Posted by Roel M. Hogervorst

Running an R Script on a Schedule: Docker Containers on gitlab

In this tutorial/howto I show you how to run a docker container on a schedule on gitlab. Docker containers are awesome because, once made, they run everywhere! It does not matter what type of computer^[Though I believe there is a problem with ARM based vs other CPU’s]. you have. Once I … Continue reading →

Running an R Script on a Schedule: Gh-Actions

In this tutorial I have an R script that runs every day on github actions. It creates a curve in ggplot2 and posts that picture to twitter. The use case is this: You have a script and it needs to run on a schedule (for instance every day). Other ways to schedule a script I will create a new post … Continue reading →

Running an R Script on a Schedule: Gitlab

In this tutorial I have an R script that creates a plot and tweets it, it runs every day on gitlab runners. The use case is this: You have a script and it needs to run on a schedule (for instance every day). Other ways to schedule a script I will create a new post for many of the other ways on … Continue reading →

Running an R Script on a Schedule: Heroku

In this tutorial I have an R script that runs every day on heroku. It creates a curve in ggplot2 and posts that picture to twitter. The use case is this: You have a script and it needs to run on a schedule (for instance every day). In 2018 I wrote a small post how to run an R script on … Continue reading →

How to Use Catboost with Tidymodels

So you want to compete in a kaggle competition with R and you want to use tidymodels. In this howto I show how you can use CatBoost with tidymodels. I give very terse descriptions of what the steps do, because I believe you read this post for implementation, not background on how the elements … Continue reading →

How to Use Lightgbm with Tidymodels

So you want to compete in a kaggle competition with R and you want to use tidymodels. In this howto I show how you can use lightgbm (LGBM) with tidymodels. I give very terse descriptions of what the steps do, because I believe you read this post for implementation, not background on how the … Continue reading →

Expressing size in bananas a dive into {vctrs}

Recently I’ve become interested in relative sizes of things. Maybe I’m paying more attention to my surroundings since I’m locked at home for so long. Maybe my inner child is finally breaking free. Whatever the reason, I channeled all of that into two packages: everydaysizes A … Continue reading →

New Package, Pinboardr

I’ve created a new package to interact with pinboard not to be confused with pinterest. I noticed there wasn’t a package yet and the API is fairly clear. So come and check it out {pinboardr} at https://github.com/RMHogervorst/pinboardr I did see a new package to interact with pocket: … Continue reading →

Munging and reordering Polarsteps data

This post is about how to extract data from a json, turn it into a tibble and do some work with the result. I’m working with a download of personal data from polarsteps. A picture of Tokomaru Wharf (New Zealand) I was a month in New Zealand, birthplace of R and home to Hobbits. I logged my … Continue reading →

Where does the output of Rscript go?

We often run R interactively, through Rstudio or in the terminal. But you can also run Rscripts without manual intervention. Using Rscript. But where does the output go? Warning: This post is very linux/unix (macos) centred, I don’t know how this works in Windows. Also I’m using the … Continue reading →

Scraping Gdpr Fines

The website Privacy Affairs keeps a list of fines related to GDPR. I heard * that this might be an interesting dataset for TidyTuesdays. The dataset contains at this moment 250 fines given out for GDPR violations and is last updated (according to the website) on 31 March 2020. All data is from … Continue reading →

Gosset part 2: small sample statistics

Simulation was the key to to achieve world beer dominance. ‘Scientific’ Brewing at scale in the early 1900s Beer bottles cheers This post is an explainer about the small sample experiments performed by William S. Gosset. This post contains some R code that simulates his simulations1 and … Continue reading →

Gosset part 2: small sample statistics

A NICE ONELINER HERE? This post is an explainer about the small sample experiment and determining the ideal sample size for inference. Economic perspectives and business logic Brewing beer at scale One of the problems William S. Gosset worked on was determining the quality of Malt. To brew … Continue reading →

Quick post – detect and fix this ggplot2 antipattern

Recently one of my coworkers showed me a ggplot and although it is not wrong, it is also not ideal. Here is the TL:DR : Whenever you find yourself adding multiple geom_* to show different groups, reshape your data In software engineering there are things called antipatterns, ways of … Continue reading →

Graphing My Daily Phone Use

How many times do I look at my phone? I set up a small program on my phone to count the screen activations and logged to a file. In this post I show what went wrong and how to plot the results. The data I set up a small program on my phone that counts every day how many times I use my phone (to be … Continue reading →