Blog Posts by Andrew B. Collier / @datawookie

In a previous post I documented the process of setting up a GitLab Runner using the gitlab/gitlab-runner Docker image. As of GitLab Runner v16.0.0 the registration process has changed somewhat. This is an update to reflect that change.

Many of my projects now involve building a Docker image. The image is generally pushed to a registry as part of a CI workflow. This is how I push an image to Docker Hub from GitLab CI.

The stability of a kayak is intimately linked to its potential speed. A faster boat will generally be less stable. A more stable boat will normally be slower. But, of course, the skill of the paddler will also influence the likelihood of taking a swim.

I prefer to have my primary key columns first in a table. I recognise that column order is irrelevant to the performance of the table, but I prefer this for personal aesthetic reasons. However, from SQLAlchemy 2.0.0 there’s a change in the way that column order works with inherited base classes.

Do you ever contribute to a Git repository from different machines? Yeah, you probably do. Sometimes you’re on your work machine. Other times you’re on your personal laptop. Or your gaming desktop. And you might have a different Git identity on each of those. And this means that your Git log ends up looking a bit messy. Who are all of these people with similar names but different email addresses? A .mailmap file can be used to tidy things up.

Getting Gatsby installed and running can be a challenge. With older versions of Ubuntu I have fought extensively with Node package versions. Docker seems to be a natural solution. This post shows how to build and run a simple Docker image for serving a development Gatsby site.

If you use BASH, then you’re probably already using the command history. BASH history allows you to access a list of previous commands executed in the shell. It can make you more productive and efficient: do more and do it quicker.

The default configuration of BASH history will suit most purposes. But, like most things in the Linux universe, it’s possible to tweak that configuration to suit your specific requirements. In this post I’ll present some of those configuration options.

Do you do any web scraping? If so, then you probably spend a lot of time scratching around in your browser’s Developer Tools, figuring out the DOM structure and understanding how various bits of a site are delivered. Wouldn’t it be cool to access the Developer Tools functionality from inside your scraper? Well, you can. The Chrome DevTools Protocol (CDP) provides a low-level interface for interacting with Chrome. And you can tap into that interface via Selenium.

Message IDs (MIDs) and Content IDs (CIDs) are used to identify and refer to email messages and specific pieces of content within those messages. They are formally described in RFC 2392.

The {emayili} package already has adapters which make it simple to send email via a variety of services. I have just added an adapter for the ZeptoMail transactional email service. The adapter is available in {emayili} version 0.7.13.

I recently upgraded to Ubuntu 22.10. Everything went very smoothly, but there are some minor teething problems. One of them is getting the AWS Workspace client to work. This is how I fixed that.

There’s one major problem with ChromeDriver: anti-bot services are able to detect that a browser session is being automated (as opposed to being used by a regular meat sack) and will often impose restrictions or deny connections altogether. The Undetected ChromeDriver (undetected-chromedriver) Python package is a patched version of ChromeDriver which avoids triggering a selection of anti-bot services, allowing it to glide under the anti-bot radar.

At Fathom Data we have been doing a lot of automated documentation and automated reporting. Although many of these documents are rendered to HTML, there’s an increasing demand for PDF documents. So we’ve had to raise out game in that department. The {pagedown} package has become invaluable. This is a short note showing how we tweak the page size and margins for PDF documents.

I’m a density plot devotee. And, using geom_density() from {ggplot2} these plots are effortless to produce. However, sometimes the results of geom_density() are not exactly what I’m after. Here’s how I tweak them to give me precisely what I need.

From time to time I find empty paragraph tags (<p></p>) inserted into my HTML when knitting an R Markdown document.

I’m not a web developer. However, I do regularly use HTML and CSS to layout pages which are then transformed into PDF documents. One of the requirements that I encounter fairly often is vertically aligning images and text. Fairly often, but not often enough to remember the solution. So I inevitably have to rediscover the solution (thanks StackOverflow!). Jotting this down for posterity.

In the previous post we looked at how to apply a linter and styler to a Python Project. Now we’re going to do the same for an R project. We’ll use the {precommit} R package to make the setup a breeze.

A linter and a styler can help you to write cleaner and more consistent code. In this post we’ll look at how to set up both for a Python project.

While trolling the internet aimlessly this morning, this TikTok video caught my attention.

In a previous post I took a look at some granular weather data that I acquired via the Weather API. One interesting application of these data is calculating the Fire Danger Index (FDI), which measures the degree of fire danger using information on dryness, wind speed, temperature and humidity.