My First Shiny App Part II: The Shiny code itself

Recap In the last post we got to the stage where we had the data for each club’s league positions since 1958/59 in a Google sheet. The next step is to visualise this data in a Shiny app. Shiny lets […]

Continue Reading

My First Shiny App: See Where Your Team Ranks in the Football Pyramid

My First Shiny App: See Where Your Team Ranks in the Football Pyramid

Here is my first Shiny app! Select a football team and the app will plot where the team has ranked in the top four divisions of English football: Shiny lets you create interactive visualisations in R. It’s a big step […]

Continue Reading

Vandalism Causing Train Delays

Vandalism Causing Train Delays

Over the past two weeks I’ve been looking at Network Rail’s delays data. The data tells us how many delays there have been to trains thanks to all kinds of problems that affect the railways, from natural causes such as […]

Continue Reading

The Losses in the Final Year of WW1

The Losses in the Final Year of WW1

Back in August 2014, around the 100th anniversary of the outbreak of the First World War, the Data Unit published our analysis of the Commonwealth War Graves Commission‘s records of fallen soldiers, airmen, sailors and other servicemen and women who […]

Continue Reading

Scraping in R: Access to mortgage petition

Scraping in R: Access to mortgage petition

Over the past few years a good source of data has been Parliament’s petitions website. Anyone can start petitions or sign them. MPs have to consider the ones that get to 100,000 signatures for debates. The most popular petitions often […]

Continue Reading

Spring Budget 2017: Circle visualisation

Spring Budget 2017: Circle visualisation

It’s time to branch out into a new area of data visualisation: proportion area plots. These plots use area to show proportion between different related values. A common type of proportional area plots are tree maps. We are going to […]

Continue Reading

Comparing Donald Trump and Hillary Clinton’s Facebook pages during the US presidential election, 2016

Comparing Donald Trump and Hillary Clinton’s Facebook pages during the US presidential election, 2016

R has a lot of packages for users to analyse posts on social media. As an experiment in this field, I decided to start with the biggest one: Facebook. I decided to look at the Facebook activity of Donald Trump […]

Continue Reading

Calculating Distances in R: How Fast is Your Train?

Calculating Distances in R: How Fast is Your Train?

Earlier this month Marie Segger, Carlos Novoa and I had a major new project published about different rail speeds between cities around Britain. We compared the distances between train stations in Britain’s largest cities and found which areas were poorly-served […]

Continue Reading

Internal Migration Part III: Plotting Age Groups

Internal Migration Part III: Plotting Age Groups

Introduction In Part I we looked at overall internal migration local authority by local authority – are more people coming than going? In Part II we looked at where people are moving from and to around the country. Here in […]

Continue Reading

Internal Migration, Part II: Homing in on individual authorities

Internal Migration, Part II: Homing in on individual authorities

In the first post we completed a hexagonal map showing internal migration at a glance around England and Wales in 2015/16. This map is very good for an overview of what’s going on around the country – is your area […]

Continue Reading