Introduction to Data Visualization with ggplot2

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# Introduction to Data Visualization with ggplot2
## bmRn CSM: An introduction to the grammar of graphics
### Martin Frigaard
### 2020-12-13

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# Intro to Data Visualization with R with `ggplot2`

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# Objectives

## 1) Introduce the grammar of graphics

## 2) Identifying graph aesthetics

## 3) Recognizing and using `geoms`

## 4) Facetting graphs

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# Resources

## Link to these slides

https://mjfrigaard.github.io/data-viz-intro/Index.html

## Link to exercises

https://mjfrigaard.github.io/r-meetup-tutorials/gg00_intro-to-data-viz.html

## Link to RStudio Project

https://rstudio.cloud/project/1973650

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# Why use `ggplot2` for data visualization?

### 1) `ggplot2` provides a comprehensive grammar for creating graphs/figures

### 2) It works hand-and-hand with the `tidyverse`

### 3) Better plots = better communication

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# Why do we create data visualizations?

### Clarification

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

### Better decision making

> "*Data visualization is a collection of methods that use visual representations to explore, make sense of, and communicate quantitative data…The ultimate purpose of data visualization, beyond understanding, is to enable better decisions and actions.*" - Stephen Few

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# How should we start creating data visualizations?

## Start with pen and paper

### *get those first bad ideas out of the way*

## Import and inspect your data

### *so you know what to expect*

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## Layered grammar of graphics

> *"appreciating the engineering design behind a sentence – **a linear ordering of phrases which conveys a gnarly network of ideas** — is the key to understanding what you are trying to accomplish when you compose a sentence."* - Stephen Pinker

> *"language is a system for making infinite use of finite means."* - Wilhelm von Humboldt

### `ggplot2` is a language of *layers*, organized linearly

`ggplot2`'s layers give us a "*linear ordering of phrases*" to build an infinite number of graphs "*which convey a gnarly network of ideas.*"

### ...infinitely extensible

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### Let's load some data!

The `NHANES` package comes with data from the [2014 American National Health and Nutrition Examination surveys](http://www.cdc.gov/nchs/data/series/sr_02/sr02_162.pdf). We will load a sample from it below:

```r
SmallNhanes <- read_csv("https://bit.ly/nhanes-small")
SmallNhanes
```

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### Quick Tip: Column Names

**Standardize names**

```r
SmallNhanes <- SmallNhanes %>% janitor::clean_names()
SmallNhanes
```

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  </script>
</div>

---
background-image: url(img/ggplot2.png)
background-position: 95% 20%
background-size: 8%

### Quick Tip: Factors

**Format factors:** We have a `health_gen` variable with the following levels:

`Excellent`, `Vgood`, `Good`, `Fair`, or `Poor`. These are ordered.

```r
SmallNhanes <- 
  SmallNhanes %>% mutate(
  health_gen = factor(x = health_gen, 
                       levels = c("Poor", "Fair", 
                                  "Good", "Vgood", 
                                  "Excellent"), 
                      ordered = TRUE))
```

```r
levels(SmallNhanes$health_gen)
```

```
## [1] "Poor"      "Fair"      "Good"      "Vgood"     "Excellent"
```

---
background-image: url(img/ggplot2.png)
background-position: 96% 8%
background-size: 8%

## Layered grammar of graphics

### How it works:

### - Graphs are *initialized* with *ggplot()*

### - Variables are *mapped* to *aesthetics*

### - Geoms are linked to *statistics*

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 8%

# Our First Graph

<br>

## What relationship do you expect to see between `height` and `weight`?

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 8%

## 1. Use data with pipe to initialize graph

`SmallNhanes %>%`

## 2. Map variables to aesthetics

`SmallNhanes %>%`   
`ggplot(mapping = aes(x = weight, y = height))`

## 3. Add geoms and layers

`SmallNhanes %>% `  
    `ggplot(mapping = aes(x = weight, y = height)) +`  
    `geom_point()`

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 6%

```r
SmallNhanes %>% 
* ggplot() # initialize
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 5%

```r
SmallNhanes %>% 
* ggplot(mapping = aes(x = weight, y = height)) # map variables
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 6%

```r
SmallNhanes %>% 
  ggplot(mapping = aes(x = weight, y = height)) + 
* geom_point() # add geoms
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 8%

# `ggplot2` template

```r
<DATA> %>% 
  ggplot(mapping = aes(<MAPPINGS>)) + 
  <GEOM_FUNCTION>()
```

#### We can add more aesthetics *inside* geoms

```r
<DATA> %>% 
  ggplot(mapping = aes(<MAPPINGS>)) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))
```

#### And we can add *more* geoms

