From 7799db0e2fa4d862f07624b7785477e883a16e10 Mon Sep 17 00:00:00 2001 From: James Turk Date: Wed, 25 Sep 2024 23:41:41 -0500 Subject: [PATCH] copy over 01 and 00 --- .gitignore | 1 + .python-version | 1 + 00.value-of-dataviz/a-day-in-data.jpg | Bin 0 -> 664642 bytes 00.value-of-dataviz/anscome.svg | 497 +++++++ 00.value-of-dataviz/challenger.jpg | Bin 0 -> 505824 bytes 00.value-of-dataviz/cholera.jpg | Bin 0 -> 3922205 bytes 00.value-of-dataviz/datagov.png | Bin 0 -> 358246 bytes 00.value-of-dataviz/feynman.jpg | Bin 0 -> 19754 bytes 00.value-of-dataviz/limitless.jpg | Bin 0 -> 310950 bytes 00.value-of-dataviz/rockets_chart.png | Bin 0 -> 889846 bytes 00.value-of-dataviz/slides.html | 464 ++++++ 00.value-of-dataviz/slides.md | 341 +++++ 00.value-of-dataviz/taxis.png | Bin 0 -> 2205935 bytes 01.gog-altair/altair-intro.py | 226 +++ 01.gog-altair/effectiveness.png | Bin 0 -> 878434 bytes 01.gog-altair/midwest_bills.csv | 13 + 01.gog-altair/outline.html | 180 +++ 01.gog-altair/outline.md | 144 ++ README.md | 16 + pyproject.toml | 13 + uv.lock | 1903 +++++++++++++++++++++++++ 21 files changed, 3799 insertions(+) create mode 100644 .gitignore create mode 100644 .python-version create mode 100644 00.value-of-dataviz/a-day-in-data.jpg create mode 100644 00.value-of-dataviz/anscome.svg create mode 100644 00.value-of-dataviz/challenger.jpg create mode 100644 00.value-of-dataviz/cholera.jpg create mode 100644 00.value-of-dataviz/datagov.png create mode 100644 00.value-of-dataviz/feynman.jpg create mode 100644 00.value-of-dataviz/limitless.jpg create mode 100644 00.value-of-dataviz/rockets_chart.png create mode 100644 00.value-of-dataviz/slides.html create mode 100644 00.value-of-dataviz/slides.md create mode 100644 00.value-of-dataviz/taxis.png create mode 100644 01.gog-altair/altair-intro.py create mode 100644 01.gog-altair/effectiveness.png create mode 100644 01.gog-altair/midwest_bills.csv create mode 100644 01.gog-altair/outline.html create mode 100644 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      +

      30239: Data Visualization for Policy Analysis

      +

      James Turk

      +
      +
      +

      Today

      +
        +
      • What is the value of data visualization?
      • +
      • Focus of this course
      • +
      • Course Logistics
      • +
      +
      +
      +

      infographic: a day in data

      +
      +
      +

      data.gov

      +
      +
      +

      taxis

      +
      +
      +

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

      +
      +
      +

      "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

      +
      +
      +

      What is Visualization?

      +

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

      +

      “Transformation of the symbolic into the geometric”
      +[McCormick et al. 1987]

      +

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

      +
      +
      +

      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?

      +
      +
      +

      Why do we create visualizations?

      +
        +
      • What visualizations have you created?
      • +
      • What visualizations have you seen that you remember?
      • +
      +
      +
      +

      Reasons for Data Visualization

      +
        +
      • Means of reasoning about large quantities without reduction/over-simplification.
      • +
      • Assist in gaining unique insights into data: clustering, correlation, trends, etc.
      • +
      • Deepen understanding, for ourselves or others.
      • +
      +
      +
      +

      Four Samples

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      x1y1x2y2x3y3x4y4
      10.08.0410.09.1410.07.468.06.58
      8.06.958.08.148.06.778.05.76
      13.07.5813.08.7413.012.748.07.71
      9.08.819.08.779.07.118.08.84
      11.08.3311.09.2611.07.818.08.47
      14.09.9614.08.1014.08.848.07.04
      6.07.246.06.136.06.088.05.25
      4.04.264.03.104.05.3919.012.50
      12.010.8412.09.1312.08.158.05.56
      7.04.827.07.267.06.428.07.91
      5.05.685.04.745.05.738.06.89
      +

      What sense can we make of this?

