This repository contains materials for the computer programming course of the EDSD cohort 2023. Eventually, there will be four folders:
- "R Code": R code for each session.
- "Slides": Slides of the first session.
- "Exercises": Voluntary exercises.
- "Assignment": Mandatory assignment.
In this course, we will use two programs:
First install R, then install RStudio.
- September 6 (Wed), 14:00-16:00 (Basics)
- September 7 (Thu), 14:00-16:00 (Descriptive)
- September 11 (Mon), 9:30-11:30 (Data viz)
- September 12 (Tue), 14:00-16:00 (Data handling)
- September 13 (Wed), 14:00-16:00 (Programming 1)
- September 25 (Mon), 14:00-16:00 (Programming 2)
- September 27 (Wed), 14:00-16:00 (Example)
Books:
- Wickham, Grolemund: R for Data Science. https://r4ds.hadley.nz/
- Wickham: Advanced R. https://adv-r.hadley.nz/
- Wickham, Navarro, Pedersen: ggplot2. https://ggplot2-book.org/
- Chang: R Graphics Cookbook. https://r-graphics.org/
- Healy: Data Visualization. https://socviz.co/
- Hernan, Robins: Causal Inference. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
- Hastie, Tibshirani, Friedman: Elements of Statistical Learning. https://web.stanford.edu/~hastie/ElemStatLearn/
- Lovelace, Nowosad, Muenchow: Geocomputation with R. https://geocompr.robinlovelace.net/
- Allerhand: A Tiny Handbook of R. http://link.springer.com/book/10.1007%2F978-3-642-17980-8 (available for free through MPIDR account)
Websites:
Journals:
- Journal of Statistical Software https://www.jstatsoft.org/index
- R Journal https://journal.r-project.org/
- Journal of Open Source Software https://joss.theoj.org/
If you have any questions (or find any errors) you can send me an email: [email protected]