Spring 2026 Slides

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# .Large[Data Visualization]

## .small[]
#### .tiny[Dr. Evangeline Reynolds | DS4IR | 2026-04-01 |Image credit: William Iven, Upsplash]

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Title slide

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# Agenda

- The Promise of Data Visualization
- ggplot2, what, why, and how (with gapminder data)?

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Next week:

- A chart that impacted you...
- aesthetic mapping

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Function of data vis:

> "The simple graph has brought more information to the data analyst’s mind than any other device"

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Fundamentals of Data Visualization:

> The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional.

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![Roger Peng's schematic (EDA = Exploratory Data Analysis)](../../../teaching/lecture_topics/images_for_lecture/peng_hypothesis_testing.png)

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![R for data science](../../../teaching/lecture_topics/images_for_lecture/data-science-steps.png)

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- grammar of graphics
- Discussion "Three waves"
- Intro to R, RStudio, to ggplot(), w/ gapminder data

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# Break out 1 - and introductions

Which are you?

exploration <--> communication

data <--> vis

grammar of graphics <--> glamour of graphics

Quadratisch. Praktisch. Gut <--> Je suis un artist

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![](../../../teaching/lecture_topics/images_for_lecture/ritter_sport.jpeg)

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# The "Grammar of Graphics" - 1999

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- non judgemental about
  - what's a good or bad chart, chart that communicates well or not
  - about beauty, thematic choices
  - how to design a chart building API

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# definition of a data visualization...

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# A data visualization is made up of ...

### .left[... geometric objects ...]

### .center[...that take on aesthetics (color, line width, x-position) ...]

### .center[...which represent variables ...]

### .right[...from a dataset (dataframe)]

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# colloquial:

## "aesthetics representing variables"

# Term of art:

## "variables mapped to aesthetics"

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# colloquial:

## "data visualization"

# Term of art:

## "statistical graphic"

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class: center, middle

.large[A statistical graphic ...]

.large[maps variables of a data set]

.large[to the aesthetics of geometric objects.]

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## Elements of the Grammar of Graphics (choices)

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### A series of choices:

- choose data

- choose aesthetic mapping (what aesthetics will represent what variables)

- choose geometric objects

- make decision to facet or not

- make choice to summarize data or not (statistics)

- choose a coordinate system

- make thematic choices

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# Hadley Wickham on grammar of graphics

> A grammar of graphics is a tool that enables us to concisely describe the components of a graphic. Such a grammar allows us to **move beyond named graphics** (e.g., the “scatterplot”) and gain insight into the **deep structure** that underlies statistical graphics.

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# Hadley Wickham on grammar of graphics

> .large[A good grammar will allow us to gain insight into the composition of complicated graphics, and reveal **unexpected connections**
between seemingly different graphics (Cox 1978).]

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# if don't know the chart name...?

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# what's a pie chart?

https://evamaerey.github.io/flipbooks/pie_chart/pie_chart

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# "Cleveland dot plot?"

![example from https://uc-r.github.io/](https://uc-r.github.io/public/images/visual/cleveland-dotplots/icon.png)

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# Implementations

# Tableau

Polaris -> Tableau - Acquired for 15.7 Billion dollars (salesforce)

Vega Lite... (dynamic, interactive data visualizations in web browsers)

ggplot2

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.right[learning ggplot - transferable skill!]

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background-image: url(images_for_lecture/ggplot_beautiful_examples.png)
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ggplot2: and quickly explore data, and iterate on explorations

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background-image: url(images_for_lecture/ggplot_beautiful_examples.png)
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A tool for making beautiful things...

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# BBC on ggplot

[From "How the BBC Visual and Data Journalism team works with graphics in R":](https://medium.com/bbc-visual-and-data-journalism/how-the-bbc-visual-and-data-journalism-team-works-with-graphics-in-r-ed0b35693535)
  
On flexibility:  
  
> ggplot2 gives you far more **control and creativity** than a chart tool and allows you to go **beyond a limited number of graphics**.

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# BBC on ggplot

On reproducibility:  
  
> Working with **scripts** saves a huge amount of time and effort, in particular when working with data that needs updating regularly, with reproducibility a key requirement of our workflow.

In sum:

*In short, it was a **game changer**, so we quickly turned our attention to how best manage this newly-discovered power.*

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# ggplot2's full name is:

A "*Layered* grammar of graphics" ...

so the promise of building up your plot step-by-step -- incrementally!

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### Building up a plot: Layered presentation of graphics

<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">My best tip on how to give better quantitative presentations is to (a) use more plots and (b) build up your plots on multiple overlays, as in:<br><br>- Just x-axis (explain it)<br>- Add y-axis (explain it)<br>- Add 1 data point (explain it)<br>- Plot the rest of the data (explain it)</p>&mdash; Matt Blackwell (@matt_blackwell) <a href="https://twitter.com/matt_blackwell/status/991004129198854145?ref_src=twsrc%5Etfw">April 30, 2018</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

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> `+` [the plus sign] is the key to constructing sophisticated ggplot2 graphics. It allows you to start simple, then get more and more complex, checking your work at each step.

``` r
ggplot() +
  aes(x = 1) +
  aes(y = n) +
  geom_col(width = 2, col = "grey", ) + 
  aes(fill = continent) +
  geom_point(aes(y = text_pos_y, x = 2.3), 
             color = "pink") +
  aes(label = continent) +
  geom_text(aes(y = text_pos_y, x = 2.3), 
            color = "navy") +
  geom_vline(xintercept = 0:2, lty = "dashed") +
  scale_x_continuous(limits = c(-1, 3)) +
  coord_polar(theta = "y")
```

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Very current relevance... Data Journalist's work ...

https://twitter.com/jburnmurdoch/status/1237737352879112194

What are some of the decisions that were made?

List at least 6 in your group...

Then try rank order as the most consequential vs. least

https://evamaerey.github.io/flipbooks/covid_19/covid_19_2

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## Elements of the Grammar of Graphics (choices)

What do you think is the key piece in terms of communication for creating graphs?  What makes visualization so effective anyway?

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## Key Aesthetics

Channels for visually encoding information...

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# Prep for Demo

# Hans Rosling

https://www.youtube.com/watch?v=jbkSRLYSojo

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What are the variables that are represented in the video?

What are the aesthetics used to represent variables?

What are the aesthetic mappings (variable aesthetic pairings)

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```
# A tibble: 1,704 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ℹ 1,694 more rows
```

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Bit of a Break...

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ggplot2 "Layered grammar of graphics"

building up your plots

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# But ... the working backward paradigm pretty strong

One rather savvy and experienced R described his experience with ggplot2:  “All good ggplots start like all good makefiles, by copying the last one you edited and deleting most of it.”

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"Hacker News" exchange about ggplot2: 	
	
sk5t 8 months ago [-]

*I can't help but wonder--as someone who has flirted with R but certainly not committed--how much time and practice it takes to acquire decent fluency with these half-magical incantations to become productive.*

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jhbadger 8 months ago [-]

*Not that long. The trick is to really understand what each clause is doing. It's easy to just find some code on the Web for a graph similar to what you want and tweak it for your data, and I did that myself in the beginning, but then they will remain "half-magical incantations" as you put it. The thing about ggplot is nothing is there arbitrarily -- it is really describing how the plot is put together.*

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"Discrete" and "Continous" data