not equal: !=
  • greater/less than: > <
  • greater/less than or equal: >= <=

Logical Operators

  • and: &
  • or: |
  • not: !

If Statements

If statements operate on length-one logical vectors.


if(cond1=true) { cmd1 } else { cmd2 }


if(1==0) {
} else {
[1] 2

Avoid inserting newlines between '} else'.

Ifelse Statements

Ifelse statements operate on vectors of variable length.


ifelse(test, true_value, false_value)


x <- 1:10 # Creates sample data
ifelse(x<5 | x>8, x, 0)
[1]  1  2  3  4  0  0  0  0  9 10


The most commonly used loop structures in R are for, while and apply loops. Less common are repeat loops. The break function is used to break out of loops, and next halts the processing of the current iteration and advances the looping index.

For Loop

For loops are controlled by a looping vector. In every iteration of the loop one value in the looping vector is assigned to a variable that can be used in the statements of the body of the loop. Usually, the number of loop iterations is defined by the number of values stored in the looping vector and they are processed in the same order as they are stored in the looping vector.


for(variable in sequence) {


mydf <- iris
myve <- NULL # Creates empty storage container
for(i in seq(along=mydf[,1])) {
    myve <- c(myve, mean(as.numeric(mydf[i, 1:3]))) # Note: inject approach is much faster than append with 'c'. See below for details.
[1] 3.333333 3.100000 3.066667 3.066667 3.333333 3.666667 3.133333 3.300000
[9] 2.900000 3.166667 3.533333 3.266667 3.066667 2.800000 3.666667 3.866667

Table of Contents

Example: condition*

x <- 1:10
z <- NULL
for(i in seq(along=x)) {
    if(x[i] < 5) {
        z <- c(z, x[i] - 1) 
    } else {
        z <- c(z, x[i] / x[i]) 
[1] 0 1 2 3 1 1 1 1 1 1

Table of Contents

Example: stop on condition and print error message

x <- 1:10
z <- NULL
for(i in seq(along=x)) {
    if (x[i]<5) {
        z <- c(z,x[i]-1) 
    } else {
        stop("values need to be <5")
Error: values need to be <5
[1] 0 1 2 3

While Loop

Similar to for loop, but the iterations are controlled by a conditional statement.


while(condition) statements


z <- 0
while(z < 5) {
    z <- z + 2
[1] 2
[1] 4
[1] 6

Apply Loop Family

For Two-Dimensional Data Sets: apply

apply(X, MARGIN, FUN, ARGs)

: array, matrix or data.frame; MARGIN: 1 for rows, 2 for columns, c(1,2) for both; FUN: one or more functions; ARGs: possible arguments for function


## Example for applying predefined mean function
apply(iris[,1:3], 1, mean)
[1] 3.333333 3.100000 3.066667 3.066667 3.333333 3.666667 3.133333 3.300000

## With custom function
x <- 1:10
test <- function(x) {
# Defines some custom function
    if(x < 5) {
    } else {
        x / x
apply(as.matrix(x), 1, test)
Returns same result as previous for loop*
[1] 0 1 2 3 1 1 1 1 1 1

## Same as above but with a single line of code
apply(as.matrix(x), 1, function(x) { if (x<5) { x-1 } else { x/x } })

[1] 0 1 2 3 1 1 1 1 1 1
Table of Contents

For Ragged Arrays: tapply
Applies a function to array categories of variable lengths (ragged array). Grouping is defined by factor.


tapply(vector, factor, FUN)


## Computes mean values of vector agregates defined by factor
tapply(as.vector(iris[,4]), factor(iris[,5]), mean)
setosa versicolor  virginica
 0.246      1.326      2.026

## The aggregate function provides related utilities
aggregate(iris[,1:4], list(iris$Species), mean)

     Group.1 Sepal.Length Sepal.Width Petal.Length Petal.Width
1     setosa        5.006       3.428        1.462       0.246
2 versicolor        5.936       2.770        4.260       1.326
3  virginica        6.588       2.974        5.552       2.026

For Vectors and Lists: lapply and sapply

Both apply a function to vector or list objects. The function lapply returns a list, while sapply attempts to return the simplest data object, such as vector or matrix instead of list.


lapply(X, FUN)
sapply(X, FUN)


## Creates a sample list
mylist <- as.list(iris[1:3,1:3])
[1] 5.1 4.9 4.7

[1] 3.5 3.0 3.2

[1] 1.4 1.4 1.3

## Compute sum of each list component and return result as list
lapply(mylist, sum)
[1] 14.7

[1] 9.7

[1] 4.1

Compute sum of each list component and return result as vector
sapply(mylist, sum)
Sepal.Length  Sepal.Width Petal.Length
        14.7          9.7          4.1

    Other Loops

    Repeat Loop


    repeat statements

    Loop is repeated until a break is specified. This means there needs to be a second statement to test whether or not to break from the loop.

    z <- 0
    repeat {
        z <- z + 1
    if(z > 100) break()

    Improving Speed Performance of Loops

    Looping over very large data sets can become slow in R. However, this limitation can be overcome by eliminating certain operations in loops or avoiding loops over the data intensive dimension in an object altogether. The latter can be achieved by performing mainly vector-to-vecor or matrix-to-matrix computations which run often over 100 times faster than the corresponding for() or apply() loops in R. For this purpose, one can make use of the existing speed-optimized R functions (e.g.: rowSums, rowMeans, table, tabulate) or one can design custom functions that avoid expensive R loops by using vector- or matrix-based approaches. Alternatively, one can write programs that will perform all time consuming computations on the C-level.

