- capsPath <- getFullPath (T.unpack (coerce dir <> coerce videoName <> ".en.vtt"))
+ capsPath <- getFullPath (T.unpack (coerce dir <> "/" <> coerce videoName <> ".en.vtt"))
--sub-langs en
All data sourced from Companies House as xbrl or pdf.
The trickiest part was all the unexpected edge cases I found in the data, but that's also where most of the learning (aka fun) was found. For instance, before starting this project I didn't know that negative turnover was possible, or that accounting periods vary between years and can be 52/53 weeks to make sure they end on a specific day of the week. The more I learned, the more aware of my ignorance in this regard!
Here is a typical example:
> Between 2024 and 2025, workers at this company each lost £4,196 due to a combination of falling pay and price inflation.
(User clicks/taps through if they want details and method)
I've also noticed a high representation of care-home providers appearing in the results. It's something I want to dig into but it's unexplained (to me) for now. Possibly it's related to a higher proportion of workers on zero-hours contracts.
It's also been challenging to present less obvious factors such as nominal and real wages alongside inflation metrics, all intended for a non-technical audience. Consequently I've spent a disproportionate amount of time on the wording for each type of inequality, and I'm still tinkering.
Not ready to share the URL just yet, as the site could easily be abused or the facts taken out of context and used to mislead or unfairly (lol) condemn. It may never be public, but I definitely have an audience in mind.
Ideas for development include - sector/industry analysis and comparisons - an inequality leader-board of some kind - sentiment analysis from director reports - search and filter