In the course of work I end up using both R and Python. I think both have their own use cases. Some of the observations I have had are:-
R
- The tidyverse ecosystem had given a huge boost to R. It had brought intuitiveness and consistency to R which was much required especially if you are a programmer coming from other languages. Also there are other ecosystems like Bioconductor which are also very mature.
- Rstudio and especially Rmarkdown notebooks are much better for reproducible analysis than Jupyter.
- It is very difficult though to develop standalone tools with R. For example it doesn't have a good argument parser.
Python
- The language is much more intuitive and more ideal for developing standalone tools.
- The ecosystem is many cases very fragmented though with a lot of libraries doing similar things.
- It lacks a good plotting system. Matplotlib is very powerful but has a very steep learning curve. In comparison ggplot2 in R is very intuitive.
R
- The tidyverse ecosystem had given a huge boost to R. It had brought intuitiveness and consistency to R which was much required especially if you are a programmer coming from other languages. Also there are other ecosystems like Bioconductor which are also very mature.
- Rstudio and especially Rmarkdown notebooks are much better for reproducible analysis than Jupyter.
- It is very difficult though to develop standalone tools with R. For example it doesn't have a good argument parser.
Python
- The language is much more intuitive and more ideal for developing standalone tools.
- The ecosystem is many cases very fragmented though with a lot of libraries doing similar things.
- It lacks a good plotting system. Matplotlib is very powerful but has a very steep learning curve. In comparison ggplot2 in R is very intuitive.