    (1) Speed comparison of for loops with an append versus an inject step:

    myMA <- matrix(rnorm(1000000), 100000, 10, dimnames=list(1:100000, paste("C", 1:10, sep="")))
    results <- NULL
    system.time(for(i in seq(along=myMA[,1])) results <- c(results, mean(myMA[i,])))
       user  system elapsed
     39.156   6.369  45.559

    results <- numeric(length(myMA[,1]))
    system.time(for(i in seq(along=myMA[,1])) results[i] <- mean(myMA[i,]))
       user  system elapsed
      1.550   0.005   1.556 

      The inject approach is 20-50 times faster than the append version.

      (2) Speed comparison of apply loop versus rowMeans for computing the mean for each row in a large matrix:

      system.time(myMAmean <- apply(myMA, 1, mean))
        user  system elapsed
       1.452   0.005   1.456

      system.time(myMAmean <- rowMeans(myMA))
         user  system elapsed
        0.005   0.001   0.006

      The rowMeans approach is over 200 times faster than the apply loop.

      (3) Speed comparison of apply loop versus vectorized approach for computing the standard deviation of each row:

      system.time(myMAsd <- apply(myMA, 1, sd))
         user  system elapsed
        3.707   0.014   3.721

              1         2         3         4
      0.8505795 1.3419460 1.3768646 1.3005428

      system.time(myMAsd <- sqrt((rowSums((myMA-rowMeans(myMA))^2)) / (length(myMA[1,])-1)))
         user  system elapsed
        0.020   0.009   0.028

              1         2         3         4
      0.8505795 1.3419460 1.3768646 1.3005428

      The vector-based approach in the last step is over 200 times faster than the apply loop.

      (4) Example for computing the mean for any custom selection of columns without compromising the speed performance:

      ## In the following the colums are named according to their selection in myList
      myList <- tapply(colnames(myMA), c(1,1,1,2,2,2,3,3,4,4), list)
      myMAmean <- sapply(myList, function(x) rowMeans(myMA[,x]))
      colnames(myMAmean) <- sapply(myList, paste, collapse="_")
          C1_C2_C3   C4_C5_C6       C7_C8     C9_C10
      1  0.0676799 -0.2860392  0.09651984 -0.7898946
      2 -0.6120203 -0.7185961  0.91621371  1.1778427
      3  0.2960446 -0.2454476 -1.18768621  0.9019590
      4  0.9733695 -0.6242547  0.95078869 -0.7245792

      ## Alternative to achieve the same result with similar performance, but in a much less elegant way
      myselect <- c(1,1,1,2,2,2,3,3,4,4) # The colums are named according to the selection stored in myselect
      myList <- tapply(seq(along=myMA[1,]), myselect, function(x) paste("myMA[ ,", x, "]", sep=""))
      myList <- sapply(myList, function(x) paste("(", paste(x, collapse=" + "),")/", length(x)))
      myMAmean <- sapply(myList, function(x) eval(parse(text=x)))
      colnames(myMAmean) <- tapply(colnames(myMA), myselect, paste, collapse="_")
          C1_C2_C3   C4_C5_C6       C7_C8     C9_C10
      1  0.0676799 -0.2860392  0.09651984 -0.7898946
      2 -0.6120203 -0.7185961  0.91621371  1.1778427
      3  0.2960446 -0.2454476 -1.18768621  0.9019590
      4  0.9733695 -0.6242547  0.95078869 -0.7245792


      A very useful feature of the R environment is the possibility to expand existing functions and to easily write custom functions. In fact, most of the R software can be viewed as a series of R functions.

      Syntax to define functions

      myfct <- function(arg1, arg2, ...) {

      The value returned by a function is the value of the function body, which is usually an unassigned final expression, e.g.: return()

      Syntax to call functions

      myfct(arg1=..., arg2=...)
      Syntax Rules for Functions

      Functions are defined by (1) assignment with the keyword function, (2) the declaration of arguments/variables (arg1, arg2, ...) and (3) the definition of operations (function_body) that perform computations on the provided arguments. A function name needs to be assigned to call the function (see below).

      Function names can be almost anything. However, the usage of names of existing functions should be avoided.

