About R

Otho Mantegazza

Quick history of R

The R Project for Statistical Computing

https://www.r-project.org/

• 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 programm your own functions.
• We are going to use R in visual studio code as you did for Python, but you might want to try out also Rstudio, which is a great open source IDE dedicated to R.

Resources

As always, stackoverflow is your friend

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… 📚

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

• R for Data Science - A gentle introducion to data science with the Tidyverse.

• Introduction to Data Science - A detailed introduction to Data science by a biostatistician.

• Advanced R - All you need to know about programming in R.

• Introduction to Statistical Learning - A detailed introductio to modern statistical methods, implemented in R.

• Text Mining in R Dealing with natural language and written text in R.
• Tidy Modeling with R R Machine learning framework.
• Designing and Building Data Science Solutions how to set up a data science project.

More ⬇️⬇️⬇️

Slack Spaces

• R for Data Science Community a slack chanel 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.