A tutorial on tidy cross-validation with R
Analyzing NetHack data, part 1: What kills the players
Analyzing NetHack data, part 2: What players kill the most
Building a shiny app to explore historical newspapers: a step-by-step guide
Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 1
Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 2
Dealing with heteroskedasticity; regression with robust standard errors using R
Easy time-series prediction with R: a tutorial with air traffic data from Lux Airport
Exporting editable plots from R to Powerpoint: making ggplot2 purrr with officer
Forecasting my weight with R
From webscraping data to releasing it as an R package to share with the world: a full tutorial with data from NetHack
Getting data from pdfs using the pdftools package
Getting the data from the Luxembourguish elections out of Excel
Going from a human readable Excel file to a machine-readable csv with {tidyxl}
How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data
Imputing missing values in parallel using {furrr}
Looking into 19th century ads from a Luxembourguish newspaper with R
Making sense of the METS and ALTO XML standards
Manipulate dates easily with {lubridate}
Manipulating strings with the {stringr} package
Maps with pie charts on top of each administrative division: an example with Luxembourg's elections data
Missing data imputation and instrumental variables regression: the tidy approach
Objects types and some useful R functions for beginners
Pivoting data frames just got easier thanks to `pivot_wide()` and `pivot_long()`
R or Python? Why not both? Using Anaconda Python within R with {reticulate}
Searching for the optimal hyper-parameters of an ARIMA model in parallel: the tidy gridsearch approach
Some fun with {gganimate}
The best way to visit Luxembourguish castles is doing data science + combinatorial optimization
The year of the GNU+Linux desktop is upon us: using user ratings of Steam Play compatibility to play around with regex and the tidyverse
Using Data Science to read 10 years of Luxembourguish newspapers from the 19th century
Using a genetic algorithm for the hyperparameter optimization of a SARIMA model
Using the tidyverse for more than data manipulation: estimating pi with Monte Carlo methods
What hyper-parameters are, and what to do with them; an illustration with ridge regression
{pmice}, an experimental package for missing data imputation in parallel using {mice} and {furrr}
Building formulae
Functional peace of mind
Get basic summary statistics for all the variables in a data frame
Getting {sparklyr}, {h2o}, {rsparkling} to work together and some fun with bash
Importing 30GB of data into R with sparklyr
Introducing brotools
It's lists all the way down
It's lists all the way down, part 2: We need to go deeper
Keep trying that api call with purrr::possibly()
Lesser known dplyr 0.7* tricks
Lesser known dplyr tricks
Lesser known purrr tricks
Make ggplot2 purrr
Mapping a list of functions to a list of datasets with a list of columns as arguments
Predicting job search by training a random forest on an unbalanced dataset
Teaching the tidyverse to beginners
Why I find tidyeval useful
tidyr::spread() and dplyr::rename_at() in action
Easy peasy STATA-like marginal effects with R
Functional programming and unit testing for data munging with R available on Leanpub
How to use jailbreakr
My free book has a cover!
Work on lists of datasets instead of individual datasets by using functional programming
Method of Simulated Moments with R
New website!
Nonlinear Gmm with R - Example with a logistic regression
Simulated Maximum Likelihood with R
Bootstrapping standard errors for difference-in-differences estimation with R
Careful with tryCatch
Data frame columns as arguments to dplyr functions
Export R output to a file
I've started writing a 'book': Functional programming and unit testing for data munging with R
Introduction to programming econometrics with R
Merge a list of datasets together
Object Oriented Programming with R: An example with a Cournot duopoly
R, R with Atlas, R with OpenBLAS and Revolution R Open: which is fastest?
Read a lot of datasets at once with R
Unit testing with R
Update to Introduction to programming econometrics with R
Using R as a Computer Algebra System with Ryacas

This semester I taught a course on applied econometrics with the R programming language. For this, I created a document that I gave to my students and shared online. This is the kind of document I would have liked to read when I first started using R. I already had some programming experience in C and Pascal but this is not necessarily the case for everyone that is confronted to R when they start learning about econometrics.

This is why the beginning of the document focuses more on general programming knowledge and techniques, and then only on econometrics. People online seemed to like the document, as I’ve received some positive comments by David Smith from Revolution R (read his blog post about the document here) and Dave Giles which links to David’s blog post here. People on twitter have also retweeted David’s and Dave’s tweets to their blog posts and I’ve received some requests by people to send them the PDF by email (because they live in places where Dropbox is not accessible unfortunately).

The document is still a work in progress (and will probably remain so for a long time), but I’ve added some new sections about reproducible research and thought that this update could warrant a new blog post.

For now, only linear models are reviewed, but I think I’ll start adding some chapters about non-linear models soonish. The goal for these notes, however, is not to re-invent the wheel: there are lots of good books about econometrics with R out there and packages that estimate a very wide range of models. What I want for these notes, is to focus more on the programming knowledge an econometrician needs, in a very broad and general sense. I want my students to understand that R is a true programming language, and that they need to use every feature offered by such a language, and not think of R as a black box into which you only type pre-programmed commands, but also be able to program their own routines.

Also, I’ve made it possible to create the PDF using a Makefile. This may be useful for people that do not have access to Dropbox, but are familiar with git.

You can compile the book in two ways: first download the whole repository:

`git clone git@bitbucket.org:b-rodrigues/programmingeconometrics.git`

and then, with Rstudio, open the file *appliedEconometrics.Rnw* and knit it. Another solution is to use the Makefile. Just type:

`make`

inside a terminal (should work on GNU+Linux and OSX systems) and it should compile the document. You may get some message about “additional information” for some R packages. When these come up, just press Q on your keyboard to continue the compilation process.

Get the notes here.

As always, if you have questions or suggestions, do not hesitate to send me an email and I sure hope you’ll find these notes useful!