R’s Growth and Comparison to Other Software
R’s growth in the data analytics field has come to the point where it is beginning to surpass the field standard software such as SAS, SPSS, Stata, and MatLab (Muenchen, 2017). This surge in the number of R users has led to an increase in job postings asking that applicants understand and can use R. The field paradigm changes are well marked by Muenchen (2017) who shows the overall trend in Figures 1, 2, and 3. These figures clearly show the spike in R’s popularity, especially over the last 5 years.
Figure 1. Number of data science jobs posted on a job search website with over 250 job hits
Figure 2. The job posting trends for SAS (orange) and R (blue)
Figure 3. The job posting trends for Python (orange) and R (blue)
As is evidenced by the figures, R is beginning to overtake the big names in the field. While it is more popular than SAS, it has not yet reached the same level of triumph over Python. However, Muenchen (2017) notes that this could be due to the fact that Python jobs are much more diverse in nature than R jobs are, by virtue of the way the two different programs are set up. Python is just as code-heavy as R, but R is restricted in its application just to data and information science applications. Therefore, one must be careful when attempting to draw conclusions from the general trend and must look closer at the data science landscape specifically.
Figure 4.
Comparison of primary of programing languages
As you can see the Figure 4, popularity of Python is increasing the most in this 10 year period. However, R is stable also showing slight growth in usage.
The popularity of R in the field is reflected by the amount of resources available with which to learn R. There are several books written devoted to the software overall and to general packages (Varma, 2016, Springer 2014) which can complement the various websites, blogs, video tutorials, and conference papers available to new and veteran users (Rickert, 2014, Mejia, 2013). This plethora of available information also makes teaching the software easier, as students are able to access information about specific packages or the debugging process with a quick web search. The packages themselves are easy to use and install; they are available from R’s CRAN depository (R Core Development Team, 2016) and need only be installed once (Arriata, 2016).