From f6d071bf170b7cb8340b51f68e0583ddea42493b Mon Sep 17 00:00:00 2001 From: James Turk Date: Sat, 28 Sep 2024 16:18:20 -0500 Subject: [PATCH] regen all slides --- 00.value-of-dataviz/slides.html | 76 ++++++++------- 01.gog-altair/slides.html | 48 +++++----- 02.perception-and-color/slides.html | 142 ++++++++++++++-------------- 03.charts/slides.html | 70 +++++++------- 4 files changed, 172 insertions(+), 164 deletions(-) diff --git a/00.value-of-dataviz/slides.html b/00.value-of-dataviz/slides.html index 8d2db86..7910459 100644 --- a/00.value-of-dataviz/slides.html +++ b/00.value-of-dataviz/slides.html @@ -1,4 +1,4 @@ -
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30239: Data Visualization for Policy Analysis

James Turk

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+

Today

  • What is the value of data visualization?
    • @@ -21,24 +25,24 @@
  • Course Logistics
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infographic: a day in data

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data.gov

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taxis

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OK, there is a lot of data, but isn't that a good thing?

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"What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it."

~Herb Simon
as quoted by Hal Varian. Scientific American, September 1995

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What is Visualization?

“... finding the artificial memory that best supports our natural means of perception.”
[Bertin 1967]

@@ -47,20 +51,20 @@

“The use of computer-generated, interactive, visual representations of data to amplify cognition.”
[Card, Mackinlay, & Shneiderman 1999]

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Information Visualization

"The use of computer-generated, interactive, visual representations of (abstract) data to amplify cognition."

What does it mean to amplify cognition?

Why do we often seek visualizations?

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Why do we create visualizations?

  • What visualizations have you created?
  • What visualizations have you seen that you remember?
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Reasons for Data Visualization

  • Means of reasoning about large quantities without reduction/over-simplification.
    • @@ -68,7 +72,7 @@
  • Deepen understanding, for ourselves or others.
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Four Samples

@@ -198,7 +202,7 @@

What sense can we make of this?

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@@ -262,14 +266,14 @@
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Anscombe's quartet

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Our understanding of the data is enhanced by these visualizations in a way summary statistics won't capture.

What things are easier to see?

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Benefits of Visualizing Data

  • outliers
    • @@ -281,7 +285,7 @@

exploratory visualization

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Explanatory Visualization

  • Highlight interesting findings
    • @@ -292,23 +296,23 @@
  • Inspire
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Visualizations and Policy

Visualizations have an outsized influence on human cognition, we seem to trust images more than words.

This means that visualizations can be used to persuade, but also mislead.

There does not need to be intention, we can easily deceive ourselves without realizing it.

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+

-
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+

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Data Visualization for Policy Analysis

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Course Goals

  • Understand & appreciate what makes a good data visualization.
    • @@ -317,7 +321,7 @@
  • Gain exposure to useful libraries in Python and JavaScript.
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Topics

  • Fundamentals, Grammar of Graphics
    • @@ -331,7 +335,7 @@
  • Special Topics: to be discussed
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Programming Workload

Key Idea: You get better at visualizations by making a lot of them. (20-30 this quarter)

    @@ -348,7 +352,7 @@

    Final project will have a place where D3 will be helpful, but other options will be presented.

    After introductory lecture, some examples will continue to be in D3, but you will not need to understand their inner workings.

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Course Structure

  • Lecture and Discussion: Introduce and explore key concepts, mostly focused on theory.
    • @@ -357,7 +361,7 @@
  • Projects: Learn to explore a topic on your own from conception to practice. Leave here with a portfolio you can show off.
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Course Staff

  • James Turk
    • @@ -366,7 +370,7 @@

All official information will be on the course site and/or Ed.

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Projects

@@ -405,19 +409,19 @@
  • D3 (Week 6)
    • -
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    Grading

    15 SNU grades

    Completion: 9 (2x Proposals, Drafts & Critiques + 2 Labs + 1 Participation)

    Quality Grades: 6 (2x Design, Narrative, Code Quality)

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    AI Policy

    In this course, all usage of generative AI must be fully cited.

    Details on specific rules: https://capp30239.netlify.app/policies/ai/

    You are, as always, expected to turn in your own work, not the work of an LLM.

