Teaching and resources
Class materials
I teach two classes here at the KDI School for which I have made my materials publicly available. In both cases:
- The raw quarto/markdown files for the slides contain plenty of useful comments and examples.
- All of the data I use as examples in class are uploaded to the repository so you can follow along and do the examples yourself.
- Please feel free to make use of any of the code. Or, if you have questions, feel free to e-mail me!
Applied microeconometrics in R
I teach an advanced masters/Ph.D. class on applied microeconometrics in R. The resources are available on my Github page, here.
- Note that I am transitioning to Quarto and HTML slides, but the older markdown files are available in the weeks folder.
Geospatial data analysis in R
I am teaching a new masters class on geospatial data analysis in R. The resources are available on my Github page, here.
- This is a new class in Fall 2024 so the website is not complete yet.
- All slides are HTML and made using Quarto (in R).
Working with others
I put together some slides for our Ph.D. students focused on setting up projects when working with others. These slides focus on two things:
- Using Dropbox to keep all files up to date
- Using relative paths so that files run on everyone’s computer
You can view the slides here.
Geospatial data and small area estimation
Stay tuned for a primer aimed at discussing the ins and outs of integrating geospatial data and survey data for small area estimation in R, with a particular emphasis on improving estimates of SDGs.
- This work is with UN Stats and is aimed at practitioners and national statistics offices, but it will also contain a short guide on using geospatial data.
- In the meantime, please check out UN Stats’ SAE4SDG page, here.
- As part of this work, I taught a workshop in Nairobi, in combination with UN Stats, UN ECA, and UN EAC, on integrating geospatial data into small area estimation. You can find my slides here. I will upload all of the slides from the Bangkok workshop in late October 2024. These will draw heavily from my geospatial analysis class, however, which you can find above.
- There are also two upcoming R packages that will make this easier. Please check back for updates!