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Analyzing NetHack data, part 1: What kills the players Analyzing NetHack data, part 2: What players kill the most 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 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 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} How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data Imputing missing values in parallel using {furrr} 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 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 {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

Why I find tidyeval useful

First thing’s first: maybe you shouldn’t care about tidyeval. Maybe you don’t need it. If you exclusively work interactively, I don’t think that learning about tidyeval is important. I can only speak for me, and explain to you why I personally find tidyeval useful.

I wanted to write this blog post after reading this twitter thread and specifically this question.

Mara Averick then wrote this blogpost linking to 6 other blog posts that give some tidyeval examples. Reading them, plus the Programming with dplyr vignette should help you get started with tidyeval.

But maybe now you know how to use it, but not why and when you should use it… Basically, whenever you want to write a function that looks something like this:

my_function(my_data, one_column_inside_data)

is when you want to use the power of tidyeval.

I work at STATEC, Luxembourg’s national institute of statistics. I work on all kinds of different projects, and when data gets updated (for example because a new round of data collection for some survey finished), I run my own scripts on the fresh data to make the data nice and tidy for analysis. Because surveys get updated, sometimes column names change a little bit, and this can cause some issues.

Very recently, a dataset I work with got updated. Data collection was finished, so I just loaded my hombrewed package written for this project, changed the path from last year’s script to this year’s fresh data path, ran the code, and watched as the folders got populated with new ggplot2 graphs and LaTeX tables with descriptive statistics and regression results. This is then used to generate this year’s report. However, by looking at the graphs, I noticed something weird; some graphs were showing some very strange patterns. It turns out that one column got its name changed, and also one of its values got changed too.

Last year, this column, let’s call it spam, had values 1 for good and 0 for bad. This year the column is called Spam and the values are 1 and 2. When I found out that this was the source of the problem, I just had to change the arguments of my functions from

generate_spam_plot(dataset = data2016, column = spam, value = 1)
generate_spam_plot(dataset = data2016, column = spam, value = 0)


generate_spam_plot(dataset = data2017, column = Spam, value = 1)
generate_spam_plot(dataset = data2017, column = Spam, value = 2)

without needing to change anything else. This is why I use tidyeval; without it, writing a function such as genereta_spam_plot would not be easy. It would be possible, but not easy.

If you want to know more about tidyeval and working programmatically with R, I shamelessly invite you to read a book I’ve been working on: https://b-rodrigues.github.io/fput/ It’s still a WIP, but maybe you’ll find it useful. I plan on finishing it by the end of the year, but there’s already some content to keep you busy!