      It is often useful to provide default values for arguments (e.g.:arg1=1:10). This way they don't need to be provided in a function call. The argument list can also be left empty (myfct <- function() { fct_body }) when a function is expected to return always the same value(s). The argument '...' can be used to allow one function to pass on argument settings to another.

      Function body
      The actual expressions (commands/operations) are defined in the function body which should be enclosed by braces. The individual commands are separated by semicolons or new lines (preferred).

      Calling functions
      Functions are called by their name followed by parentheses containing possible argument names. Empty parenthesis after the function name will result in an error message when a function requires certain arguments to be provided by the user. The function name alone will print the definition of a function.

      Variables created inside a function exist only for the life time of a function. Thus, they are not accessible outside of the function. To force variables in functions to exist globally, one can use this special assignment operator: '<<-'. If a global variable is used in a function, then the global variable will be masked only within the function.

      Example: Function basics

      myfct <- function(x1, x2=5) {
          z1 <- x1/x1
          z2 <- x2*x2
          myvec <- c(z1, z2)
      myfct # prints definition of function
      myfct(x1=2, x2=5) # applies function to values 2 and 5
       [1]  1 25

      myfct(2, 5) # the argument names are not necessary, but then the order of the specified values becomes important
      myfct(x1=2) # does the same as before, but the default value '5' is used in this case

      Example: Function with optional arguments

      myfct2 <- function(x1=5, opt_arg) {
              if(missing(opt_arg)) { # 'missing()' is used to test whether a value was specified as an argument
                  z1 <- 1:10
              } else {
                  z1 <- opt_arg
              cat("my function returns:", "\n")

      myfct2(x1=5) # performs calculation on default vector (z1) that is defined in the function body
      my function returns:
       [1] 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

      myfct2(x1=5, opt_arg=30:20) # a custom vector is used instead when the optional argument (opt_arg) is specified
      my function returns:
       [1] 6.0 5.8 5.6 5.4 5.2 5.0 4.8 4.6 4.4 4.2 4.0

      Control utilities for functions: return, warning and stop

      The evaluation flow of a function may be terminated at any stage with the return function. This is often used in combination with conditional evaluations.

      To stop the action of a function and print an error message, one can use the stop function.

      To print a warning message in unexpected situations without aborting the evaluation flow of a function, one can use the function warning("...").

      myfct <- function(x1) {
              if (x1>=0) print(x1) else stop("This function did not finish, because x1 < 0")
              warning("Value needs to be > 0")
      [1] 2
      Warning message:
      In myfct(x1 = 2) : Value needs to be > 0

      Error in myfct(x1 = -2) : This function did not finish, because x1 < 0

      Useful Utilities

      Debugging Utilities

      Several debugging utilities are available for R. The most important utilities are: traceback(), browser(), options(error=recover), options(error=NULL) and debug(). The Debugging in R page provides an overview of the available resources.

      Regular Expressions

      R's regular expression utilities work similar as in other languages. To learn how to use them in R, one can consult the main help page on this topic with ?regexp. The following gives a few basic examples.

      The grep function can be used for finding patterns in strings, here letter A in vector[grep("A",]
      [1] "April"  "August"

      Example for using regular expressions to substitute a pattern by another one using the sub/gsub function with a back reference. Remember: single escapes '\' need to be double escaped '\\' in R.

      gsub("(i.*a)", "xxx_\\1", "virginica", perl = TRUE)
      [1] "vxxx_irginica"

      Example for split and paste functions

      x <- gsub("(a)", "\\1_",[1], perl=TRUE) # performs substitution with back reference which inserts in this example a '_' character
      [1] "Ja_nua_ry"

      strsplit(x, "_") # splits string on inserted character from above
      [1] "Ja"  "nua" "ry"

      paste(rev(unlist(strsplit(x, NULL))), collapse="") # reverses character string by splitting first all characters into vector fields and then collapsing them with paste
      [1] "yr_aun_aJ"

      Example for importing specific lines in a file with a regular expression. The following example demonstrates the retrieval of specific lines from an external file with a regular expression. First, an external file is created with the cat function, all lines of this file are imported into a vector with readLines, the specific elements (lines) are then retieved with the grep function, and the resulting lines are split into vector fields with strsplit.

      cat(, file="zzz.txt", sep="\n")
      x <- readLines("zzz.txt")
      x <- x[c(grep("^J", as.character(x), perl = TRUE))]
      t(, "u")))
                      [,1]  [,2]
      c..Jan....ary.. "Jan" "ary"    "J"   "ne"    "J"   "ly"

      Interpreting Character String as Expression


      mylist <- ls() # generates vector of object names in session
      mylist[1] # prints name of 1st entry in vector but does not execute it as expression that returns values of 10th object
      get(mylist[1]) # uses 1st entry name in vector and executes it as expression
      eval(parse(text=mylist[1])) # alternative approach to obtain similar result

