Comment by rizoic | Hacker News Reader

Comment by rizoic | Hacker News Reader

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.

Tidyverse is so overrated that I don't even know where to start... sure dplyr is nice to use if you're working interactively and ggplot2 has good first principles as the basis of its design, but that's about as far as the tidyverse niceness goes. The apis, the documentation, the inconsistencies (on a language level, between tidy packages and within each package), the problems with backwards compatibility, the evaluation (standard vs non-standard vs tidyeval) - it's a big mess.

If you're writing code that's deployed in any way, it's best to avoid the tidyverse as much as possible. This is also acknowledged to some extent by the main developer - https://www.tidyverse.org/articles/2018/06/tidyverse-not-for...

The statement in the link means that one should avoid to include the term tidyverse as a pkg dependency. Instead one should name the specific pkgs from the tidyverse individually. This makes sense, as the tidyverse is a collection of pkgs and referring to it as a whole just blows up the pkg. However, there is no indication to not use tidyverse pkgs (dplyr, ggplot2, stringr,...) in packages or production.

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