<p><span style=\"text-decoration: underline;\">Overview and Setup</span></p> <p>R is a free open source programing language and statistical environment maintained by the GNU. R contains powerful libraries for parallel computing and is especially adept at computing on large data sets. The R website maintains and distributes the necessary files to install on all major operating systems. The R executable itself operates inside a command line environment; however, an optional and separate program Rstudio can provide a graphical development environment.</p> <p><span style=\"text-decoration: underline;\">Features</span></p> <p>R was not originally designed for textual analysis, and it abilities go beyond the direct needs of the average text analysis researcher. However, R&rsquo;s vector data type, its ability to work on several data sources simultaneously, and its built-in visualization tools make graphing data exceptionally easy. With some coaxing R provides an ideal environment for large-scale data processing and analysis. R provides all tools necessary for splitting and combining texts, yet prior knowledge of regular expressions is mandatory in order to get the most out of these options. The R language vector data structure scales easily between small and large data sets. R also includes one command graph generation for all common graph and data types.</p> <p><span style=\"text-decoration: underline;\">Conclusion</span></p> <p>R is a programing language, yet it does several things differently then most conventional scripting languages. R does not rely heavily on control structures, which makes it easier for someone not familiar with recursive programing to learn. Anyone with a solid understanding of flows and tokenization should be able to pick up the programing style easily. However, R is a tool far bigger then text analysis; its features may cause some confusion for the self-learning especially those not immediately familiar with statistical concepts. R is an excellent tool for large and long-term research projects as well as toying around with smaller data sets. An excellent introduction to R targeted at humanist scholars can be found in the book &lsquo;Quantitative Corpus Linguistics with R,&rsquo; by Stefan Th. Gries. (Link below) While more experience programmers might want to look at the official R tutorial on the R website.</p> <p><a href=\"http://www.amazon.ca/Quantitative-Corpus-Linguistics-Practical-Introduction/dp/0415962706%20\">http://www.amazon.ca/Quantitative-Corpus-Linguistics-Practical-Introduction/dp/0415962706 </a></p> <p><a href=\"http://cran.r-project.org/doc/manuals/R-intro.html\">http://cran.r-project.org/doc/manuals/R-intro.html</a></p>