Appendix B — Ressources

B.1 General ressources

• R for Data Science, this is THE R classic by Hadley Wickham, the founder of the tidyverse, you should definitely have a look to better understand what we see in this course
• Telling stories with data by Rohan Alexander : one of my favorite book on data science with R. (a bit more advanced)
• Computational analysis of communication by van Atteveldt et al. 
• Computational Thinking for Social Scientists by Jae Yeon Kim
• Introduction to data science by Rafael Irizarry

B.2 Other introductions to R

There are plently others introduction to R on the web so feel free to visit some of them.

• Introduction to R, by Felix Lennert

• Introduction to R by Alex Douglas et al. 

• On best practices in R, What They Forgot to Teach You About R by Jennifer Bryan et al If you want ressources in french, these are the two most comprehensive introduction you will find :

• Introduction à R et au tidyverse by Julien Barnier

• Guide pour l’analyse de données d’enquêtes avec R by Joseph Larmarange

B.3 Reproducibility

• Learn how to use Quarto

• Learn more about reprodubility with Building reproducible analytical pipelines with R by Bruno Rodrigues

• Learn how to connect Github and Rstudio with Happy git by Jennifer Bryan

B.4 Statistics

• Max Blackwell’s Harvard course on statistical inference with R for a more technical introduction.

B.5 For IR students

• Federica Genovese’ Syllabus on Quantitative Methods in R with different specialized articles and ressources

B.6 To go further

Here, different ressources if you want to go further in some areas that are not covered in this course :

B.6.1 Text analysis and machine learning

• Text Mining with R
• Supervised Machine Learning for Text Analysis with R by Hvitfeldt and Silge
• Tidy Modeling with R by Kuhn and Silge