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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

Fast food, causality and R packages, part 1

I am currently working on a package for the R programming language; its initial goal was to simply distribute the data used in the Card and Krueger 1994 paper that you can read here (PDF warning).

The gist of the paper is to try to answer the following question: Do increases in minimum wages reduce employment? According to Card and Krueger’s paper from 1994, no. The authors studied a change in legislation in New Jersey which increased the minimum wage from $4.25 an hour to $5.05 an hour. The neighbourghing state of Pennsylvania did not introduce such an increase. The authors thus used the State of Pennsylvania as a control for the State of New Jersey and studied how the increase in minimum wage impacted the employment in fast food restaurants and found, against what economic theory predicted, an increase and not a decrease in employment. The authors used a method called difference-in-differences to asses the impact of the minimum wage increase.

This result was and still is controversial, with subsequent studies finding subtler results. For instance, showing that there is a reduction in employment following an increase in minimum wage, but only for large restaurants (see Ropponen and Olli, 2011).

Anyways, this blog post will discuss how to create a package using to distribute the data. In a future blog post, I will discuss preparing the data to make it available as a demo dataset inside the package, and then writing and documenting functions.

The first step to create a package, is to create a new project:

Select “New Directory”:

Then “R package”:

and on the window that appears, you can choose the name of the package, as well as already some starting source files:

Also, I’d highly recommend you click on the “Create a git repository” box and use git within your project for reproducibility and sharing your code more easily. If you do not know git, there’s a lot of online resources to get you started. It’s not super difficult, but it does require making some new habits, which can take some time.

I called my package {diffindiff}, and clicked on “Create Project”. This opens up a new project with a hello.R script, which gives you some pointers:

# Hello, world!
# This is an example function named 'hello' 
# which prints 'Hello, world!'.
# You can learn more about package authoring with RStudio at:
#   http://r-pkgs.had.co.nz/
# Some useful keyboard shortcuts for package authoring:
#   Install Package:           'Ctrl + Shift + B'
#   Check Package:             'Ctrl + Shift + E'
#   Test Package:              'Ctrl + Shift + T'

hello <- function() {
  print("Hello, world!")

Now, to simplify the creation of your package, I highly recommend you use the {usethis} package. {usethis} removes a lot of the pain involved in creating packages.

For instance, want to start by adding a README file? Simply run:

✔ Setting active project to '/path/to/your/package/diffindiff'
✔ Writing 'README.md'
● Modify 'README.md'

This creates a README.md file in the root directory of your package. Simply change that file, and that’s it.

The next step could be setting up your package to work with {roxygen2}, which is very useful for writing documentation:

✔ Setting Roxygen field in DESCRIPTION to 'list(markdown = TRUE)'
✔ Setting RoxygenNote field in DESCRIPTION to '6.1.1'
● Run `devtools::document()`

See how the output tells you to run devtools::document()? This function will document your package, transforming the comments you write to describe your functions to documentation and managing the NAMESPACE file. Let’s run this function too:

Updating diffindiff documentation
First time using roxygen2. Upgrading automatically...
Loading diffindiff
Warning: The existing 'NAMESPACE' file was not generated by roxygen2, and will not be overwritten.

You might have a similar message than me, telling you that the NAMESPACE file was not generated by {roxygen2}, and will thus not be overwritten. Simply remove the file and run devtools::document() again:

Updating diffindiff documentation
First time using roxygen2. Upgrading automatically...
Loading diffindiff

But what is actually the NAMESPACE file? This file is quite important, as it details where your package’s functions have to look for in order to use other functions. This means that if your package needs function foo() from package {bar}, it will consistently look for foo() inside {bar} and not confuse it with, say, the foo() function from the {barley} package, even if you load {barley} after {bar} in your interactive session. This can seem confusing now, but in the next blog posts I will detail this, and you will see that it’s not that difficult. Just know that it is an important file, and that you do not have to edit it by hand.

Next, I like to run the following:

✔ Adding 'magrittr' to Imports field in DESCRIPTION
✔ Writing 'R/utils-pipe.R'
● Run `devtools::document()`

This makes the now famous %>% function available internally to your package (so you can use it to write the functions that will be included in your package) but also available to the users that will load the package.

Your package is still missing a license. If you plan on writing a package for your own personal use, for instance, a collection of functions, there is no need to think about licenses. But if you’re making your package available through CRAN, then you definitely need to think about it. For this package, I’ll be using the MIT license, because the package will distribute data which I do not own (I’ve got permission from Card to re-distribute it) and thus I think it would be better to use a permissive license (I don’t know if the GPL, another license, which is stricter in terms of redistribution, could be used in this case).

✔ Setting License field in DESCRIPTION to 'MIT + file LICENSE'
✔ Writing 'LICENSE.md'
✔ Adding '^LICENSE\\.md$' to '.Rbuildignore'
✔ Writing 'LICENSE'

We’re almost done setting up the structure of the package. If we forget something though, it’s not an issue, we’ll just have to run the right use_* function later on. Let’s finish by preparing the folder that will contains the script to prepare the data:

✔ Creating 'data-raw/'
✔ Adding '^data-raw$' to '.Rbuildignore'
✔ Writing 'data-raw/DATASET.R'
● Modify 'data-raw/DATASET.R'
● Finish the data preparation script in 'data-raw/DATASET.R'
● Use `usethis::use_data()` to add prepared data to package

This creates the data-raw folder with the DATASET.R script inside. This is the script that will contain the code to download and prepare datasets that you want to include in your package. This will be the subject of the next blog post.

Let’s now finish by documenting the package, and pushing everything to Github:


The following lines will only work if you set up the Github repo:

git add .
git commit -am "first commit"
git push origin master

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.

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