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    Static Visualization Project

    https://capp30239.netlify.app/coursework/static/

    End Product

    @@ -429,11 +433,11 @@

    Use of Altair is strongly recommended, but other libraries allowed.

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    Examples

    https://capp-30239-winter-2021.netlify.app/#staticShowcase

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    Milestones

    • Milestone 1 (Week 1): Draft proposal.
      • @@ -442,8 +446,8 @@
    • Milestone 4 (Week 5): Final Deliverable
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    Acknowledgements

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    Acknowledgements & References

    Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials.

    Why Is Data Visualization Important? What Is Important in Data Visualization? - An tony Unwin

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:is(h3,marp-h3){font-size:1.1em}div#\:\$p>svg>foreignObject>section :is(h4,marp-h4){font-size:1.05em}div#\:\$p>svg>foreignObject>section :is(h5,marp-h5){font-size:1em}div#\:\$p>svg>foreignObject>section :is(h6,marp-h6){font-size:.9em}div#\:\$p>svg>foreignObject>section :is(h1,marp-h1) strong,div#\:\$p>svg>foreignObject>section :is(h2,marp-h2) strong,div#\:\$p>svg>foreignObject>section :is(h3,marp-h3) strong,div#\:\$p>svg>foreignObject>section :is(h4,marp-h4) strong,div#\:\$p>svg>foreignObject>section :is(h5,marp-h5) strong,div#\:\$p>svg>foreignObject>section :is(h6,marp-h6) strong{color:var(--heading-strong-color);font-weight:inherit}div#\:\$p>svg>foreignObject>section :is(h1,marp-h1)::part(auto-scaling),div#\:\$p>svg>foreignObject>section :is(h2,marp-h2)::part(auto-scaling),div#\:\$p>svg>foreignObject>section :is(h3,marp-h3)::part(auto-scaling),div#\:\$p>svg>foreignObject>section :is(h4,marp-h4)::part(auto-scaling),div#\:\$p>svg>foreignObject>section :is(h5,marp-h5)::part(auto-scaling),div#\:\$p>svg>foreignObject>section :is(h6,marp-h6)::part(auto-scaling){max-height:563px}div#\:\$p>svg>foreignObject>section hr{height:0;padding-top:.25em}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre){border:1px solid var(--color-border-default);line-height:1.15;overflow:visible}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre)::part(auto-scaling){max-height:529px}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs){color:var(--color-prettylights-syntax-storage-modifier-import)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-doctag),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-keyword),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-meta .hljs-keyword),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-template-tag),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-template-variable),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-type),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-variable.language_){color:var(--color-prettylights-syntax-keyword)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-title),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-title.class_),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-title.class_.inherited__),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-title.function_){color:var(--color-prettylights-syntax-entity)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-attr),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-attribute),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-literal),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-meta),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-number),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-operator),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-selector-attr),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-selector-class),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-selector-id),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-variable){color:var(--color-prettylights-syntax-constant)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-meta .hljs-string),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-regexp),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-string){color:var(--color-prettylights-syntax-string)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-built_in),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-symbol){color:var(--color-prettylights-syntax-variable)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-code),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-comment),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-formula){color:var(--color-prettylights-syntax-comment)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-name),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-quote),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-selector-pseudo),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-selector-tag){color:var(--color-prettylights-syntax-entity-tag)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-subst){color:var(--color-prettylights-syntax-storage-modifier-import)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-section){color:var(--color-prettylights-syntax-markup-heading);font-weight:700}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-bullet){color:var(--color-prettylights-syntax-markup-list)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) 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footer{bottom:21px}div#\:\$p>svg>foreignObject>section{--h1-color:#246;--header-footer-color:hsla(0,0%,40%,.75);--heading-strong-color:#48c;--paginate-color:#777;align-items:stretch;display:flex;flex-flow:column nowrap;font-size:29px;height:720px;justify-content:center;padding:78.5px;width:1280px}div#\:\$p>svg>foreignObject>section{--marpit-root-font-size:29px}div#\:\$p>svg>foreignObject>section:where(.invert){--h1-color:#cee7ff;--header-footer-color:hsla(0,0%,60%,.75);--heading-strong-color:#7bf;--paginate-color:#999}div#\:\$p>svg>foreignObject>section>:last-child,div#\:\$p>svg>foreignObject>section[data-footer]>:nth-last-child(2){margin-bottom:0}div#\:\$p>svg>foreignObject>section>:first-child,div#\:\$p>svg>foreignObject>section>header:first-child+*{margin-top:0}div#\:\$p>svg>foreignObject>section:after{bottom:21px;color:var(--paginate-color);font-size:24px;padding:0;position:absolute;right:30px}div#\:\$p>svg>foreignObject>section:after{--marpit-root-font-size:24px}div#\:\$p>svg>foreignObject>section[data-color] :is(h1,marp-h1),div#\:\$p>svg>foreignObject>section[data-color] :is(h2,marp-h2),div#\:\$p>svg>foreignObject>section[data-color] 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    Grammar of Graphics with Altair

