WTF Re: Data Visualization

Heidi Steiner

Spring 2023

This “Diagram of the causes of mortality in the army in the East” was published in Notes on Matters Affecting the Health, Efficiency, and Hospital Administration of the British Army and sent to Queen Victoria in 1858.

This graphic indicates the number of deaths that occured from preventable diseases (in blue), those that were the results of wounds (in red), and those due to other causes (in black).

Nightingale’s key persuasion tactic was to convey statistics in exciting ways.

Today’s Map

• Why visualize?

• Limitations

• Types of Visualizations

• Tools + tips + takeaways

Why visualize?

Discover patterns that may not be obvious from numerical summaries

For Example…

datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise(
    n = n(),
    `Average x` = round(mean(x), 1),
    `Average y` = round(mean(y), 1)
  )

dataset	n	Average x	Average y
Dataset 1	142	54.3	47.8
Dataset 2	142	54.3	47.8
Dataset 3	142	54.3	47.8
Dataset 4	142	54.3	47.8
Dataset 5	142	54.3	47.8
Dataset 6	142	54.3	47.8
Dataset 7	142	54.3	47.8
Dataset 8	142	54.3	47.8
Dataset 9	142	54.3	47.8
Dataset 10	142	54.3	47.8
Dataset 11	142	54.3	47.8
Dataset 12	142	54.3	47.8
Dataset 13	142	54.3	47.8

We have 13 datasets, each with 142 observations. For each observation we have values on two variables recorded: an X and a Y.

Summary statistics for these two variables for each of the datasets is given on the right.

How, if at all, are these 13 datasets different from each other?

More summary statistics…

How, if at all, are these 13 datasets different from each other?

dataset	n	Average x	Average y	St Dev x	St Dev y
Dataset 1	142	54.3	47.8	16.8	26.9
Dataset 2	142	54.3	47.8	16.8	26.9
Dataset 3	142	54.3	47.8	16.8	26.9
Dataset 4	142	54.3	47.8	16.8	26.9
Dataset 5	142	54.3	47.8	16.8	26.9
Dataset 6	142	54.3	47.8	16.8	26.9
Dataset 7	142	54.3	47.8	16.8	26.9
Dataset 8	142	54.3	47.8	16.8	26.9
Dataset 9	142	54.3	47.8	16.8	26.9
Dataset 10	142	54.3	47.8	16.8	26.9
Dataset 11	142	54.3	47.8	16.8	26.9
Dataset 12	142	54.3	47.8	16.8	26.9
Dataset 13	142	54.3	47.8	16.8	26.9

Even more stats…

dataset	n	Average x	Average y	St Dev x	St Dev y	Correlation
Dataset 1	142	54.3	47.8	16.8	26.9	-0.1
Dataset 2	142	54.3	47.8	16.8	26.9	-0.1
Dataset 3	142	54.3	47.8	16.8	26.9	-0.1
Dataset 4	142	54.3	47.8	16.8	26.9	-0.1
Dataset 5	142	54.3	47.8	16.8	26.9	-0.1
Dataset 6	142	54.3	47.8	16.8	26.9	-0.1
Dataset 7	142	54.3	47.8	16.8	26.9	-0.1
Dataset 8	142	54.3	47.8	16.8	26.9	-0.1
Dataset 9	142	54.3	47.8	16.8	26.9	-0.1
Dataset 10	142	54.3	47.8	16.8	26.9	-0.1
Dataset 11	142	54.3	47.8	16.8	26.9	-0.1
Dataset 12	142	54.3	47.8	16.8	26.9	-0.1
Dataset 13	142	54.3	47.8	16.8	26.9	-0.1

Hidden patterns!

EDA Tools

Software for Exploratory Data Analysis & Beyond

MS Excel, LibreOffice - Calc

R (e.g. ggplot2) and Python (e.g. seaborn)

Tableau, PowerBI, Grafana

Orange

Why visualize?

Convey information in a way that is otherwise difficult/impossible to describe

Major Data Viz Limitations

Computational

Do you have the compute power to visualize every data point? Probably not…

Do you need a graphics processing unit?

Do you know how to utilize your lab’s GPU?

Coordinate

Forget putting all your great insights into one figure!

Cognitive Limitations

Ranking of visual communication channels

Cognitive Limitations: Distance

Principle of Proportional Ink
For example:

Cognitive Limitations: Color

Cognitive Limitations: Color

Cognivite Limitations: Clutter

Cognivite Limitations: Clutter

Cognitive Limitations

Often the same visualization won’t work for every audience…

#SciComm

• Graphical Abstracts

• Infographics

#SciComm

• Graphical Abstracts

• Infographics

#SciComm

#SciComm Tools

Extra Data Viz Tools

Rate your data visualization

InkSkape

JavaScript

Is there a field specific tool you might utilize?

Data are not neutral.

It’s easy to make a “bad” plot.

Bad Plots

• Taste

• Data

• Perception

Bad Plots: Data

this graph sucks for a few reasons: cherry picking data,

cross sectional design aka lines aren’t time but are differences

title misleads audience into assuming the survey question. we assume yes/no “is democracy essential?” actual question 1-10 how essential is democracy and this graph shows % of surveyed who answered only 10.

Bad Plots: Data

Data Viz Ethics

It is not good enough to commit to not misleading your audience.

Data Viz Ethics

It is OK for visualizations to invoke feelings!

Extra Thoughts: Exporting

Acronym	Name	Type	Application
pdf	Portable Document Format	vector	general purpose
eps	Encapsulated PostScript	vector	general purpose, outdated; use pdf
svg	Scalable Vector Graphics	vector	online use
png	Portable Network Graphics	bitmap	optimized for line drawings
jpeg	Joint Photographic Experts Group	bitmap	optimized for photographic images
tiff	Tagged Image File Format	bitmap	print production, accurate color reproduction
raw	Raw Image File	bitmap	digital photography, needs post-processing
gif	Graphics Interchange Format	bitmap	outdated for static figures, Ok for animations

• Vector graphics redrawn “on the fly” vs bitmap always bitmap

• Bitmap Compression (to keep file sizes small)

  • Lossless = pixel-for-pixel identical

  • Lossy = accepts some image degradation in return for smaller file sizes

    • great for photos, can’t make lines

• Rule of Thumb: Store visualization in maximum resolution format, copy at lower resolutions when necessary

resolution properly refers to the pixel density, the number of pixels per unit distance or area, not the total number of pixels. In digital measurement, the display resolution would be given in pixels per inch (PPI).

Extra Thoughts: Text

• Is your font readable at the smallest printed size?
• Do you need to left align?

Extra Thoughts: Accessibility

• Check that your plots are color blind friendly

• Add alt-text to #scicomm

Questions?