[comment]: # (Compile this presentation with the command below) [comment]: # (mdslides index.md && mv index/index.html .) [comment]: # (The list of themes is at https://revealjs.com/themes/) [comment]: # (The list of code themes is at https://highlightjs.org/) [comment]: # (Pass optional settings to reveal.js:) [comment]: # (markdown: { smartypants: true }) [comment]: # (Other settings are documented at https://revealjs.com/config/) ### Data Visualization Workshop ---------- Dr. Kevin Buffardi, Professor, Chico State <small>This presentation is accessible at [learnbyfailure.com/data-viz/](https://learnbyfailure.com/data-viz/) and its source is available on [GitHub](https://github.com/kbuffardi/data-viz/).</small> <img src="qr-data-viz.png" alt="QR code for this presentation" width="30%"> </img> <small>[Back to LearnByFailure](https://learnbyfailure.com/research/) </small>

#### Goals - Communicate meaning of data - Relationships - Comparisons - Distributions - Composition - Interact and explore data

#### Modes of Visualization - What is necessary? - **Tables** enable lookup of specific values - **Charts** provide nuance - simplify multivariate visualization - **Interactive visualizations** provide custom control

#### Get to Know Your Data - How many variables? - Categorical - Qualitative data - Clusters - Ordinal - Ordered, but not on numerical scale - "Strongly Disagree" to "Strongly Agree" Likert-type - Numeric - Continuous - Discrete

#### Choosing the Right Chart <a href="https://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf" target="_blank"> <img src="extreme-presentation-chart.jpg" alt="Decision tree for choosing the right chart, via Abela"> </img> </a>

#### Choosing the Right Colors - **Sequential** - vary only lightness/opacity - **Continuous** - continuous scale - **Discrete** - ordinal, discrete, or "buckets" <a href="https://www.atlassian.com/data/charts/how-to-choose-colors-data-visualization" target="_blank"> <img src="https://wac-cdn.atlassian.com/dam/jcr:f2340687-9e19-4859-9c2c-87465f001678/continuous-vs-discrete-choropleth.png?cdnVersion=2635" alt="Comparing continuous color scale vs. buckets"> </img> </a>

#### Choosing the Right Colors - **Divergent** - vary between hues to signify magnitude and category <a href="https://www.flerlagetwins.com/2019/04/predominance-maps.html" target="_blank"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgnOr7w8BsVOBvofx12YOM3k0p0emSW5Z_hNNECGFH0lNbgqZ8vkQ3mYB7nhxslpw01c5oamv8LsrBaqUGE83AVXQOhrbTkGW5lSVKgkGYRtsHWrO3jgFfwxnUyMRsQLbV0L9UaqpMTsRKM/s1600/Heading.png" alt="Diverging scale illustrating voting preference by county in USA"> </img> </a>

#### Choosing the Right Colors - **Categorical** - categorize by hue <a href="https://analysisfunction.civilservice.gov.uk/policy-store/data-visualisation-colours-in-charts/" target="_blank"> <img src="https://analysisfunction.civilservice.gov.uk/wp-content/uploads/2022/03/Clustered-bar-chart-new-order.svg" alt="Categorical scales for comparing fruit sales between multiple shops"> </img> </a>

#### Viz Sins Common bad practices ("Viz Sins") - Misrepresent data - Difficult to interpret

#### Viz Sins: Colors - Color scale does not match data type - Too close to differentiate - Ignoring Color Blindness - [Simulate Color Blindness](https://www.color-blindness.com/coblis-color-blindness-simulator/) - Antidotes - [Color blind safe palettes](https://davidmathlogic.com/colorblind) - Redundant encoding data (shapes/patterns/size/etc.)

#### Viz Sins: Axes - Axes - Label scale and units - Avoid misleading "broken axis" - Unnecessary dimensions - Linear vs Logarithmic scale

#### Viz Sins: Where People Live - Graphing by location - Population density is a dominant factor - [Reddit People Live in Cities](https://www.reddit.com/r/PeopleLiveInCities/)

#### Viz Sins: Over-sliced Pie - Pie charts with too many slices <a href="https://www.atlassian.com/data/charts/how-to-choose-colors-data-visualization" target="_blank"> <img src="https://wac-cdn.atlassian.com/dam/jcr:60eecca0-6c0b-406e-b491-be1c88fc619c/pie-chart-grouping-slices.png?cdnVersion=2635" alt="Contrasting pie chart with 12 slices versus one with 6"> </img> </a>

#### Interactive Visualizations - Interaction gives control over visualization - [Shneiderman's Information Seeking Mantra](https://www.cs.umd.edu/~ben/papers/Shneiderman1996eyes.pdf) - Overview first - Zoom and filter - Details on demand

#### Tools - Charts - R [ggplot](https://ggplot2.tidyverse.org/) - Python [plotly](https://plotly.com/python/) - Interactive visualizations - Shiny for [R](https://shiny.posit.co/py/docs/comp-r-shiny.html) or [Python](https://shiny.posit.co/py/) - [Tableau](https://www.tableau.com/)

#### Data Visualization Workshop <small>This presentation is accessible at [learnbyfailure.com/data-viz/](https://learnbyfailure.com/data-viz/) and its source is available on [GitHub](https://github.com/kbuffardi/data-viz/).</small> <img src="qr-data-viz.png" alt="QR code for this presentation" width="30%"> </img> <small>[Back to LearnByFailure](https://learnbyfailure.com/research/) </small>