PRDV420: Introduction to R Programming

R's capabilities are extended through user-created packages, which offer statistical techniques, graphical devices, import/export, reporting (RMarkdown, knitr, Sweave), etc. R's packages and the ease of installing and using them have been cited as driving the language's widespread adoption in data science. The packaging system is also used by researchers to create compendia to organize research data, code, and report files in a systematic way for sharing and archiving.

Multiple packages are included with the basic installation. Additional packages are available on CRAN, Bioconductor, Omegahat, GitHub, and other repositories.

The "Task Views" on the CRAN website list packages in fields including Finance, Genetics, High-Performance Computing, Machine Learning, Medical Imaging, Social Sciences, and Spatial Statistics. The FDA has identified R as suitable for interpreting data from clinical research. Microsoft maintains a daily snapshot of CRAN dating to Sept. 17, 2014.

Other R package resources include R-Forge, a platform for the collaborative development of R packages. The Bioconductor project provides packages for genomic data analysis, including object-oriented data handling and analysis tools for data from Affymetrix, cDNA microarray, and next-generation high-throughput sequencing methods.

A group of packages called the Tidyverse, considered a "dialect" of the R language, is increasingly popular among developers. It strives to provide a cohesive collection of functions to deal with common data science tasks, including data import, cleaning, transformation, and visualization (notably with the ggplot2 package). Dynamic and interactive graphics are available through additional packages.text

R is one of 5 languages with an Apache Spark API, along with Scala, Java, Python, and SQL.