R Resources

The preferred software for performing statistical analysis is R, an open-source, free software that is vastly used by the scientific community to perform all sorts of analyses. If you are not familiar with R and have to perform some flavour of data analysis, this language is definitely worth learning: the syntax is easy, and there are hundreds of additional libraries (~ which are like packages in Python) which will allow you to do your analysis swiftly and easily!

Installing and Learning R

• For additional help on installing R, read the following instructions.
• Here are a few introductory slides for understanding and getting started in R.
• Our consultants have also started to put together a few resources for learning R. You can consult their tutorial by clicking on this link .

Textbooks

• “R for Data Science”, by Garrett Grolemund and Hadley Wickham.
• “Data Analysis for the Life Sciences”, by Rafael A Irizarry and Michael I Love.
• “Modern Statistics for Modern Biology”, by Susan Holmes and Wolfgang Huber
• “An Introduction to Statistical Learning with Applications in R”, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.

R-cheatsheets

Here are a couple of cheatsheets with basic R commands:

• Short R-reference card
• R cheatsheet
• List of Rstudio Cheatsheets

Here are a couple of cheatsheets with data import, transformation, visualization, and rmarkdown:

• Data Import
• Data Transformation (dplyr)
• Data Visualization (ggplot2)
• Rmarkdown

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