```r
<DATA> %>% 
  ggplot(mapping = aes(<MAPPINGS>)) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>)) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))
```

---
class: inverse, center
background-image: url(img/ggplot2.png)
background-position: 50% 60%
background-size: 30%

# Graph Aesthetics

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 7%

# Aesthetics

**Is the relationship between `weight` and `height` the same for both `gender`s?**

*We can explore this by mapping the variables to different aesthetics*

### Aesthetics as graph elements

*Examples of aesthetics are `color`, `size`, `shape`, and `alpha`*

---
class: inverse, center
background-image: url(img/ggplot2.png)
background-position: 50% 60%
background-size: 30%

# Mapping (global vs. local)

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 7%

# Global `ggplot2` mapping

### ***inside the `ggplot()` function*** = setting variables ***globally***

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 7%

# Local `ggplot2` mapping

### ***inside the `geom()` function*** = setting variables ***locally***

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 8%

# `ggplot2` tip

## Note the different syntax:

### *Initializing plots with `%>%`*

```r
*<DATA> %>% # initialize
```

### *Adding layers with `+`*

```r
<DATA> %>% 
* ggplot(mapping = aes(<MAPPINGS>)) + # adding layers
  <GEOM_FUNCTION>()
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 8%

## Your Turn

### Set local aesthetic mappings

*From here...*

```r
SmallNhanes %>% 
* ggplot(mapping = aes(x = weight, y = height)) +
  geom_point() + 
  geom_smooth()
```

*...to here.*

```r
SmallNhanes %>% 
  ggplot() + 
* geom_point(mapping = aes(x = weight, y = height))
* geom_smooth(mapping = aes(x = weight, y = height))
```

### What do you expect to see?

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 8%

### Your Turn (solution 1)

```r
SmallNhanes %>% 
* ggplot(mapping = aes(x = weight, y = height)) +
  geom_point() + 
  geom_smooth()
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 8%

### Your Turn (solution 2)

```r
SmallNhanes %>% 
  ggplot() + 
* geom_point(mapping = aes(x = weight, y = height)) +
* geom_smooth(mapping = aes(x = weight, y = height))
```

---
class: inverse, center
background-image: url(img/ggplot2.png)
background-position: 50% 60%
background-size: 30%

# Variables, Aestheitcs, and Geoms

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 8%

### Variables, Aestheitcs, and Geoms (1)

Each graph needs a variable or value, an aesthetic, and geom (the accompanying graphic, geometry)

```r
*geom_point(mapping = aes(x = weight, y = height)) + # layer 1
*geom_smooth(mapping = aes(x = weight, y = height)) # layer 2
```

These have the same aesthetics! What if we added a layer with a variable mapped to a different aesthetic?

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 8%

### Variables, Aestheitcs, and Geoms (2)

But we can add *more* variables, map them to *different* aesthetics, and *adding* another `geom` layer

Add another layer, coloring the points by `gender`

```r
SmallNhanes %>% 
  ggplot() + 
* geom_point(mapping = aes(x = weight, y = height)) +
* geom_point(mapping = aes(color = gender))
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 8%

### Variables, Aestheitcs, and Geoms (3)

### ERROR!

```r
SmallNhanes %>% 
  ggplot() + 
  geom_point(mapping = aes(x = weight, y = height)) + 
  geom_point(mapping = aes(color = gender))

*# <error/rlang_error>
*# geom_point requires the following missing aesthetics: x and y
```

### SOLUTION

All `geom`s have required aesthetics--map variables globally

```r
SmallNhanes %>% 
* ggplot(aes(x = weight, y = height)) +
*   geom_point(aes(color = gender))
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 4%

### Aesthetics: color

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
*   geom_point(aes(color = gender))
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 4%

### Aesthetics: size

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
*   geom_point(aes(color = gender, size = gender))
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 4%

### Aesthetics: shape

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
*   geom_point(aes(color = gender, size = gender, shape = gender))
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 4%

### Aesthetics: alpha (opacity)

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
*   geom_point(aes(color = gender, alpha = gender))
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 4%

# Aesthetic mappings

<br>

.pull-left[

### Legend is automatically included

### Continuous variables best with `size`

]

.pull-right[

]

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 4%

## Setting values vs. mapping variables

### How can we create this plot?

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 4%

### Inside `aes()`

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
*   geom_point(aes(color = "red")) # inside aes
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 4%

### Outside `aes()`

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
* geom_point(color = "red") # outside aes
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 4%

## What happened?

This expected a variable, not a value (`"red"`).

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
* geom_point(aes(color = "red")) # "value" in aes
```