    CAPP 30239

    -
    +

    Today

      -
    • Grammar of Graphics
      • -
    • Types of Data
      • -
    • Intro to Altair
      • +
    • What is a grammar of graphics and how do we use it in practice?
      • +
    • What types of data do we encounter, and how does that affect visualizations?
      • +
    • Introduction to Altair
    -
    +

    Grammar of Graphics

    Hadley Wickham, creator of ggplot2 and tidyverse, "A Layered Grammar of Graphics".

    Key Idea: move beyond pre-defined composites like "scatter plot" and "bar chart" into a composable grammar from which we can construct a wide variety of visualizations.

    -
    +

    Wickham's Components:

    1. @@ -51,7 +55,7 @@
    -
    +

    Types of Data

    N - Nominal

    "strings" with no order (alphabetical does not count)

    @@ -64,7 +68,7 @@ Countries

  • Rankings: 1st, 2nd, 3rd
    • -
    +

    Types of Data (Quantitative)

    Q - Interval (arbitrary zero)

      @@ -79,7 +83,7 @@ Countries

      Physical measurements, counts, amounts.

      "4 km is twice as far as 2 km"

    -
    +

    Types of Data (Operations)

    @@ -123,7 +127,7 @@ Countries

    -
    +

    Data Model to N, O, Q

    • string?
      • @@ -132,7 +136,7 @@ Countries

    Possible exceptions?

    -
    +

    Data Model to N, O, Q

    Typically:

      @@ -147,7 +151,7 @@ Countries

    • ratio data stored with units (e.g. "10km")
    -
    +

    Mapping of Variables to Aesthetics

    • position (X, Y, Z)
      • @@ -165,18 +169,18 @@ Countries

    • shape
    -
    +

    Mackinlay's "effectiveness"

    -
    +

    Altair

    Altair is a Python visualization library that allows us to work from a grammar of graphics perspective.

    It also is very flexible in output formats, which will be useful if you want to modify your graphics or make them interactive.

    Altair is built on top of Vega-Lite.

    Vega-Lite is a system that represents graphics in a JSON schema, and a set of tools that convert these JSON representations to images or interactive graphics.

    -
    +

    Vega-Lite Example

    {
   "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
@@ -191,10 +195,10 @@ Countries

    Vega condenses several of the different pieces of the grammar to "encoding channels".

    -
    +

    -
    +

    Altair

    import pandas as pd
 import altair as alt
@@ -207,10 +211,10 @@ alt.Chart(df).encode(

    Altair is a Pythonic wrapper to create Vega-Lite JSON. If you use it in a notebook, the resulting graphs will render inline.

    -
    +

    Altair Notebook

    -
    +

    Learning Altair

    To master a library like Altair, you'll go through the following phases:

      @@ -234,7 +238,7 @@ alt.Chart(df).encode(
    -
    +

    Altair Assignment

    credit: https://en.wikipedia.org/wiki/File:Linear_visible_spectrum.svg

    source https://commons.wikimedia.org/wiki/File:Cone-fundamentals-with-srgb-spectrum.svg

    Source: https://www.color-meanings.com/color-symbolism-different-cultures/

    image from wikipedia: UK House of Commons

    image source: https://upload.wikimedia.org/wikipedia/commons/3/34/RGB_pixels.jpg

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    Chart Design

    CAPP 30239

    -
    +

    Today

    • What general principles of visual design are relevant to our work?
      • @@ -25,12 +25,12 @@
    • When and how do we break the rules?
    -
    +

    Edward Tufte

    The Visual Display of Quantitative Information

    -
    +

    Key Ideas

    • Graphical Integrity: Above all else, show the data.
      • @@ -40,10 +40,10 @@
    • Revision & Editing are essential.
    -
    +

    Tufte's Principles for Graphical Integrity

    -
    +
    1. The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the numerical quantities represented.
    @@ -52,20 +52,20 @@ Graph length increase: 783%
    "Lie Factor": 14.8x

    -
    +
    1. Clear, detailed and thorough labeling should be used to defeat graphical distortion and ambiguity.

