copy over 01 and 00

2024-09-25 23:41:41 -05:00 · 2024-09-25 23:41:41 -05:00 · 7799db0e2f
commit 7799db0e2f
21 changed files with 3799 additions and 0 deletions
--- a/.gitignore
+++ b/.gitignore
@ -0,0 +1 @@
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--- a/00.value-of-dataviz/a-day-in-data.jpg
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--- a/00.value-of-dataviz/challenger.jpg
+++ b/00.value-of-dataviz/challenger.jpg
--- a/00.value-of-dataviz/cholera.jpg
+++ b/00.value-of-dataviz/cholera.jpg
--- a/00.value-of-dataviz/datagov.png
+++ b/00.value-of-dataviz/datagov.png
--- a/00.value-of-dataviz/feynman.jpg
+++ b/00.value-of-dataviz/feynman.jpg
--- a/00.value-of-dataviz/limitless.jpg
+++ b/00.value-of-dataviz/limitless.jpg
--- a/00.value-of-dataviz/rockets_chart.png
+++ b/00.value-of-dataviz/rockets_chart.png
--- a/00.value-of-dataviz/slides.html
+++ b/00.value-of-dataviz/slides.html
--- a/00.value-of-dataviz/slides.md
+++ 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)
+
+<!--
+Modern data visualization is largely a product of the sheer amount of data we produce.
+-->
+
+---
+
+![data.gov](datagov.png)
+
+<!--
+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.
+-->
+
+---
+
+![taxis](taxis.png)
+
+<!--
+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.
+-->
+
+---
+
+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)
+
+<!-- images from presentation on O-ring temperature anomaly -->
+
+---
+
+![bg](challenger.jpg)
+![bg](feynman.jpg)
+
+<!-- the result -->
+
+---
+
+![](cholera.jpg)
+
+<!-- Cholera outbreak. London, 1854. John Snow.  -->
+
+---
+
+## 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.
+
+<!--
+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.
+-->
+
+---
+
+## 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: <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?](https://hdsr.mitpress.mit.edu/pub/zok97i7p/release/4) - An tony Unwin
--- a/00.value-of-dataviz/taxis.png
+++ b/00.value-of-dataviz/taxis.png
--- a/01.gog-altair/altair-intro.py
+++ b/01.gog-altair/altair-intro.py
@ -0,0 +1,226 @@
+import marimo
+
+__generated_with = "0.8.20"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import marimo as mo
+    import altair as alt
+    import polars as pl
+    from pathlib import Path
+    return Path, alt, mo, pl
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        """
+        ## Tidy Data
+
+        Altair expects our data to be [tidy](http://vita.had.co.nz/papers/tidy-data.html).
+
+        - Each variable is a column.
+        - Each observation is a row. 
+        - Each type of observational unit is a table.
+
+        You can use `pandas` or `polars` DataFrames.
+        """
+    )
+    return
+
+
+@app.cell
+def __(Path, __file__, pl):
+    # first let's load and look at a dataframe with three columns
+    #  there is an observation for each state legislature, showing how many bills they introduced in a given year
+    df = pl.read_csv(Path(__file__).parent / "midwest_bills.csv")
+    # (having a dataframe or chart as the last line in a notebook cell will automatically display it)
+    df
+    return (df,)
+
+
+@app.cell
+def __(alt, df):
+    # Let's make our own charts of this dat, first we bind the data to a new chart object
+    chart = alt.Chart(df)
+    return (chart,)
+
+
+@app.cell
+def __(chart):
+    # we add a geometry, we'll start with a point (at this point *something* can be displayed, but it won't be useful)
+    chart.mark_point()
+    return
+
+
+@app.cell
+def __(chart):
+    # We use encodings to map our data to particular dimensions.
+    # Altair will make then make appropriate choices based upon the type of data.
+    chart.mark_point().encode(
+        y="state",
+        x="num_bills"
+    )
+    return
+
+
+@app.cell
+def __():
+    return
+
+
+@app.cell
+def __(alt, chart):
+    # what happens when we try to add color?
