>About R
> An open source programming language about statistics.
\ Otho Mantegazza _ Dataviz for Scientists _ Part 1.0
The R Project for Statistical Computing
- Started in 1991 by Ross Ihaka and Robert Gentleman at the University of Auckland (NZ).
- Made open source in 1995.
- Official “peer reviewed” packages are hosted on CRAN.
- Has multiple “universes” of packages, including Bioconductor dedicated to bioinformatics, and the Tidyverse for Data Science.
- Great for analyzing data, for statistics, for research and for communicating data to others.
A bit more on R
- We are going to use the Tidyverse. But you might have to learn also a bit of the original idiom, now called base R, which sometimes helps if you have to program your own functions.
- Rstudio, which is a great open source IDE dedicated to R. But you can use it also from visual studio code or any IDE of your choice.
Resources
If you need help, as always, Google and Stackoverflow are your friends.
But sometimes you need to learn on a support that is structured, nuanced and detailed, such as books. 📚
A helpful and welcoming community 📚
One of the things that makes R great is its community of users and programmer.
It is open source and open access oriented and highly dedicated to lowering the barrier to learning R and data science, and make their tools available and usable to everyone.
Open Books
Bookdown: a package and a repository for open access books about R.
Open Books
Many authors have made their books on R open access, you can find a selection of my favourites here.
More ⬇️⬇️⬇️
Slack Spaces
- R for Data Science Community a slack channel for R networking.
Blogs
Alison Hill’s Blog; great for everything Rmarkdown.
Simply Statistics; modern statistics and R.
Julia Silge’s Blog; learn about R machine learning framework from one of its main developer.
Gina Reynolds Flipbooks; for more tidyverse and Rmarkdown mastery.
Fornkonstin; at the interface between math, art and coding.
Data imaginist; more art in R 🎨.
Collaborative Challenges
- Tidytuesday Tidy and visualize a dataset and share your results with the R community every week on Tuesday.