ISA 401: Business Intelligence & Data Visualization

class: center, middle, inverse, title-slide

.title[
# ISA 401: Business Intelligence & Data Visualization
]
.subtitle[
## 14: Fundamentals of Data Visualization
]
.author[
### <br>Fadel M. Megahed, PhD <br><br>Professor of Information Systems and Business Analytics <br> Farmer School of Business<br> Miami University<br><br> <a href="https://twitter.com/FadelMegahed"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z" /></svg> <span class="citation">@FadelMegahed</span></a> <br> <a href="https://github.com/fmegahed/"><svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z" /></svg> fmegahed</a> <br> <a href="mailto:fmegahed@miamioh.edu"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z" /></svg> fmegahed@miamioh.edu</a><br> <a href="https://calendly.com/fmegahed"><svg viewBox="0 0 384 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z" /></svg> Automated Scheduler for Office Hours</a><br><br>
]
.date[
### Fall 2024
]

---

# Refresher: Organization of this Course

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#../../figures/course_overview.png" alt="How the ISA 401/501 course is organized." width="100%" />
<p class="caption">How the ISA 401/501 course is organized.</p>
</div>

---

# Learning Objectives for Today's Class

- Explain the concept of "graphical excellence"

- Explain the theory of data graphics

- Optimize visual encoding based on data types

- Understand why color should be used sparingly and how to select appropriate colors (when color is a must)

---
class: center, inverse, middle

# Graphical Excellence

---

# Non-graded activity: Terrible Charts

.panelset[

.panel[.panel-name[Activity]

> Over the next 5 minutes, please identify the **1-2 main problems** in the charts in the following tabs.

- Write down your answers in the editable area of each chart.  
  
  - Discuss your answers with your neighboring classmates.  
  
  - Be prepared to share these answers with class.

]

.panel[.panel-name[Russia's Defense Budget]

.tiny[**Source:** The chart was embedded in [this tweet](https://twitter.com/CedScherer/status/1498593405408059394?s=20&t=b0aOBtP77mq0WZinrfaH5g) by Cedric Scherer; however, it is unclear which news outlet have created the original chart.]

.pull-left-2[
<img src="data:image/png;base64,#https://pbs.twimg.com/media/FMwSwFOWQAM9eek?format=jpg&name=large" alt="Defence budgets for Russia vs Ukraine (2020)" height="350px" style="display: block; margin: auto;" />
]

.pull-right-2[
.can-edit.key-activity1_russia[

**Main Issue(s):** .font70[(Insert below)]

]
]
]

.panel[.panel-name[White House Economy Growth]

.tiny[**Source:** The chart was created by the White House and shared via [this tweet from the verified White House account](https://twitter.com/WhiteHouse/status/1486709480351952901?s=20&t=b0aOBtP77mq0WZinrfaH5g). Note that the chart was latter corrected.]

.pull-left-2[
<img src="data:image/png;base64,#https://pbs.twimg.com/media/FKHaZFyWYAAP962?format=jpg&name=large" alt="A bar chart, created by The White House, capturing America's Growth Economy in the 21st century" height="350px" style="display: block; margin: auto;" />
]

.pull-right-2[
.can-edit.key-activity1_whitehouse[

**Main Issue(s):** .font70[(Insert below)]

]
]
]

.panel[.panel-name[Tucker Carlson]

.tiny[**Source:** The chart was created by Fox and was highlighted in [this Fox News Clip](https://video.foxnews.com/v/6274513508001#sp=show-clips).]

.pull-left-2[
<img src="data:image/png;base64,#https://www.ft.com/__origami/service/image/v2/images/raw/https%3A%2F%2Fd1e00ek4ebabms.cloudfront.net%2Fproduction%2F8597019d-978b-40cf-a2a8-fa47610e62cc.png?fit=scale-down&source=next&width=700" alt="A bar chart, created by Fox News, capturing the decline in the number of Americans who self-identify as Christians." height="350px" style="display: block; margin: auto;" />
]

.pull-right-2[
.can-edit.key-activity1_tucker[

**Main Issue(s):** .font70[(Insert below)]

]
]
]

]

---

# Graphical Excellence: What should Graphs Do?

- **Show the data**  
 
 - Lead to thinking about the **substance**  rather than something else  
 
 - Avoid **distorting** what the data have to say  
 
 - Present **many numbers in a small space**  
 
 - Make **large datasets coherent**  
 
 - Encourage the eye to **compare different pieces of the data**  
 
 - **Reveal the data at several levels of detail**, from a broad overview to the
fine structure

- Serve **a purpose:** description, exploration, tabulation, decoration  
 
 - Be **closely integrated with the statistical & verbal descriptions of the data** 
 
.footnote[
<html>
<hr>
</html>

**Source:** Tufte, E. R. (2001). The visual display of quantitative information. Cheshire, Conn: Graphics Press, P. 13.
]

---

# Show/Reveal the Data: Anscombe's Dataset

**In a seminal paper, Anscombe stated:**

> **Few of us escape being indoctrinated with these notions:** 
>  - numerical **calculations are exact, but graphs are rough**; 
>  - for any particular kind of **statistical data there is just one set of calculations constituting a correct statistical analysis**;  
>  - performing **intricate calculations is virtuous**, whereas **actually looking at the data is cheating**.

He proceeded by stating that 
> a computer should **make both calculations and graphs**. Both sorts of output should be studied; each will contribute to understanding.

Now, let us consider his four datasets, each consisting of eleven (x,y) pairs.

