Data Visualization

class: center, middle, inverse, title-slide

# Data Visualization
## CORE Lab
### Department of Defense Analysis
### 2019-08-08

---

# Introduction

* Purpose

* Scope

* Data visualization in R
---

# Why visualize data?

* Leverage capabilities and bandwidth of visual system to move information (often huge amounts) into the brain quickly.

* Amplifies cognition in several ways (Card et al., 1999):
  * Increase cognitive resources
  * Reduce search
  * Enhance recognition of patterns and outliers
  * Support inferences of relationships
  * Can provide a medium with which to interact

* We think visually.

---

# Close Personal Network

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---

# Close Personal Network

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---

# Close Personal Network

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---

# Close Personal Network

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---

# Close Personal Network

<div id="htmlwidget-3bfe0f1b8e67339cccb4" style="width:504px;height:504px;" class="visNetwork html-widget"></div>
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---

# Common Mistakes

* **Inappropriate use of visual cues/properties**

* Too many dimensions

* Lack of context

* Attempting to substitute visual appeal for substance

* Accidentially misleading audience

* Quality of underlying data

---

# Inappropriate use of visual properties

.center[
![](inapprop_vis_cues.png)]

---

# Common Mistakes

* Inappropriate use of visual cues/properties

* **Too many dimensions**

* Lack of context

* Attempting to substitute visual appeal for substance

* Accidentially misleading audience

* Quality of underlying data

---

# Too many dimensions

.center[
![](dimensions.jpg)]

---

# Common Mistakes

* Inappropriate use of visual cues/properties

* Too many dimensions

* **Lack of context**

* Attempting to substitute visual appeal for substance

* Accidentially misleading audience

* Quality of underlying data

---

# Lack of Context

.center[
![](context.png)]

---

# Common Mistakes

* Inappropriate use of visual cues/properties

* Too many dimensions

* Lack of context

* **Attempting to substitute visual appeal for substance**

* Accidentially misleading audience

* Quality of underlying data

---

# Visual appeal for substance

.center[
![](context.png)]

---

# Common Mistakes

* Inappropriate use of visual cues/properties

* Too many dimensions

* Lack of context

* Attempting to substitute visual appeal for substance

* **Accidentially misleading audience**

* Quality of underlying data

---

# Misleading audience

.center[
![](mislead.png)]

---

# Common Mistakes

* Inappropriate use of visual cues/properties

* Too many dimensions

* Lack of context

* Attempting to substitute visual appeal for substance

* Accidentially misleading audience

* **Quality of underlying data**

---

# Quality of underlying data

* Who? What? Where? When? Why? How?

* Codebooks

---

# General Principles and Guidelines

* Answer questions and effectively communicate

* Aesthetic, substantive, and perceptual considerations (Healy, 2019)
  * Gestalt laws of pattern recognition
  * Functionality over aesthetics
  * Choose right type of visualization
  * Avoid including extraneous data and dimensions (e.g., 3D)
  * Use colors appropriately
  * Label usage
  * Legends, titles, axes, scales, etc.
  
---

# Example

.center[
![](labels.png)]

---

# Example

.center[
![](choropleth.png)]

---

# Questions?

.center[
![](corelogo5.png)]

Dan Cunningham - dtcunnin@nps.edu