In any industry, data analysis is often supplemented by powerful statistical packages. Currently, competition exists between some of the big named programs like SAS and SPSS, both of which are used extensively in educational and professional settings. Powerful and efficient, these programs have paved the way for major improvements in data analysis, and are often the cornerstone backing research and major projects. Complex analyses that in the past would take days if not weeks to complete, can now be done at the click of a button.
So what is the catch? These programs often carry large price tags for annual licenses. Furthermore, I have experienced a SAS installation and it was not pretty. The entire process required three installation disks, four people, two days, and one software downgrade as Windows 8 did not support SAS at the time.
Enter R. Upon discovering the statistical software package known as R (also known as GNU S), we selected the base package and downloaded it within five minutes. R is free of cost and is available to anyone, and it can be useful to anyone from the field expert to the curious beginner. Moreover, R is an open-source program, meaning that any user can create and share packages to use for analysis. There are currently over 1,000 different packages for everything ranging from graphing, SQL, to complex statistical analyses. The program uses C, C++, and S code to conduct analysis.
R is easily compatible with everyday work processes, and as such can be a useful tool for analysts. The program is able to import and export .csv files, and can be linked to databases. Using R, data can be easily manipulated using SQL queries, making valuable subsets of data for analysis. Additionally, we’ve run a wide variety of statistical tests including, but in no way limited to, linear and logistic regression, ANOVA/ANCOVA , t-tests, and correlation analyses. R is also capable of several different data mining approaches including Tree Based and Bayesian Methods.
You might be wondering why companies will shell out the big bucks for SAS licenses when R is free. I have personally run the same tests on the same data using R and SAS and achieved the same output. Some even consider R to be superior to SAS in terms of graphing capability and aesthetics.
So when you’re considering a new program to bolster your analytics efforts, give R a shot. Odds are you will not be disappointed.