Data Visualisation with D3 workshop

Last week I attended the 4th HSS Digital Day of Ideas 2015. Amongst networking and some interesting presentations on the use of digital technologies in humanities research (the two presentations I attended focused on analysis and visualisation of historical records), I attended the hands-on `Data Visualisation with D3′ workshop run by Uta Hinrichs, which I thoroughly enjoyed.

The workshop was a crash course to start visualising data combining d3.js and leaflet.js libraries, with HTML, SVG, and CSS. For this, we needed to have installed a text editor (e.g. Notepad++, TextWrangler) and a server environment for local development (e.g. WAMP, MAMP). With the software installed beforehand, I was ready to script as soon as I got there. We were recommended to use Chrome (or Safari), for it seems to work best for JavaScript, and the developer tools it offers are pretty good.

First, we started with the basics of how the d3.js library and other JavaScript libraries, such as jquery or leaflet, are incorporated into basic HTML pages. D3 is an open source library developed by Mike Bostocks. All the ‘visualisation magic’ happens in the browser, which takes the HTML file and processes the scripts as displayed in the console. The data used in the visualisation is pulled into the console, thus you cannot hide the data.

For this visualisation (D3 Visual Elements), the browser uses the content of the HTML file to call the d3.js library and the data into the console. In this example, the HTML contains a bit of CSS and SVG (Scalable Vector Graphics) element with a d3.js script which pulls data from a CSV file containing the details: author and number of books. The visualisation displays the authors’ names and bars representing the number of books each author has written. The bars change colour and display the number of books when you hover over.

Visualising CSV data with D3 JavaScript library

The second visualisation we worked on was the combination of geo-referenced data and leaflet.js library. Here, we combine the d3.js and leaflet.js libraries to display geographic data from a CSV file. First we ensured the OpenStreetMap loaded, then pulled the CSV data in and last customised the map using a different map tile. We also added data points to the map and pop-up tags.

Visualising CSV data using leaflet JavaScript library

In this 2-hour workshop, Uta Hinrichs managed to give a flavour of the possibilities that JavaScript libraries offer and how ‘relatively easy’ it is to visualise data online.

Workshop links:

Other links:

Rocio von Jungenfeld
EDINA and Data Library


New data analysis and visualisation service

Statistical Analysis without Statistical Software

The Data Library now has an SDA server (Survey Documentation and Analysis), and is ready to load numeric data files for access by either University of Edinburgh users only, or ‘the world’. The University of Edinburgh SDA server is available at:

SDA provides an interactive interface, allowing extensive data analysis with significance tests. It also offers the ability to download user-defined subsets with syntax files for further analysis on your platform of choice.

SDA can be used to teach statistics, in the classroom or via distance-learning, without having to teach syntax. It will support most statistical techniques taught in the first year or two of applied statistics. There is no need for expensive statistical packages, or long learning curves. SDA has been awarded the American Political Science Association Best Instructional Software.

For data producers concerned about disclosure control, SDA provides the capability of defining usage restrictions on a variable-by-variable basis. For example, restrictions on minimum cell sizes (weighted or unweighted), use of particular variables without being collapsed (recoded), or restrictions on particular bi- or multivariate combinations.

For data managers and those concerned about data preservation, SDA can be used to store data files in a generic, non-software dependant format (fixed-field format ASCII), and includes capability of producing the accompanying metadata in the emerging DDI-standard XML format.

Data Library staff can mount data files very quickly if they are well documented with appropriate metadata formats (eg SAS or SPSS), depending on access restrictions appertaining to the datafile. To request a datafile be made available in SDA, contact

Laine Ruus
EDINA and Data Library