      Time, Date and Sleep


      system.time(ls()) # returns CPU (and other) times that an expression used, here ls()
         user  system elapsed
            0       0       0

      date() # returns the current system date and time
      [1] "Wed Dec 11 15:31:17 2012"

      Sys.sleep(1) # pause execution of R expressions for a given number of seconds (e.g. in loop)

      Calling External Software with System Command

      The system command allows to call any command-line software from within R on Linux, UNIX and OSX systems.

      system("...") # provide under '...' command to run external software e.g. Perl, Python, C++ programs

      Related utilities on Windows operating systems

      x <- shell("dir", intern=T) # reads current working directory and assigns to file
      shell.exec("C:/Documents and Settings/Administrator/Desktop/my_file.txt") # opens file with associated program

      Miscellaneous Utilities

      (1)  Batch import and export of many files.
      In the following example all file names ending with *.txt in the current directory are first assigned to a list (the '$' sign is used to anchor the match to the end of a string). Second, the files are imported one-by-one using a for loop where the original names are assigned to the generated data frames with the assign function. Consult help with ?read.table to understand arguments row.names=1 and comment.char = "A". Third, the data frames are exported using their names for file naming and appending the extension *.out.

      files <- list.files(pattern=".txt$")
      for(i in files) {
              x <- read.table(i, header=TRUE, comment.char = "A", sep="\t")
              assign(i, x)
              write.table(x, paste(i, c(".out"), sep=""), quote=FALSE, sep="\t", col.names = NA)

      (2)  Running Web Applications (basics on designing web client/crawling/scraping scripts in R)
      Example for obtaining MW values for peptide sequences from the EXPASY's pI/MW Tool web page.

      myentries <- c("MKWVTFISLLFLFSSAYS", "MWVTFISLL", "MFISLLFLFSSAYS")                                                                                                                                        
      myresult <- NULL                                                                                                                                                                                              
      for(i in myentries) {                                                                                                                                                                                         
              myurl <- paste("", i, "&resolution=monoisotopic", sep="")                  
              x <- url(myurl)                                                                                                                                                                                       
              res <- readLines(x)                                                                                                                                                                                   
              mylines <- res[grep("Theoretical pI/Mw:", res)]                                                                                                                                                        
              myresult <- c(myresult, as.numeric(gsub(".*/ ", "", mylines)))                                                                                                                                         
              Sys.sleep(1) # halts process for one sec to give web service a break                                                                                                                                     
      final <- data.frame(Pep=myentries, MW=myresult)                                                                                                                                                               
      cat("\n The MW values for my peptides are:\n")                                                                                                                                                                
                       Pep      MW
      2          MWVTFISLL 1108.60
      3     MFISLLFLFSSAYS 1624.82

      Running R Programs

      (1) Executing an R script from the R console


      (2.1) Syntax for running R programs from the command-line. Requires in first line of my_script.R the following statement: #!/usr/bin/env Rscript

      $ Rscript my_script.R # or just ./myscript.R after making file executable with 'chmod +x my_script.R'

      All commands starting with a '$' sign need to be executed from a Unix or Linux shell.
      (2.2) Alternatively, one can use the following syntax to run R programs in BATCH mode from the command-line.

      $ R CMD BATCH [options] my_script.R [outfile]

      The output file lists the commands from the script file and their outputs. If no outfile is specified, the name used is that of infile and .Rout is appended to outfile. To stop all the usual R command line information from being written to the outfile, add this as first line to my_script.R file: options(echo=FALSE). If the command is run like this R CMD BATCH --no-save my_script.R, then nothing will be saved in the .Rdata file which can get often very large. More on this can be found on the help pages: $ R CMD BATCH --help or ?BATCH.

      (2.3) Another alternative for running R programs as silently as possible.

      $ R --slave < my_infile > my_outfile

      Argument --slave makes R run as 'quietly' as possible.

      (3) Passing Command-Line Arguments to R Programs
      Create an R script, here named test.R, like this one:

      myarg <- commandArgs()
      print(iris[1:myarg[6], ])

      Then run it from the command-line like this:
      $ Rscript test.R 10 
      In the given example the number 10 is passed on from the command-line as an argument to the R script which is used to return to STDOUT the first 10 rows of the iris sample data. If several arguments are provided, they will be interpreted as one string that needs to be split it in R with the strsplit function.

      (4) Submitting R script to a Linux cluster via Torque
      Create the following shell script
      cd $PBS_O_WORKDIR
      R CMD BATCH --no-save my_script.R
      This script doesn't need to have executable permissions. Use the following qsub command to send this shell script to the Linux cluster from the directory where the R script my_script.R is located. To utilize several CPUs on the Linux cluster, one can divide the input data into several smaller subsets and execute for each subset a separate process from a dedicated directory.

