I used to think like that. Programmers hate R. But I took a biostatistics class and it really is the best tool for that job. Plus the graphic output can't be beat (ggplot2) and fairly easy to install packages make it quite valuable tool.
The statistics built in are great. They're just there, less need to find a package (general stats, ttest, chi_squared test...). We tend to use the "tidyverse" packages [1] https://r4ds.hadley.nz/. Bio-python is amazing for manipulating biodata, but once the data is extracted and you need statistics, our scientist seem to use R. I really don't love R's syntax, but I get why they use it. I use python all the time for data wrangling (right now I'm pulling sequences from a fasta file to inject into a table).
Rstudio is like an IDE for your data. You can view the data tables, graph different things etc. If you try the first chapter of the R4data Science book, you can see how get up and graphing and analyzing quite quickly.
https://r4ds.hadley.nz/data-visualize.html
Though at this point Python and R are necessary depending on what package/ algorithm you want to use.
There are some good packages for single cell analysis: We use "Seurat".
