Merge branch 'lec02'

00.value-of-dataviz/slides.md

@@ -335,7 +335,7 @@ Use of Altair is **strongly** recommended, but other libraries allowed.
 
 ---
 
-## Acknowledgements
+## Acknowledgements & References
 
 Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials.
 

01.gog-altair/slides.md

@@ -6,9 +6,9 @@
 
 ## 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**
 
 ---
 

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02.perception-and-color/slides.md

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+---
+theme: custom-theme
+---
+
+# Perception & Color
+
+## CAPP 30239
+
+---
+
+## Today
+
+- What matters most when creating a visualization?
+- How does human **perception** factor into visualization design?
+- Understanding **color**, and computational representations of it.
+
+---
+
+## What is the most important question when creating a visualization?
+
+---
+
+## What is the most important question when creating a visualization?
+
+<ul>
+<li><s>Where will the data come from?</s>
+<li><s>What type of chart do I use?</s></li>
+<li>Who is the audience?</li>
+</ul>
+
+---
+
+## Audience First
+
+- Who are you presenting to?
+- How familiar are they with the data?
+- What is their numerical & visualization literacy?
+- Via what medium will they receive the information?
+- What are you trying to do? (Persuade, Inform, Inspire?)
+
+*Only now can we start thinking about data and presentation.*
+
+---
+
+## Perception
+
+- **Selective** - We can only pay attention to so much.
+- **Patterns** - Our brains are pattern-matching machines, audience will benefit from intentional patterns & be distracted by unintentional ones.
+- **Limited working memory** - We hold a very limited set of information in our minds at once.
+
+---
+
+## What do you see?
+
+<div class="container">
+<div class="col">
+
+![](viz-1.png)
+
+</div><div class="col">
+
+```python
+alt.Chart(random_df).mark_point().encode(
+    alt.X("a"),
+    alt.Y("c"),
+    alt.Color("b"),
+    alt.Size("c"),
+    alt.Shape("a:N"),
+    alt.Fill("b"),
+    alt.Opacity("b"),
+) 
+```
+</div>
+</div>
+
+---
+
+## What do you see?
+
+<div class="container">
+<div class="col">
+
+![](viz-2.png)
+
+```
+alt.Chart(random_df).mark_line().encode(
+    x="a",
+    y="c",
+)`
+```
+
+</div>
+</div>
+
+---
+
+## Effectiveness Revisited
+
+![width:800px](effectiveness.png)
+
+---
+
+<div class="container">
+
+<div class="col">
+
+**Altair Channels**
+
+- Position (`X, Y`)
+- Angle (`Angle`)
+- Area (`Radius`, `Size`)
+- Hue, Saturation (`Color`)
+- Texture (`Opacity`, `Fill`)
+- Shape (mark type, `Shape`)
+</div>
+
+<div class="col">
+
+**What about?**
+- Length
+- Slope
+- Volume
+- Density
+- Connection
+- Containment
+</div>
+</div>
+
+---
+
+**Derived Properties**
+
+
+- Length/Area - size of bars (`X`, `Y`)
+- Slope & Density - affected by scale
+- Connection - ex. layering of lines w/ points
+- Containment - achieved with layering
+
+What about *volume*?
+
+---
+
+## Stevens' Power Law
+
+Stevens (1975): Human response to sensory stimulus is characterized by a power law with different exponents with different stimuli. 
+
+perception = (magnitude of sensation)<sup>a</sup>
+
+Smaller <sup>a</sup> exponent: harder to perceive changes.
+
+Stevens measured values of a by exposing people to varied stimulus and asking them to compare magnitudes.
+
+---
+
+
+<div class="container"><div class="col">
+
+![](stevens.png)
+
+</div><div class="col">
+
+
+| Continuum | Exponent |
+|-|-|
+| Color **Brightness**| 0.33-0.5 |
+| Smell| 0.6 |
+| Loudness | 0.67 |
+| **Depth Perception** | 0.67 | 
+| Area | 0.7 |
+| 2D Planar Position | 1.0 |
+| Warmth | 1.3-1.6 |
+| Color **Saturation** | 1.7 |
+| Electric Shock | 3.5 |
+
+</div></div>
+
+---
+
+## 3D Graphs
+
+![](stunning-3d-chart.jpg)
+
+---
+
+![](datavizproject.png)
+
+---
+
+![](3d-scatter.png)
+
+---
+
+## Instead of 3D Graphs
+
+- Find other channels: hue & size are good candidates. (bubble chart)
+- Or make multiple 2D graphs with XY/YZ/XZ pairs.
+
+![bg left](scatter-matrix.png)
+
+---
+
+## What is Color?
+
+Wavelengths of light are perceived as particular colors:
+![](linear_visible_spectrum.svg.png)
+
+What's missing?
+
+<!-- credit: https://en.wikipedia.org/wiki/File:Linear_visible_spectrum.svg -->
+
+---
+
+## Color & the Eye
+
+### Rods
+
+- spread throughout retina
+- more sensitive in low light conditions
+- brightness ("lightness")
+
+### Cones
