About Me Blog
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 Curly-Curly, the successor of Bang-Bang 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 Fast food, causality and R packages, part 1 Fast food, causality and R packages, part 2 For posterity: install {xml2} on GNU/Linux distros 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 Get text from pdfs or images using OCR: a tutorial with {tesseract} and {magick} 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} Historical newspaper scraping with {tesseract} and R How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data Imputing missing values in parallel using {furrr} Intermittent demand, Croston and Die Hard 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 Modern R with the tidyverse is available on Leanpub 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} Split-apply-combine for Maximum Likelihood Estimation of a linear model Statistical matching, or when one single data source is not enough The best way to visit Luxembourguish castles is doing data science + combinatorial optimization The never-ending editor war (?) 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 cosine similarity to find matching documents: a tutorial using Seneca's letters to his friend Lucilius Using linear models with binary dependent variables, a simulation study 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 {disk.frame} is epic {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 is a list of software I use for work. I only use Free Software (whenever I have a choice, at least). Free here is not about price, but about freedom. Think of free as in free speech. The reasons I chose to use free software are both practical and philosophical. Free software gives me infinite flexibility: I can install it on any machine I own without having to worry about licenses or worry that the software may become deprecated some day. As for the philosophical reasons, I think that free software is in line with the way science should be done: sharing knowledge, helping each other and disapprove of unethical behaviour. To know more about Free Software, consult the FSF site and the Wikipedia page about free software.

  • I use openSUSE as my operating system. It’s fast, stable, doesn’t nag me with forced and unwanted updates and has a lot of software available in the default repositories.
  • For my statistical needs I use the R programming language. It is a very extensive and powerful programming language used worldwide by both researchers and firms and is rapidly taking the place of other statistical software programs like SAS. You can install it on any linux distribution via its package manager. For Windows and OSX versions, you can get R for free on the project’s page. It works best if you also install Rstudio, which is a modern IDE for R.
  • Setting up Python for scientific purposes can be a tricky. To simplify the installation of the different Python libraries and have a consistent environment across my different computers (and coworkers’ computers) I use Anaconda by Continuum Analytics. It is a free, commercially supported Python distribution and it works very well. It comes with everything you’d need from a Python distribution, Numpy, Scipy, Matplotlib and Pandas, one of the most useful Python libraries out there.
  • LaTeX is used extensively in academia to write scientific papers. It takes care of the formatting for you, allowing you to focus on what really matters. Writing mathematical equations in LaTeX is also very easy and the result is beautiful. On any GNU+Linux distribution, installing LaTeX is done via the system’s package manager. For Windows users, you’ll need to install Miktex and for OSX MacTeX.
  • I’ve been looking for the killer editor for quite some time and I can confidently say that Spacemacs is the one! Spacemacs truly is the best of both Vim and Emacs; it is Emacs at its core, but the user can enable Vim keybindings. This means that you can use all the great Emacs packages. Using ESS, it is possible to work with R, and it’s also possible to edit LaTeX source files, Python files, etc.
  • I’m not a designer by any means, but when I need to design a poster for a conference, or a call for papers, I use Gimp and Scribus. Gimp is an image manipulation tool not unlike Adobe Photoshop and Scribus is a desktop publishing tool like Adobe Indesign.