      +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      Sample 1Sample 2Sample 3Sample 4
      Mean of x9999
      Variance of x11111111
      Mean of y7.507.507.507.50
      Variance of y (±0.003 )4.1254.1254.1254.125
      Correlation x & y0.8160.8160.8160.816
      Linear Regressiony = 3.00 + 0.500xy = 3.00 + 0.500xy = 3.00 + 0.500xy = 3.00 + 0.500x
      R² coefficient0.670.670.670.67
      +
      +
      +

      Anscombe's quartet

      +
      +
      +

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

      +

      What things are easier to see?

      +
      +
      +

      Benefits of Visualizing Data

      +
        +
      • outliers
      • +
      • "shape of data"
      • +
      • clusters
      • +
      • intuition and questions - "why is it like that?"
      • +
      • tap into human capacity for pattern recognition
      • +
      • often easier to test theories or models
      • +
      +

      exploratory visualization

      +
      +
      +

      Explanatory Visualization

      +
        +
      • Highlight interesting findings
      • +
      • Tell a story
      • +
      • Present a thesis
      • +
      • Persuade
      • +
      • Support larger story/argument
      • +
      • Inspire
      • +
      +
      +
      +

      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.

      +
      +
      +

      +
      +
      +
      +

      +
      +
      +

      Data Visualization for Policy Analysis

      +
      +
      +

      Course Goals

      +
        +
      • Understand & appreciate what makes a good data visualization.
      • +
      • Learn practical visualization techniques that will apply in any language & library.
      • +
      • Build a portfolio of static & interactive visualizations using real-world policy data.
      • +
      • Gain exposure to useful libraries in Python and JavaScript.
      • +
      +
      +
      +

      Topics

      +
        +
      • Fundamentals, Grammar of Graphics
      • +
      • Design principles. How to use color, human perception, chart design.
      • +
      • How to evaluate and critique visualizations.
      • +
      • Uncertainty & Narrative
      • +
      • HTML/CSS/JS overview
      • +
      • D3.js
      • +
      • Interactive & non-chart data visualization.
      • +
      • Geospatial visualization
      • +
      • Special Topics: to be discussed
      • +
      +
      +
      +

      Programming Workload

      +

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

      +
        +
      • Expect to write code every week, mostly fairly short Python functions.
      • +
      • You will be learning at least one library (Altair) mostly independently.
      • +
      • You'll also need to be comfortable with pandas or polars.
      • +
      +

      JavaScript and D3

      +

      "You aren't going to make them learn D3 are you?"

      +
        +
      • 1 Assignment
      • +
      • 1-2 Lectures
      • +
      +

      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.

      +
      +
      +

      Course Structure

      +
        +
      • Lecture and Discussion: Introduce and explore key concepts, mostly focused on theory.
      • +
      • Readings: Supplement course materials with more examples, technical tutorials.
      • +
      • 2 "minor" assignments: Altair & D3 - Gain practice with commonly used tools in a structured setting.
      • +
      • Projects: Learn to explore a topic on your own from conception to practice. Leave here with a portfolio you can show off.
      • +
      +
      +
      +

      Course Staff

      +
        +
      • James Turk
      • +
      • TODO
      • +
      +

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

      +
      +
      +

      Projects

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      StaticInteractive
      ProposalWeek 1Week 6
      Feedback DraftWeek 3Week 8
      Peer CritiqueWeek 4Week 9
      FinalWeek 5Week 10
      +

      Practice Assignments

      +
        +
      • Altair (Week 2)
      • +
      • D3 (Week 6)
      • +
      +
      +
      +

      Grading

      +

      15 SNU grades

      +

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

      +

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

      +
      +
      +

      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.

