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

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)
```

to

```
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!