I'm in bioinformatics. R is huge, Python is a distant second. So I am forced to use both.

R is a terrible programming language. It's slow and syntactically inconsistent. For interactive statistical analysis it can be OK, but anything beyond a small program becomes unmanageable quickly. In particular, if you want to manipulate strings or hierarchical data structures quickly in R, good luck.

For ML, scikit-learn is almost always sufficient. For statistics, statsmodels is OK but very underdeveloped compared to what is available in R. IMO plotting is equally painful in both Python and R (seaborn is a good Python library to ease the pain if you haven't seen it).

Personally, I write everything in Python and call out to R as infrequently as possible using rpy2, usually only for specific statistical routines or bioinformatics-specific libraries.

Probably not any strong reasons. That said, if you're a software engineer, you shouldn't find it too hard to pick up enough R to be useful. You might enjoy <http://adv-r.had.co.nz> which describes R from more of a programming language perspective.

If you use M$ products you will find that SQL Server 2016 has R baked in.

https://www.r-bloggers.com/demo-r-in-sql-server-2016/

I use both R and python quite a bit. I prefer python as a programming language. Here's my take on 'Why learn R?': (1) R/ggplot is hands-down better for plotting than anything in python. I also think that R is better for EDA generally. (2) Many smart, knowledgable people use R and publish their code. To learn from it, you need to know enough R to read and modify it. (3) R has better package support than python in several common data analysis domains. For example, in forecasting and in graph analysis, the best R packages available are much better than the best python packages.