      +
      +
      +

      Static Visualization Project

      +

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

      +

      End Product

      +

      Using real data of your choosing:

      +
        +
      • 8-12 distinct images, of at least 5 different types.
      • +
      • Presented as part of a narrative: an article, infographic, poster.
      • +
      • Cohesive visual design: custom theme for graphs, matching colors and fonts with supplementary material.
      • +
      +

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

      +
      +
      +

      Examples

      +

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

      +
      +
      +

      Milestones

      +
        +
      • Milestone 1 (Week 1): Draft proposal.
      • +
      • Milestone 2 (Week 3): Draft of 8 visualizations for review & critique.
      • +
      • Milestone 3 (Week 4): Peer Critique
      • +
      • Milestone 4 (Week 5): Final Deliverable
      • +
      +
      +
      +

      Acknowledgements

      +

      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

      +
      +

      Modern data visualization is largely a product of the sheer amount of data we produce.

      In part this is a function of the digitization of most of our lives. + +15 years ago there was no data.gov, it has added 300k data sets since then.

      As technology advances, our data gets to be incredibly fine grained. +This is a sample of ride share rides over a six year period. Each pixel is a pick up or drop off.

      images from presentation on O-ring temperature anomaly

      the result

      Cholera outbreak. London, 1854. John Snow.

      It has however, become a "library's library" in some ways. Most developers interact with d3 through a higher-level interface. + +We will be learning Altair, which generates Vega JSON, which in turn is drawn using D3. + +D3 underpins dozens of other charting libraries as well, and D3's own documentation says: + + D3 makes things possible, not necessarily easy; even simple things that should be easy are often not. To paraphrase Amanda Cox: “Use D3 if you think it’s perfectly normal to write a hundred lines of code for a bar chart.” + +Learning D3 both requires working in an unfamiliar environment (JavaScript) and with a very unique style of programming based around their concept of the ["data join"](https://d3js.org/d3-selection/joining), which requires a decent understanding of the HTML Document Object Model. + +So, if you are here to learn visualization, I think that it is fair that you can succeed in this class without putting yourself through that.

