![]() ![]() ![]() Getting started with Python, meanwhile, feels like you’re in a maze. Once in the comfort of RStudio, you can do pretty much anything R-related you’d want: write and test code, create and run entire scripts, install and update packages, produce and export visuals, build and share Markdown files that export to PDF, etc. To start programming in R on your own, you download R and RStudio… and then you’re done. While this is handy for the very first steps of learning a language, you still need to actually install the program once the course is over if you want to keep coding. (I can vouch for the content from DataCamp and Codecademy especially.) A major convenience of these classes is that you can type code directly into a terminal on the screen, as opposed to needing to install R or Python on your computer first. Thanks to the explosion of interest in data science over the last decade, there are tons of excellent online classes for getting started with R and Python for free. This post outlines some of the major differences between R and Python, as well as why those differences exist. Picking up a second language went much faster than the first, but there was a lot to get used to when I transitioned. I coded in R throughout my Ph.D., but I needed to switch to Python for my first non-academic job. I fell in love with the nuance R granted for visualizing data, and how with a little practice it was straightforward to pull off complex statistical analyses. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |