R + the tidyverse (dplyr, ggplot2 etc.) is easily the most elegant data analysis combo I've used. However, I tend to use Python for this usecase nowadays, primarily to reduce friction when working with others where Python is the lingua franca. Also, RMarkdown is superior to Jupyter notebooks in my opinion.
Python, Numpy, Pandas is very inconsistent in comparison to R. For example, calculating and adding a column to existing dataframe using df.assign.
Coming from R, working in Python Pandas world, I am constantly googling, and checking the results of each command to make sure no errors and consistent output.