      \ No newline at end of file diff --git a/00.value-of-dataviz/slides.md b/00.value-of-dataviz/slides.md new file mode 100644 index 0000000..2e0fd46 --- /dev/null +++ b/00.value-of-dataviz/slides.md @@ -0,0 +1,341 @@ +# 30239: Data Visualization for Policy Analysis + +## James Turk + +--- + +## Today + +- What is the value of data visualization? +- Focus of this course +- Course Logistics + +--- + +![infographic: a day in data](a-day-in-data.jpg) + + + +--- + +![data.gov](datagov.png) + + + +--- + +![taxis](taxis.png) + + + +--- + +OK, there is **a lot** of data, but isn't that a **good thing**? + +--- + +"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_ + +--- + +## What is Visualization? + +“... finding the **artificial memory** that best supports our natural means of perception.” +_[Bertin 1967]_ + +“Transformation of the symbolic into the geometric” +_[McCormick et al. 1987]_ + +“The use of computer-generated, interactive, visual representations of data to **amplify cognition**.” +_[Card, Mackinlay, & Shneiderman 1999]_ + +--- + +## 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? + +--- + +## Why do we create visualizations? + +- What visualizations have you created? +- What visualizations have you seen that you remember? + +--- + +## Reasons for Data Visualization + +- Means of reasoning about large quantities without reduction/over-simplification. +- Assist in gaining unique insights into data: clustering, correlation, trends, etc. +- Deepen understanding, for ourselves or others. + +--- + +### Four Samples + +| x1 | y1 | x2 | y2 | x3 | y3 | x4 | y4 | +| ---- | ----- | ---- | ---- | ---- | ----- | ---- | ----- | +| 10.0 | 8.04 | 10.0 | 9.14 | 10.0 | 7.46 | 8.0 | 6.58 | +| 8.0 | 6.95 | 8.0 | 8.14 | 8.0 | 6.77 | 8.0 | 5.76 | +| 13.0 | 7.58 | 13.0 | 8.74 | 13.0 | 12.74 | 8.0 | 7.71 | +| 9.0 | 8.81 | 9.0 | 8.77 | 9.0 | 7.11 | 8.0 | 8.84 | +| 11.0 | 8.33 | 11.0 | 9.26 | 11.0 | 7.81 | 8.0 | 8.47 | +| 14.0 | 9.96 | 14.0 | 8.10 | 14.0 | 8.84 | 8.0 | 7.04 | +| 6.0 | 7.24 | 6.0 | 6.13 | 6.0 | 6.08 | 8.0 | 5.25 | +| 4.0 | 4.26 | 4.0 | 3.10 | 4.0 | 5.39 | 19.0 | 12.50 | +| 12.0 | 10.84 | 12.0 | 9.13 | 12.0 | 8.15 | 8.0 | 5.56 | +| 7.0 | 4.82 | 7.0 | 7.26 | 7.0 | 6.42 | 8.0 | 7.91 | +| 5.0 | 5.68 | 5.0 | 4.74 | 5.0 | 5.73 | 8.0 | 6.89 | + +What sense can we make of this? + +--- + +| | Sample 1 | Sample 2 | Sample 3 | Sample 4 | +| ----------------------- | ----------------- | ----------------- | ----------------- | ----------------- | +| Mean of x | 9 | 9 | 9 | 9 | +| Variance of x | 11 | 11 | 11 | 11 | +| Mean of y | 7.50 | 7.50 | 7.50 | 7.50 | +| Variance of y (±0.003 ) | 4.125 | 4.125 | 4.125 | 4.125 | +| Correlation x & y | 0.816 | 0.816 | 0.816 | 0.816 | +| Linear Regression | y = 3.00 + 0.500x | y = 3.00 + 0.500x | y = 3.00 + 0.500x | y = 3.00 + 0.500x | +| R² coefficient | 0.67 | 0.67 | 0.67 | 0.67 | + + +--- + +![Anscombe's quartet](anscome.svg) + +--- + +Our understanding of the data is enhanced by these visualizations in a way summary statistics won't capture. + +What things are easier to see? + +--- + + +## Benefits of Visualizing Data + +- outliers +- "shape of data" +- clusters +- _intuition_ and _questions_ - "why is it like that?" +- tap into human capacity for pattern recognition +- often easier to test theories or models + +_exploratory visualization_ + +--- + +## Explanatory Visualization + +- Highlight interesting findings +- Tell a story +- Present a thesis +- Persuade +- Support larger story/argument +- Inspire + +--- + +## 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. + +--- + + +![](rockets_chart.png) + + + +--- + +![bg](challenger.jpg) +![bg](feynman.jpg) + + + +--- + +![](cholera.jpg) + + + +--- + +## Data Visualization for Policy Analysis + +--- + +## Course Goals + +- Understand & appreciate what makes a good data visualization. +- Learn practical visualization techniques that will apply in any language & library. +- Build a portfolio of static & interactive visualizations using real-world policy data. +- Gain exposure to useful libraries in Python and JavaScript. + +--- + +## Topics + +- Fundamentals, Grammar of Graphics +- Design principles. How to use color, human perception, chart design. +- How to evaluate and critique visualizations. +- Uncertainty & Narrative +- HTML/CSS/JS overview +- D3.js +- Interactive & non-chart data visualization. +- Geospatial visualization +- Special Topics: to be discussed + +--- + +## Programming Workload + +Key Idea: You get better at visualizations by making *a lot* of them. (20-30 this quarter) + +- Expect to write code every week, mostly fairly short Python functions. +- You will be learning at least one library (Altair) mostly independently. +- You'll also need to be comfortable with `pandas` or `polars`. + +### JavaScript and D3 + +*"You aren't going to make them learn D3 are you?"