WEEK 1 Visualizing data Flashcards
Data visualization by David McCandless
Information: the data you are working with
Story: a clear and compelling narrative or concept
Goal: a specific objective or function for the visual
Visual form: an effective use of metaphor or visual expression
Kaiser Fung’s Junk Charts Trifecta Checkup
This approach is a useful set of questions that can help consumers of data visualization critique what they are consuming and determine how effective it is. The Checkup has three questions:
What is the practical question?
What does the data say?
What does the visual say?
Pre-attentive attributes
Are the elements of a data visualization that people recognize automatically without conscious effort.
Marks
Are basic visual objects like points, lines, and shapes.
Every mark can be broken down into four qualities
Position
Size
Shape
Color
Channels
Are visual aspects or variables that represent characteristics of the data.
They will vary in terms of how effective they are at communicating data based on three elements:
Accuracy (Are the channels helpful in accurately estimating the values being represented?)
Popout (How easy is it to distinguish certain values from others?)
Grouping (How good is a channel at communicating groups that exist in the data? )
Engage your audience
An important component of being a data analyst is the ability to communicate your findings in a way that will appeal to your audience.
Static visualizations
Do not change over time unless they’re edited. They can be useful when you want to control your data and your data story.
Dynamic visualizations
Are interactive or change over time. The interactive nature of these graphics means that users have some control over what they see. This can be helpful if stakeholders want to adjust what they’re able to view.
Having an interactive visualization can be useful for both you and the audience you share it with. But it’s good to remember that the more power you give the user, the less control you have over the story you want the data to tell.
Type of Charts
Change: This is a 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: A collection of data points with similar or different values. This is best represented through a distribution graph.
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.
best practices that are helpful to keep in mind
Your audience should know what they are observing within five seconds of being shown a data visualization.
In the five seconds after that, your audience should understand the conclusion your visualization is making—even if they aren’t familiar with your research.
As long as it’s not misleading, you should visually represent only the data that your audience needs in order to understand your findings.
Elements of art
line, shape, color, space and movement
Principles of design
Balance
Emphasis
Movement
Pattern
Repetition
Proportion
Rhythm
Variety
Unity
3 esentials elements of effective visuals
clear meaning: good visualizations clearly communicate their intended insight.
sophisticated use of contrast: which helps separate the most important data from the rest using visual context that our brains naturally look for.
refined execution: Visuals with refined execution include deep attention to detail, using visual elements like lines, shapes, colors, value, space and movement.
Charts for change over time
Line graphs; bar graphs and stacked bar graphs, along with area charts.
