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Learning R
- “R for Data Science” — seminal Tidyverse book by Hadley Wickham & Garrett Grolemund
- “Learning R Step by Step” – hard copy book by Richard Cotton
- Statistics with R — online Duke courses via Coursera
- R Programming — online Johns Hopkins class via Coursera
- Advanced R – advanced R primer by Hadley Wickham
Sourcing
Wrangling
- Getting and Cleaning Data in R — online Johns Hopkins class via Coursera
- Data wrangling in dplyr – tutorials by Susan Baert
- Tidyverse tricks – Keith McNulty
- Regular expressions
- Simplify lists to data frames
Modelling
Visualizing
Fundamentals
- “Fundamentals of Data Visualization” — free online book by Claus Wilke. Source code here.
- “Data Visualization” — free online book by
Kieran Healy
- Graphics principles cheatsheet
Charts
- From Data to Viz – selecting chart types
- Top 50 ggplot visualizations
- Ggplot2 extensions
- Ggplot aesthetics cheat sheet
- Automating charts using ggplot2 and purrr
Maps
- Making maps with R – introduction to geom_polygon and ggmap methodologies
- Google Maps API developers guide
- Creating street Maps with ggplot2
Aesthetics
- What to consider when choosing colors
- R color key – view names of all R colors
- Viridus color scales
- Colorbrewer standard schemes and ad hoc schemes
- Google s
- Adding custom s to R
Programming
- R Markdown Cheat Sheet
- Core Purrr lessons – functional programming primer on automating repeated tasks
- Converting strings to symbols
- Folder structure and here package
Online Communities
- Tidy Tuesday Data – data sets to practice Tidyverse concepts
- Tidy Tuesday Rocks – compilation of Tidy Tuesday submissions across users
- R for Data Science (R4DS) Online Learning Community
Misc
Data Science Blogs
- R Bloggers – curated centralized source for numerous R blogs
- Keith McNulty
- Christian Burkhart
- Data Imaginist – Thomas Lin Pederson
- Georgios Karamanis
- Computational Ecology and Data Visualization – Cédric Scherer
- Hugo Toscano
- Julia Silge
- Variance Explained — David Robinson
- Garrick Aden-buie
- Calum Webb
- David Smale
- Visualizing Data — Andy Kirk
- Little Miss Data – Laura Ellis
- Very Statisticious – Ariel Muldoon
- Haystacks – Caitlan Hudon
- Claus Wilke
- Econometrics — Bruno Rodrigues
- Kaylin Pavlik
- NateDayta – Nathan Day
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