+
+- 3 types with peak sensitivity at different frequencies
+- concentrated in center of eye
+- less sensitive in low light conditions
+- hue & saturation
+
+![bg right](cone-fundamentals.png)
+
+<!-- source https://commons.wikimedia.org/wiki/File:Cone-fundamentals-with-srgb-spectrum.svg -->
+
+---
+
+## Spectrum vs. What We See
+
+What we actually see is always a blend of multiple peaks.
+
+This is impacted by ambient light conditions, as well as quirks of our visual processing.
+
+![height:400px](the-dress.jpg)
+
+In actuality, multiple combinations of light can give same color (**metamers**).
+
+---
+
+## Chromatic Adaptation
+
+![](demo-bw-illusion-andrew-steele.gif)
+
+Source: Andrew Steele <https://www.youtube.com/channel/UC-XYsDNh4-886rMNLnnwR_w>
+
+---
+
+## Color Naming
+
+Color naming is highly subjective, and research has shown that the ability to name a color correlates highly with the ability to distinguish it.
+
+![bg right](xkcd-color-map.png)
+
+Be particularly careful with blue/green boundaries, as there are significant cultural differences.
+
+Source: https://blog.xkcd.com/2010/05/03/color-survey-results/
+
+
+---
+
+## Cultural Considerations
+
+![bg right width:600px](hok-uk.svg)
+
+- American audiences associated <span color="red">red</span> & <span color="blue">blue</span> with political parties on any map in a political context.
+- Also international meaning of <span color="red">red</span> & <span color="blue">blue</span> is flipped: red is left, blue is right.
+- Most other colors have contradictory meanings depending on culture. For example, yellow might be chosen to denote success (parts of Africa) or be associated with death (Middle East).
+
+<!--Source: https://www.color-meanings.com/color-symbolism-different-cultures/-->
+
+<!-- image from wikipedia: UK House of Commons -->
+
+---
+
+## Color Vision Deficiency
+
+More accurate name for what is commonly known as colorblindness.
+
+- Red-Green CVD - most common
+    - four types: Dueteranomaly and Protanomaly (mild) to Protanopia and Dueteranopia (complete)
+- Tritanomaly/Tritanopia: blue/green and yellow/red confusion.
+- rarest, complete lack of color vision, usually corresponds to other vision issues as well
+
+![bg right width:600px](colorblind.jpg)
+
+---
+
+## Color on a Page
+
+![bg right](cmyk.svg)
+
+Ink absorbs light, so we work with subtractive blending.  Our base colors are cyan, magenta, and yellow.  To save on ink costs, we throw in black/contrast as well.
+
+We call this CMYK color.
+
+---
+
+## Color on a Screen
+
+Screens emit light, which means we use **additive blending** of red, green, and blue light. Every pixel of a screen can emit these three colors in different intensities.
+
+
+
+![bg right](additive.png)
+
+---
+
+## Color Spaces
+
+Ways of describing a color mathematically, usually have 3 components to match our perception of color:
+
+- RGB (early photography)
+- CIE XYZ (1931)
+- HSB/HSV/HSL (1970s)
+
+![bg right width:700px](rgb-khan.jpg)
+
+<!-- https://commons.wikimedia.org/wiki/File:Rgb-compose-Alim_Khan.jpg -->
+
+---
+
+A common way to refer to colors is by their intensity in each of these three channels.
+
+<span style="color: rgb(0% 100% 0%)">this is 0% red, 100% green, 0% blue intensity (#00ff00)</span>
+<span style="color: rgb(20% 60% 20%)">this is 20% red, 60% green, 20% blue intensity: (#143c14)</span>
+<span style="color: #ff00ff">this is 100% red, 0% green, 100% blue intensity: #ff00ff</span>
+
+This is sometimes expressed in hexadecimal:
+
+![height:120px](hexrgb.png)
+
+![bg right](rgb-pixels.jpg)
+
+---
+
+### RGB space as a cube
+
+![cube](LinearRGBCube.png)
+
+---
+
+### RGB as pair plots
+
+![pair plots](RGBPairPlots.png)
+
+Remember this trick for your own 3-dimensional data!
+
+---
+
+![height:500px](TriangleSliceRGB.png)
+
+A slice through the middle of the cube gives colors of comparable brightness.  (You may have seen such a triangle in color pickers.)
+
+---
+
+## HSL
+
+![height:500px](HSL.png)
+
+An alternative color space that's very useful for visualization is HSL color space.
+
+Hue, Saturation, Lightness | <https://hslpicker.com/>
+
+---
+
+## Aside: What about "alpha"?
+
+You will often see a fourth channel: RGB**A**, HSL**A**.
+
+This is known as alpha transparency (translucency).
+
+This has to do with how the program in question *blends* the colors. The final pixel values on the screen will still be converted to RGB components.
