R programming language
The R programming language (or just "R"), sometimes described as "GNU S", is a mathematical language and environment used for statistical analysis and display.
R is highly extensible through the use of packages, which are user submitted libraries for specific functions or specific areas of study. A core set of packages are included with the installation of R, with many more available at the comprehensive R archive network, CRAN. The bioinformatics community has seeded a successful effort to use R for the analysis of data from molecular biology laboratories. The bioconductor project started in the fall of 2001 provides R packages for the analysis of genomic data. e.g. Affymetrix and cDNA microarray object-oriented data handling and analysis tools.
- 1 Installation
- 2 Comparison with other programs
- 3 Basics
- 4 Packages (add-ons)
- 5 See also
- 6 External links
First make sure you have the following installed (check http://www.rpmfind.net for the packages):
compat-g77 compat-gcc gcc-g77
It also sometimes helps to create a soft link to gfortran like so (changing the directory to suit your needs):
ln -s /usr/bin/g77 /usr/bin/gfortran
Then, and this is important, add the following to your config.site (found in your R source directory):
Now you are ready to install R on SuSE:
./configure --x-includes=/usr/include/X11 # sometimes necessary
make make check make pdf # optional make info # optional make install # as superuser ('root')
That's it. You are now ready to use R
Comparison with other programs
Although R is mostly used by statisticians, and other people in need of statistics, it can also be used as a general matrix calculation toolbox in a program such as GNU Octave or its proprietary counterpart, MATLAB.
It should not be confused with the R package , a collection of programs for multidimensional and spatial analysis available on Macintosh and VAX/VMS systems.
How to get help:
- Opens browser
- For more on using help
- For help on ..
- To search for ..
How to leave again:
- Image can be saved to .RData
Basic R commands
Most arithmetic operators work like you would expect in R:
4+2 #Prints '6' 3*4 #Prints '12'
Operators have precedence as known from basic algebra:
1+2*4 #Prints '9', while (1+2)*4 #Prints '12'
A function call in R looks like this:
cos(pi/3) #Prints '0.5' exp(1) #Prints '2.718282'
A function is identified in R by the parentheses
- That's why it's: help(), and not: help
Variables (objects) in R
To assign a value to a variable (object):
x<-4 #Assigns 4 to x x=4 #Assigns 4 to x (new) x #Prints '4' y<-x+2 #Assigns 6 to y
Functions for managing variables:
- lists all existing objects
- tells the structure (type) of object 'x'
- removes (deletes) the object 'x'
A vector in R is like a sequence of elements of the same mode.
x<-1:10 #Creates a vector y<-c("a","b","c") #So does this
Handy functions for vectors:
- Concatenates arguments into a vector
- Returns the smallest value in vector
- Returns the largest value in vector
- Returns the mean of the vector
Elements in a vector can be accessed individually:
x #Prints first element x[1:10] #Prints first 10 elements x[c(1,3)] #Prints element 1 and 3
Most functions expect one vector as argument, rather than individual numbers
mean(1,2,3) #Replies '1' mean(c(1,2,3)) #Replies '2'
The Recycling Rule
The recycling rule is a key concept for vector algebra in R.
When a vector is too short for a given operation, the elements are recycled and used again.
Examples of vectors that are too short:
x<-c(1,2,3,4) y<-c(1,2) #y is too short x+y #Returns '2,4,4,6'
All simple numerical objects in R function like a long string of numbers. In fact, even the simple:
x<-1, can be thought of like a vector with one element.
str(x) returns information on the dimensionality of
- A series of numbers
- Tables of numbers
- More 'powerful' matrix (list of vectors)
- Collections of other objects
- Intelligent(?) lists
Matrices are created with the matrix() function.
m<-matrix(1:12,nrow=3) #This produces something like this: – [,1] [,2] [,3] [,4] – [1,] 1 4 7 10 – [2,] 2 5 8 11 – [3,] 3 6 9 12
The recycling rule still applies:
m<-matrix(c(2,5),nrow=3,ncol=3) #Gives the following matrix: – [,1] [,2] [,3] – [1,] 2 5 2 – [2,] 5 2 5 – [3,] 2 5 2
For vectors we could specify one index vector like this:
x<-c(2,0,1,5) x[c(1,3)] #Returns '2' and '1'
For matrices we have to specify two vectors:
m<-matrix(1:3,nrow=3,ncol=3) m[c(1,3),c(1,3)] #Return 2*2 matrix m[1,] #First row as vector
Beyond two dimensions
You can actually assign to dim():
x<-1:12 dim(x) #Returns 'NULL' dim(x)<-c(3,4) #3*4 Matrix dim(x) #Returns '3 4' dim(x)<-c(2,3,2) #x is now in 3d dim(x) #Returns '2 3 2'
But functions like mean() still work:
mean(x) #Returns '6.5'
Graphics and visualisation
Visualization is one of R's strong points.
R has many functions for drawing graphs, including:
hist(x) #Draws a histogram of values in x plot(x,y) #Draws a basic xy plot of x against y
Adding stuff to plots:
points(x,y) #Add point (x,y) to existing graph. lines(x,y) #Connect points with line.
A graphical device is what 'displays' the graph. It can be a window, it can be the printer.
Functions for plotting "Devices":
X11() #This function allows you to change the size and composition of the plotting window. par(mfrow=c(x,y)) #Splits a plotting device into x rows and y columns. dev.print(postscript, file="???.ps") #Use this device to save the plot to a file.
To install packages from the CLI, execute the following:
R CMD INSTALL /path/to/pkg_version.tar.gz
- Journal of Statistical Software — peer-reviewed journal publishing many R related papers
- CRAN — Comprehensive R Archive Network for the R programming language.
- R graphics — a long list of techniques with examples.
- The R Project for Statistical Computing
- The CRAN (Comprehensive R Archive Network) Project
- The R Reference Manual - Base Package by the R Development Core Team. ISBN 0-9546120-0-0 (vol. 1), ISBN 0-9546120-1-9 (vol. 2)
- R Wiki
- The R Wiki User contributed R documentation and how to information.
- Collection of examples — from Wikimedia Commons.
Packages / Resources
- Rcmdr, an open source GUI for R
- List of IDEs and script editors for R
- Tinn-R, an advanced open source script editor for R under Windows
- Web-based interface to R