A programming language and environment developed for statistical analyses.
R was built for end-users tackling a wide variety of statistical problems. R has an extensive assortment of libraries to address statistical computing and data visualization.
This rich analytical environment can improve your data scientists effectiveness and productivity.
What is it?
A programming language and environment suited to statistical analyses. It is frequently used by data scientists to explore and analyze datasets, statistics and rich data visualizations. R has a rich, vast library of packages of R functions, data and compiled code — to make your data scientists’ lives easier. R has a large user base in both business and academia.
What’s in for you?
This rich statistical analysis environment helps data scientists innovate since they can experiment quickly with a variety of hypotheses. These short iterations enable them to quickly learn what works and what doesn’t.
R is regarded as being an easy language to learn and designed for those with a background in statistics.
R is also seen as an expressive language, which makes it easier for your data scientists to concisely represent the models they’re creating — for instance, models exploring pricing elasticity or promotion effectiveness.
And because it is widely used in academia, R continues to be at the forefront of data analytics. As researchers develop innovative algorithms, they’re quickly added to R libraries.
What are the trade offs?
There are other well-established language alternatives to R, such as Python. Many data scientists that come from a statistics background won’t have experience with R.
R is not readily scalable. Because of the way it was designed, it’s not well suited to run in today’s high-performance environments. If you want to run R in production environments, you may have to consider a number of workarounds, which can be time consuming and costly to build. So R may work better in an analytical environment while another solution might be better in production.