Flexibility
Another benefit of open source is that anybody can access the source code, modify and improve it. As a result, many excellent programmers contribute to improving existing R code and developing new capabilities. Researchers from all walks of life (academic institutions, industry, and focus groups such as RStudio and rOpenSci) are contributing to advancements of R’s capabilities and best practices. This has resulted in some powerful tools that advance both statistical and non-statistical modeling capabilities that are taking data analysis to new levels.
Many researchers in academic institutions are using and developing R code to develop the latest techniques in statistics and machine learning. As part of their research, they often publish an R package to accompany their research articles6. This provides immediate access to the latest analytic techniques and implementations. And this research is not soley focused on generalized algorithms as many new capabilities are in the form of advancing analytic algorithms for tasks in specific domains. A quick assessment of the different task domains for which code is being developed illustrates the wide spectrum - econometrics, finance, chemometrics & computational physics, pharmacokinetics, social sciences, etc.
Powerful tools are also being developed to perform many tasks that greatly aid the data analysis process. This is not limited to just new ways to wrangle your data but also new ways to visualize and communicate data. R packages are now making it easier than ever to create interactive graphics and websites and produce sophisticated html and pdf reports. R packages are also integrating communication with high-performance programming languages such as C, Fortran, and C++ making data analysis more powerful, efficient, and posthaste than ever.
So although the analytic montra “use the right tool for the problem” should always be in our prefrontal cortex, the advancements and flexibility of R is making it the right tool for many problems.