On the RStudio Developer Blog we’ve recently written a series on interoperability and R, including why enterprises should embrace workflows that are open to diverse toolsets.
The ecosystem around R has striven to strike the right balance between a domain specific environment optimized for data science workflows and output, and a general programming environment. For example CRAN, Bioconductor, rOpenSci, and GitHub provide collections of packages written with data science in mind, which extend core R’s functionality, letting you tap into (and share) statistical methods and field-specific tools — when and only when you need them.
RStudio – design philosophy and development priorities
Our mission at RStudio is to create free and open source software for data science, scientific research, and technical communication. R is a wonderful environment for data analysis, and we’ve focused on making it easier to use. We do this through our IDE and open sources packages, such as the tidyverse. We also do this by making data science easier to learn through RStudio Cloud and our support for data science education. And we help make R easier to manage and scale out across an organization through our our professional products, supporting best practices for data science in the enterprise through our solutions team.