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Data Visualization Guide

Selecting Visualization Elements

Marks - Any graphical symbol visible on a display medium - it is the most primitive building block that can encode some useful information. i.e the points, bars, lines on a graph.

Channels - The attributes of a mark, such as its position, shape, size, or colour. It’s called channels because it’s serving as channels in which we can encode underlying data values.

(Adapted from Ignatius & Senay, Visual Concepts)


There has been a lot of research an visual techniques. Studies have shown that for quantitative data, showing them through scales helps people much better understand the information. For example the scales that we’ve seen in the bar graphs, and this is in comparison to colour or area which were a little less accurate.

But for categorical data, using colours like red or blue actually works really well. However as you vary how many shapes and colours and data points you are using, the cognitive load increases and breaks down so in general

You should have about 5 different types of shapes and sizes at most in your visualizations! 

Ranking Systems

Quantitative Data:

Data that can be counted or measured, and given a numerical value. Examples: distance, rainfall in ml, weight etc.

Ordinal Data:

This type of data is classified into categories within a variable that have a natural rank order. However, the distances between the categories are uneven or unknown. For example, socio-economic status (high-income, middle-income, low-income), or rating satisfaction (“extremely dislike”, “dislike”, “neutral”, “like”, “extremely like").

Nominal Data:

This is data that you can't really order in a meaningful way but can be labelled or classified into mutually exclusive categories within a variable. Examples: hair colour, country, race etc.

Accessibility Elements

  • Descriptions that are used well will both ensure your visual is accessible as well as indicate to your audience what you want them to understand in the data. There are some words that must be there: every graph needs a title, and every axis needs a title (exceptions will be rare!). Don’t make your audience work or make assumptions to try to decipher what they are looking at. Beyond that, think about how you can use words to make the “so what?” of your visual clear. You can use a “takeaway title”—meaning, if there is something important that you want your audience to know (and there should be!), put it in the title so they don’t miss it. That way, when your audience reads the takeaway in the title, they are primed to know what to look for in the data.
  • Graphs are usually more readable if you get rid of the legend and just directly label the data. That way the user doesn't have to keep going back and forth with the data and make guesses if there are subtle changes between the marks. This is another thing that reduces the cognitive burden and allows the user to take in other elements of the visualization.
  • Check the colour contrast. This is important for people who have colour blindness, low vision and a variety of other conditions that affect vision. The W3 WCAG guidelines specify necessary contrast and text sizes for readability on screen. There are many tools online that can help you abide by these contrast and size standards.
  • Alternative text (referred to as Alt text) is displayed when the image cannot be. This is important for screen readers, the assistive technology used by people who are visually impaired, as it reads alt text out loud in place of the image. It’s important to have valuable alt text instead of a basic one word description, which doesn’t help a user understand the content they are missing. Screen readers speak alt text without allowing users to speed up or skip, so make sure you find the balance between being descriptive but succinct.
  • Use white space in your graphs. When information is too densely packed, it can be hard to decipher the boundaries and the image overall can feel overwhelming. Just leaving small gaps can be helpful to leave a gap between sections of a chart. This can also supplement accessible color choices by helping users distinguish the difference between colors that identify separate sections.

(from Accessbile Data Viz is Better Data Viz)

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