R

R is a dynamic statistical focused programming language supported by the R Foundation for Statistical Computing. Its power relies in the fact that is a open-source (GNU project) programming language with a strong, large and active community. This is why R programming language is complemented with a lot of tools as RStudio or a lot of specific packages that helps you to face different problems related specially with Statistical analysis, statistical learning, machine learning, but also other problems more general problems. R language has a good plotting and reporting tools as well as a good documented packages. That features made R language is widely used among statisticians and data miners for developing statistical software and data analysis. The source code for the R software environment is written primarily in C, Fortran, and R.

The main features of R are:

• Huge amount of libraries. Some of theme coded by top-level researchers and very reliable. Others are low-quality libraries sometimes not tested properly. You should be cautious. The libraries has a
  wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, among others.
• Easy to link with C, C++, and Fortran code, for being called at run time. Extremely useful for computationally intensive tasks. The advance users can also write C, C++, Java, .NET or Python code to manipulate R objects directly.
• High-quality static graphics, which can produce graphs for publication, including mathematical symbols. Dynamic and interactive graphics are available through additional packages.
• Easy documentation formats as Rd, and Rmarkdown.

See also

Python, Julia, SAS, Matlab, Go (Programming language), Java, C, Fortran

Material

• https://intellipaat.com/blog/choosing-between-sas-r-and-python-for-big-data-solution/
• https://cran.r-project.org/
• https://www.rstudio.com/

Books

• Kabacoff, Robert. (2011) R in Action. Manning Publications
• Teetor, Paul. (2011). R Cookbook. O’Reilly Media
• Matloff, Norman. (2011). The Art of R Programming: A Tour of Statistical Software Design. No Starch Press
• Adler, Joseph. (2010). R in a Nutshell: A Desktop Quick Reference. O’Reilly Media
• Torgo, Luís. (2010). Data Mining With R: Learning By Case Studies. CRC Press
• James, G.; Witten, D.; Hastie, T.;Tibshirani, R. (2015). An Introduction to Statistical Learning: With Applications in R. Springer
• Zumel, Nina; Mount, John. (2014) Practical Data Science with R. Manning Publications.
• Chang, Winston. (2012). R Graphics Cookbook. O’Reilly Media.
• Wickham, Hadley. (2009). ggplot2: Elegant Graphics for Data Analysis. Springer