Traffic flows and other stuff

I’ve recently started using Obsidian for my notes. I was looking at Notion as a possible replacement for using a combination of OneNote and Google Keep but came across Obsidian and got hooked. Here are my thoughts after a month or so of using it.

I’ve moved my blog posts from a paid Wix site to GitHub, using GitHub Pages and the Jekyll website/blog engine. This enables me to write posts in Markdown and integrate with my Obsidian vault (I’ll write about that another day).

I’ve done the hard work in porting the Wix blog files and images over to GitHub Pages. Please let me know if you see any bad links, poorly formatted code or the wrong image. Getting the code and the images across was by the far the hardest part - I’ll post about the process later.

I’m in the process of porting my blog from Wix to Jekyll, hosted on GitHub Pages. I’ll post a how-to once I’m done but in the meantime please bear with me. D

This is a revised version of my earlier post, having considered responses to the original and having had a little time to order my thoughts more carefully. I had originally planned a Twitter thread on my thoughts but there is just too much to cover for that format. I am sure there are some logical, factual and theoretical errors, for which I apologise in advance. I suspect this will remain a work in progress, to...

I’ve played around with the new Shiny website (https://trafficflows.shinyapps.io/Traffic/) and added some new data for Burntwood Lane (two sites from May 2018) and Tranmere Road (two sites from March 2018).

I’ve been thinking of moving away from Power BI for a while for several reasons. First, there’s nothing I like more than some change and learning a new package. Secondly, I felt that R just offers more flexibility when designing charts. So with that in mind, I’ve moved the visuals to Shiny, which is an app that enables you to publish your R work and make it interactive.

An appendum to my previous post in which I discussed UK choropleths in R.

This follows on from the previous post on UK choropleths, except now we’ll be using real data.

Addendum to my last post on ONS data.

If there’s one thing I love, it’s maps (ok, that’s many things). Combined with data and computers and I’m in heaven. I put these notes together for work but I felt they’re worth sharing wider as there don’t seem to be many very easy to follow guides to UK choropleths in R.

I know many of my friends/followers on Twitter are in favour of “smart” road user charging. As with most things in life I’m sceptical. I want to believe, I really do. But I don’t think it is the panacea people think it is.

A quick list of some useful books on Power Query, Power BI (PBI) and R, with some very brief notes. Be warned that the PBI user interface has changed a lot over the past 12 months and that older books may be out of date, at least in terms of the look and feel, the underlying query language hasn’t changed as much.

5 new surveys all along Priory Lane added. Nice when the hardest part of getting the data in is working out the exact latitude and longitude. Enjoy! (link removed as out of date)

Well, it had to happen didn’t it? The next traffic surveys, a bunch of slightly dated Richmond Park data, use a different layout. So this brings forward what I was thought I was going to do anyway - have separate tables for each input type and then merge together into the main tables. It also allows me to introduce several other features of Power Query/M - fill-down and merge. This is necessarily a long read...

In the last post, we added some visuals into our Power BI report. I hope you had fun playing with the visuals! Now we’re going to share our work with other people.

In this post, we’ll get to see what Power BI can do to visualise the data. OK, so this isn’t really Excel related any more but it’s part of a series so please don’t get at me about the title.

In part 3, we finally looked at bringing the survey data into Power BI. Let’s now tidy that up a bit and bring all the different data tables together so we can start to analyse the data. We’ll meet a useful new technique - unpivot.

In Part 2 of this series, we read in the accompanying data, in this part I show you how to read the traffic data from the survey workbooks into our Power BI dataset. I’ve deliberately separated this out from the other data sources as this is more complex. We want to write the query in such a way that when we add a new survey, all we need to do is refresh the query without...

Assuming you’ve downloaded Power BI (see previous post), let’s use it to read in some data.

OK, so reading the data into R was too much for you, is there an easier way? Yes! This post will introduce you to Power BI and its half-sister Power Query. This will be the first in a series of posts that will take you through the process of producing interactive charts on the Internet sitting on top of a scalable database.

So you’ve got some traffic data, how do we get the data into a dataset so we can analyse it? Let’s use the R language to explore the data.

A brief post on how to make Freedom of Information Requests to request traffic data.

A quick look at what you might get back from a local authority. In a previous post, I looked at how you might request data from a local authority. Now, what do you get?

(this is the original text except I’ve removed the link to the Power BI report)

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