This course will provide an introduction to R for data science and policy evaluation. During each class, a series of applications useful to develop autonomous research projects will be presented. At the end of the course you will be able to perform from scratch a data analysis in R.
Interactive approach: we will perform simple analyses during the lectures after the main concepts are introduced. Remote lectures are available at this link. A shared folders with mock datasets is available here. The exam will consist in an individual project/presentation using one or multiple tools introduced during the course.
R and R Studio should be installed before the first lecture. Here a breif guide on how to install both.
| Date | Topic | Readings | |
|---|---|---|---|
| Week 1 June 3rd |
Introduction to Data Science and R Slides | Lecture Notes | R Code |
Peng and Matsui, 2017 Meng, 2019 |
|
| Week 1 June 5th |
Exploratory Data Analysis Slides | Lecture Notes | R Code |
Wickham and Grolemund, 2017 | |
| Week 2 June 10th |
Data Modeling Slides | Lecture Notes | R Code |
Tibshirani, 1996 Meinshausen and Bühlmann, 2010 |
|
| Week 2 June 11th |
Predictive Analysis Slides | Lecture Notes | R Code |
Friedman et al., 1984 Breiman, 2001 Chipman, 2010 |
|
| Week 2 June 12th |
Causal Machine Learning Slides | Lecture Notes | R Code |
Athey and Imbens, 2016 Wager and Athey, 2018 |
|