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

Yesterday I released an ebook on Leanpub,
called *Modern R with the tidyverse*, which you can also
read for free here.

In this blog post, I want to give some context.

*Modern R with the tidyverse* is the second ebook I release on Leanpub. I released the first one, called
Functional programming and unit testing for data munging with R around
Christmas 2016 (I’ve retired it on Leanpub, but you can still read it for free
here) . I just had moved back to my home country of
Luxembourg and started a new job as a research assistant at the statistical national institute.
Since then, lots of things happened; I’ve changed jobs and joined PwC Luxembourg as a data scientist,
was promoted to manager, finished my PhD, and most importantly of all, I became a father.

Through all this, I continued blogging and working on a new ebook, called *Modern R with the tidyverse*.
At first, this was supposed to be a separate book from the first one, but as I continued writing,
I realized that updating and finishing the first one, would take a lot of effort, and also, that
it wouldn’t make much sense in keeping both separated. So I decided to merge the content from the
first ebook with the second, and update everything in one go.

My very first notes were around 50 pages if memory serves, and I used them to teach R at the
University of Strasbourg while I employed there as a research and teaching assistant and working
on my PhD. These notes were the basis of *Functional programming and unit testing for data munging with R*
and now *Modern R*. Chapter 2 of *Modern R* is almost a simple copy and paste from these notes
(with more sections added). These notes were first written around 2012-2013ish.

*Modern R* is the kind of text I would like to have had when I first started playing around with R,
sometime around 2009-2010. It starts from the beginning, but also goes quite into details in the
later chapters. For instance, the section on
modeling with functional programming
is quite advanced, but I believe that readers that read through all the book and reached that part
would be armed with all the needed knowledge to follow. At least, this is my hope.

Now, the book is still not finished. Two chapters are missing, but it should not take me long to finish them as I already have drafts lying around. However, exercises might still be in wrong places, and more are required. Also, generally, more polishing is needed.

As written in the first paragraph of this section, the book is available on Leanpub. Unlike my previous ebook, this one costs money; a minimum price of 4.99$ and a recommended price of 14.99$, but as mentioned you can read it for free online. I’ve hesitated to give it a minimum price of 0$, but I figured that since the book can be read for free online, and that Leanpub has a 45 days return policy where readers can get 100% reimbursed, no questions asked (and keep the downloaded ebook), readers were not taking a lot of risks by buying it for 5 bucks. I sure hope however that readers will find that this ebook is worth at least 5 bucks!

Now why should you read it? There’s already a lot of books on learning how to use R. Well, I don’t
really want to convince you to read it. But some people do seem to like my style of writing and my
blog posts, so I guess these same people, or similar people, might like the ebook. Also, I think
that this ebook covers a lot of different topics, enough of them to make you an efficient R user.
But as I’ve written in the introduction of *Modern R*:

*So what you can expect from this book is that this book is not the only one you should read.*

Anyways, hope you’ll enjoy *Modern R*, suggestions, criticisms and reviews welcome!

By the way, the cover of the book is a painting by John William Waterhouse, depicting Diogenes of Sinope, an ancient Greek philosopher, and absolute mad lad. Read his Wikipedia page, it’s worth it.

Hope you enjoyed! If you found this blog post useful, you might want to follow me on twitter for blog post updates and buy me an espresso or paypal.me, or buy my ebook on Leanpub