.footnote[
<html>
<hr>
</html>
**Source:** Anscombe, Francis J. 1973. "Graphs in Statistical Analysis." *The American Statistician* 27 (1): 17–21. ([PDF Link](https://www.sjsu.edu/faculty/gerstman/StatPrimer/anscombe1973.pdf)).

---
count: false
# Show/Reveal the Data: Anscombe's Dataset

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---
count: false
# Show/Reveal the Data: Anscombe's Dataset

.font80[
<div class="datatables html-widget html-fill-item" id="htmlwidget-ab63986235339ed68986" style="width:100%;height:auto;"></div>
<script type="application/json" data-for="htmlwidget-ab63986235339ed68986">{"x":{"filter":"none","vertical":false,"data":[["I","II","III","IV"],[9,9,9,9],[3.32,3.32,3.32,3.32],[7.5,7.5,7.5,7.5],[2.03,2.03,2.03,2.03],[0.82,0.82,0.82,0.82]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>set<\/th>\n      <th>x.mean<\/th>\n      <th>x.sd<\/th>\n      <th>y.mean<\/th>\n      <th>y.sd<\/th>\n      <th>corr<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":5,"dom":"tip","columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5]},{"name":"set","targets":0},{"name":"x.mean","targets":1},{"name":"x.sd","targets":2},{"name":"y.mean","targets":3},{"name":"y.sd","targets":4},{"name":"corr","targets":5}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]},"selection":{"mode":"multiple","selected":null,"target":"row","selectable":null}},"evals":[],"jsHooks":[]}</script>
]

---
count: false
# Show/Reveal the Data: Anscombe's Dataset

---

# A Modern Version of Anscombe's Dataset

.footnote[
<html>
<hr>
</html>
**Source:** Matejka, J. and Fitzmauricem G. 2023. "Same Stats, Different Graphs" *Proceedings of the 2017 CHI conference on human factors in computing systems*. ([Blog Post Link](https://www.research.autodesk.com/publications/same-stats-different-graphs/)).

---

# Substance

.panelset[

.panel[.panel-name[Activity]

> In 5 minutes, please **sketch** a better (non-bubble) chart than the one use by William Playfair for plotting the populations of 22 European cities at the end of the 1700s.

<img src="data:image/png;base64,#14_fundamentals_data_viz_files/figure-html/playfair_data-1.png" style="display: block; margin: auto;" />
]

.panel[.panel-name[Your Solution]

**Ideally, on a piece of paper sketch out your solution.** Otherwise, please feel free to download the plot's data (using the code below) and use a software of your choice for plotting a better chart for the data.

``` r
pacman::p_load(tidyverse)

playfair = read.table("http://www.stat.uiowa.edu/~luke/data/Playfair") |> 
  rownames_to_column(var = 'city') |> # converting row names to city var
  as_tibble() |>  # converting it to a tibble
  arrange( desc(population) ) # arranging the rows in a descending order by population

write_csv(x = playfair, file = 'playfair_data.csv')
```

**Be prepared to share your solution with the entire class.**

]

.panel[.panel-name[My Solution]

<details>
<summary>In my opinion, a <strong>dot chart is more effective</strong> than the bubble chart. The <strong>population would be mainly encoded using the position</strong>; you can still use area as a secondary encoding mechanism.</summary>
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</details>

]

---

# Avoid Distortion of Data

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**Source:** Graph from Tufte, E. R. (2001). The visual display of quantitative information. Cheshire, Conn: Graphics Press, P. 54.
]

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# Avoid Distortion of Data

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**Source:** Nathan Yau (2012). [Fox News continues charting excellence](https://flowingdata.com/2012/08/06/fox-news-continues-charting-excellence/).
]

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# Avoid Distortion of Data: The Lie Factor

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**Source:** Graph from Tufte, E. R. (2001). The visual display of quantitative information. Cheshire, Conn: Graphics Press, P. 57.
]

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# Graphical Integrity Principles: A Summary

- Clear, detailed, and thorough labeling and appropriate scales

- Size of the graphic effect should be directly proportional to the numerical quantities (“lie factor”)  
 
 - Show data variation, not design variation
 
---
class: center, inverse, middle

# Theory of Data Graphics

---

# Definition of Data Ink

- **Data-ink refers to the non-erasable ink used for presenting the data**.  
  
  * If data-ink would be removed from the image, the graphic would lose the content.

$$
`\begin{split}
\text{Data-ink ratio} &= \frac{\text{Data-ink}}{\text{Total ink used to print the graphic}} \\
\\
&= \text{proportion of a graphic's ink devoted to the} \\
& \quad \text{ non-redudant display of data-information} \\
\\
&= 1.0 - \text{proportion of a graphic that can be erased}
\end{split}`
$$

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# Definition of Data Ink

- **Data-ink refers to the non-erasable ink used for presenting the data**.  
  
  * If data-ink would be removed from the image, the graphic would lose the content.
  
<img src="data:image/png;base64,#../../figures/dataink2.jpg" alt="Data ink" width="65%" style="display: block; margin: auto;" />

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**Source:** Slide Adapted from Hanspeter Pfister. Lecture Notes for CS 171. Harvard University. <http://cs171.org/>
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count: false
# Definition of Data Ink

- **Data-ink refers to the non-erasable ink used for presenting the data**.  
  
  * If data-ink would be removed from the image, the graphic would lose the content.
  
<img src="data:image/png;base64,#../../figures/dataink3.jpg" alt="Data ink" width="65%" style="display: block; margin: auto;" />

.footnote[
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**Source:** Slide Adapted from Hanspeter Pfister. Lecture Notes for CS 171. Harvard University. <http://cs171.org/>
]

---