ODSC: Data Visualization with ggplot2

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<div class="footer-small"><span>https://github.com/mjfrigaard/talks/tree/main/odsc-eda-2022-04</span></div>

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# ODSC: Data Visualization with ggplot2
#.fancy[Part 1: Thinking with graphs]

### https://bit.ly/odsc-ggplot2

<br>

.large[by Martin Frigaard & Peter Spangler]

Written: February 08 2022

Updated: July 23 2022

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# Resources

<br>

.leftcol[

## Links:

### - [Conference Website](https://odsc.com/boston/)

### - [Website](https://mjfrigaard.github.io/odsc-ggplot2-2022/)

### - [Part 1](https://mjfrigaard.github.io/odsc-ggplot2-2022/ggplot2-slides-01.html#1)

### - [Part 2](https://mjfrigaard.github.io/odsc-ggplot2-2022/ggplot2-slides-02.html#1)

]

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## Materials:

### - [RStudio.Cloud](https://rstudio.cloud/project/3941178)

### - [Github Repo](https://github.com/mjfrigaard/odsc-ggplot2-2022/tree/gh-pages)

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## Outline

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### .red[Part 1]

.font70[

**Exploratory data analysis**

- *What is it, who does it, and why it's important*

**A Bayesian mindset**

- *Priors -> new information -> posteriors*

**The grammar of graphics**

- *Layers, aesthetics, and geoms*

]

--
 
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### Part 2

.font70[

**Build labels first**

- *Set expectations*

**Exercises & solutions**

- *RStudio.Cloud*

**Creating graphs**

- *Building graphs layer-by-layer, global vs. local mapping, visual encodings*

**Applying the grammar**

- *Mapping vs. setting aesthetics, combining layers, facets*

]

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# .fancy[.large[PART 1]]

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<br>

# **.fancy[.darkblue[.large[Exploratory Data Analysis (EDA)]]]**

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# .center["EDA"]

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### "Exploratory Data Visualization" first coined by American mathematician John Tukey in 1977

]

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# What is EDA?

#### John T. Behrens, [Principles and Procedures of Exploratory Data Analysis](https://psycnet.apa.org/record/1997-06270-001):

<br>

> .blue[*Emphasis on substantive understanding of data*]

>   .blue[*- i.e. "what is going on here?"*]

> .blue[*Iterative process with a focus on graphic representations of data*]

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# What is EDA?

#### John T. Behrens, [Principles and Procedures of Exploratory Data Analysis](https://psycnet.apa.org/record/1997-06270-001):

<br>

> .blue[*- Includes subset analyses, skepticism, and flexibility*]

> .red[*- The role of the data analyst is to listen to the data in as many ways as possible until a plausible "story" of the data is apparent*]

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# Who does EDA?

#### John Tukey, [Exploratory Data Analysis](http://www.ru.ac.bd/wp-content/uploads/sites/25/2019/03/102_05_01_Tukey-Exploratory-Data-Analysis-1977.pdf):

<br>

> .blue[*A detective investigating a crime needs both tools and understanding.*]

> .blue[*If he has no fingerprint powder, he will fail to find fingerprints on most surfaces.*]

> .blue[*If he does not understand where the criminal is likely to have put his fingers, he will not look in the right places.*]

> .red[*Equally, the analyst of data needs both tool and understanding.*]

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# EDA is a 'state of mind'

#### Hadley Wickham, [R for Data Science](https://r4ds.had.co.nz/exploratory-data-analysis.html):

<br>

> .red[*More than anything, EDA is a state of mind.*]

> .blue[*During the initial phases of EDA you should feel free to investigate every idea that occurs to you. Some of these ideas will pan out, and some will be dead ends.*]

> .blue[*As your exploration continues, you will home in on a few particularly productive areas that you’ll eventually write up and communicate to others.*]

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# Why is EDA important?

> .blue[*"Data are becoming the new raw material of business"* - Craig Mundie, CEO at Microsoft]

> .blue[*"Data is the oil of the digital era"* - [The  Economist](https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data)]

<div class="footer-small"><span>https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data</span></div>

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# Why is EDA important?

### Data are complex:

*It's hard to derive insight from data in it's raw form!*

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# EDA is a means of .blue[visualizing complexity]

<br>

### - *It's hard to make sense of a dataset or database with millions of rows and thousands of columns*

### - *Fortunately, humans are excellent at seeing patterns:*

.footer[.small[.right[[Superior pattern processing is the essence of the evolved human brain](https://www.frontiersin.org/articles/10.3389/fnins.2014.00265/full) - Frontiers in Neuroscience]]]

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# What do you need?

<br>

## **Tools** = R, RStudio, Adobe, sketch pad, text editor (Atom, Sublime Text, Vim)

<br>

## **Understanding** = *...experience and feedback*

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<br>

# **.fancy[.darkblue[.large[A Bayesian Mindset]]]**

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## A Bayesian Mindset

### .center[*What we thought we knew (.orange[what we expect])*]

# .large[.center[+]]

### .center[*New information (.orange[what we see])*]

# .large[.center[=]]

### .center[*What we think now (.orange[what we've learned])*]

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## A Bayesian Mindset

<br>

### We all have implicit beliefs (.red[*'priors'*]) about the world

<br>

### When we encounter new data or information, our *priors* get updated

<br>

### These updated beliefs (.red[*'posteriors'*]) depend on our implicit beliefs and our **perceptions** of the new information

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# A Bayesian Mindset

.leftcol40[

<br>

### *Before EDA, we start with expectations and/or assumptions about the data*

]

