This year I’ve focussed on deepening my coding skills, so I’ve finally been able to give d3 a proper go. And let me tell you, it’s brilliant. It’s exciting to hook up a data source to a custom chart that changes dynamically, and be able to see it on a live website, which other people can view.
In this post, I’m going to discuss the steps I took to create this d3 visualization of the GitHub API.
Testing your web applications can seem like a rather daunting and complex field at first. That’s how I felt when I started learning about it. It took a while to get comfortable with the lingo, and see how the different pieces related to each other. I made notes as I went and have collected them together below, into some semblance of an introductory post on the subject. Hopefully, it’ll lay out the lie of the land and point you in the direction of some useful resources.
In the steps outlined below, I walk through creating a CRUD app in Rails that saves images to, and serves images from, Amazon S3, using the paperclip gem. The images are displayed in a grid using Flexbox with some Ruby fun to change the grid layout each time the page is refreshed.
This issue arose when I asked my brother to test drive the Rails app I’m working on, UpLearn, without any supervision. It was really useful to have a second person use the software without any knowledge of how it was built, as issues surfaced that I might have otherwise missed.
One issue was the handling of URLs submitted by the user without an “http(s)” at the front. My brother had typed a link in to the submission form directly, rather than copy-pasting the URL, so it was missing an “http://” or “https://” at the front. As a result, my Rails app treated this as a relative path, rather than an absolute path, and the result was a broken link that didn’t take the user to the correct resource page.
Earlier this year, I finished General Assembly’s Back-End Web Development course in Washington DC, where I learnt Ruby and how to build data-driven websites using the Rails framework. It was a brilliant experience. I did a lot of work outside the course to learn as much as I could about web development, treating the 10 weeks as my own full-time dev bootcamp.
This year The Writer’s Bundle 2015 was bigger and better than ever, so I wanted to create an even more useful dashboard for the team. The dashboard was a key tool for the team, to monitor both overall and individual sales channel performance, as well as to be a motivating force by giving everyone a visual sense of progress.
This is a tutorial on how to create a slick-looking, single-letter logo using only CSS. For example:
In the code below, I’ve also added functionality to change the colour of the logo for hover overs. Frustratingly, it doesn’t work on this WordPress site (it looks screwy), so for now, this GIF will have to suffice to show the hover over:
Recently, I’ve been busy redesigning the website for Socialexis, my wife’s fantastic content marketing firm. I used the Corsa theme from UpSolution. It’s a modern, single-page parallax theme, which is perfect for the Socialexis brand.
The screenshot below shows the old website. Whilst there was nothing wrong with it per se, it wasn’t particularly exciting and didn’t represent the creativity and passion at the core of Lexi’s business. Since this original website was built, the business has also focused more on blog management and content creation, so the message needed tweaking. Hence, it was a good opportunity to also redesign the website.
I’m working my way through this book, Data Analysis Using SQL and Excel, at the moment and chapter 6 is all about survival and retention modelling. I learn best when I can attack real life problems, so I took some of the lessons from this chapter and applied them to the MailChimp email data I already had in a MySQL database.
This post takes it a step further by looking at subscriber behaviour within the email campaign data. What can we say about how long people remain active subscribers? For a subscriber who has been active for a given length of time, how likely are they to continue being active?
This post digs deeper into the MailChimp data which I analyzed in my previous post. In this analysis, I’ve focused on the distribution of subscribers and what was the most recent date they opened an email.
Specifically, I wanted to answer:
What is the distribution of subscribers by campaign date that opened an email in 2014?
And what are their email addresses?
The way I approached this problem was to break it down into its constituent parts, tackle each of those and then build that back up into a single query.