eliashaddad

In this case you should probably use the "here" or the "rprojroot" packages (libraries in conventional R parlance). They both simplify the usage of relative paths inside a project/repository.

If you have a project root with the folders code, data, etc and are running a project on /path/root/code, you can then just call data_dir <- here::here("data") for the data folder, as the here package uses several always to find the root of a project (e.g., looking for a .git folder).

Vimium also works in FF. I am using it right now.

Regarding TensorFlow, you could use the docker images and run everything in containers instead. They tend to work pretty fine out of the box.

That is not true at all.

R has inbuilt great parallel tools (check for example the doSnow and future frameworks);

the best packages for data manipulation are mostly written in C (for example data.table and a good part of the tidyverse);

and with frameworks like Drake you can easilly create a Dag out of it that can process complex iterations millions of times. Check the uses of the Rcpp package that makes interfacing C code to R a breeze.

But of course, if you were comparing R to a pure compiled language, you are out of luck.

Besides the aforementioned browser(), try() and tryCatch(), you would check the packages assertive and assertr to guarantee the expected inputs, and testthat to create tests for your scripts. All awesome.

But then, to avoid that, you can embrace the concept of copyleft (free software) instead of open source.

So, you can make your software free as in 'free speech', and obligate other companies to keep the products they construct above yours still free.

Better than to make them free as in 'free beer' where they can just consume your work and not give back to the overall movement.