R's killer app is interactive analysis. Import some data, graph it, look at the graph, do some clean-up based on what you noticed, graph it again, run some summary statistics, fit a model, graph the residuals from the model, etc. It's amazing for this - arguably the best available open source software for this type of task. Comparable tools for this would be MATLAB, Stata, Mathematica, etc. (all with strengths in slightly different domains).

It's comparatively very weak for "software development" - writing libraries, long-running programs, etc. Even data cleaning/data analysis scripts are a headache beyond a certain length or number of contributors. People tend to have the same complaints about MATLAB, Stata, and Mathematica.

These are related - a lot of the features that make interactive use concise and easy to pick up end up being kind of a mess to handle consistently in a larger program.

Python is at a different point on this spectrum. It's moderately good for interactive analysis, but there are still a lot of tasks that are concise and easy in R that require a bunch of verbose object manipulation in Python. This is a choice - "explicit is better than implicit", etc. Python is also moderately good for "software development" - it has enough consistency and code structuring features that you can write libraries, systems, and infrastructure while collaborating with a small number of other people pretty easily. You still hit a point where Python's dynamic features make things like refactoring harder than you'd like, but it's usable.