---
class: inverse, center
background-image: url(img/ggplot2.png)
background-position: 50% 60%
background-size: 30%

# Geoms (geometric objects)

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 8%

## Geoms

### These are visual elements used to represent the data of the graph

### Examples include:

- `geom_boxplot`  
- `geom_col` 
- `geom_line` 
- `geom_smooth`

### See the cheatsheet for more examples:

https://bit.ly/ggplot2-cheat

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 8%

## Your Turn

### *How does BMI vary across levels of self-reported general health?*

### Complete the code below:

Map the variables locally inside the `geom_boxplot()` function

```r
SmallNhanes %>% 
  ggplot() %>% 
  geom_boxplot(mapping = aes(x = __________, y = ___))
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 4%

```r
SmallNhanes %>% 
  ggplot() +
* geom_boxplot(mapping = aes(x = health_gen, y = bmi))
```

#### Box-plots are great for seeing how a continuous variable varies across a categorical variable

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 8%

## Your Turn

<br>

### Fill in the code below to change the colors in the boxplot for each level of `health_gen`

```r
SmallNhanes %>% 
  ggplot() +
  geom_boxplot(
*   aes(x = health_gen, y = bmi, _____ = health_gen))
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 4%

```r
SmallNhanes %>% 
  ggplot() +
  geom_boxplot(
*   aes(x = health_gen, y = bmi, color = health_gen))
```

*Color is not the setting we want here...*

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 4%

```r
SmallNhanes %>% 
  ggplot() +
  geom_boxplot(
*   aes(x = health_gen, y = bmi, fill = health_gen))
```

*Fill is better*

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 8%

# Adding layers

### The 'infinitely extensible' part of `ggplot2` is where we start to really see it's power

### Consider the relationship between `height` and `weight` again

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 7%

```r
SmallNhanes %>% 
* ggplot(aes(x = weight, y = height)) + # global
  geom_point(aes(color = gender))
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 7%

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
  geom_point(aes(color = gender)) + 
* geom_smooth(data = # data 2
*               filter(SmallNhanes, gender == "male"), # layer 2
              aes(x = weight, y = height), 
              color = "blue") 
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 93%
background-size: 7%

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
  geom_point(aes(color = gender)) + 
  geom_smooth(data = 
                filter(SmallNhanes, gender == "male"), 
              aes(x = weight, y = height), 
              color = "blue") + 
*   geom_smooth(data = # data 3
*                 filter(SmallNhanes, gender == "female"), # layer 3
              aes(x = weight, y = height), 
              color = "red") 
```

---
class: inverse, center
background-image: url(img/ggplot2.png)
background-position: 50% 60%
background-size: 30%

# Facets

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 9%

## Facetting

### Facet layers display subplots for levels of categorical variables

<br>

| Facet layer                  |   Display                                  |
|:-----------------------------|:-------------------------------------------|
| `facet_wrap(. ~ gender)`     | Plot for each level of `gender`            |
| `facet_wrap(race1 ~ gender)` | Plot for each level of `gender` and `race` |
| `facet_wrap(. ~ gender, ncol = 1)` | Specify the number of columns        |
| `facet_wrap(. ~ gender, nrow = 1)` | Specify the number of rows           |

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 9%

## Facet Single Variable

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
  geom_point(aes(color = gender)) + 
* facet_wrap(. ~ gender)
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 9%

## Facet Two Variables

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
  geom_point(aes(color = gender)) + 
* facet_wrap(race1 ~ gender)
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 9%

## Facet: Set Columns

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
  geom_point(aes(color = gender)) + 
* facet_wrap(race1 ~ gender, ncol = 5)
```

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 9%

## Facet: Set Rows

```r
SmallNhanes %>% 
  ggplot(aes(x = weight, y = height)) + 
  geom_point(aes(color = gender)) + 
* facet_wrap(race1 ~ gender, nrow = 2)
```

---
class: left, top
background-image: url(img/ggplot2.png)
background-position: 90% 7%
background-size: 8%

# Recap

### 1) Introduction the grammar of graphics syntax

### 2) Identifying graph aesthetics (position, color, shape, opacity, etc.)

### 3) Recognizing and using `geoms` (`geom_point`, `geom_smooth`, etc.)

### 4) Facetting graphs (`facet_wrap` with 1 or two variables)

---
background-image: url(img/ggplot2.png)
background-position: 95% 7%
background-size: 12%

# More resources

## The [`ggplot2` book](https://ggplot2-book.org/)

## [`ggplot2`](https://ggplot2.tidyverse.org/) on the tidyverse website

## [Flowing Data](https://flowingdata.com/)