Lecture 6 - Intro to Visualization: When and Why; Visualization Aesthetics

Announcements

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

Goals

• Understand the importance of visualization as a tool for understanding data.
• Know some of the different settings in which visualization is used.
• Understand some principles of how to make good visualizations
  • Maximize data-ink ratio
  • Minimize lie factor
  • Minimize chartjunk
  • Use scales and labeling well
  • Use Color Well
  • Use Repetition Well

Big Idea: Why visualize?

Consider Anscombe's Quartet:

import seaborn as sns
sns.set_theme(style="ticks")

# Load the example dataset for Anscombe's quartet
df = sns.load_dataset("anscombe")

# if you want to look at the raw data, this makes it into a nicer shape:
# df["idx"] = df.groupby("dataset").cumcount()
# df.pivot(index="idx", columns="dataset").swaplevel(0, 1, axis=1).sort_index(axis=1)

df.groupby("dataset").describe()

Hey, they're all the same! ...right? Let's confirm by visualizing:

# Show a scatter plot with a regression line for each dataset
sns.lmplot(x="x", y="y", col="dataset", hue="dataset", data=df,
           col_wrap=2, ci=None, palette="muted", height=4,
           scatter_kws={"s": 50, "alpha": 1})

Hmm, that didn't come out how I thought it would.

Takeaway: visualization is often the best (and sometimes the only) way to understand a dataset.

When should you visualize?

• When exploring data
  • for me, this often looks like df.plot.*
  • Goal: show you what's going on; answer questions for yourself.
• When presenting data
  • for me, this often looks like sns.*(...) along with a bunch of matplotlib code to fine-tune the appearance.
  • Goal: show your reader what's going on; tell a story about the data, clearly and faithfully.
• When providing interactive visualization tools for consumers of your data; examples:
  • https://www.mountwashington.org/experience-the-weather/current-summit-conditions.aspx
  • https://pudding.cool/projects/vocabulary/

What makes a good visualization?

Two concerns:

• Telling the truth
• Telling it clearly and with style

This is like asking what makes a good painting - it requires a sense of aesthetics.

Some principles to live by, based on the work of visualization pioneer Edward Tufte:

Maximize data-ink ratio

The data-ink ratio is the amount of "ink" used to represent data divided by the total amount of "ink" in the graphic:

$$ \frac{\textrm{ink used to represent data}}{\textrm{total ink in the graphic}}$$

Minimize lie factor

The lie factor is the ratio between the size of the effect in your graphic and the size of the effect in the data:

$$ \frac{\textrm{size of effect in the graphic}}{\textrm{size of effect in the data}}$$

Minimize chartjunk

Chartjunk is loosely defined as extraneous visual elements that do not further the purpose of the graphic.

Use scales and labeling well

• Fill the available space with data (without increasing the lie factor)
• Use clear labels

Use color and shading well

• Colors can be used to differentiate categorical or numerical values.
• For numerical/continuous, use perceptually uniform colormaps.
• Avoid large areas of bright colors; small areas of sharp color contrast can be powerful visual elements.

Use repetition well

• Reuse the cognitive effort your reader puts in to understand one plot
• Small multiples - many small charts of the same thing, e.g., for different categories
  • Example: sns.pairplot
• Multiple time series on a single set of axes

Activity: analyze a plot!

Write:

• Your plot number
• The names of your group members
• Analysis of the plot with respect to at least three of the above principles
  • Maximize data-ink ratio
  • Minimize lie factor
  • Minimize chartjunk
  • Use scales and labeling well
  • Use Color Well
  • Use Repetition Well
• Be prepared to share the most pertinent principle with the class in 1 minute or less.