Visualising Data Flashcards
Data Visualisation
The graphic representation and presentation of data
The McCandless Method
- Information (data)
- story (concept)
- goal (function)
- visual form (metaphor)
Approaching the method to see parts of the graphic where there is incomplete overlap between all four elements. Visual form without a goal, story, or data could be a sketch or even art.
Kaiser Fung’s Junk Chart Triefecta Checkup
- What is the practical question?
- What does the data say?
- What does the visual say?
Captures how to organise the thinking behind the critique pieces
The pre-attentive attributes
are elements of a data visualisation that people recognise automatically without conscious effort. The essential, basic building blocks that make visuals understandable are called marks can channels
Marks
Basic visual object like points, lines, and shapes. Every mark can be broken down into 4 qualities
1. Position - Where specific marks is in space to a scale or to other marks.
2.Size. How big,small, or tall a mark is
3.Shape - Whether a specific object is given a shape that communicates something about it.
4. Colour
Channels
Visual aspects or variables that represent characteristics of the data. Channels are marks that have been used to visualise data. Channels vary in terms of how effective they are based on 3 points
- Accuracy - Are channels helpful in accurately estimating the represented value. Colour is good at differentiating apples and oranges but not 5 from 5.5
- Popout—How easy it is to distinguish certain values from others, such as drawing attention to a specific part of a visual.
- Grouping - How good is a channel at communicating groups that exist in the data - proximity, similarity, enclosure, connectedness, and continuity of the channel
Histogram
A chart that shows how often data values fall into certain ranges
Correlation charts
show relationships among data
Causation
Occurs when an action directly leads to an outcome
Correlation
In statistics is the measure of the degree to which two variables move in relationship to each other. An example of correlation is idea that “as the temputure goes up, ice cream sales go up”. It means they have a pattern or some relationship towards each other, butit does not suggest causation.
Static Visualisations
Do not change over time unless they’re edited
Dynamic visualisations
Interactive or change over time
Tableau
A business intelligence and analytics platform that helps people see, understand, and make decisions with data
Line Chart
Use to track changes over short and long periods of time. When smaller changes exist, line charts are better to use than bar charts.
Column chart
Use size to contrast and compare two or more values, using height or lengths to represent the specific values.
Heatmap
Mainly used to show relationships between two variables and use a system of colour-coding to reprsent different values.
Pie chart
Is a circular graph that is divided into segments representing proportions corresponding to the quantity it represented, especially when dealing with parts of a whole.
Scatterplot
Show relationship between different variables. Typically used for two variables for a set of data, although variables can be displayed.
Distribution graph
Displays the spread of various outcomes in a dataset
Change
Trend or instance of observations that become different over time. A great way to measure change in data is through a line or column chart
Clustering
Collection of data points with similar or different values. This is best represented through a distribution chart.
Relativity
These are observations considered in relation or in proportion to something else. You have probably seen examples of relativity data in a pie chart.
Ranking
This is a position in a scale of achievement or status. Data that requires ranking is best represented by a column chart.
Correlation
This shows a mutual relationship or connection between two or more things. A scatterplot is an excellent way to represent this type of data pattern.
Decision Tree
tool that allows data analyst to make decisions based on key questions that you can ask yourself.
The elements of art in data analytics
Line
Shape
Colour
Space
Movement (Sense or flow or action in visualisation)
Colour
Hue (Red blue green)
Intensity (bright or dull)
Value (How much light is being reflected)
All of these aspects are within visualisations. You can have a shade of grey, that is dull, but with different tints to display data.
9 Principles of Design
Balance
Emphasis
Movement
Pattern
Repetition
Proportion
Rhythm
Variety
Unity
6 key design principles when you are creating your viz?
Balance
Emphasis
Movement
Pattern
Repetition
Proportion
3 key design principle when you are checking you viz
Rhythm
Variety
Unity
Data composition
Combining the individual parts in a viz and displaying them together as a whole
Elements for effective visuals
Clear meaning
Sophisticated use of contrast - separate most important data from the rest
Refine execution - deep attention to detail using elements of art.
Design Thinking
A process used to solve complex problems in a user-centric way
Five phases of the design process
Empathise - consider how your audience will visualise the data (too birght/dramatic for seriousness of data ect)
Define - define audience needs, problems and insights, goes hand in hand with emphasise phase.
Ideate - Start to generate data viz ideas from define and emphasise phase - drafts example. Creating as many examples as possible.
Prototype - Putting your chart/dashboard together
Test- putting your chart/dashboards together.
Headline
A line of words printed in large letters at the top of the visualisation to communicate what data is being presented
Subtitle
Supports the headline by adding more context and description
Legend
Identifies the meaning of various element in a data visualisation
Alternative text
Alternative text provides a textual alternative to non-text content
