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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

Unit testing with R

I've been introduced to unit testing while working with colleagues on quite a big project for which we use Python.

At first I was a bit skeptical about the need of writing unit tests, but now I must admit that I am seduced by the idea and by the huge time savings it allows. Naturally, I was wondering if the same could be achieved with R, and was quite happy to find out that it also possible to write unit tests in R using a package called testthat.

Unit tests (Not to be confused with unit root tests for time series) are small functions that test your code and help you make sure everything is alright. I'm going to show how the testthat packages works with a very trivial example, that might not do justice to the idea of unit testing. But you'll hopefully see why writing unit tests is not a waste of your time, especially if your project gets very complex (if you're writing a package for example).

First, you'll need to download and install testthat. Some dependencies will also be installed.

Now, you'll need a function to test. Let's suppose you've written a function that returns the nth Fibonacci number:

Fibonacci <- function(n){
    a <- 0
    b <- 1
    for (i in 1:n){
        temp <- b
        b <- a
        a <- a + temp

You then save this function in a file, let's call it fibo.R. What you'll probably do once you've written this function, is to try it out:

## [1] 5

You'll see that the function returns the right result and continue programming. The idea behind unit testing is write a bunch of functions that you can run after you make changes to your code, just to check that everything is still running as it should.

Let's create a script called test_fibo.R and write the following code in it:

test_that("Test Fibo(15)",{
  phi <- (1 + sqrt(5))/2
  psi <- (1 - sqrt(5))/2
  expect_equal(Fibonacci(15), (phi**15 - psi**15)/sqrt(5))

The code above uses Binet's formula, a closed form formula that gives the nth Fibonacci number and compares it our implementation of the algorithm. If you didn't know about Binet's formula, you could simply compute some numbers by hand and compare them to what your function returns, for example. The function expect_equal is a function from the package testthat and does exactly what it tells. We expect the result of our implementation to be equal to the result of Binet's Formula. The file test_fibo.R can contain as many tests as you need. Also, the file that contains the tests must start with the string test, so that testthat knows with files it has to run.

Now, we're almost done, create yet another script, let's call it run_tests.R and write the following code in it:



test_results <- test_dir("path/to/tests", reporter="summary")

After running these lines, and if everything goes well, you should see a message like this:

> library(testthat)
> source("path/to/fibo.R")
> test_results <- test_dir("path/to/tests", reporter="summary")

Your tests are dandy! 

Notice the small . over the message? This means that one test was run successfully. You'll get one dot per successful test. If you take a look at test_results you'll see this:

> test_results
         file context          test nb failed skipped error  user system  real
1 test_fibo.R         Test Fibo(15)  1      0   FALSE FALSE 0.004      0 0.006

You'll see each file and each function inside the files that were tested, and also whether the test was skipped, failed etc. This may seem overkill for such a simple function, but imagine that you write dozens of functions that get more and more complex over time. You might have to change a lot of lines because as time goes by you add new functionality, but don't want to break what was working. Running your unit tests each time you make changes can help you pinpoint regressions in your code. Unit tests can also help you start with your code. It can happen that sometimes you don't know exactly how to start; well you could start by writing a unit test that returns the result you want to have and then try to write the code to make that unit test pass. This is called test-driven development.

I hope that this post motivated you to write unit tests and make you a better R programmer!