As much as I love R, it’s clear that Python is also a great language—both for data science and general-purpose computing. And there can be good reasons an R user would want to do some things ...

But, Python and R also bring their own unique strengths to data science, making it harder to decide which to use. R vs. Python: The main differences.

Reticulate is a handy way to combine Python and R code. From the reticulate help page suggests that reticulate allows for: "Calling Python from R in a variety of ways including R Markdown, sourcing ...

The decision between R and Python, two strong and adaptable languages for data science research, may come down to personal taste, project specifications, and domain knowledge. Each language has ...

Python has a more limited and less mature set of visualization tools than R, as it relies on external libraries and modules that are not always compatible or consistent. R can produce more elegant and ...

Statistical programming language R has fallen off Tiobe index's list of the 20 most popular languages, having spent three years in the top tier. Tiobe now places R in 21st position and suggests ...