Using Python:

Python was originally developed as a programming language for software development (with data analytics tools added later), so people with a background in computer science or software development may be more comfortable using it.

As a result, the transition from other popular programming languages (such as Java or C ++) to Python is easier than the transition from those languages to R.

Using R:

R has a set of packages called the Tidyverse that provide powerful but easy-to-learn tools for importing, manipulating, visualizing, and reporting on data. Using these tools, people without any programming or data analysis experience (at least anecdotally) can be more productive faster than Python.

Overall, if we or our employees don't have a background in data analytics or programming, R may make more sense.