For the white paper titled “R Is Hot” about four years ago, the goal was to introduce the R programming language to a larger audience of statistical analysts and data scientists. As it turned out, the timing couldn’t have been better: R has now blossomed into the language of choice for data scientists worldwide. Today, R is widely used by scientists, researchers, and statisticians for modeling data and solving problems quickly and effectively. When people ask me which factors are driving the broader adoption of R among data analysts, I usually offer two key points:
1. R was designed specifically for statistical analysis, which means that analytics written in R typically require fewer lines of code (and hence less work) than analytics written in Java, Python, or C++.
2. R is an open source project, which means it is continually improved, upgraded, enhanced, and expanded by a global community of incredibly passionate developers and users.
R Is Still Hot–and Getting Hotter