There are many free resources and online books available to learn social data science methods in R. Here is a selection that I have found useful.
RLadies Sydney RYouWithMe course: An introductory course split into three: BasicBasics, CleanItUp (data manipulation), VizWhiz (data visualisation), MarkyMark (output in RMarkdown). Some very nice video walk-throughs
Modern dive by Chester Ismay and Albert Y. Kim: This book includes a very nice introduction to R and RStudio in chapter 1 using flights data. Chapters 2-4 cover basic data science; Chapters 5-6 cover data modelling; Chapters 7-10 cover statistical inference, including using bootstrap as an alternative to parametric methods
Learning statistics with R by Dani Navarro: basic statistics for psychologists online book written by one of the RLadies Sydney group, with an emphasis on ANOVA, regression and factorial ANOVA, plus an intro to Bayesian statistics
Quick-R by Rob Kabacoff: quick introductions to how to implement a range of statistical methods
R Cookbook second edition by James Long and Paul Teetor: a book with a large number of ‘how to’ recipes
Cookbook for R by Winston Chang: similar idea to the Long & Teetor book, but with less detail. Particularly useful for how to modify charts in ggplot2
The peer (and often very expert) help forum on Stack Overflow
R for Data Science by Hadley Wickham and Garrett Grolemund: co-authored by Hadley Wickham, the person responsible for developing the tidyverse. Exploration, wrangling, programming, modelling, communicating
Hand-on programming with R: Chapters 1-3 cover a basic dice-rolling project that introduces basic arithmetic, functions, objects, script files and help
Getting used to R, RStudio and RMarkdown: a short book, with a lot of emphasis on basic programming concepts and RMarkdown
RStudio ‘basics’ primers: visualisation basics - the basics of using ggplot to visualise data; programming basics - functions, arguments, objects, vectors, types, lists, packages. Short videos alongside quiz questions and R exercises to check comprehension
Advanced R by Hadley Wickham - for those who really want to get under the hood of R programming
The tidyverse coding
style guide
RStudio suggested resources for beginners
Cheatsheets for important R packages
RStudio webinars: lots of useful stuff. Videos are approx. 50 mins each, so many are more appropriate for intermediate / advanced R users
R Markdown Cookbook by Yihui Xie, Christophe Dervieux and Emily Riederer
R Markdown: The Definitive Guide by Yihui Xie, J. J. Allaire and Garrett Grolemund
bookdown: Authoring Books and Technical Documents with R Markdown by Yihui Xie: write your PhD thesis or your next book in R Markdown
blogdown: Creating Websites with R Markdown, by Yihui Xie, Amber Thomas, Alison Presmanes Hill
An introduction to Bayesian statistics in Learning Statistics with R by Dani Navarro
Statistical Rethinking: a series of YouTube lectures by Richard McElreath, with code in R, to accompany his book of the same name (available via the Bodleian for Oxford University members)
Causal inference: the mixtape, Scott Cunningham - introduction to modern methods of causal inference with examples in R
Introduction to Econometrics with R by Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer
Applied Causal Analysis (with R) by Paul C. Bauer
ggplot2 book by Hadley Wickham: an online version of the original book on ggplot2
ggplot2 grammar guide: a visual guide to ggplot2
R Graphics Cookbook, 2nd edition by Winston Chang
Data Visualization: A practical introduction by Kieran Healy
Fundamentals of Data Visualisation by Claus O. Wilke
An Introduction to Statistical Learning: with Applications in R, 2nd edition, by James, Witten, Hastie and Tibshirani; there are also video lectures that follow the first edition
Introduction to Data Science: Data Analysis and Prediction Algorithms with R, by Rafael A. Irizarry
Doing Meta-Analysis in R by Harrer et al.: a comprehensive applied guide, with very useful links to free resources on the theory
How to perform a meta-analysis with R by Balduzzi et al.: a short tutorial in the Evidence-Based Mental Health on doing meta-analysis with the package meta
The website for the metafor package has
data and replication code for around 25 meta-analysis papers using a variety of meta-analysis techniques
R packages for meta-analysis: an overview of the R packages available for meta-analysis
No R, but there are some useful resources on the companion site to Borenstein et al (2021, 2nd edn) Introduction to Meta-Analysis
Flexible Imputation of Missing Data by Stef van Buuren
Centre for Multilevel Modelling LEMMA course: a free online course with instructions in R and Stata
NCRM introductory videos on multilevel modelling
Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R by Paul Roback and Julie Legler
Code and walkthrough videos to accompany Leite (2017) Practical Propensity Score Methods Using R
Introduction to GIS in R: an introductory tutorial from the Office for National Statistics
sf,
Simple Features for R - spatial data, simplified
Geocomputation with R by Lovelace, Nowosad and Muenchow
Spatial Modelling for Data Scientists: an introduction to working with spatial analysis from the University of Liverpool’s Geographic Data Science Lab
Synthetic control methods for the evaluation of single-unit interventions in epidemiology: a tutorial, Bonander et al (2021): article | replication code
Comparative Politics and the Synthetic Control Method, Abadie et al (2014): article | replication code
Survey analysis in R: the homepage of the survey package, written and maintained by Thomas Lumley
RQDA package:
Qualitative Analysis Using R
Text Mining with R by Julia Silge and David Robinson
quanteda package:
Quantitative Analysis of Textual Data
Text Analysis in R (2017): introductory article by Kasper Welbers , Wouter Van Atteveldt and Kenneth Benoit
Forecasting: Principles and Practice (3rd ed), Hyndman and Athanasopoulos