* + +- 1 Assignment +- 1-2 Lectures + +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. + + + +--- + +## Course Structure + +- **Lecture and Discussion**: Introduce and explore key concepts, mostly focused on theory. +- **Readings**: Supplement course materials with more examples, technical tutorials. +- **2 "minor" assignments: Altair & D3** - Gain practice with commonly used tools in a structured setting. +- **Projects**: Learn to explore a topic on your own from conception to practice. Leave here with a portfolio you can show off. + +--- + +## Course Staff + +- James Turk +- TODO + +**All official information will be on the course site and/or Ed as appropriate.** + + +--- + +## Projects + +| | Static | Interactive | +| -------------- | ------ | ----------- | +| Proposal | Week 1 | Week 6 | +| Feedback Draft | Week 3 | Week 8 | +| Peer Critique | Week 4 | Week 9 | +| Final | Week 5 | Week 10 | + +## Practice Assignments + +- Altair (Week 2) +- D3 (Week 6) + +--- + +## Grading + +15 SNU grades + +**Completion:** 9 (2x Proposals, Drafts & Critiques + 2 Labs + 1 Participation) + +**Quality Grades:** 6 (2x Design, Narrative, Code Quality) + +--- + +## AI Policy + +In this course, all usage of generative AI must be *fully cited*. + +Details on specific rules: + +You are, as always, expected to turn in **your own work**, not the work of an LLM. + +--- + +## Static Visualization Project + + + +**End Product** + +Using real data of your choosing: + +- 8-12 distinct images, of at least 5 different types. +- Presented as part of a **narrative**: an article, infographic, poster. +- Cohesive visual design: custom theme for graphs, matching colors and fonts with supplementary material. + +Use of Altair is **strongly** recommended, but other libraries allowed. + +--- + +## Examples + + + +--- + +## Milestones + +- Milestone 1 (Week 1): Draft proposal. +- Milestone 2 (Week 3): Draft of 8 visualizations for review & critique. +- Milestone 3 (Week 4): Peer Critique +- Milestone 4 (Week 5): Final Deliverable + +--- + +## Acknowledgements + +Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials. + +[Why Is Data Visualization Important? 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z#HoqF0qOWgl8jF#>9_t*!%JKst-?#IhWVtA{P14if`kNNL`I%$E{#DkK%cz};`RAS zRHoX;Fvd)0n>xBP@8`@ziY<=*O%3unt>S(#)?atN}DvrN+o?M z2mBN@C4X?R$n3BYQu0#4KEW=?%$h?g`XUfM%T% Channels (skim Channel Options) + - Marks (skim a few of the mark guides, including Bar & Point) + - Data Transformations (skim a few, including Regression) + - Layered and Multi-View Charts + - Customizing Visualizations + + Once you've read the above you have the core ideas of Altair. + The remaining sections are useful as reference, and as you use Altair you will find your way to them as you ask yourself questions like "how do I work with geospatial data" or "how can I combine these axes"? + + The other common thing you will use the documentation for is "what arguments can I pass to this?" + + For that, use the [API Reference](https://altair-viz.github.io/user_guide/api.html) and find the class you're working with. + + Example: + + - Let's say we want to adjust the color scheme, start with + - Note that it can take a scale, and click to + """ + ) + return + + +@app.cell +def __(alt, chart): + color_scheme = alt.Scale(scheme="set2") + chart.mark_line().encode( + alt.Y("num_bills"), + alt.X("session_start_year:N"), + alt.Color("state", scale=color_scheme), + ) + chart.mark_point().encode( + alt.Y("num_bills").title("Bills Introduced"), + alt.X("session_start_year:N").title("Session Year"), + alt.Color("state", scale=color_scheme), + alt.Shape("state"), + ).properties( + title='Midwest Bills by Session', + ) + return (color_scheme,) + + +@app.cell +def __(): + return + + +if __name__ == "__main__": + app.run() diff --git a/01.gog-altair/effectiveness.png b/01.gog-altair/effectiveness.png new file mode 100644 index 0000000000000000000000000000000000000000..8df91b6931c300be7767e70204810c0d55443735 GIT binary patch literal 878434 zcmeEtWmsF=)-EnZ3oRPFNK5hJ+G1_d;#x|Blp?_$f=h8PZY>lq?oc#nuo5J=yAveH z%|2)Ev%mY@`#kr2|L%{IXRXYcwWh2&=a^%?;~gX6>Z*!F4`?2sp`j7Ic`c`bhK7qk 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