More effective data visualisation

Dr Nicola Rennie

Welcome!

Who am I?

Data visualisation specialist.

Mainly working with R, Python, and D3.

Background in statistics, operational research, and data science consultancy.

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What to expect during this workshop

Combines slides, examples, and discussions for you to participate in.
Ask questions throughout!

What to expect during this workshop

I hope you end up with more questions than answers after this workshop!

Source: giphy.com

Workshop resources

Course website: nrennie.rbind.io/training-data-visualisation

What’s this session about?

In this session we will cover…

why you should visualise data;
choosing a chart type;
some guidelines for making better charts;
examples of good and bad charts!

The role of visualisation

Why visualise data?

Data visualisation has two main purposes:

Exploratory data analysis and identifying data issues
Communicating insights and results

Exploratory data visualisation

Statistic	Value
Mean(x)	54.26
Mean(y)	47.83
Standard deviation(x)	16.77
Standard deviation(y)	26.94
Correlation(x, y)	-0.06

Because summary statistics aren’t enough…

Communicating insights with data visualisation

Grab attention

Visualisations stand out. If a reader is short on time or uncertain about whether a document is of interest, an attention-grabbing visualisation may entice them to start reading.

Improve access to information

Textual descriptions can be lengthy and hard to read, and are frequently less precise than a visual depiction showing data points and axes.

Summarise content

Visual displays allow for summarising complex textual content, aiding the reader in memorising key points.

Communicating insights with data visualisation

John Snow collected data on cholera deaths and created a visualisation where the number of deaths was represented by the height of a bar at the corresponding address in London.

This visualisation showed that the deaths clustered around Broad Street, which helped identify the cause of the cholera transmission, the Broad Street water pump.

Snow. 1854.

Choosing a chart type

What are you trying to communicate?

Data visualisations must serve a purpose.

Ask yourself:

What is the purpose?
Does the visualisation support the purpose?
Is it quick, accurate, and intuitive?

Common relationships

Correlation: The relationship between two variables.
Deviation: The difference between a value and an average or another value.
Distribution: How data values are spread for a variable.
Geography: The pattern of data across different locations or areas.
Magnitude: The size of values.
Parts of a whole: The relative sizes of components within a whole.
Ranking: The position of data within a hierarchy or scale.
Time: How a value changes over time.

Why do pie charts have a bad reputation?

Why do 3D charts have a bad reputation?

On the plot on the left, how tall is the bar?

Choosing a chart type

Data visualisation best practices

Elements of charts

Layout
Aspect ratio
Lines
Points

Colours
Axes
Symbols
Legends

Orientation
Auxiliary elements
Dimensionality

Layouts, aspect ratios, and axes

Layouts, aspect ratios, and axes

Layouts, aspect ratios, and axes

Layouts, aspect ratios, and axes

Longer labels are best on the y-axis, horizontally.

Layouts, aspect ratios, and axes

Should the axes start at 0?

Layouts, aspect ratios, and axes

They don’t always have to start at zero…

Layouts, aspect ratios, and axes

Order categories appropriately…

Layouts, aspect ratios, and axes

Source: Georgia Department of Public Health

Layouts, aspect ratios, and axes

Default:

Magnitude ordered:

Naturally ordered:

Plotting multiple values

Avoid spaghetti plots!

Effectively plotting multiple values

Alternatives to spaghetti:

Show a smaller number of lines (e.g. compare a few countries to average)
Use facets (AKA small multiples)
Use colour only to highlight lines

Plotting multiple variables

Effectively plotting multiple variables

Some alternatives:

Separate plots, each with their own axis, and place the plots side-by-side.
Plot different variables on the x- and y- axis.
Rescale the variables, rather than the axis.

Lines aren’t always appropriate

Suggest an order
Suggest continuity

Colours

Why use colours in data visualisation?

Colours should serve a purpose, e.g. discerning groups of data
Colours can highlight or emphasise parts of your data.
Not always the most effective for, e.g. communicating differences between variables.