Data on James Jarvis

📉 Inflation adjusted net worth/salary spreadsheet

hello@jamesjarvis.io (James Jarvis) — Sat, 20 Jan 2024 00:00:00 +0000

TL;DR

You can copy the following spreadsheet and play around with the data yourself: PUBLIC: Inflation adjusted personal finance

If you, like me, enjoy watching number go up graphs, then you’ve probably also got some method of tracking basic financial metrics in a spreadsheet, or via some other sort of means.

Well, we are but simple creatures, and we do like to see number go up.

The thing is, the last few years have been a bit more, shall we say, tricky than normal years. The swinging 20’s have been at it again, with the following bargain bucket of little bombs being dropped on the economy in the last 4 years:

A Global Pandemic
Climate Emergency
Russo-Ukraine War
China-Taiwan escalation
Various shipping route disruptions
AI hysteria (this one has barely begun)

Overall, this spells for an atypical combination of over-demand and supply-restriction, that has lead to fairly shocking inflation figures in much of the world.

UK Inflation Data for the last 30 years. Source: ons.gov.uk

Whilst this is not entirely random on the scale of human history, this is certainly my first experience of meaningful inflation, and it coincides just nicely with the beginning of my professional career, so I’m left feeling a bit like Harold here.

My thoughts exactly. Credit to Liz Farmer

Since I’m so unused to this, I thought I would humble myself and rebase my personal finance tracking, by inflation-adjusting all net-worth and salary data. The results were very insightful and only a little bit disheartening.

Now you can too!

The spreadsheet below allows you to calculate your own inflation adjusted data, however it is very UK focused, and I’m not entirely convinced my maths is correct, but it should be good-enough to grok whether you are on the right track or not.

Google Sheets PUBLIC: Inflation adjusted personal finance

All you need to do is copy the sheet to your own drive, and modify data under “Net Worth”. It will calculate your real-term (inflation adjusted) YoY (Year over Year) percentage growth. If you wish, you can even repurpose the data by replacing it with your salary!

This is probably slightly more depressing, as salary is sadly not cumulative.

And before you ask, no, these figures do not reflect my personal finances. I wish life were that simple.

Sources:

Office for National Statistics UK

🦠 COVID-19 (Coronavirus) automatic predictions

hello@jamesjarvis.io (James Jarvis) — Thu, 19 Mar 2020 00:00:00 +0000

This project was a slightly morbid experiment with data plotting and predictions.

The idea was to have a set of graphs that can automatically update as new data is published, since the coronavirus pandemic is such a rapidly changing event. A Python script was written to collect the latest data from John Hopkins University’s repo, and generate graphs (both exponential and logistical curve predictions) based on this data.

Then, using the wonderful GitHub Actions, this script was rerun every hour, and if the graphs generated were different to previously, it would automatically commit and push these changes to the repo.

I am not en expert, I’m just curious with how well the data can predict the future.

Another morbid graph (if you want to check out the previous predictions, you are welcome to go through the repo’s history to see how well it predicted as time progressed):

📈 Graphing Monzo Growth

hello@jamesjarvis.io (James Jarvis) — Sun, 01 Mar 2020 00:00:00 +0000

Monzo (here) is a digital “challenger” bank from the UK.

I thought it would be interesting to use GitHub to automatically update and store data regarding the growth of Monzo’s current account user numbers. You can visit the page here.

Since their website includes an auto updating tally of users, I just found the api and the python scripts utilise this.

Similar to the COVID-19 infection predictions, this uses GitHub actions to automatically update and store data regarding the growth of Monzo’s current account user numbers. It also builds a simple react webpage to display the data in a graph. Or, if you prefer you can download the data for yourself.

Feel free to make a PR if you have some data that can fill in the gaps.

Some (better) visualisations can be found here:

🌩️ Automated speedtest internet monitoring

hello@jamesjarvis.io (James Jarvis) — Fri, 10 Jan 2020 00:00:00 +0000

A quick monitoring project.

