2 Learning R

R is a free, open-source software and programming language developed in 1995 at the University of Auckland as an environment for statistical computing and graphics (Ikaha and Gentleman, 1996). Since then R has become one of the dominant software environments for data analysis and is used by a variety of scientific disciplines, including biomedicine, environmental epidemiology, social sciences, ecology, genetics and geoinformatics among others. CRAN Tasks provides an excellent overview of existing R packages for a given discipline (see for instance Genetics Task View ; Envirometrics Task View; Spatial Task View).

R offers numerous advantages, such as:

1. Free and Open source

2. Reproducible Research

   • repeatable:
     • code + output in a single document
     • easier the re-analyses
   • scalable: applicable to small or large datasets
   • extensible: several

3. Getting help

   • Numerous Discipline Specific R Groups
   • Numerous Local R User Groups (including R-Ladies Groups)
   • Stack Overflow

4. Learning Resources

   • R books
   • (Free Online) R Books

While some people find the use of a command line environment daunting, it is becoming a necessary skill for scientists as the volume and variety of data has grown. Additionally, GUI interfaces can easily be implemented in R (see this review) being Shiny a widely used R package that makes it easy to build interactive web apps straight from R.