In this post, that I will update regularly as I come across new resources, I will drop a list of useful material to learn R, for beginners and not-so-beginners. Some of the references will be particularly focused on R for social/political scientists, while others are tailored for general interest R users.

For social scientists

1. R for Political Data Science: A Practical Guide, by Francisco Urdinez and Andres Cruz. Great introduction for political scientists who are new to R, or are migrating from a different software/language. It provides a general overview of the basics of R and introduces many tools that are frequently useful in political science.

2. Quantitative Politics with R, by Erik Gahner Larsen and Zoltán Fazekas. Similar in spirit to the previous one, it is tailored for political scientists who are new to R, but it is a bit shorter and less ambitious. Main advantage: it can be fully accessed for free.

3. Quantitative Social Science, by Kosuke Imai. Unlike the previous two, it is primarily a quantitative methods textbook, but it does an excellent job in introducing at the same time methods and their applications in R. It covers a wide range of topics and is an excellent companion to a first course in quantitative methods for social scientists.

4. Data Analysis for Social Science, by Kosuke Imai and Elena Llaudet is also an introductory textbook to quantitative social science. Simpler in its technical content and friendlier for beginners than the previous one, it is ideal for people without prior knowledge in statistics and programming.

For general interest in R

1. Stat 545: Data Wrangling, Exploration, and Analysis with R, by Jenny Bryan. This is probably my favorite introduction to all things related to data manipulation and analysis in R. It is very clear and friendly to users with different levels of experience in R. Notably, it does not cover topics of modeling and inference (for good reasons), but it is incredibly useful for all things data wrangling/analysis. Another advantage: accessible for free.

2. R Programming for Data Science, by Roger Peng. Another good introduction for newcomers, with an interesting and useful history of R section. It is the companion book to the popular Coursera’s R Programming course.

3. Introduction to Data Science. Data Analysis and Prediction Algorithms with R. Excellent general introduction to R with applications to most common data science problems. Very clear and straightforward presentation of the most frequently needed tools in R, with some coverage of statistical concepts as well. Advantages: accessible for free, presents topics in different package ecosystems within R (tidyverse, data.table).

4. R for Data Science, by Hadley Wickham and Garrett Grolemund. A must. Although I would not start with it if I had absolutely no prior knowledge of R, it is an excellent and thorough resource to learn all of the most popular techniques for data wrangling, exploration, analysis and modelling in R, all presented in the most popular (and probably most straightforward) ecoysistem of packages that has been developed for data science in R: the Tidyverse. Advantages: accessible for free, yo get to learn the Tidyverse from its very own creator, and it covers R Markdown, which is an excellent framework for integrating document typesetting in LaTex and data analysis in R.

En español

1. Introducción a la ciencia de datos: Análisis de datos y algoritmos de predicción con R de Rafael Irizarry. Versión en castellano del tercer item de la sección anterior. Excelente presentación de la mayoría de las herramientas habitualmente necesarias para ciencia de datos en R, con explicación conceptual de algunos temas de probabilidad y estadística también. Texto completo disponible gratuitamente.

2. R para ciencia de datos, de Hadley Wickham y Garrett Grolemund. Es la traducción al castellano del cuarto item de la sección anterior. No es ideal como primer contacto con R, pero es un excelente manual de las técnicas más populares en manipulación, análisis y modelado de datos, presentadas en el ecosistema de paquetes más popular, el Tidyverse, que además fue desarrollado en buena medida por el propio Hadley Wickham. Otra ventaja es que enseña a usar R Markdown, una forma muy sencilla y eficaz de integrar la elaboración de documentos en LaTex con análisis de datos en R. Texto completo disponible gratuitamente.

3. Ciencia de datos para curiosos, de Martín Montané. Introducción a R muy amigable para principiantes sin experiencia ni en R ni en estadística en general. Texto completo disponible gratuitamente.

4. AnalizaR Datos Políticos, de Francisco Urdinez y Andres Cruz. Este libro está enfocado a las necesidades más frecuentes de cientistas sociales/políticos, con una presentación detallada y ejemplos claros de aplicaciones de métodos populares en esas disciplinas, como distintos tipos de modelos de regresión, de texto o de inferencia causal, entre otros. Texto completo disponible gratuitamente.