+
+- Use sparingly.
+- Variations are very subtle, and background dependent.
+
+<!-- image source: https://upload.wikimedia.org/wikipedia/commons/3/34/RGB_pixels.jpg -->
+
+---
+
+## CIE (RGB / XYZ / CIELAB)
+
+Based on human perception experiments where people would adjust dials to recreate colors out of red, green, and blue light.
+
+First from 1920s, revised in 1970s.
+
+*Commission internationale de l'éclairage*  (Illumination)
+
+![bg right](cie-xyz.png)
+
+---
+
+## Screen Gamut
+
+Screens can't show the entire range of visible colors accurately, they define a "gamut".  Since ~1996 most devices aim at a standard gamut to ensure similar representations of color, but even high end devices are not perfectly aligned.
+
+![bg right](gamut2.png)
+
+Projectors (like the one you're likely viewing this on) usually have skewed gamut. 
+
+Moral of the story: **Consider your medium!**
+
+---
+
+## What does all this mean for visualization?
+
+Color choices should be made with respect to:
+
+  - medium (screen vs. print, type of screen)
+  - audience (culture, vision differences, expectations)
+  - differentiability
+
+---
+
+## Role of Color
+
+- **Identify** - Different color per category/actor.
+  - opt for distinct hues
+- **Group** - Group like entities using same/like colors.
+  - often with similar hues
+- **Layer** - Overlay different information while keeping contrast.
+  - saturation differences very important to not overwhelm eye
+- **Highlight** - Call out important/relevant information.
+  - brightness and hue differences important
+
+--- 
+
+## Color Channels & Data Types
+
+### Lightness is perceived as ordered
+
+Good for **Ordinal** variables
+
+![height:50px](ordinal.png)
+
+**Quantitative** (Continuous) variables harder to discern
+
+![height:50px](qual.png)
+
+### Hue typically unordered
+
+**Nominal** variables.
+
+![height:50px](nominal.png)
+
+---
+
+### Types of Palettes
+
+- Qualitative - Nominal data
+- Sequential - Quantitative data
+- Diverging - Data with a meaningful zero-point (increase/decrease, more/less)
+
+![](palette-types.jpg)
+
+<!-- source: Peter Aldhous, NICAR 2016 -->
+
+---
+
+### Hue Separation
+
+Pick distinct hues for unrelated variables.
+
+Grouped schemes can be used where there are relationships among the categories.
+
+![bg left width:600px](vega-schemes.png)
+
+<https://vega.github.io/vega/docs/schemes/>
+
+---
+
+## Color Tips
+
+- Aim for no more than ~6 colors that need to be distinguished.
+- Colors should be distinct & differentiable by name.
+- Be mindful of cultural considerations & symbolism.
+- Ensure color schemes chosen appropriately for types of data.
+- "Get it right in black & white"
+
+
+---
+
+## "Get it right in black & white"
+
+A common mantra among visual designers.
+
+Ensure that your hues have different brightness levels.
+
+Ensure that you aren't using hue alone for your image.
+
+![bg left width:600px](vega-schemes-bw.jpg)
+
+---
+
+## Text Legibility
+
+An important issue when using colored text and/or backgrounds is **legibility**.
+
+Web Content Accessibility Guidelines require a 4.5 color contrast (3:1 for large text).
+
+Minimize *saturation* in backgrounds, pick a font color with opposing *lightness*.
+
+<https://webaim.org/resources/contrastchecker/>
+
+![bg left width:600px](contrast.png)
+
+
+---
+
+## Tools
+
+- Vega Schemes: https://vega.github.io/vega/docs/schemes/
+- Contrast/theme exploration: https://schubert-da.github.io/dataviz-palette-tool/
+- Theme exploration for cartography: <https://colorbrewer2.org/>
+- Color-theory based theme creator: https://meodai.github.io/poline/
+- Theme creator w/ theme sharing: https://coolors.co
+- HSL/RGB picker: <https://hslpicker.com/>
+- Contast checker: <https://webaim.org/resources/contrastchecker/>
+
+### Color-Blindness
+
+- MacOS/iOS app: https://michelf.ca/projects/sim-daltonism/
+- Browser extensions (search "colorblindness" in your browser of choice)
+
+---
+
+## Acknowledgements & References
+
+Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials.
+
+Color space images are from <https://jamie-wong.com/post/color/>, which is an incredible resource if you'd like to go deeper into both the biology and math of color.
+
+- https://www.math.csi.cuny.edu/~mvj/GC-DataViz-S23/lectures/L6.html
+- https://en.wikipedia.org/wiki/Stevens%27s_power_law
+- https://colorusage.arc.nasa.gov
+- https://vega.github.io/vega/docs/schemes/

