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Skip to main content# R (statistical software)

Andresen, M.A. (2021). R (statistical software). In J.C. Barnes & D.R. Forde (Eds.), Encyclopedia of research methods and statistical techniques in criminology and criminal justice (pp. 865 – 866). Hoboken, NJ: Wiley Blackwell.

Published onJan 01, 2021

R (statistical software)

The R Project for Statistical Computing <https://www.r-project.org/> provides a software environment that is freeware for statistical computing and graphics. R is available for UNIX/Linux, Windows, and MacOS. R is a GNU Project, meaning that users have the freedom to use the program as they wish, study how the program works, redistribute the program, and improve the program—for more information on GNU projects, visit <http://www.gnu.org/>.

R and all of its libraries, or packages, allow its users to undertake many statistical techniques that include data handling and manipulation, classical statistical tests/analyses, Bayesian analyses, data reduction techniques, and cutting-edge statistics. Though users have the option of implementing these techniques directly using the command line interface in R, many of these techniques are available through the use of user-defined packages, or libraries, that allow user to implement statistical techniques with simple commands. As of December 2017, there were more than 14,000 of these packages available for use within R < https://www.rdocumentation.org/>.

There are three primary ways in which users may use R. The first is to simply use the R Console. The R Console allows the user to open R scripts, save projects, as well as install, load, and update packages. This would be considered one of the more basic ways of using R and is probably the most intimidating for the new user who is not experienced in using programming languages. The second is to implement one of the graphical user interfaces (GUI) available for R. One of the more popular GUIs available for R is *R Commander: A Basic-Statistics GUI for R* < https://socialsciences.mcmaster.ca/jfox/Misc/Rcmdr/>. R Commander is easily installed using the following code in the R Console:

install.packages ("Rcmdr")

library(Rcmdr)

When installing and loading R Commander, the user will notice that R installs all of the “dependencies” for this package. Dependencies are the R packages others have developed that are used by subsequent R packages. Once installed the user can import data, view data, edit data, generate distributions, make graphics, implement statistics, and run statistical models all using a drop-down menu within R Commander. Consequently, with two lines of R code, any user can obtain the power of R using drop-menus similar to those in commercial statistical software programs. Moreover, for those who wish to learn R code, R Commander provides the use with the code necessary to run the R commands, in a format to save as a R script file. This provides an excellent opportunity for new R users to become exposed to and use R code.

The most common method of using R is through R Studio <https://www.rstudio.com/products/rstudio/>. R Studio is an integrated development environment (a software application that provides an environment to programmers or code writers for the development of software) for R. R Studio includes a console, syntax-highlighting editor, code completion, smart indentation, tools for plotting, history, debugging, and workspace management, including package installation and updates. The benefit of using R Studio is the ease of use for all of these aspects of statistical computing. R Studio is available in both open source and commercial editions and is available for UNIX/Linux, Windows, and MacOS.

The R Project for Statistical Computing provides *The R Journal* <https://journal.r-project.org/>, an open-access peer-reviewed journal that is intended to be a resource for R users and programmers. This journal provides a variety of context for the use of R through news items, package demonstrations, comparisons, and benchmarking, as well as articles highlighting challenges and opportunities for the R community.

Support for R is available through a variety of media. There are, of course, a large number of books available that may be purchased to help users learn R. These books range from the very general to the very specific, some focusing on generating graphics, R modeling within specific disciplines, and books focusing on specific statistical techniques. There are also a wide variety of books, or documents, written that are available free online, including some documentation available from the R website <https://cran.r-project.org/manuals.html>. Though the commercially-based books on R are instructive, the online materials are rather extensive and cover most of what any user will need to get the job done. Moreover, there are a number of community forums <https://r-dir.com/community/forums.html> that focus on applications in R and videos that are set up as how-to manuals on a variety of topics. These include online courses in statistics and econometrics that have been written using R as the software of choice for demonstrating statistical concepts.

The R Project for Statistical Computing is often not considered an easy software program to learn, particularly if the user has little or no experience working with code or syntax in other statistical software. However, because of the flexibility and power contained within R, the learning curve for R is worth the work. Other commercial statistical software programs are available that may have slightly more elegant code than R, but there is a high price paid to use these statistical software programs. With a little patience and hard work, most R users will quickly build a long list of R scripts for a variety of statistical techniques that will serve them well in their research.