R Programming/Introduction

What is R ?

R is statistical software which is used for data analysis. It includes a huge number of statistical procedures such as t-test, chi-square tests, standard linear models, instrumental variables estimation, local polynomial regressions, etc. It also provides high-level graphics capabilities.

Why use R?

• R is free software. R is an official GNU project and distributed under the Free Software Foundation General Public License (GPL).
• R is a powerful data-analysis package with many standard and cutting-edge statistical functions. See the Comprehensive R Archive Network's Task Views to get an idea of what you can do with R.
• R is a programming language, so its abilities can easily be extended through the use of user-defined functions. A large collection of user-contributed functions and packages can be found at CRAN's Contributed Packages page.
• R is widely used in political science, statistics, econometrics, actuarial sciences, sociology, finance, etc.
• R is available for all major operating systems (Windows, Mac OS, Linux).
• R is object oriented. Virtually anything (e.g., complex data structures) can be stored as an R object.
• R is a matrix language.
• R syntax is much more systematic than Stata or SAS syntax.
• R can be installed on your USB stick.

Alternatives to R

• S-PLUS is a commercial version of the same S programming language that R is a free version of.
• Gretl is free software for econometrics. It has a graphical user interface and is nice for beginners.
• SPSS is proprietary software which is often used in sociology, psychology and marketing. It is known to be easy to use.
• GNU PSPP is a free-software alternative to SPSS.
• SAS is proprietary software that can be used with very large datasets such as census data.
• Stata is proprietary software that is often used in economics and epidemiology.
• MATLAB is proprietary software used widely in the mathematical sciences and engineering.
• Octave is free software similar to MATLAB. The syntax is the same and MATLAB code can be used in Octave.

Beginners can have a look at GNU PSPP or Gretl. Intermediate users can check out Stata. Advanced users who like matrix programming may prefer MATLAB or Octave. Very advanced users may use C or Fortran.

See also

• "R Relative to Statistical Package" by Patrick Burns

R programming style

• R is an object oriented programming language. This means that virtually everything can be stored as an R object. Each object has a class. This class describes what the object contains and what each function does with it. For instance, plot(x) does not produce the same output if x is the result of a regression or a vector.
• The assignment symbol is "<-". Alternatively it is also possible to use the classical "=" symbol.

The two following statements are equivalent :

 > a <- 2
 > a = 2

• Arguments are passed inside round brackets (parentheses).
• It is often better to put quotes around names but this is not always required.
• One can easily combine functions. For instance you can directly type

mean(rnorm(1000)^2)

• "#" comments to the end of the line

 # This is a comment
 5 + 7 # This is also a comment

• Commands are separated by a semicolon ";" or newline. If you want to put more than one statement on a line, you can use the ";" delimiter.

 a <- 1:10 ; mean(a) ;

• You can also have one statement on multiple lines.
• R is case sensitive : "a" and "A" are two different objects.
• Traditionally underscore "_" are not used in names. It is often better to use dots ".". One should avoid underscore as the first character of an object name.

See Also

• Google's R Style Guide : a set of rules for R programmers