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

Hi! My name is Bruno Rodrigues, and I’m a manager in the data science team at PwC Luxembourg.

I mostly work on data related projects using R and Python.

I’m an economist by training and hold a PhD from the University of Strasbourg, but am interested in any subject that deals with statistics and machine learning in general!

I program mostly in R and love sharing my knowledge, that’s why I started this blog. I share my posts also on R-bloggers and Rweekly. In my posts, I discuss new packages I discovered or new ways of using packages.

If you find this blog useful, you might want to follow me on twitter for blog post updates and buy me an espresso or paypal.me.

I wrote an ebook called *Modern R with the tidyverse* that you can read for free
here.
If you find it useful and want to contribute to this blog and future ebooks, you can also
purchase a copy on leanpub.
This ebook is partly based of my old one.

You can also download my package, called {brotools},
which contains some functions I use daily. You might find them useful too! I also made a package
called `{nethack}`

that contains data from NetHack games played online from 2001 to 2018. You
can get it here. I also made another package,
{michelRodange}, which contains
texts written by the Luxembourguish author Michel Rodange, that can be used for text mining.

- Cluster multiple time series using K-means
- Split-apply-combine for Maximum Likelihood Estimation of a linear model
- {disk.frame} is epic
- Modern R with the tidyverse is available on Leanpub
- Using linear models with binary dependent variables, a simulation study
- Statistical matching, or when one single data source is not enough
- Curly-Curly, the successor of Bang-Bang
- Intermittent demand, Croston and Die Hard
- Using cosine similarity to find matching documents: a tutorial using Seneca's letters to his friend Lucilius
- The never-ending editor war (?)
- For posterity: install {xml2} on GNU/Linux distros
- Fast food, causality and R packages, part 2
- Fast food, causality and R packages, part 1
- Historical newspaper scraping with {tesseract} and R
- Get text from pdfs or images using OCR: a tutorial with {tesseract} and {magick}
- Pivoting data frames just got easier thanks to `pivot_wide()` and `pivot_long()`
- Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 2
- Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 1
- Manipulating strings with the {stringr} package
- Building a shiny app to explore historical newspapers: a step-by-step guide
- Using Data Science to read 10 years of Luxembourguish newspapers from the 19th century
- Making sense of the METS and ALTO XML standards
- Looking into 19th century ads from a Luxembourguish newspaper with R

- R or Python? Why not both? Using Anaconda Python within R with {reticulate}
- Some fun with {gganimate}
- Objects types and some useful R functions for beginners
- Using the tidyverse for more than data manipulation: estimating pi with Monte Carlo methods
- Manipulate dates easily with {lubridate}
- What hyper-parameters are, and what to do with them; an illustration with ridge regression
- A tutorial on tidy cross-validation with R
- The best way to visit Luxembourguish castles is doing data science + combinatorial optimization
- Using a genetic algorithm for the hyperparameter optimization of a SARIMA model
- Searching for the optimal hyper-parameters of an ARIMA model in parallel: the tidy gridsearch approach
- Easy time-series prediction with R: a tutorial with air traffic data from Lux Airport
- Analyzing NetHack data, part 2: What players kill the most
- Analyzing NetHack data, part 1: What kills the players
- From webscraping data to releasing it as an R package to share with the world: a full tutorial with data from NetHack
- Maps with pie charts on top of each administrative division: an example with Luxembourg's elections data
- Getting the data from the Luxembourguish elections out of Excel
- Exporting editable plots from R to Powerpoint: making ggplot2 purrr with officer
- How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data
- Going from a human readable Excel file to a machine-readable csv with {tidyxl}
- 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
- Dealing with heteroskedasticity; regression with robust standard errors using R
- Missing data imputation and instrumental variables regression: the tidy approach
- Forecasting my weight with R
- Getting data from pdfs using the pdftools package
- {pmice}, an experimental package for missing data imputation in parallel using {mice} and {furrr}
- Imputing missing values in parallel using {furrr}
- Get basic summary statistics for all the variables in a data frame
- Keep trying that api call with purrr::possibly()
- Getting {sparklyr}, {h2o}, {rsparkling} to work together and some fun with bash
- Importing 30GB of data into R with sparklyr
- Predicting job search by training a random forest on an unbalanced dataset
- Mapping a list of functions to a list of datasets with a list of columns as arguments
- It's lists all the way down, part 2: We need to go deeper
- It's lists all the way down

- Building formulae
- Teaching the tidyverse to beginners
- Functional peace of mind
- Easy peasy STATA-like marginal effects with R
- Why I find tidyeval useful
- tidyr::spread() and dplyr::rename_at() in action
- Lesser known dplyr 0.7* tricks
- Make ggplot2 purrr
- Introducing brotools
- Lesser known purrr tricks
- Lesser known dplyr tricks
- How to use jailbreakr

- My free book has a cover!
- Functional programming and unit testing for data munging with R available on Leanpub
- Work on lists of datasets instead of individual datasets by using functional programming
- I've started writing a 'book': Functional programming and unit testing for data munging with R
- Merge a list of datasets together
- Read a lot of datasets at once with R
- Data frame columns as arguments to dplyr functions
- Unit testing with R
- Careful with tryCatch

- Bootstrapping standard errors for difference-in-differences estimation with R
- Update to Introduction to programming econometrics with R
- Export R output to a file
- Introduction to programming econometrics with R

- R, R with Atlas, R with OpenBLAS and Revolution R Open: which is fastest?
- Object Oriented Programming with R: An example with a Cournot duopoly

- Using R as a Computer Algebra System with Ryacas
- Simulated Maximum Likelihood with R
- Method of Simulated Moments with R
- Nonlinear Gmm with R - Example with a logistic regression