      $ qsub 
      Here is a short R script that generates the required files and directories automatically and submits the jobs to the nodes: submit2cluster.R. For more details, see also this 'Tutorial on Parallel Programming in R' by Hanna Sevcikova

      (5) Submitting jobs to Torque or any other queuing/scheduling system via the BatchJobs package. This package provides one of the most advanced resources for submitting jobs to queuing systems from within R. A related package is BiocParallel from Bioconductor which extends many functionalities of BatchJobs to genome data analysis. Useful documentation for BatchJobs: Technical Report, GitHub page, Slide Show, Config samples.

      loadConfig(conffile = ".BatchJobs.R")
              ## Loads configuration file. Here .BatchJobs.R containing just this line:
              ## cluster.functions <- makeClusterFunctionsTorque("torque.tmpl")
              ## The template file torque.tmpl is expected to be in the current working
              ## director. It can be downloaded from here:

      getConfig() # Returns BatchJobs configuration settings
      reg <- makeRegistry(id="BatchJobTest", work.dir="results")
              ## Constructs a registry object. Output files from R will be stored under directory "results",
              ## while the
      standard objects from BatchJobs will be stored in the directory "BatchJobTest-files"

      ## Some test function
      f <- function(x) {
              system("ls -al >> test.txt")

      ## Adds jobs to registry object (here reg)
      ids <- batchMap(reg, fun=f, 1:10)

      ## Submit jobs or chunks of jobs to batch system via cluster function
      done <- submitJobs(reg, resources=list(walltime=3600, nodes="1:ppn=4", memory="4gb"))

      ## Load results from BatchJobTest-files/jobs/01/1-result.RData
      loadResult(reg, 1)

      Object-Oriented Programming (OOP)

      R supports two systems for object-oriented programming (OOP). An older S3 system and a more recently introduced S4 system. The latter is more formal, supports multiple inheritance, multiple dispatch and introspection. Many of these features are not available in the older S3 system. In general, the OOP approach taken by R is to separate the class specifications from the specifications of generic functions (function-centric system). The following introduction is restricted to the S4 system since it is nowadays the preferred OOP method for R. More information about OOP in R can be found in the following introductions: Vincent Zoonekynd's introduction to S3 Classes, S4 Classes in 15 pages, Christophe Genolini's S4 Intro, The R.oo package, BioC Course: Advanced R for Bioinformatics, Programming with R by John Chambers and R Programming for Bioinformatics by Robert Gentleman.

        Define S4 Classes

        (A) Define S4 Classes with setClass() and new()

        y <- matrix(1:50, 10, 5) # Sample data set
            prototype=prototype(a=y[1:2,]), # Defines default value (optional)
            validity=function(object) { # Can be defined in a separate step using setValidity
                if(class(object@a)!="matrix") {
                    return(paste("expected matrix, but obtained", class(object@a)))
                } else {

        Table of Contents
        The setClass function defines classes. Its most important arguments are
        • Class: the name of the class
        • representation: the slots that the new class should have and/or other classes that this class extends.
        • prototype: an object providing default data for the slots.
        • contains: the classes that this class extends.
        • validity, access, version: control arguments included for compatibility with S-Plus.
        • where: the environment to use to store or remove the definition as meta data.
        (B) The function new creates an instance of a class (here myclass)

        myobj <- new("myclass", a=y)
        An object of class "myclass"
        Slot "a":
              [,1] [,2] [,3] [,4] [,5]
         [1,]    1   11   21   31   41
         [2,]    2   12   22   32   42

        new("myclass", a=iris) # Returns an error message due to wrong input type (iris is data frame)
        Error in validObject(.Object) :
          invalid class “myclass” object: expected matrix, but obtained data.frame

        Table of Contents
        Its arguments are:
        • Class: the name of the class
        • ...: Data to include in the new object with arguments according to slots in class definition.
        (C) A more generic way of creating class instances is to define an initialization method (details below)

        setMethod("initialize", "myclass", function(.Object, a) {
            .Object@a <- a/a
        new("myclass", a = y)
        [1] "initialize"
        new("myclass", a = y)> new("myclass", a = y)
        An object of class "myclass"
        Slot "a":
              [,1] [,2] [,3] [,4] [,5]
         [1,]    1    1    1    1    1
         [2,]    1    1    1    1    1

        (D) Usage and helper functions

        myobj@a # The '@' extracts the contents of a slot. Usage should be limited to internal functions!
        initialize(.Object=myobj, a=as.matrix(cars[1:3,])) # Creates a new S4 object from an old one.
        # removeClass("myclass") # Removes object from current session; does not apply to associated methods.

