Curated videos, books, papers, articles, podcasts, blogs, etc. to help deepen your understanding of the R programming language.
Please feel free to contribute your suggestions
Contents
- Andrew Couch
- Ben Stenhaug
- Colin Quirk
- Data Analysis and Visualization Using R
- Data Science with Tom
- datascienceR
- David Jablonski
- David Robinson
- Dragonfly Statistics
- Julia Silge
- Karandeep Singh
- Lander Analytics
- LearnR
- Richard Webster
- Roger Peng
- Ronak Shah
- Simplilearn
- Statistics Globe
- StatQuest with Josh Starmer
- TidyX
- A Sufficient Introduction to R by Derek L. Sonderegger
- An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
- Advanced R by Hadley Wickham
- An Introduction to R by W. N. Venables, D. M. Smith and the R Core Team
- Answering Questions with Data by Matthew J. C. Crump
- Exploratory Data Analysis with R by Roger D. Peng
- ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham
- Handling and Processing Strings in R by Gaston Sanchez
- Learning Statistics with R by Danielle Navarro
- Practical Regression and Anova using R by Julian Faraway
- Practicals and Exercises by Charles DiMaggio, PhD
- R Cookbook, 2nd Edition by James (JD) Long, Paul Teetor
- R for Data Science by Garrett Grolemund, Hadley Wickham
- R for Data Science: Exercise Solutions by Jeffrey B. Arnold
- R for Spatial Analysis by Charles DiMaggio, PhD
- R Language for Programmers by John Cook
- R Packages by Hadley Wickham
- R Programming for Data Science by Roger Peng
- R Programming Tutorial by Data & Tools
- R programming Wikibook by WikiBooks
- Statistical Inference via Data Science by Chester Ismay and Albert Y. Kim
- Text Mining with R by Julia Silge and David Robinson
- The Art of R Programming by Norman Matloff
- The Caret Package by Max Kuhn
- The R Inferno byPatrick Burns
- The R Language by the Authors of R
- The Tidyverse Cookbook Edited by Garrett Grolemund
- YaRrr! The Pirate’s Guide to R by Nathaniel D. Phillips
Math
- Descriptive Statistics by Salvatore S. Mangiafico
- Essential Math for Data Science by Tirthajyoti Sarkar
- Gallery of Statistical Distributions by NIST/SEMATECH
- Plotting distributions (ggplot2) by Winston Chang
Viz
- Chart Suggestions — A Thught-Starter by Andrew Abela, Ph.D.
- Color Hex Color Codes
- Combine Multiple GGPlots into a Figure by Alboukadel Kassambara
- From Data to Viz by Yan Holtz
- ggplot2 extensions - gallery maintained by Daniel Emaasit
- ggplot2 - Modify components of a theme
- paletteer maintained by Emil Hvitfeldt
- The Data Visualization Catalogue by Severino Ribecca
- The Graphic Continuum by Jonathan Schwabish, and Severino Ribecca
- The R Graph Gallery by Yan Holtz
- Time Based Heatmaps in R by Laura Ellis
- Top 50 ggplot2 Visualizations - The Master List (With Full R Code) by Selva Prabhakaran
Spatial
- An Introduction to Choropleth maps in R by Henry Cann
- Getting latitude & longitude for any address by Brecht Vermeire
- Map Plots Created With R And Ggmap by Laura Ellis
- Plot Spatial Data / Shapefiles in R from the math et al YoutTube channel
Wrangling
- Data Wrangling Part 1: Basic to Advanced Ways to Select Columns by Suzan Baert
- Data Wrangling Part 2: Transforming your columns into the right shape by Suzan Baert
- Data Wrangling Part 3: Basic and more advanced ways to filter rows by Suzan Baert
- Data Wrangling Part 4: Summarizing and slicing your data by Suzan Baert
- Not so Standard Deviations by Roger Peng and Hilary Parker
- Avery Robbins
- Cédric Scherer
- Data Imaginist by Thomas Lin Pedersen
- Data meets Narrative by Rebecca Barter
- HighlandR by John Mackintosh
- Julia Silge
- r-bloggers by Tal Galili
- r4stats by Bob Muenchen
- Ryo Nakagawara
- Statistics Globe by Joachim Schork
- TidyTuesday created by Thomas Mock. TidyTuesday is a weekly data project aimed at the R ecosystem with an emphasis placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem.
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