    How many children get a spinal injury every year? (out of 74,000,000 children in US)

    -
    +
    1. Write out explanation of the data on the graphic itself. Label important events in the data.

    -
    +
    1. Show data variation, not design variation.
    @@ -73,7 +73,7 @@ Graph length increase: 783%

    The number of information-carrying (variable) dimensions depicted should not exceed the number of dimensions in the data. (roughly 1:1 channel mapping)

    Exception: It is OK/common to pair color & shape, or for print color & texture to address issues that color presents.

    -
    +

    Data-Ink Ratio

    • Data-ink: Ink (pixels) used to show data.
      • @@ -81,27 +81,27 @@ Graph length increase: 783%

    -
    +

    -
    +

    Optimizing Data Density

    Number of entries in DataFrame / Area of Graphic.

    Classic example of high data density is the sparkline, which can fit on a line of text.

    -
    +

    Chart Junk

    Anything that isn't relevant to understanding the data.

    -
    +


    via junkcharts.typepad.com

    -
    +

    Common Chart Types

    -
    +

    Bar Charts & Histograms

    • X/Y: Nominal (Binned Numerical - Histogram)
      • @@ -109,19 +109,19 @@ via junkcharts.typepad.com

    -
    +

    Line & Area Charts

    • X: Temporal
    • Y: Quantitative
    -
    +

    When to use stacked area charts?

    Combined X axis variable has meaning.

    -
    +

    Heatmap

      @@ -130,7 +130,7 @@ via junkcharts.typepad.com

    • mark_rect
    -
    +

    Strip Plot

      @@ -140,7 +140,7 @@ via junkcharts.typepad.com

    • mark_tick
    -
    +

    Pie / Donut / Radial Charts

    Theta: Quantitative (ratio)
    @@ -148,18 +148,18 @@ Color: Nominal

    Direct comparison of segments is very difficult at n > 2.

    Only use when most important information is ratio between sizes, and relatively few categories.

    -
    +


    https://www.storytellingwithdata.com/blog/2020/5/14/what-is-a-pie-chart

    -
    +

    Bump / Rank Line Chart

    left

    left

    Useful for showing changes in relative positioning.

    Require some data manipulation using transform_window or pre-computing ranks. (see Altair gallery examples.)

    -
    +

    Scatter & Bubble Plots

      @@ -167,17 +167,17 @@ Color: Nominal

    Bubble charts use size as a 3rd dimension.

    -
    +

    Small Multiples / Faceting

    Most useful when there is a nominal variable that charts vary on.

    -
    +

    Distributions

    Map Basics

    -
    +

    When & How to Break the Rules

    When in doubt...

      @@ -185,7 +185,7 @@ Color: Nominal

    • Focus on Tufte's rules for clarity.
    -
    +

    Case Study: Two Innovations

    Two visualization types that have had their moment in the past 10-15 years:

      @@ -193,33 +193,33 @@ Color: Nominal

    • Word Clouds
    -
    +

    Grid Map

    Introduced in https://blog.apps.npr.org/2015/05/11/hex-tile-maps.html

    -
    +

    Word Cloud

    -
    +


    Derived from same data as word cloud.

    source: NYTimes via https://www.niemanlab.org/2011/10/word-clouds-considered-harmful/

    -
    +

    Narrative-supporting graphics

    by ulaniulani on flickr

    -
    +

    When it's OK to use 3D

    You have data that truly makes more sense in 3D.

    and/or

    You work at CERN.

    -
    +

    Acknowledgements & References

    Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials.

      @@ -230,4 +230,4 @@ Color: Nominal

    .0000003%

    source: https://www.juiceanalytics.com/writing/better-know-visualization-small-multiples

    discuss: is this a good thing?

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