Dealing with heteroskedasticity; regression with robust standard errors using R
Forecasting my weight with R
Getting data from pdfs using the pdftools package
Imputing missing values in parallel using {furrr}
Missing data imputation and instrumental variables regression: the tidy approach
{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

I have started writing a ‘book’ using the awesome `bookdown`

package. In the book I explain and show why using functional programming
and putting your functions in your own packages is the way to go when you want to clean, prepare and transform large data sets.
It makes testing and documenting your code easier. You don’t need to think about managing paths either. The book is far from complete,
but I plan on working on it steadily. For now, you can read an intro to functional programming, unit testing and creating your own packages
that will hold your code. I also show you can write documentation for your functions. I am also looking for feedback; so if you have any
suggestions, do not hesitate to shoot me an email or a tweet! You can read the book by clicking here.