215 lines
4.6 KiB
Markdown
215 lines
4.6 KiB
Markdown
# Grammar of Graphics with Altair
|
|
|
|
## CAPP 30239
|
|
|
|
---
|
|
|
|
## Today
|
|
|
|
- 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"](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"
|
|
|
|
|
|
|
|
---
|
|
|
|
## 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
|
|
|
|
```json
|
|
{
|
|
"$schema": "https://vega.github.io/schema/vega-lite/v5.json",
|
|
"description": "A scatterplot showing horsepower and miles per gallons for various cars.",
|
|
"data": {"url": "data/cars.json"},
|
|
"mark": "point",
|
|
"encoding": {
|
|
"x": {"field": "Horsepower", "type": "quantitative"},
|
|
"y": {"field": "Miles_per_Gallon", "type": "quantitative"}
|
|
}
|
|
}
|
|
```
|
|
Vega condenses several of the different pieces of the grammar to _"encoding channels"_.
|
|
|
|
---
|
|
|
|
|
|
|
|
---
|
|
|
|
## Altair
|
|
|
|
```python
|
|
import pandas as pd
|
|
import altair as alt
|
|
|
|
df = pd.read_csv("cars.csv")
|
|
alt.Chart(df).encode(
|
|
x="Horsepower:Q", # shorthand for simple features
|
|
alt.Y("Miles_per_Gallon:Q").title("Miles Per Gallon"), # longer form w/ customization
|
|
)
|
|
```
|
|
|
|
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
|
|
|
|
<!-- at this point, see the marimo notebook in this directory -->
|
|
|
|
---
|
|
|
|
## Learning Altair
|
|
|
|
To master a library like Altair, you'll go through the following phases:
|
|
|
|
1. Learn the key concepts.
|
|
- Goal: Understand how the authors of Altair think about visualization.
|
|
- Achieved by: Reading user guide & watching tutorials.
|
|
2. Internalize concepts & API.
|
|
- Goal: Be able to do common tasks without referring to documentation. (You'll always lean on documentation for specifics.)
|
|
- Achieved by: Working on assignments & experimentation. Reading API reference as needed.
|
|
3. Mastery (not this quarter!)
|
|
- Goal: Be able to manipulate library to achieve most tasks. Understand limits.
|
|
- Achieved by: Regular use over months/years. Reading API reference and/or source code.
|
|
|
|
---
|
|
|
|
## Altair Assignment
|
|
|
|
<!-- walk through of assignment setup & how it'll be graded -->
|