Andrew B. Collier / @datawookie

When writing an R package I usually create a README.Rmd file that I render to README.md. I use {pkgdown} to then create documentation. I run the last step via CI, so once it’s set up I never need to think about it again.

The problem is that I regularly forget to process the README.Rmd file, which means that despite keeping that up to date, everything else lags behind.

What if I automated the process? I created a simple pre-commit hook which processes README.Rmd whenever I make a commit and automatically adds any changes to the commit.

A small new feature added to {emayili}: the ability to interpolate content into the email message body.

In a previous post I looked at how to set up Websockify behind an NGINX proxy. The ultimate goal was to accommodate multiple simultaneous users. Although the setup in that post worked, if the number of users is large then it becomes very resource hungry because there’s a Websockify instance running for each user.

A recent issue on the {emayili} GitHub repository prompted me to think a bit more about email address validation. When I started looking into this I was somewhat surprised to learn that it’s such a complicated problem. Who would have thought that something as apparently simple as an email address could be linked with such complexity?

I recently moved from suburban South Africa to rural England. I’m figuring out my new environment. Making some maps seemed to be a good way to get familiar with the surroundings.

In the process I wanted to figure out two things:

• how to get maps with a consistent aspect ratio at different latitudes; and
• how to overlay a partially transparent map layer.

To make things more interesting I’ll create maps of both my old and new locations.

If you’re going to be exposing noVNC on the (public) internet, then it’s vital that you take some security measures. You should install a suitable SSL certificate and serve noVNC via HTTPS rather than HTTP. Getting that all up and running can be moderately tricky. Here’s a quick recipe to get a minimal setup working.

At Fathom Data we are developing a framework which will enable remote access to Linux desktops via a browser. There’s nothing new to this idea. However, we have a very specific application in mind, so we need to roll our own solution. Importantly, there need to be multiple independent connections catering for a group of users. In this post I’ll show how we used the following tools to make this possible:

This post describes the process of building a custom AMI (Amazon Machine Image) using the AWS CLI. The goal is to automate the entire process, making it completely repeatable.

In the previous post I introduced the {tomtom} package and showed how it can be used for geographic routing. Now we’re going to look at the traffic statistics returned by the TomTom API.

While working with the Google Mobility Data I stumbled upon the TomTom Traffic Index. I then learned that TomTom has a public API which exposes a bunch of useful and interesting data.

Seemed like another opportunity to create a smaller R package. Enter {tomtom}.

{tomtom} Package

The {tomtom} package can be found here.

Install the package.

remotes::install_github("datawookie/tomtom")

Load the package.

library(tomtom)

API Key

Getting a key for the API is quick and painless. I stored mine in the environment then retrieved it with Sys.getenv().

I’ve got a few CI/CD jobs running on GitLab that produce long logs, which in turn get truncated. Since the most interesting stuff normally happens towards the end of the logs (like errors!), this can be really counter-productive.

Job's log exceeded limit of 4194304 bytes.

There’s a fundamental problem with this though: if something’s going to break then it’s inevitably going to happen after the logs have been truncated so I won’t be able to actually see what’s broken.

Did the recent unrest in South Africa have a detectable effect on mobility patterns?

I’m building a crawler which I’m going to wrap up in a Docker image. The crawler writes data to a remote MySQL database. However, there’s a catch: the database connection is via an SSH tunnel. Another wrinkle: the crawler is going to be run on ECS, so the whole thing (including setting up the SSH tunnel) needs to be baked into the Docker image.

This post illustrates the process of connecting to a remote MySQL data via a SSH tunnel from Docker. I’m not sure how secure this is. And there are probably better ways to do this. But it’s a start and it works!

A recipe for setting up a simple Shiny app on ECS.

Most people running a Linux system would agree that you should set up swap. According to the poll below, only 28% believe that no swap is required. And I think that they are misguided. Always put some swap on your system. You’ll never regret it.

This post shows an approach to using a rotating Tor proxy with Scrapy.

I’m using the scrapy-rotating-proxies download middleware package to rotate through a set of proxies, ensuring that my requests are originating from a selection of IP addresses. However, I need to have those IP addresses evolve over time too, so I’m using the Tor network.

Setup

I’ve got the following in the settings.py for my Scrapy project:

How much memory and CPU resources should be allocated to a simple Selenium crawler? I’ve been fudging these parameters but the time has come to man up and do this right.

I want my task to have sufficient resources that it’s able to perform its function. It should never be starved of resources! But, at the same, I also don’t want to extravagantly allocate excess resources. More resources → higher costs. I want to allocate the minimal resources to get the job done.

I’m busy helping a colleague with a Shiny application. The application includes HTML content rendered from a .Rmd document. However, there’s a catch: the .Rmd uses the {DT} package to render a dynamic DataTable. It turns out that this doesn’t immediately work because the JavaScript in the embedded document isn’t run.

I’ll use a simple document and application structure to illustrate the problem.

Static Document

Let’s start with a .Rmd document which renders two different static views of the Palmer Archipelago (Antarctica) Penguin Data.

We’re developing some training about Apache Airflow and need to have a robust and portable environment for running demos and labs which we can make available to the class. This will reduce the frustration and time wasted getting everybody set up and ensure that everybody is working in the same environment.

We’re building a new training program around Apache Airflow. The major technical challenge with delivering this sort of program is ensuring that everybody in the class has access to a working version of the technology. Since there is generally a diverse range of setups (operating systems, corporate firewalls and personal configurations) this can really be a nightmare.