I use python as my main programming language, I’m doing an MBA in actuarial sciences and all my professors use R, so all the classes and exercises are in R. They are kind enough to accept my exercises and exams in python, but I spent half my time translating R functions to python. This pass week I found the first function that doesn’t exists in python and had to learn how to run R code inside python. Just the cell of that function took 6hs processing, because of the back and forth between python to R to python again.
I barely even write base R anymore, I mostly use it for data wrangling these days so my code is almost entirely tidyverse. Every once in a while I get to bust out some statistics, but rarely.
I’m a tidyverse zealot and I just cannot stand fixing people’s 300 line base R spaghetti that can easily be refactored into 10 lines of dplyr. Especially annoying when researchers can’t move away from doing everything in matrix format (when it’s unnecessary).
I have been IT support for medical researchers and that application is a BEAST. Installing and configuring it can be a nightmare. Especially when the researchers aren’t proficient in it already. Watching someone who is good at it is like watching the pinball wizard.
I’ve almost entirely switched to using pak for managing my R packages. I’m not 100% sure what the magic is in pak, but it’s really made my life easier when installing packages.
For the untrained eye can look complicated because it had a pretty unorthodox syntaxes, like <- to variable assign, c() to create a vector, df$column… and other little R specific things that are not common on other languages.
Eli5?
R is a complicated statistics programming language usually used by people in undergraduate and post graduate STEM degrees.
I use python as my main programming language, I’m doing an MBA in actuarial sciences and all my professors use R, so all the classes and exercises are in R. They are kind enough to accept my exercises and exams in python, but I spent half my time translating R functions to python. This pass week I found the first function that doesn’t exists in python and had to learn how to run R code inside python. Just the cell of that function took 6hs processing, because of the back and forth between python to R to python again.
I barely even write base R anymore, I mostly use it for data wrangling these days so my code is almost entirely tidyverse. Every once in a while I get to bust out some statistics, but rarely.
I’m a tidyverse zealot and I just cannot stand fixing people’s 300 line base R spaghetti that can easily be refactored into 10 lines of dplyr. Especially annoying when researchers can’t move away from doing everything in matrix format (when it’s unnecessary).
I have been IT support for medical researchers and that application is a BEAST. Installing and configuring it can be a nightmare. Especially when the researchers aren’t proficient in it already. Watching someone who is good at it is like watching the pinball wizard.
I’ve almost entirely switched to using pak for managing my R packages. I’m not 100% sure what the magic is in pak, but it’s really made my life easier when installing packages.
… complicated? it’s a pretty easy language in comparison to others, it’s simple to use (although it can be quite terse)
For the untrained eye can look complicated because it had a pretty unorthodox syntaxes, like <- to variable assign, c() to create a vector, df$column… and other little R specific things that are not common on other languages.
R is made infinitely better with tidyverse.