Data visualization basics

# Data visualization basics
## Introduction to ggplot2
### Byron C. Jaeger
### last updated: 2020-04-07

---

## Catch up

- Any questions on material from last time?

- Any questions on the reading / primer?

- Any questions on workflow / course structure?
    
- Catch up on informal "requirements":

+ You should have a GitHub profile.
    
    + You should have an RStudio cloud profile. 
    
    + Ideally add your photo to both profiles.

---

## Agenda

- Tips on getting help (`reprex`)

- Exploratory data analysis

- Data visualization

- Visualizing Star Wars

- Aesthetics

- Faceting

---

# `reprex`

---

## What is `reprex`?

`reprex` stands for reproducible example. The `reprex` [R package](https://reprex.tidyverse.org/) helps you prepare reproducible examples for posts on GitHub, StackOverflow, or Slack.

- If you want someone to help you solve a problem...

- widdle the problem down to its essential components.

- keep code minimal, don't overwhelm your helpers.

- describe issues concisely.

]

.footnote[</br>Image source and great place to get help: [https://reprex.tidyverse.org/](https://reprex.tidyverse.org/) </br> see R/reprex_example in [Rstudio cloud](https://rstudio.cloud/spaces/15174/project/1048666)]

---

# Exploratory data analysis </br> (EDA)

---

## What is EDA?

- Interactively learning about data by summarizing its main characteristics.

- Often, this is visual. That's what we'll focus on today.

- We might also calculate summary statistics and perform

+ data tidying (coming up much later)
    
    + data isolation (coming up next time)
    
    + data transformation (coming up later)

at (or before) this stage of the analysis.

---

# Data visualization

---

## Data visualization

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

- Data visualization is the creation and study of the visual representation of data.

- There are many tools for visualizing data (R is one of them), and many approaches/systems within R for making data visualizations (**ggplot2** is one of them, and that's the one we're going to use).

---

## `ggplot2`

- `ggplot2` is a data visualization package. Like any package, `ggplot2` should be loaded before its functions are used.

```r
library(ggplot2)
```

- Code for `ggplot` can often be written as

```r
ggplot + geom_yyy()
```

- More generally:

```r
ggplot(data = [dataset]) +
  aes(x = [x-variable], y = [y-variable]) +
  geom_xxx() +
  other_functions()
```

- `geoms` (geometric objects) determine the type of plot produced.

---

## About `ggplot2`

- `ggplot2` is the name of the package

- The `gg` in "`ggplot2`" stands for Grammar of Graphics

- Inspired by the book **Grammar of Graphics** by Leland Wilkinson

- `ggplot()` is the main function in ggplot2

- For help with `ggplot2`, see http://ggplot2.tidyverse.org/

---

# Visualizing Star Wars

---

## Dataset terminology

__Question__: What does each row represent? What does each column represent?

```r
starwars[, 1:5]
```

```
## # A tibble: 87 x 5
##    name               height  mass hair_color    skin_color 
##    <chr>               <int> <dbl> <chr>         <chr>      
##  1 Luke Skywalker        172    77 blond         fair       
##  2 C-3PO                 167    75 <NA>          gold       
##  3 R2-D2                  96    32 <NA>          white, blue
##  4 Darth Vader           202   136 none          white      
##  5 Leia Organa           150    49 brown         light      
##  6 Owen Lars             178   120 brown, grey   light      
##  7 Beru Whitesun lars    165    75 brown         light      
##  8 R5-D4                  97    32 <NA>          white, red 
##  9 Biggs Darklighter     183    84 black         light      
## 10 Obi-Wan Kenobi        182    77 auburn, white fair       
## # ... with 77 more rows
```

---

## Luke Skywalker

![luke-skywalker](img/luke-skywalker.png)

