Motivation I believe the most common discussion on data science teams is R vs Python. I saw myself in some of these discussions a couple of times and my position is always “Why not use both?”. The idea of this post is not to compare R vs Python but to show how easy it is to integrate both languages using APIs (LIKE A PRO), this way we can use the best of each.
Motivation Recently I was introduced by some friends to the GitHub Actions and how it could help me execute tasks like: deploy my Shiny Apps, deploy this Blogdown, perform automated tests in packages, refresh data, and more. So, I decided to give it a try, and it was so simple and saved me so many work hours that I decided to write this post explaining how R developers can make good use of this amazing tool.
Motivation If you are using a script more than 3 times it is time to create a function and if you are using a function in 3 different projects it is time to create a package. “I heard it from someone, but I don’t remember who..” Recently I experienced the whole flow to create a new R package with the help of amazing packages like devtools, usethis, pkgdown, and roxygen2.
Motivation I had the idea of writing some “walking through” about blogdown, first because it was my first time using the package and I still have all the necessary steps fresh in my mind and second because probably I will need to have it all documented if I decided to refresh this blog one day. Of course, there are a lot of great tutorials about blogdown over the internet (the blogdown book for example), but maybe someone could find this one easier in some way…
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