        (E) Inheritance: allows to define new classes that inherit all properties (e.g. data slots, methods) from their existing parent classes

        setClass("myclass1", representation(a = "character", b = "character"))
        setClass("myclass2", representation(c = "numeric", d = "numeric"))
        setClass("myclass3", contains=c("myclass1", "myclass2"))
        new("myclass3", a=letters[1:4], b=letters[1:4], c=1:4, d=4:1)
        An object of class "myclass3"
        Slot "a":
        [1] "a" "b" "c" "d"

        Slot "b":
        [1] "a" "b" "c" "d"

        Slot "c":
        [1] 1 2 3 4

        Slot "d":
        [1] 4 3 2 1

        Class "myclass1" [in ".GlobalEnv"]

        Name:          a         b
        Class: character character

        Known Subclasses: "myclass3"

        Class "myclass2" [in ".GlobalEnv"]

        Name:        c       d
        Class: numeric numeric

        Known Subclasses: "myclass3"

        Class "myclass3" [in ".GlobalEnv"]

        Name:          a         b         c         d
        Class: character character   numeric   numeric

        Extends: "myclass1", "myclass2"

        The argument contains allows to extend existing classes; this propagates all slots of parent classes.

        (F) Coerce objects to another class

        setAs(from="myclass", to="character", def=function(from) as.character(as.matrix(from@a)))
        as(myobj, "character")
        [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13" "14" "15"

        (G) Virtual classes are constructs for which no instances will be or can be created. They are used to link together classes which may have distinct representations (e.g. cannot inherit from each other) but for which one wants to provide similar functionality. Often it is desired to create a virtual class and to then have several other classes extend it. Virtual classes can be defined by leaving out the representation argument or including the class VIRTUAL:

        setClass("myVclass", representation(a = "character", "VIRTUAL"))
        Table of Contents

        (H) Functions to introspect classes
        • getClass("myclass")
        • getSlots("myclass")
        • slotNames("myclass")
        • extends("myclass2")

        Assign Generics and Methods

        Assign generics and methods with setGeneric() and setMethod()

        (A) Accessor function (to avoid usage of '@')

        setGeneric(name="acc", def=function(x) standardGeneric("acc"))
            setMethod(f="acc", signature="myclass", definition=function(x) {
              [,1] [,2] [,3] [,4] [,5]
         [1,]    1   11   21   31   41
         [2,]    2   12   22   32   42

        Table of Contents

        (B.1) Replacement method using custom accessor function (acc <-)

        setGeneric(name="acc<-", def=function(x, value) standardGeneric("acc<-"))
        setReplaceMethod(f="acc", signature="myclass", definition=function(x, value) {
            x@a <- value
        ## After this the following replace operations with 'acc' work on new object class
        acc(myobj)[1,1] <- 999 # Replaces first value
        colnames(acc(myobj)) <- letters[1:5] # Assigns new column names
        rownames(acc(myobj)) <- letters[1:10] # Assigns new row names
        An object of class "myclass"
        Slot "a":
            a  b  c  d  e
        a 999 11 21 31 41
        b   2 12 22 32 42


        (B.2) Replacement method using "[" operator ([<-)

        setReplaceMethod(f="[", signature="myclass", definition=function(x, i, j, value) {
            x@a[i,j] <- value
        myobj[1,2] <- 999
        An object of class "myclass"
        Slot "a":
            a   b  c  d  e
        a 999 999 21 31 41
        b   2  12 22 32 42

        (C) Define behavior of "[" subsetting operator (no generic required!)

        setMethod(f="[", signature="myclass",
            definition=function(x, i, j, ..., drop) {
            x@a <- x@a[i,j]
        myobj[1:2,] # Standard subsetting works now on new class
        An object of class "myclass"
        Slot "a":
            a   b  c  d  e
        a 999 999 21 31 41
        b   2  12 22 32 42


        (D) Define print behavior

        setMethod(f= show", signature="myclass", definition=function(object) {
            cat("An instance of ", "\"", class(object), "\"", " with ", length(acc(object)[,1]), " elements", "\n", sep="")
            if(length(acc(object)[,1])>=5) {
                print([1:2,], ...=rep("...", length(acc(object)[1,])),
            } else {
        myobj # Prints object with custom method
        An instance of "myclass" with 10 elements
              a   b   c   d   e
        a   999 999  21  31  41
        b     2  12  22  32  42
        ... ... ... ... ... ...
        i     9  19  29  39  49
        j    10  20  30  40  50

          (E) Define a data specific function (here randomize row order)

          setGeneric(name="randomize", def=function(x) standardGeneric("randomize"))
          setMethod(f="randomize", signature="myclass", definition=function(x) {
              acc(x)[sample(1:length(acc(x)[,1]), length(acc(x)[,1])), ]
              a   b  c  d  e
          j  10  20 30 40 50
          b   2  12 22 32 42

          (F) Define a graphical plotting function and allow user to access it with generic plot function

          setMethod(f="plot", signature="myclass", definition=function(x, ...) {
              barplot(as.matrix(acc(x)), ...)