03.charts/Untitled.ipynb

@@ -0,0 +1,77 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "52cc5dd5-93ce-4370-adc0-8842764a359e",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'vega_datasets'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01maltair\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01malt\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mvega_datasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data\n\u001b[1;32m      4\u001b[0m source \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mstocks()\n\u001b[1;32m      6\u001b[0m lines \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m      7\u001b[0m     alt\u001b[38;5;241m.\u001b[39mChart(source)\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;241m.\u001b[39mmark_line()\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;241m.\u001b[39mencode(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprice\u001b[39m\u001b[38;5;124m\"\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msymbol\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     10\u001b[0m )\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'vega_datasets'"
+     ]
+    }
+   ],
+   "source": [
+    "import altair as alt\n",
+    "from vega_datasets import data\n",
+    "\n",
+    "source = data.stocks()\n",
+    "\n",
+    "lines = (\n",
+    "    alt.Chart(source)\n",
+    "    .mark_line()\n",
+    "    .encode(x=\"date\", y=\"price\", color=\"symbol\")\n",
+    ")\n",
+    "\n",
+    "xrule = (\n",
+    "    alt.Chart()\n",
+    "    .mark_rule(color=\"cyan\", strokeWidth=2)\n",
+    "    .encode(x=alt.datum(alt.DateTime(year=2006, month=\"November\")))\n",
+    ")\n",
+    "\n",
+    "yrule = (\n",
+    "    alt.Chart().mark_rule(strokeDash=[12, 6], size=2).encode(y=alt.datum(350))\n",
+    ")\n",
+    "\n",
+    "\n",
+    "lines + yrule + xrule"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a208413-acd4-4ec5-a4ac-3e77558ec9bb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