+    chart.mark_point().encode(
+        alt.Y("state"),
+        alt.X("num_bills"),
+        alt.Color("session_start_year"),
+    )
+    return
+
+
+@app.cell
+def __(alt, chart):
+    # the prior example treated year as an Ordinal because it was numeric
+    # instead we would treat it as Nominal for this data
+    # we can use :Q, :O, :N, :T to mark the type that should be used
+    by_year = chart.mark_point().encode(
+        alt.Y("state:N"),
+        alt.X("num_bills:Q"),
+        alt.Color("session_start_year:N"),
+    )
+    # we're saving this one for later
+    by_year
+    return (by_year,)
+
+
+@app.cell
+def __(alt, chart):
+    # Here we make a different chart from the same base data 
+    # by re-using our `chart` variable.
+    #
+    # We choose a different shape (parameters that don't need to vary can be passed into the mark_* functions)
+    # We also use an aggregate function average(num_bills)
+    avgs = chart.mark_point(shape="wedge", color="black").encode(
+        alt.Y("state"),
+        alt.X("average(num_bills)"),
+    )
+    avgs
+    return (avgs,)
+
+
+@app.cell
+def __(avgs, by_year):
+    # two charts with compatible data can be layered with +
+    by_year + avgs
+    return
+
+
+@app.cell
+def __(alt, by_year, chart):
+    # perhaps we don't want to use mark_point anymore, maybe a bar?
+    bar_avgs = chart.mark_bar(color="#ccc").encode(
+        alt.Y("state"),
+        alt.X("average(num_bills)"),
+    )
+    bar_avgs + by_year
+    return (bar_avgs,)
+
+
+@app.cell
+def __(alt, chart):
+    # We can customize titles and other details by using `.title` and `.properties`
+    # the latter sets chart-wide properties.
+    final = chart.mark_point(shape="diamond").encode(
+        alt.Y("state:N"),
+        alt.X("num_bills:Q"),
+        alt.Color("session_start_year:N").title("Session Year"),
+    ) + chart.mark_bar(color="#70905050").encode(
+        alt.Y("state"),
+        alt.X("average(num_bills)").title("Number of Bills Introduced"),
+    )
+    final.properties(
+        title='Midwest Bills by State',
+        background='#f5f5dc'
+    )
+    return (final,)
+
+
+@app.cell
+def __(alt, chart):
+    # Let's say we instead want to see if there are trends by year.
+    # create a new chart object with year on the X-axis, and bills on the Y-axis
+    # Also, make the chart print/colorblind friendly by encoding state in multiple ways.
+    new_chart = chart.mark_point().encode(
+        alt.Y("num_bills"),
+        alt.X("session_start_year:N"),
+        alt.Color("state"),
+        alt.Shape("state"),
+    )
+    new_chart.properties(
+        title='Midwest Bills by Year',
+        background='#f5f5dc'
+    )
+    return (new_chart,)
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        """
+        ### Recommended Reading
+
+        Altair Tutorial
+
+        - Specifying Data (you can stop when you hit 'Generated Data')
+        - Encodings
+        - Encodings -> 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 <https://altair-viz.github.io/user_guide/generated/channels/altair.Color.html>
+        - Note that it can take a scale, and click to <https://altair-viz.github.io/user_guide/generated/core/altair.Scale.html#altair.Scale>
+        """
+    )
+    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()
--- a/01.gog-altair/effectiveness.png
+++ b/01.gog-altair/effectiveness.png
--- a/01.gog-altair/midwest_bills.csv
+++ b/01.gog-altair/midwest_bills.csv
@ -0,0 +1,13 @@
+state,session_start_year,num_bills
+IL,2017,13616
+IL,2019,12760
+IL,2021,12847
+IL,2023,11951
+MI,2017,4818
+MI,2019,4450
+MI,2021,4520
+MI,2023,3424
+WI,2017,1820
+WI,2019,2264
+WI,2021,2618
+WI,2023,2656
--- a/01.gog-altair/outline.html
+++ b/01.gog-altair/outline.html
--- a/01.gog-altair/outline.md
+++ b/01.gog-altair/outline.md
@ -0,0 +1,144 @@
+# Grammar of Graphics with Altair
+
+## CAPP 30239
+
+---
+
+## Today
+
+- Grammar of Graphics
+- Types of Data
+- Intro to Altair
+
+---
+
+## Grammar of Graphics
+
+Hadley Wickham, creator of `ggplot2` and `tidyverse`, ["A Layered Grammar of Graphics"](http://vita.had.co.nz/papers/layered-grammar.pdf).