          (G) Functions to inspect methods
          • showMethods(class="myclass")
          • findMethods("randomize")
          • getMethod("randomize", signature="myclass")
          • existsMethod("randomize", signature="myclass")

          Building R Packages

          To get familiar with the structure, building and submission process of R packages, users should carefully read the documentation on this topic available on these sites:

          Short Overview of Package Building Process

          (A) Automatic package building with the package.skeleton function:

          package.skeleton(name="mypackage", code_files=c("script1.R", "script2.R"))
          Note: this is an optional but very convenient function to get started with a new package. The given example will create a directory named mypackage containing the skeleton of the package for all functions, methods and classes defined in the R script(s) passed on to the code_files argument. The basic structure of the package directory is described here. The package directory will also contain a file named 'Read-and-delete-me' with the following instructions for completing the package:
          • Edit the help file skeletons in man, possibly combining help files for multiple functions.
          • Edit the exports in NAMESPACE, and add necessary imports.
          • Put any C/C++/Fortran code in src.
          • If you have compiled code, add a useDynLib() directive to NAMESPACE.
          • Run R CMD build to build the package tarball.
          • Run R CMD check to check the package tarball.
          • Read Writing R Extensions for more information.

          (B) Once a package skeleton is available one can build the package from the command-line (Linux/OS X):

          $ R CMD build mypackage
          This will create a tarball of the package with its version number encoded in the file name, e.g.: mypackage_1.0.tar.gz.
          Subsequently, the package tarball needs to be checked for errors with:

          $ R CMD check mypackage_1.0.tar.gz
          All issues in a package's source code and documentation should be addressed until R CMD check returns no error or warning messages anymore.

          (C) Install package from source:


          install.packages("mypackage_1.0.tar.gz", repos=NULL)
          OS X:

          install.packages("mypackage_1.0.tar.gz", repos=NULL, type="source")
          Table of Contents
          Windows requires a zip archive for installing R packages, which can be most conveniently created from the command-line (Linux/OS X) by installing the package in a local directory (here tempdir) and then creating a zip archive from the installed package directory:

          $ mkdir tempdir
          $ R CMD INSTALL -l tempdir mypackage_1.0.tar.gz
          $ cd tempdir
          $ zip -r mypackage mypackage

          ## The resulting archive can be installed under Windows like this:
          install.packages("", repos=NULL)

          Table of Contents
          This procedure only works for packages which do not rely on compiled code (C/C++). Instructions to fully build an R package under Windows can be found here and here.

          (D) Maintain/expand an existing package:
          • Add new functions, methods and classes to the script files in the ./R directory in your package
          • Add their names to the NAMESPACE file of the package
          • Additional *.Rd help templates can be generated with the prompt*() functions like this:
          source("myscript.R") # imports functions, methods and classes from myscript.R
          prompt(myfct) # writes help file myfct.Rd
          promptClass("myclass") # writes file myclass-class.Rd
          promptMethods("mymeth") # writes help file mymeth.Rd
            • The resulting *.Rd help files can be edited in a text editor and properly rendered and viewed from within R like this:
          Rd2txt("./mypackage/man/myfct.Rd") # renders *.Rd files as they look in final help pages
          checkRd("./mypackage/man/myfct.Rd") # checks *.Rd help file for problems

          (E) Submit package to a public repository

          The best way of sharing an R package with the community is to submit it to one of the main R package repositories, such as CRAN or Bioconductor. The details about the submission process are given on the corresponding repository submission pages:

          Reproducible Research by Integrating R with Latex or Markdown

          R Programming Exercises

          Exercise Slides

          Slides ]   [ Exercises ]   [ Additional Exercises ]  

          Download on of the above exercise files, then start editing this R source file with a programming text editor, such as Vim, Emacs or one of the R GUI text editors. Here is the HTML version of the code with syntax coloring.

          Sample Scripts

          Batch Operations on Many Files

          ## (1) Start R from an empty test directory
          ## (2) Create some files as sample data

          for(i in {
                  mydf <- data.frame(, Rain=runif(12, min=10, max=100), Evap=runif(12, min=1000, max=2000))
                  write.table(mydf, file=paste(i , ".infile", sep=""), quote=F, row.names=F, sep="\t")

          ## (3) Import created files, perform calculations and export to renamed files
          files <- list.files(pattern=".infile$")
          for(i in seq(along=files)) { # start for loop with numeric or character vector; numeric vector is often more flexible
                  x <- read.table(files[i], header=TRUE, row.names=1, comment.char = "A", sep="\t")
                  x <- data.frame(x, sum=apply(x, 1, sum), mean=apply(x, 1, mean)) # calculates sum and mean for each data frame
                  assign(files[i], x) # generates data frame object and names it after content in variable 'i'
                  print(files[i], quote=F) # prints loop iteration to screen to check its status
                  write.table(x, paste(files[i], c(".out"), sep=""), quote=FALSE, sep="\t", col.names = NA)

