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How to Create Datasets With GitHub Actions

  • Expert Jakob Gepp
  • Date 25. March 2022
  • Topic
  • Format Blog
  • Category Technology
How to Create Datasets With GitHub Actions

In the field of Data Science – as the name suggests – the topic of data, from data cleaning to feature engineering, is one of the cornerstones. Having and evaluating data is one thing, but how do you actually get data for new problems?

If you are lucky, the data you need is already available. Either by downloading a whole dataset or by using an API. Often, however, you have to gather information from websites yourself – this is called web scraping. Depending on how often you want to scrape data, it is advantageous to automate this step.

This post will be about exactly this automation. Using web scraping and GitHub Actions as an example, I will show how you can create your own data sets over a more extended period. The focus will be on the experience I have gathered over the last few months.

The code I used and the data I collected can be found in this GitHub repository.

Search for data – the initial situation

During my research for the blog post about gasoline prices, I also came across data on the utilization of parking garages in Frankfurt am Main. Obtaining this data laid the foundation for this post. After some thought and additional research, other thematically appropriate data sources came to mind:

  • Road utilization
  • S-Bahn and subway delays
  • Events nearby
  • Weather data

However, it quickly became apparent that I could not get all this data, as it is not freely available or allowed to be stored. Since I planned to store the collected data on GitHub and make it available, this was a crucial point for which data came into question. For these reasons, railway data fell out completely. I only found data for Cologne for road usage, and I wanted to avoid using the Google API as that definitely brings its own challenges. So, I was left with event and weather data.

For the weather data of the German Weather Service, the rdwd package can be used. Since this data is already historized, it is irrelevant for this blog post. The GitHub Actions have proven to be very useful to get the remaining event and park data, even if they are not entirely trivial to use. Especially the fact that they can be used free of charge makes them a recommendable tool for such projects.

Scraping the data

Since this post will not deal with the details of web scraping, I refer you here to the post by my colleague David.

The parking data is available here in XML format and is updated every 5 minutes. Once you understand the structure of the XML, it’s a simple matter of accessing the right index, and you have the data you want. In the function get_parking_data(), I have summarized everything I need. It creates a record for the area and a record for the individual parking garages.

Example data extract area

0.351417;2021-12-01T01:07:00Z;801;0;1235;5[Dom / Römer];2021-12-01T01:07:02.720Z

Example data extract facility

0.11547912;open;2021-12-01T01:02:00Z;47;360;0;407;407;18944[Alte Oper];2021-12-01T01:07:02.720Z
0.0027472528;open;2021-12-01T01:02:00Z;1;363;0;364;364;24281[Hauptbahnhof Süd];2021-12-01T01:07:02.720Z
0.609375;open;2021-12-01T01:02:00Z;234;150;0;384;384;105479[Baseler Platz];2021-12-01T01:07:02.720Z

For the event data, I scrape the page Since it is a HTML that is quite well structured, I can access the tabular event overview via the tag “kalenderListe”. The result is created by the function get_event_data().

Example data extract event

Magical Sing Along - Das lustigste Mitsing-Event;12576;Bürgerhaus;64546 Mörfelden-Walldorf, Westendstraße 60;Freitag;2022-03-04;2022-03-04T02:24:14.234833Z
Velvet-Bar-Night;1460;Velvet Club;60311 Frankfurt, Weißfrauenstraße 12-16;Freitag;2022-03-04;2022-03-04T02:24:14.234833Z
Basta A-cappella-Band;465;Zeltpalast am Deutsche Bank Park;60528 Frankfurt am Main, Mörfelder Landstraße 362;Freitag;2022-03-04;2022-03-04T02:24:14.234833Z
BeThrifty Vintage Kilo Sale | Frankfurt | 04. & 05. …;1302;Batschkapp;60388 Frankfurt am Main, Gwinnerstraße 5;Freitag;2022-03-04;2022-03-04T02:24:14.234833Z

Automation of workflows – GitHub Actions

The basic framework is in place. I have a function that writes the park and event data to a .csv file when executed. Since I want to query the park data every 5 minutes and the event data three times a day for security, GitHub Actions come into play.

With this function of GitHub, workflows can be scheduled and executed in addition to actions triggered during merging or committing. For this purpose, a .yml file is created in the folder /.github/workflows.

The main components of my workflow are:

  • The schedule – Every ten minutes, the functions should be executed
  • The OS – Since I develop locally on a Mac, I use the macOS-latest here.
  • Environment variables – This contains my GitHub token and the path for the package management renv.
  • The individual steps in the workflow itself.

The workflow goes through the following steps:

  • Setup R
  • Load packages with renv
  • Run script to scrape data
  • Run script to update the README
  • Pushing the new data back into git

Each of these steps is very small and clear in itself; however, as is often the case, the devil is in the details.

Limitation and challenges

Over the last few months, I’ve been tweaking and optimizing my workflow to deal with the bugs and issues. In the following, you will find an overview of my condensed experiences with GitHub Actions from the last months.

Schedule problems

If you want to perform time-critical actions, you should use other services. GitHub Action does not guarantee that the jobs will be timed exactly (or, in some cases, that they will be executed at all).

Time span in minutes <= 5 <= 10 <= 20 <= 60 > 60
Number of queries 1720 2049 5509 3023 194

You can see that the planned five-minute intervals were not always adhered to. I should plan a larger margin here in the future.

Merge conflicts

In the beginning, I had two workflows, one for the park data and one for the events. If they overlapped in time, there were merge conflicts because both processes updated the README with a timestamp. Over time, I switched to a workflow including error handling.
Even if one run took longer and the next one had already started, there were merge conflicts in the .csv data when pushing. Long runs were often caused by the R setup and the loading of the packages. Consequently, I extended the schedule interval from five to ten minutes.

Format adjustments

There were a few situations where the paths or structure of the scraped data changed, so I had to adjust my functions. Here the setting to get an email if a process failed was very helpful.

Lack of testing capabilities

There is no way to test a workflow script other than to run it. So, after a typo in the evening, one can wake up to a flood of emails with spawned runs in the morning. Still, that shouldn’t stop you from doing a local test run.

No data update

Since the end of December, the parking data has not been updated or made available. This shows that even if you have an automatic process, you should still continue to monitor it. I only noticed this later, which meant that my queries at the end of December always went nowhere.


Despite all these complications, I still consider the whole thing a massive success. Over the last few months, I’ve been studying the topic repeatedly and have learned the tricks described above, which will also help me solve other problems in the future. I hope that all readers of this blog post could also take away some valuable tips and thus learn from my mistakes.

Since I have now collected a good half-year of data, I can deal with the evaluation. But this will be the subject of another blog post. Jakob Gepp Jakob Gepp

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