---

## What's in the Star Wars data?

Take a `glimpse` at the data:

```r
glimpse(starwars, width = 60)
```

```
## Rows: 87
## Columns: 14
## $ name       <chr> "Luke Skywalker", "C-3PO", "R2-D2", ...
## $ height     <int> 172, 167, 96, 202, 150, 178, 165, 97...
## $ mass       <dbl> 77, 75, 32, 136, 49, 120, 75, 32, 84...
## $ hair_color <chr> "blond", NA, NA, "none", "brown", "b...
## $ skin_color <chr> "fair", "gold", "white, blue", "whit...
## $ eye_color  <chr> "blue", "yellow", "red", "yellow", "...
## $ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0,...
## $ sex        <chr> "male", "none", "none", "male", "fem...
## $ gender     <chr> "masculine", "masculine", "masculine...
## $ homeworld  <chr> "Tatooine", "Tatooine", "Naboo", "Ta...
## $ species    <chr> "Human", "Droid", "Droid", "Human", ...
## $ films      <list> [<"The Empire Strikes Back", "Reven...
## $ vehicles   <list> [<"Snowspeeder", "Imperial Speeder ...
## $ starships  <list> [<"X-wing", "Imperial shuttle">, <>...
```

---

## What's in the Star Wars data?

Run the following **in the Console** to view the help

```r
?starwars
```

![starwars-help](img/starwars-help.png)

__Question__: How many rows and columns does this dataset have?

Make a prediction: What relationship do you expect to see between height and mass?

---

## Mass vs. height

```r
ggplot(data = starwars) +
  aes(x = height, y = mass) +
  geom_point()
```

```
## Warning: Removed 28 rows containing missing values (geom_point).
```

![](index_files/figure-html/unnamed-chunk-9-1.png)

---

## What's that warning?

- Not all characters have height and mass information (hence 28 of them not plotted)

```
## Warning: Removed 28 rows containing missing values (geom_point).
```

- Going forward I'll supress the warning to save room on slides, but it's important to note it

---

## Mass vs. height

__Questions__: 
- How would you describe this relationship?
- Who is the not so tall but really chubby character?

![](index_files/figure-html/unnamed-chunk-10-1.png)

---
class: center, middle
# Jabba!

---
class: center, middle

# Aesthetics

---

## Aesthetics options

Visual characteristics that can be **mapped to data** are

- `color`

- `size`

- `shape`

- `alpha` (transparency)

---

## Mass vs. height + gender

```r
ggplot(data = starwars) + 
  aes(x = height, y = mass, color = gender) +
  geom_point()
```

![](index_files/figure-html/unnamed-chunk-13-1.png)

---

## Aesthetics summary

- Discrete variables are measured (often counted) on a discrete scale

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</style>
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      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Aesthetics</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1">Discrete</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
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      <td class="gt_row gt_left gt_stub">color</td>
      <td class="gt_row gt_center">different color for each category</td>
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      <td class="gt_row gt_left gt_stub">size</td>
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- Continuous variable are measured on a continuous scale

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  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: double;
  border-top-width: 6px;
  border-top-color: #D3D3D3;
}

#kzhdpenkwn .gt_table_body {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#kzhdpenkwn .gt_footnotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#kzhdpenkwn .gt_footnote {
  margin: 0px;
  font-size: 90%;
  padding: 4px;
}

#kzhdpenkwn .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#kzhdpenkwn .gt_sourcenote {
  font-size: 90%;
  padding: 4px;
}

#kzhdpenkwn .gt_left {
  text-align: left;
}

#kzhdpenkwn .gt_center {
  text-align: center;
}

#kzhdpenkwn .gt_right {
  text-align: right;
  font-variant-numeric: tabular-nums;
}

#kzhdpenkwn .gt_font_normal {
  font-weight: normal;
}

#kzhdpenkwn .gt_font_bold {
  font-weight: bold;
}

#kzhdpenkwn .gt_font_italic {
  font-style: italic;
}

#kzhdpenkwn .gt_super {
  font-size: 65%;
}

#kzhdpenkwn .gt_footnote_marks {
  font-style: italic;
  font-size: 65%;
}
</style>
<div id="kzhdpenkwn" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;"><table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Aesthetics</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1">Continuous</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr>
      <td class="gt_row gt_left gt_stub">color</td>
      <td class="gt_row gt_center">color gradient</td>
    </tr>
    <tr>
      <td class="gt_row gt_left gt_stub">size</td>
      <td class="gt_row gt_center">linear mapping between radius and value</td>
    </tr>
    <tr>
      <td class="gt_row gt_left gt_stub">shape</td>
      <td class="gt_row gt_center">shouldn't (and doesn't) work</td>
    </tr>
  </tbody>
  
  
</table></div>

---

## Your turn

- Switch to Rstudio cloud and find the data visualization basics project.

- Work on completing the problems in `exercises.Rmd` with your teammates.