In talking to our many customers and others in the data science field, as well as in the surveys we’ve done of the data science community, we’ve seen that many data science teams today are bilingual, leveraging both R and Python in their work.
However, this turns out to be a false choice. Some data scientists, and even some organizations, believe they have to pick between R or Python. It gives data scientists superpowers to tackle the hardest problems because code is flexible, reusable, inspectable, and reproducible. Coding is the most powerful and efficient path to tackle complex, real-world data science challenges.This enhances the production and consumption of knowledge and facilitates collaboration and reproducible research in science, education and industry. It’s better for everyone if the tools used for data science are free and open.From the very beginning, two key ideas have driven the work we do at RStudio:
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