Andrew B. Collier / @datawookie

The concept of “wide data” is relative. In some domains 100 columns is considered “wide”, while in others that’s perfectly normal and you’d need to have thousands (or tens of thousands!) of columns for it to be considered even remotely “wide”. The data that we work with at Fathom Data generally lies in the first domain, but from time to time we do work on data that is considerably wider.

At Fathom Data we have a few projects which require us to send HTTP requests from an evolving selection of IP addresses. This post details the Medusa proxy docker image which uses Tor (The Onion Router) as a proxy.

What is a Proxy Server?

A proxy server acts as an intermediary between a client and a server. When a request goes through a proxy server there is no direct connection between the client and the server. The client connects to the proxy and the proxy then connects to the server. Requests and responses pass through the proxy.

Yoav Raskin suggested that it would be useful to support right-to-left (RTL) text in {emayili}, so that languages like Hebrew, Arabic and Aramaic would render properly. I’ll be honest, this was not something that I had previously considered. But agreed, it would be a cool feature.

By default <img> tags are wrapped in a tight <p></p> embrace by {knitr}. In general this works really well. However, I want to have more control over image formatting for {emayili}.

I love the clean simplicity of an R Markdown document. But sometimes it can feel a little bare and utilitarian. This is especially the case if it’s rendered into the body of an email. How about injecting a little more pizzazz?

I don’t frequently use parameters in R Markdown documents. The initial implementation of render() in {emayili} did not cater for them. A small tweak makes it possible though.

In a previous post I documented a new feature in {emayili}, the ability to render Plain Markdown directly into the body of an email message.

We’ve been able to attach text and HTML content to messages with {emayili}. But something that I’ve really been wanting to do is render Markdown directly into an email.

In version 0.4.19 I’ve added the ability to directly render Plain Markdown into a message. That version is not on CRAN, so you’ll need to install from GitHub.

At Fathom Data we use Clockify to keep detailed records of the time that we spend working on our clients’ projects. Up until fairly recently we manually generated timesheets at the end of each month that were sent through to the clients along with their invoices. Our experience has been that providing detailed timesheets helps foster trust and transparency. However, with a growing team and an expanding clientele, generating these timesheets has become progressively more laborious. Time to automate!

It’s often handy to have access to a HTTP proxy. I use this recipe from time to time to quickly fling together a proxy server which I can use to relay HTTP requests from a different origin.

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().