03.charts/slides.md

@@ -0,0 +1,373 @@
+---
+theme: custom-theme
+---
+
+# Chart Design
+
+## CAPP 30239
+
+---
+
+## Today
+
+- What general **principles of visual design** are relevant to our work?
+- What are the **common types of charts** and how do we use them?
+- When and how do we break the rules?
+
+---
+
+## Edward Tufte
+
+### The Visual Display of Quantitative Information
+
+![](tufte.png)
+
+---
+
+## Key Ideas
+
+- Graphical Integrity: Above all else, show the data.
+- Maximize the data-ink ratio.
+- Minimize chart junk.
+- Aim for high chart density, consider *small multiples*.
+- 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.
+
+![](liefactor.jpg)
+
+Mileage increase: 53%
+Graph length increase: 783%
+"Lie Factor": 14.8x
+
+---
+
+2. Clear, detailed and thorough **labeling** should be used to defeat graphical distortion and ambiguity.
+
+![bg left](spinal.webp)
+
+How many children get a spinal injury every year? (out of 74,000,000 children in US)
+
+Note: there are only 22,000 total spinal cord injuries a year in America (and most are 16-30yo).
+
+<!-- .0000003% -->
+
+---
+
+3. Write out explanation of the data on the graphic itself. **Label important events** in the data.
+
+![bg right width:600px](labeled.png)
+
+---
+
+4. Show **data variation, not design variation**.
+
+Deflated & standardized units of money are almost almost superior to nominal units.
+
+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.
+
+---
+
+## Axes/Scale Mistakes
+
+- Carefully consider not starting at zero.
+- Beware dual axes.
+- Consider audience when using log scale.
+- Related: No pie charts that don't add up to 100%
+
+---
+
+![bg fit](tools_misleading_axes.png)
+
+---
+
+![bg fit](fox-bar.jpg)
+
+---
+
+![bg vertical fit](1080.png)
+
+---
+
+![bg fit](5920.png)
+
+
+---
+
+## Data-Ink Ratio
+
+- **Data-ink**: Ink (pixels) used to show data.
+- Data-ink ratio: data-ink / total-ink
+
+![](francetrains.jpg)
+
+---
+
+![](eec.gif)
+
+![bg right width:600px](sizecycle.gif)
+
+---
+
+## 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.
+
+![](sparkline.png)
+
+---
+
+![bg left height:700px](age-junk.png)
+
+## Chart Junk
+
+Anything that isn't relevant to understanding the data.
+
+---
+
+![](chartjunk-bullet.webp)
+via junkcharts.typepad.com
+
+---
+
+## Common Chart Types
+
+---
+
+## How to Pick?
+
+- Quantitative / Quantitative: 
+- Quantitative / Temporal: 
+- Quantitative / Nominal: 
+- Nominal / Nominal: 
+
+---
+
+### Bar Charts & Histograms
+
+- X/Y: Nominal (Binned Numerical - Histogram)
+- Y/X: Quantitative
+- Area must be relevant on bar charts: no log scales/cut axes!
+
+![](bars.png)
+
+---
+
+### Line & Area Charts
+
+- X: Temporal / Quantitative
+- Y: Quantitative (means / sums)
+
+
+![bg right width:600px](lines.png)
+
+---
+
+### When to use stacked area charts?
+
+![bg left width:600px](area.png)
+
+Sum of stacked axis variable **must have meaning**.
+
+---
+
+### Heatmap
+
+![bg right width:600px](heatmap.png)
+
+- X & Y: Quantitative or Nominal
+- Color: Quantitative
+- `mark_rect`
+
+---
+
+### Strip Plot
+
+![bg left width:600px](strip.png)
+
+- Y: Nominal
+- X: Temporal or Quantitative
+- Color: Optional (any type)
+- `mark_tick`
+
+---
+
+### Pie / Donut / Radial Charts
+
+![bg right fit](pyramid.png)
+
+Theta: Quantitative (ratio)
+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.
+
+**Must add up to 100%**
+
+---
+
+![](pie-comparison.png)
+https://www.storytellingwithdata.com/blog/2020/5/14/what-is-a-pie-chart
+
+---
+
+### Bump / Rank Line Chart
+
+![width:200px left](rankline.png)
+
+![width:500px left](bump.png)
+
+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
+
+![bg left width:600px](bubble.png)
+
+- X / Y: Quantitative
+
+Bubble charts use size as a 3rd dimension.
+
+(Note subtle but useful transparency usage as well.)
+
+---
+
+### Small Multiples / Faceting
+
+![facet](facet.png)
+
+![bg right fit](small-maps.png)
+
+<!-- source: https://www.juiceanalytics.com/writing/better-know-visualization-small-multiples -->
+
+Useful when there is a nominal variable being compared across two other dimensions.
+
+---
+
+![bg fit](obama-treemap.png)
+
+<!-- source https://obamawhitehouse.archives.gov/interactive-budget -->
+
+---
+
+### Map Basics
+
+Two most common:
+
+- point maps
+- choropleths
+
+![bg left width:600px](london-trees.png)
+
+*Image: Trees in London, data.london.gov.uk*
+
+<!-- source: https://data.london.gov.uk/dataset/local-authority-maintained-trees#:~:text=The%20data%20does%20not%20represent,streets%2C%20private%20gardens%20and%20more. -->
+
+**We will revisit maps later in this course.**
+
+
+--- 
+
+## Two choropleths, same data.
+
+![bg right vertical width:600px](arcgis-chorolpleth.png)
+
+![bg right width:600px](arcgis-choropleth2.png)
+
+<!-- source: https://carto.maps.arcgis.com/apps/webappviewer/index.html?id=7475c5788efe4c75a9642f552f61d568 -->
+
+Color scale & unit of measurement is incredibly important.
+
+Consider alternatives if district/population sizes vary significantly.
+
+---
+
+## When & How to Break the Rules
+
+**When in doubt...**
+
+9 out of 10 visualizations should be some variation of the common types.
+
+This does not need to hamper creativity, in the right context a little flourish can add a lot. But ensure that it does not obfuscate the data.
+
+Focus on Tufte's principles & ask for feedback!
+
+---
+
+### Case Study: Two Innovations
+
+Two visualization types that have had their moment in the past 10-15 years:
+
+- Hex/Grid Maps
+- Word Clouds
+
+---
+
+## Grid Map
+
+![](npr-side-by-side.png)
+
+Introduced in <https://blog.apps.npr.org/2015/05/11/hex-tile-maps.html>
+
+
+<!-- discuss: is this a good thing? -->
+
+---
+
+## Word Cloud
+
+![](word-cloud.jpg)
+
+
+---
+
+![bg left](nyt1.png)
+![](nyt2.png)
+
+Derived from same data as word cloud.
+
+source: NYTimes via https://www.niemanlab.org/2011/10/word-clouds-considered-harmful/
+
+---
+
+## Narrative-supporting graphics
+
+![bg left width:500px](crochet.jpg)
+
+by ulaniulani on flickr
+
+---
+
+### When it's OK to use 3D
+
+You have data that relates to a spatial third dimension.
+
+![bg vertical right](snowfall.png)
+![bg right fit](lhc.png)
+
+(Image: Snowfall, NY Times)
+(Image: CERN Large Hadron Collider)
+
+---
+
+## Acknowledgements & References
+
+Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials.
+
+- https://www2.cs.uh.edu/~ceick/NO/COSC3337-DV2.pdf 
+- Images from Tufte's Visual Display of Quantitative Information
+- Images from Altair <https://altair-viz.github.io/gallery/index.html>
+