          ## (4) Same as above, but file naming by index data frame. This way one can organize file names by external table.
          name_df <- data.frame(Old_name=sort(files), New_name=sort(
          for(i in seq(along=name_df[,1])) {
                  x <- read.table(as.vector(name_df[i,1]), header=TRUE, row.names=1, comment.char = "A", sep="\t")
                  x <- data.frame(x, sum=apply(x, 1, sum), mean=apply(x, 1, mean))
                  assign(as.vector(name_df[i,2]), x) # generates data frame object and names it after 'i' entry in column 2
                  print(as.vector(name_df[i,1]), quote=F)
                  write.table(x, paste(as.vector(name_df[i,2]), c(".out"), sep=""), quote=FALSE, sep="\t", col.names = NA)

          ## (5) Append content of all input files to one file.
          files <- list.files(pattern=".infile$")
          all_files <- data.frame(files=NULL, Month=NULL, Gain=NULL , Loss=NULL, sum=NULL, mean=NULL) # creates empty data frame container
          for(i in seq(along=files)) {
                  x <- read.table(files[i], header=TRUE, row.names=1, comment.char = "A", sep="\t")
                  x <- data.frame(x, sum=apply(x, 1, sum), mean=apply(x, 1, mean))
                  x <- data.frame(file=rep(files[i], length(x[,1])), x) # adds file tracking column to x
                  all_files <- rbind(all_files, x) # appends data from all files to data frame 'all_files'
                  write.table(all_files, file="all_files.xls", quote=FALSE, sep="\t", col.names = NA)

          ## (6) Write the above code into a text file and execute it with the commands 'source' and 'BATCH'.
          source("my_script.R") # execute from R console 
          $ R CMD BATCH my_script.R # execute from shell


          Large-scale Array Analysis

          Sample script to perform large-scale expression array analysis with complex queries: lsArray.R. To demo what the script does, run it like this:


          Graphical Procedures: Feature Map Example

          Script to plot feature maps of genes or chromosomes: featureMap.R. To demo what the script does, run it like this:


          Sequence Analysis Utilities

          Includes sequence batch import, sub-setting, pattern matching, AA Composition, NEEDLE, PHYLIP, etc. The script 'sequenceAnalysis.R' demonstrates how R can be used as a powerful tool for managing and analyzing large sets of biological sequences. This example also shows how easy it is to integrate R with the EMBOSS project or other external programs. The script provides the following functionality:
          • Batch sequence import into R data frame
          • Motif searching with hit statistics
          • Analysis of sequence composition
          • All-against-all sequence comparisons
          • Generation of phylogenetic trees
          To demonstrate the utilities of the script, users can simply execute it from R with the following source command:


          Pattern Matching and Positional Parsing of Sequences

          Functions for importing sequences into R, retrieving reverse and complement of nucleotide sequences, pattern searching, positional parsing and exporting search results in HTML format: patternSearch.R. To demo what the script does, run it like this:


          Identify Over-Represented Strings in Sequence Sets

          Functions for finding over-represented words in sets of DNA, RNA or protein sequences: wordFinder.R. To demo what the script does, run it like this:


          Translate DNA into Protein

          Script 'translateDNA.R' for translating NT sequences into AA sequences (required codon table). To demo what the script does, run it like this:


          Subsetting of Structure Definition Files (SDF)

          Script for importing and subsetting SDF files: sdfSubset.R. To demo what the script does, run it like this:


          Managing Latex BibTeX Databases

          Script for importing BibTeX databases into R, retrieving the individual references with a full-text search function and viewing the results in R or in HubMed: BibTex.R. To demo what the script does, run it like this:


          Loan Payments and Amortization Tables

          This script calculates monthly and annual mortgage or loan payments, generates amortization tables and plots the results: mortgage.R. A Shiny App using this function has been created by Antoine Soetewey here. To demo what the script does, run it like this:


          Course Assignment: GC Content, Reverse & Complement

          Apply the above information to write a function that calculates for a set of DNA sequences their GC content and generates their reverse and complement. Here are some useful commands that can be incorporated in this function:

          ## Generate an example data frame with ID numbers and DNA sequences
          fx <- function(test) {
              x <- as.integer(runif(20, min=1, max=5))
              x[x==1] <- "A"; x[x==2] <- "T"; x[x==3] <- "G"; x[x==4] <- "C"
              paste(x, sep = "", collapse ="")
          z1 <- c()
          for(i in 1:50) {
              z1 <- c(fx(i), z1)
          z1 <- data.frame(ID=seq(along=z1), Seq=z1)

          ## Write each character of sequence into separate vector field and reverse its order
          my_split <- strsplit(as.character(z1[1,2]),"")
          my_rev <- rev(my_split[[1]])
          paste(my_rev, collapse="")

          ## Generate the sequence complement by replacing G|C|A|T by C|G|T|A
          ## Use 'apply' or 'for loop' to apply the above operations to all sequences in sample data frame 'z1'
          ## Calculate in the same loop the GC content for each sequence using the following command


          Table of Contents

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