SEN & Academisation

Documentation for Shiny app "SEN & Academisation"

This is a repost of the my blog post on the research project website.

Table of Contents


Our shiny app has hit a prototype release milestone!

As part of the research project we wish to build a visualisation tool to provide overview and insights into the trends and distributions of academisation and educational inclusion in England. We build this visualisation tool using these amazing technologies:

In this blog post I will walk through the design of the shiny app and discuss the major features of the app.

Topic 1: Primary dashboard

The primary dashboard is intended to provide a quick overview of the statistics, and this is the default view you see when you visit the Shiny app:

Settings in the Global settings can be used to update the statistics in the dashboard (you can also navigate to other topics using this sidebar):

Topic 2: Time series trends

The second topic aims to provide more details about the trends of academisation and educational inclusion over the sample periods. By default the plots shown are for all England, and using the settings you can limit the time series plots to Region / Local Authority / Parliamentary Constituency level respectively.

Untick Facetted by school types will display the plots for all school types.

Topic 3: Breakdown by region

The third topic provides in depth view to regional breakdowns of academisation or educational inclusion. By default you will see an empty map as the map is not initialised yet, and clicking Render map will render a map on academisation or educational inclusion:

You can choose the map to be rendered on Local Authority level (default) or Parliamentary Constituency level:

Dual map mode! This is done by ticking Render a second map in Global settings. Using dual map mode you can compare regional breakdowns of academisation and educational inclusion side by side:

Or compare the same breakdown from different time periods:

Check out this Shiny app here!

Yi Liu
Senior research associate in health data science

data science, machine learning, and complex network analysis.