Justfile

@@ -1,4 +1,7 @@
+preview lecture:
+    marp -pw --html --theme custom-theme.css {{lecture}}/slides.md
+
 slides:
     for file in `ls */slides.md`; do \
-      marp $file; \
+      marp --theme custom-theme.css $file; \
     done

custom-theme.css

@@ -0,0 +1,11 @@
+/* custom-theme.css */
+/* @theme custom-theme */
+
+@import "default";
+
+.container {
+  display: flex;
+}
+.col {
+  flex: 1;
+}

pyproject.toml

@@ -10,4 +10,5 @@ dependencies = [
   "pandas>=2.2.3",
   "polars>=1.8.2",
   "ruff>=0.6.8",
+  "vega-datasets>=0.9.0",
 ]

uv.lock

@@ -16,6 +16,7 @@ dependencies = [
     { name = "pandas" },
     { name = "polars" },
     { name = "ruff" },
+    { name = "vega-datasets" },
 ]
 
 [package.metadata]
@@ -25,6 +26,7 @@ requires-dist = [
     { name = "pandas", specifier = ">=2.2.3" },
     { name = "polars", specifier = ">=1.8.2" },
     { name = "ruff", specifier = ">=0.6.8" },
+    { name = "vega-datasets", specifier = ">=0.9.0" },
 ]
 
 [[package]]
@@ -1696,6 +1698,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ce/d9/5f4c13cecde62396b0d3fe530a50ccea91e7dfc1ccf0e09c228841bb5ba8/urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac", size = 126338 },
 ]
 
+[[package]]
+name = "vega-datasets"
+version = "0.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pandas" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8f/a0/ce608d9a5b82fce2ebaa2311136b1e1d1dc2807f501bbdfa56bd174fff76/vega_datasets-0.9.0.tar.gz", hash = "sha256:9dbe9834208e8ec32ab44970df315de9102861e4cda13d8e143aab7a80d93fc0", size = 215013 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/9f/ca52771fe972e0dcc5167fedb609940e01516066938ff2ee28b273ae4f29/vega_datasets-0.9.0-py3-none-any.whl", hash = "sha256:3d7c63917be6ca9b154b565f4779a31fedce57b01b5b9d99d8a34a7608062a1d", size = 210822 },
+]
+
 [[package]]
 name = "wcwidth"
 version = "0.2.13"