[mikorym]

I use both python and R almost every day.

Although I like R and often use R to quickly order tabulated data, there are a few things to take into account that in recent times are building a strong case for me not to use R habitually.

Development in R is frustrating. If you don't need to do dev, then on this point you are home free. Testing things that you deploy in R is not simple.

Scripting in R can be frustrating. I have a script that traverses Excel files and using tryCatch() is just so much more complicated with it being a function. In Python the try-catch functionality is part of the design syntax.

There are scenarios where R is better. If you are in actuarial science, research or academics then often you'll find R libraries that just work.

R treats tabular data with grace. Everything in R is an array.

The takeaway for me is that I should use R less and Python more. I personally can't deal with something like tryCatch() being overcomplicated, but for people who don't do dev anyway and maybe need to analyse DNA sequences for a living, R can be rewarding. For me: the ggplot2 library is great; stay away from Shiny and dev in R.

[mh12345]

Interesting, why do you advise people to stay away from Shiny?

[mikorym]

It tries to do html, but it is limited. So I'd rather use Javascript to manipulate the frontend directly.

It tries to do functional programming, but the documentation is not satisfying. The responses and behaviour is perplexing.

I spent around 5—10 hours trying to get a Shiny GUI to work and eventually got to the conclusion that 1) if you want a big project do all the frontend stuff in something else, like JS and 2) if you want a small project try something established (I am not advocating, it's just an example) like Power BI.

[mh12345]

Regarding the limited frontend capabilities, I had a similar opinion at one point, but with Shiny's HTML templating (https://shiny.rstudio.com/articles/templates.html) functionality one can circumvent the limited HTML that Shiny has out of the box. Besides that, there is also the possibility to communicate with R using JavaScript (https://shiny.rstudio.com/articles/communicating-with-js.htm...). These two functionalities combined allow for a frontend that is much more flexible, when compared to traditional Shiny applications. Of course, there might definitely be better solutions out there that fit your use case and Shiny's real use is primarily in sharing data analysis.