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# A Bayesian Mindset

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<br>

### *During EDA, we observe new information that either confirms or contradicts our prior beliefs*

]

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## A Bayesian Mindset

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<br>

### *After EDA, we have a new set of beliefs which account for the observed data*

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# EDA is .green[systematic, technical creativity]

<br>

## The .orange['exploration'] stems from:

### 1) articulating our prior beliefs,

### 2) having clear ideas for what we expect to see, and

### 3) accurately describing our discoveries

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# **.fancy[.darkblue[.large[A Grammar Of Graphics]]]**

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# `ggplot2`: grammar & syntax

<br>

## **.fancy[Grammar]:** the system of rules for any given language

<br>

## **.fancy[Syntax]:** the form, structure and order for constructing statements

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## `ggplot2`: the benefits of grammar & syntax

<br>

### ".blue[objects] are like the R language’s nouns, and functions (**.red[fn]**) are like verbs"

<br>

### .center[*.red[functions] do things to .blue[objects]*]

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# `ggplot2`: a layered language for graphs

<br>

.leftcol[

### `ggplot2` is comprised of layers

- Data 
- Mapping 
- Statistics 
- Geometric objects 
- Position adjustments

]

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]

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# `ggplot2`: data

### The data layer consists of a rectangular object (like a spreadsheet) with columns and rows

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# `ggplot2`: mapping

### The mapping layer assigns columns (variables) from the data to a visual property (i.e. graph '.red[aes]thetic')

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# `ggplot2`: geoms

### `geom_*()` functions include statistical transformations, shapes, and position adjustments for how to 'draw' the data on the graph

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# `ggplot2`: layers

### We can have multiple layers (data, mappings, geoms) in a single graph

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## `ggplot2`: layers = infinitely extensible

<br>

### Language is a system for

> .red[“making infinite use of finite means.”] - [Wilhelm von Humboldt](https://en.wikipedia.org/wiki/Wilhelm_von_Humboldt)

<br>

### With a finite number of .blue[objects] & .red[functions], we can combine `ggplot2`s grammar and syntax to create an infinite number of graphs!

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## `ggplot2`: layers = infinitely extensible

#### We can build graphs layer-by-layer

.cols3[.code50[

<br>

.center[.red[code]]

```r
ggplot(data = penguins, 
  mapping = aes(x = flipper_length_mm, 
                y = bill_length_mm)) + 
  geom_point() 
```

]]

.cols3[

.center[.blue[layer]]

]

.cols3[

<br>

.center[.green[graph]]

]

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## `ggplot2`: layers = infinitely extensible

#### New layers can 'inherit' data from previous layers (or include their own data)

.cols3[.code50[

<br>

.center[.red[code]]

```r
ggplot(data = penguins, 
       
   mapping = aes(x = flipper_length_mm, 
                  y = bill_length_mm)) +
  
  geom_point() + 
  
* geom_smooth(
*   mapping = aes(x = flipper_length_mm,
*                    y = bill_length_mm,
*                    color = species))
```

]]

.cols3[

.center[.blue[layer]]

]

<br>

.center[.green[graph]]

.cols3[

]

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## `ggplot2`: layers = infinitely extensible

#### Additional functions for facets, themes, etc.

.cols3[.code50[

<br>

.center[.red[code]]

```r
ggplot(data = penguins, 
       
    mapping = aes(x = flipper_length_mm, 
                  y = bill_length_mm)) +
    geom_point() + 
  
    geom_smooth(
    mapping = aes(x = flipper_length_mm, 
                     y = bill_length_mm, 
                     color = species)) + 
  
*   facet_wrap(facets = . ~ island)
```

]]

.cols3[

.center[.blue[layer]]

]

.cols3[

<br>

.center[.green[graph]]

]

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# `ggplot2`: templates

.leftcol55[

### Basic Template: Data, aesthetic mappings, geom

.code70[

```r
ggplot(data = <DATA>) +
     geom_*(mapping = aes(<AESTHETIC MAPPINGS>)) 
```

]
]

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<br>

]

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# `ggplot2`: templates

.leftcol55[

### Template + 1 Layer: More geoms and aesthetic mappings

.code70[

```r
ggplot(data = <DATA>) +
     geom_*(mapping = aes(<AESTHETIC MAPPINGS>)) + 
*    geom_*(mapping = aes(<AESTHETIC MAPPINGS>))
```

]]

.rightcol45[

]

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# `ggplot2`: templates

.leftcol55[

### Template + 2 Layers: Faceting

.code70[

```r
ggplot(data = <DATA>) +
     geom_*(mapping = aes(<AESTHETIC MAPPINGS>)) + 
     geom_*(mapping = aes(<AESTHETIC MAPPINGS>)) +
     facet_* 
```

]
]

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# .red[templates] = .blue[infinitely extensible!]

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### Themes

<br>

.code60[

```r
ggplot(data = <DATA>) +
     geom_*(mapping = aes(<AESTHETIC MAPPINGS>)) + 
     geom_*(mapping = aes(<AESTHETIC MAPPINGS>)) +
     facet_* + 
*    theme_*
```

]

.rightcol[

### Don't forget labels!

<br>

.code60[

```r
ggplot(data = <DATA>) +
     geom_*(mapping = aes(<AESTHETIC MAPPINGS>)) + 
     geom_*(mapping = aes(<AESTHETIC MAPPINGS>)) +
     facet_* + 
     theme_* +
*    <LABELS>
```

]

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# Next up: Part 2!

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