As anyone from the UK will know, our internet speeds aren’t quite up to par with the rest of the developed world (Don’t believe me? Check it out). From personal experience, I very rarely achieve the advertised internet speeds, even when directly connected to the router. Similarly, some ISP’s I have used seem to have loads of dropouts, and fairly poor response to criticisms of speed. So, I though I would try to monitor the internet a bit closer, I had a spare RPI hanging around not really doing much besides a minecraft server, and thought it would be an interesting experiment.

So, I set up an IFTTT webhook, and a cronjob on the RPI to automatically update a Google Sheets document with the latest speedtest results.

Inspiration thanks to this project, and I’ve had a lot of fun experimenting with the data retrieved.

Part 1: University

Whilst at university, my internet was being provided by BT, and the fastest we could supposedly get was 35mb download speeds. However, we were having constant dropouts and nothing was being done about it.

As it turns out, we have NEVER achieved our advertised internet speed, and dropped out a number of times in the few months I have run this (although as it only runs every half hour, I’m pretty sure we have dropped out more frequently than this).

Other interesting insights from this data include seeing how the Coronavirus internet surge did in fact slow down our internet as everyone moved to remote work.

Part 2: Back Home

As mentioned previously, thanks to Coronavirus, the university campus (and all associated societies, events, basically anything fun) just abruptly stopped. So, I moved back in with the ‘rents.

Our internet here is provided by Virgin Media, on a 350mb/s(!!) plan. Now unfortunately thanks to the Raspberry Pi I am using, the Ethernet port only supports 100 Base. As a result, none of the speedtests will actually reach 350mb/s (but if it drops below that, then we should see a difference).

(Update - June 2020), upgraded to a newer Raspberry Pi, so should be able to show faster speeds.

However, this connection actually has far more significant variance, with the most noticeable pattern contained within the upload speedtest (presumably virgin media throttles this channel to support more downloads at peak times). Something also to note is that this environment has ~ 5x more devices on the network, which could be a bit of an issue as well.

📆 WhatsUpKent: Everything happening at the University of Kent

hello@jamesjarvis.io (James Jarvis) — Mon, 04 Nov 2019 00:00:00 +0000

In October 2019, I was looking for a challenge.

University (look mum, I’m on the front page!) was going ok, but I didn’t feel stimulated enough, and wanted to see what else there was on offer. Turns out paying £9k per year to learn from 20 year old textbooks isn’t exactly riveting, so I was looking to see what other interesting lectures I could discover on campus. Also, my friends and I liked to use disused seminar rooms to work in since the library was often overcrowded and I wanted an easy way to discover which seminar rooms were not being used.

I also wanted to stretch my legs and learn a bit of Golang, Kubernetes, Typescript and Graph databases.

So, I decided to create a service that would gather all student’s timetables and present the information in an easy to use dashboard, to offer students the opportunity to explore other courses in their spare time.

Thanks to the university (accidentally?) publishing student timetables as publicly accessible ical links, it proves fairly simple to just brute force and scrape all ical timetables from their website. Of course, having to iterate through 50,000 timetables can be quite time consuming, so I chose Golang to write the server, thanks to it’s remarkably easy multithreading syntax.

Since the data being retrieved was fundamentally relational, I chose to store it in a graph database called DGraph. This allowed me to achieve not only some quick queries on the data, I was also able to just easily expose a read only graphql endpoint directly to the database. Simple! :)

At this point, we are now looking at a few different services for the api: the scraper, a caching service (just to speed up repetetive large queries), and the dgraph database instance (which itself requires 2 pods). So, for deployment I chose to look towards Kubernetes. I bought the cheapests little VPS I could, and whacked on a k3s lightweight kubernetes instance and set that up in the CI/CD (ran with GitHub actions). As always, the frontend was hosted with GitHub pages.

Overall, the project was a success. It managed to scrape (and continuously check for updates and re-scrape) thousands of lectures and seminars on campus, from all students, and all courses. It was hosted at whatsupkent.com, with the backend code hosted in GitHub. Unfortunately, I decided to close down the service before the uni got mad at me (also, I couldn’t be bothered to pay the £5 a month hosting fees), but relaunching it would be as easy as pie if demand ever increases for a service like this.

A few screenshots

The API:

Event info:

Room finder:

Agenda view:

Filtered week: