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Communicating Research: Visualising Data

Plotting your data

When plotting your data, here are some best practices to keep in mind:

  1. Order your data by ascending or descending order to improve the readability of your chart
  2. Plot your charts from zero (0) to avoid confusion or exaggeration of data
  3. Avoid 3D graphs - They are difficult to read, can distract from your data or gives false impressions. Do not use them unless you are displaying 3D data.
  4. Use labels instead of legends to make it easier and faster for your audience to understand your charts



    Esteban Ortiz-Ospina and Max Roser (2016) - "Taxation". Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/taxation' [Online Resource]
     
  5. When presenting many categories in a pie chart, highlight the main categories and combine the rest into an “others” section. Shade sections in muted hues.

     
  6. Use commonly understood colours – e.g. red to highlight concerns, and blue to convey a sense of safety. 



    Hannah Ritchie (2020) - "Where in the world do people have the highest CO2 emissions from flying?". Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/carbon-footprint-flying' [Online Resource

Choosing Effective Charts and Graphs

When choosing the type of charts and graphs to use, consider the story that you want to tell with your data. These tools are useful starting points for creating effective visualisations and are meant to be non-exhaustive. 
 

From Data to Viz (Beginners - start with this!)

  • Classification of chart types in the form of a decision tree
  • Decision tree leads to a set of potentially appropriate visualisations to represent your dataset (includes information about chart type and common mistakes)
  • See Caveats to learn about when a chart type may be misleading or misrepresent your data


Visual Vocabulary

  • Chart suggestions with examples of use based on the type of data relationship important in your story


Data Viz Project

  • A library of visualisations classified by family, input and function with examples of real-life uses

Improving your Charts

Data-Ink Ratio

The Data-Ink ratio is defined as the amount of data-ink divided by the total ink required to print a graphic.

Following the principles of Data-Ink ratio, here are some practical considerations:

  1. Aim to reduce ink & clutter
  2. Remove background and reduce the number of intervals and grid lines
  3. If grid lines are necessary, make them subtle so as to not distract the audience


Watch this video for a more detailed explanation of the concept, and for a visual representation of how to improve your visualisations.

This concept was introduced by Edward Tufte in his book “The Visual Display of Quantitative Information".

Colours in your visualisations

Using the right colours and contrasts can enhance the effectiveness of your data visualisations. Three colour schemes that can be used for impactful data visualisations are:

  1. Sequential 
  2. Divergent 
  3. Categorical

This video provides a brief introduction to these colour schemes and highlights common misuse of colour.

   

For a more comprehensive presentation on colours, watch this video below. 

Suitable Tool for Beginners: Data Visualization Color Picker

Generate single scale (sequential), divergent scale and multi-colour (categorical) palettes for your charts and graphs with the Data Visualization Color Picker tool .

Features include previews of how your palette would look like on different charts or graphs, and a detailed "How To Use" guide for each colour scheme. More information in the video below:

Tips
A small group of your audience may have colour blindness and are unable to distinguish colours such as red / green or blue / yellow. When creating coloured charts, consider using a colour blind friendly palette or add textual labels to indicate important data points.

Additional Tools & Resources

Colour Palette Generator


Data Visualisation Tools


Resources

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