Introduction

# Data visualization with ggplot2

.class-info[
<figure>
 <img src="img/00/ggplot-logo.png" alt="ggplot" title="ggplot" width="15%">
</figure>
]

---

.box-4.large[Why do *you* want to visualize data?]

---

---

# Just show me the data!

.pull-left-3.small-code[

```r
head(my_data, 10)
```

```
## # A tibble: 10 x 2
## x y
## <dbl> <dbl>
## 1 55.4 97.2
## 2 51.5 96.0
## 3 46.2 94.5
## 4 42.8 91.4
## 5 40.8 88.3
## 6 38.7 84.9
## 7 35.6 79.9
## 8 33.1 77.6
## 9 29.0 74.5
## 10 26.2 71.4
```
]

.pull-middle-3.small-code[

```r
mean(my_data$x)
```

```
*## [1] 54.26327
```

```r
mean(my_data$y)
```

```
*## [1] 47.83225
```

```r
cor(my_data$x, my_data$y)
```

```
*## [1] -0.06447185
```
]

&nbsp;

&nbsp;

---

# oh no

.center[
<figure>
 <img src="img/00/DinoSequentialSmaller.gif" alt="Datasaurus Dozen" title="Datasaurus Dozen" width="100%">
 <figcaption><a href="https://www.autodeskresearch.com/publications/samestats" target="_blank">The Datasaurus Dozen</a></figcaption>
</figure>
]

---

# Raw data is not enough

.box-inv-4.small[Each of these has the same mean, standard deviation, variance, and correlation]

---

.box-4.large[andhs.co/gpl-dataviz]

---

# Applied data science

.center[
<figure>
 <img src="img/00/cover.png" alt="R for Data Science" title="R for Data Science" width="28%">
 <figcaption><a href="">R for Data Science, free online!</a></figcaption>
</figure>
]

---

# Applied data science

.center[
<figure>
 <img src="img/00/data-science-workflow.png" alt="Data science workflow" title="Data science workflow" width="100%">
</figure>
]

---

---

# Plan for today

---

# Visualization resources

.small[
- Andrew Heiss, ["Data Visualization with R"](https://datavizm20.classes.andrewheiss.com/) (free online course)
- Kieran Healy,[ *Data Visualization for Social Science: A practical introduction with R and ggplot2*](http://socviz.co/)
- Claus Wilke, [*Fundamentals of Data Visualization*](https://serialmentor.com/dataviz/)
- Alberto Cairo, *The Truthful Art: Data, Charts, and Maps for Communication* 
- Stephanie D. H. Evergreen, *Effective Data Visualization: The Right Chart for the Right Data*
- Dona M. Wong, *The Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting Data, Facts, and Figures* 
- Hadley Wickham and Garrett Grolemund, *R for Data Science: Import, Tidy, Transform, Visualize, and Model Data*
- Alberto Cairo, *The Functional Art: An Introduction to Information Graphics and Visualization*
]

---

# Design resources

.center[
<figure>
 <img src="img/02/williams-design-book.jpg" alt="Robin Williams's The Non-Designers Design Book" title="Robin Williams's The Non-Designers Design Book" width="30%">
 <figcaption>Buy this book and your life will change forever.</figcaption>
</figure>
]

---

# Design resources

## Accessibility

- [**Vischeck**](http://www.vischeck.com/vischeck/vischeckImage.php): Simulate how your images look for people with different forms of colorblindness (web-based)
- [**Color Oracle**](http://colororacle.org/index.html): Simulate how your images look for people with different forms of colorblindness (desktop-based, more types of colorblindness)

## Fonts

- [**Google Fonts**](https://fonts.google.com/): Huge collection of free, well-made fonts.
- [**The Ultimate Collection of Google Font Pairings**](https://www.reliablepsd.com/ultimate-google-font-pairings/): A list of great, well-designed font pairings from all those fonts hosted by Google (for when you're looking for good contrasting or complementary fonts).
]

## Colors

- [**Adobe Color**](https://color.adobe.com): Create, share, and explore rule-based and custom color palettes.
- [**ColourLovers**](http://www.colourlovers.com/): Like Facebook for color palettes.
- [**viridis**](https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html): Percetually uniform color scales.
- [**ColorBrewer**](http://colorbrewer2.org/): Sequential, diverging, and qualitative color palettes that take accessibility into account.
- [**Colorgorical**](http://vrl.cs.brown.edu/color): Create color palettes based on fancy mathematical rules for perceptual distance.
- [**Colorpicker for data**](http://tristen.ca/hcl-picker/): More fancy mathematical rules for color palettes ([explanation](https://www.vis4.net/blog/posts/avoid-equidistant-hsv-colors/)).
- [**iWantHue**](http://tools.medialab.sciences-po.fr/iwanthue/): Yet another perceptual distance-based color palette builder.
- [**Photochrome**](https://photochrome.io/): Word-based color palettes.
- [**PolicyViz Design Color Tools**](https://policyviz.com/better-presentations/design-resources/design-color-tools/): Large collection of useful color resources

]
]

---

# Storytelling

.pull-left.small[

## Videos

- Ben Wellington, ["Making data mean more through storytelling"](https://www.youtube.com/watch?v=6xsvGYIxJok)
- Jonathan Schwabish, ["Better data communication"](https://vimeo.com/230757062)

]

.pull-right.small[

## Books

- Cole Nussbaumer Knaflic, *Storytelling with Data: A Data Visualization Guide for Business Professionals*
- Alan Alda, *If I Understood You, Would I Have This Look on My Face? My Adventures in the Art and Science of Relating and Communicating*
- Nancy Duarte, *Resonate: Present Visual Stories That Transform Audiences*
]