+
+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. data and aesthetic mappings,
+2. one or more layers, each with
+
+    - a geometric object (line, point, etc.)
+    - (optional) statistical transformation
+    - (optional) position adjustment
+
+3. one scale per aesthetic mapping (color, size, etc.)
+4. a coordinate system
+5. facet specification
+
+---
+
+## Types of Data
+
+### **N** - Nominal
+
+"strings" with no **order** (alphabetical does not count)
+
+Species
+States
+Countries
+
+### **O** - Ordered
+
+- Grades: A, B, C, D, E, F
+- Rankings: 1st, 2nd, 3rd
+
+---
+
+## Types of Data (Quantitative)
+
+### **Q** - Interval (arbitrary zero)
+
+- Dates (1 CE, Jan 1 1970, or...)
+- Location (lat, lon)
+
+Only differences matter, can't compare ratios.
+_(What is 2024 / 1990?)_
+
+### **T**emporal
+
+Some systems (like Altair) will also offer this option specifically for dates and times.
+
+### **Q** - Ratio (zero fixed)
+
+Physical measurements, counts, amounts.
+
+"4 km is _twice as far_ as 2 km"
+
+---
+
+## Types of Data (Operations)
+
+|          | =, != | <, >, <=, >= | +, - | ÷   |
+| -------- | ----- | ------------ | ---- | --- |
+| Nominal  | ✓     |              |      |     |
+| Ordered  | ✓     | ✓            |      |     |
+| Interval | ✓     | ✓            | ✓    |     |
+| Ratio    | ✓     | ✓            | ✓    | ✓   |
+
+---
+
+## Data Model to N, O, Q
+
+- string?
+- bool?
+- float/int?
+
+Possible exceptions?
+
+---
+
+## Data Model to N, O, Q
+
+_Typically:_
+
+- string - nominal or ordered
+- bool - nominal
+- float/int - interval or ratio
+
+Possible Exceptions?
+
+- Numeric IDs
+- ZIP Codes
+- ratio data stored with units (e.g. "10km")
+
+---
+
+## Mapping of Variables to Aesthetics
+
+- position (X, Y, Z)
+- length
+- angle
+- slope
+- area
+- volume
+- density
+- hue
+- saturation
+- texture
+- connection
+- containment/grouping
+- shape
+
+---
+
+### Mackinlay's "effectiveness"
+
+![width:800px](effectiveness.png)
+
+---
+
+## Altair's Grammar
+
+Altair condenses several of the different pieces of the grammar to _"encoding channels"_.
+
+We've seen X, Y, and color, let's take a look at some examples of other encoding channels.
+
+---
--- a/README.md
+++ b/README.md
@ -0,0 +1,16 @@
+# 30239 Notes Repository
+
+## How to use this Repo
+
+I will push notes to this repository throughout the quarter, so if you are keeping your own notes in this repository you should generally keep them in separate files to avoid accidental git conflicts.
+
+Either make companion files in each directory, or keep your notes in a separate directory/notebook altogether.
+
+### Marimo Notebooks
+
+Marimo notebooks are similar to Jupyter notebooks, but work much better with Git and have some other nice features I appreciate.
+
+If you have ever looked at a Jupyter notebook file (.ipynb) in an editor, you know they are large JSON files, and once they are checked into Git changes become very difficult to track.
+
+
+`uv run marimo`
--- a/pyproject.toml
+++ b/pyproject.toml
@ -0,0 +1,13 @@
+[project]
+name = "30239-notes"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "altair>=5.4.1",
+    "jupyter>=1.1.1",
+    "marimo>=0.8.20",
+    "pandas>=2.2.3",
+    "polars>=1.8.2",
+]
--- a/uv.lock
+++ b/uv.lock