Research Data Management

Think before you draw

Data visualisation is about more than technical tools. Choosing the right form of visualisation matters more in communicating and understanding data than what tool you used for this. This guide will walk you through a whole process to think about how to visualise a dataset according to your goals:
https://depictdatastudio.com/data-visualization-design-process-step-by-step-guide-for-beginners/

Books are also available on the subject of visualisation in our collection:

• Storytelling with data: a data visualization guide for business professionals (call number 004, HEIA 128570)
• The Wall Street Journal guide to information graphics: the dos and don'ts of presenting data, facts and figures (call number 371.3, HEIA 126122)

Did you try... Excel?

You should not underestimate Excel's ability to let you design aesthetically pleasing charts. Here are some tips. and more.

• With Pivot tables and charts, you can reach interesting results and get a lot out of your data.
• Excel also has a map-based visualisation tool called Microsoft Power Map. It can only create color-filled maps for countries and states (cantons in Switzerland for example), but if that is what you want, there is no need to look any further.

The Data Visualisation Catalogue
This platform lets you find tools for any specific type of visualisation.

Datawrapper
This free, lightweight online tool is used by many high-quality newspapers. It also offers advice and methods on how to produce great visualisations with little experience. It is especially great at producing maps for various data types at a very granular level.

Tableau Public
This free online version of Tableau is efficient, but it does not include a license for the desktop version.
If you do want to explore Tableau further, check out the book "Data Analytics and Visualization in Quality Analysis using Tableau".

Voyant Tools
This is a simple web-based text reading and analysis environment.

Many data visualisation packages were created for R, including ggplot2.

You can also publish your visualisation on the web using Shiny from RStudio.

Books in our collection about data visualisation with R:

• Data visualization: a practical introduction (call number 004, HEIA 126551)
• Modern Data Visualization with R: https://rkabacoff.github.io/datavis/

You can use multiple libraries to create visualisations in Python:

• matplotlib "is an easy-to-use, low-level data visualization library".
• seaborn "is a high-level interface built on top of matplotlib" to create more beautiful graphs.
• bokeh brings some interactivity to your visualisations.
• plotly is also a great Python library for data visualisation and includes some useful functionalities.

Watch this tutorial to learn more about Data visualisation in Python.

Gephi is a free standalone visualisation platform for Windows, OSX and Linux.

Pajek is another free standalone network analysis and visualisation tool.

igraph is a network analysis package for R, Python, Mathematica, and C/C++.

migraph is an R package developed to offer solutions for multimodal analysis, with contributions by prof. James Hollway.
It is the software companion to the book Multimodal political networks by Knoke et al. (Cambridge, 2021).

Canva is a multifaceted tool for visual communication, which includes graph and diagram design.

Draw.io (also known as diagrams.net) allows you to create diagrams and flowcharts. They can be stored on your preferred cloud solution and there is a desktop version.

Google Drawings is another Google Drive-integrated solution for visual design.