R Markdown Visualizations

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<div class="footer-small">https://github.com/mjfrigaard/ph-lacounty-r/</div>

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# R Markdown Visualizations
#.fancy[Creating Graphs in R Markdown]

.large[by Martin Frigaard]

Written: October 03 2022

Updated: December 15 2022

.footer-large[.right[.fira[
 [Created using the "λέξις" theme](https://jhelvy.github.io/lexis/index.html#what-does-%CE%BB%CE%AD%CE%BE%CE%B9%CF%82-mean)
]]]

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# Materials

The slides are in the `slides.pdf` file

The materials for this training are in the `worksheets` folder:

```
worksheets
├── import.Rmd
├── export.Rmd
├── objects.Rmd
├── rmd-basic.Rmd
├── rmd-tables.Rmd
└── rmd-visualizations.Rmd
```

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# Outline

.leftcol[

#### 1. Importing data

#### 2. Common Data Objects

#### 3. R Markdown

]

.rightcol[

#### 4. .red[R Markdown Data Visualizations]

#### 5. R Markdown Tables

#### 6. Exporting Data

]

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# .large[R Markdown Data Visualizations]

.font90[.green[Open `rmd-visualizations.Rmd` to follow along]]

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# R Markdown Data Visualizations

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
library(NHANES)
SmallNhanes <- NHANES |> 
 select(ID, Gender, Age, AgeDecade, Race1, HealthGen,
 Height, BMI, Weight, Pulse, BPSysAve) 
```

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### Quick Tip: Column Names

Standardize names with `janitor::clean_names()`

```r
SmallNhanes <- SmallNhanes |> janitor::clean_names()
glimpse(SmallNhanes)
```

.code50[

```
 Rows: 10,000
 Columns: 11
 $ id <int> 51624, 51624, 51624, 51625, 51630, 51638, 51646, 51647, 51647, 51647, 51654, 51…
 $ gender <fct> male, male, male, male, female, male, male, female, female, female, male, male,…
 $ age <int> 34, 34, 34, 4, 49, 9, 8, 45, 45, 45, 66, 58, 54, 10, 58, 50, 9, 33, 60, 16, 56,…
 $ age_decade <fct> 30-39, 30-39, 30-39, 0-9, 40-49, 0-9, 0-9, 40-49, 40-49, 40-49, 60-6…
 $ race1 <fct> White, White, White, Other, White, White, White, White, White, White, White, Wh…
 $ health_gen <fct> Good, Good, Good, NA, Good, NA, NA, Vgood, Vgood, Vgood, Vgood, Vgood, Fair, NA…
 $ height <dbl> 164.7, 164.7, 164.7, 105.4, 168.4, 133.1, 130.6, 166.7, 166.7, 166.7, 169.5, 18…
 $ bmi <dbl> 32.22, 32.22, 32.22, 15.30, 30.57, 16.82, 20.64, 27.24, 27.24, 27.24, 23.67, 23…
 $ weight <dbl> 87.4, 87.4, 87.4, 17.0, 86.7, 29.8, 35.2, 75.7, 75.7, 75.7, 68.0, 78.4, 74.7, 3…
 $ pulse <int> 70, 70, 70, NA, 86, 82, 72, 62, 62, 62, 60, 62, 76, 80, 94, 74, 92, 96, 84, 76,…
 $ bp_sys_ave <int> 113, 113, 113, NA, 112, 86, 107, 118, 118, 118, 111, 104, 134, 104, 127, 142, 9…
```
]

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### Formating 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"
```

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# `ggplot2`

### The *.red[Layered]* grammar of graphics

How it works:

1) Graphs are _initialized_ with `ggplot()`

2) Variables are _mapped_ to aesthetics

3) Geoms are linked to _statistics_

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.large[*What relationship do we expect to see between height and weight?*]

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#### .font90[1) Use data with pipe to initialize graph]

.font80[
`SmallNhanes |>`   
]

#### .font90[2) Map variables to aesthetics]

.font80[
`SmallNhanes |>`   
`ggplot(mapping = aes(x = weight, y = height))`
]

#### .font90[3) Add geoms and layers]

.font80[
`SmallNhanes |>`  
    `ggplot(mapping = aes(x = weight, y = height)) +`  
    `geom_point()`
]

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.border[

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

]

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.border[

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

]

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.border[

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

]

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# `ggplot2` template

#### Initialize the plot the `ggplot()`, map the aesthetics, and add a `<GEOM_FUNCTION>`

```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>))
```

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# `ggplot2` template

#### Because `ggplot2` is a language of layers, we can continue adding *more* geoms

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

#### Note the different syntax (.red[%>%] vs. .red[+])

```r
<DATA> %>% #<< pipe!
 ggplot(mapping = aes(<MAPPINGS>)) + #<< plus!
 <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))
```

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# 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 (`color`, `size`, `shape`, and `alpha`)

.border[

]

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# Global `ggplot2` mapping

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

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# Local `ggplot2` mapping

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

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# Your Turn

### Set local vs. global aesthetic mappings

.leftcol[

*From here...*

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

]

.rightcol[

*...to here.*

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

]

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# Your Turn (solution 1)

.border[

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

]

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# Your Turn (solution 2)

.border[

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

]

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# Variables, Aestheitcs, and Geoms

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# 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?

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# 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))
```

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

.leftcol55[

#### ERROR!

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

```r
# Error: geom_point requires the following missing aesthetics: x and y
```

]

.rightcol45[

#### SOLUTION

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

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

]

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### Aesthetics: color

.border[

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

]

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### Aesthetics: size

.border[

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

]

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### Aesthetics: shape

.border[

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

]

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### Aesthetics: alpha (opacity)

.border[

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

]

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# Aesthetic mappings

.pull-left[

#### Legend is automatically included

#### Continuous variables best with `size`

]

.pull-right[.border[

]]

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## Setting values vs. mapping variables

### How can we create this plot?

.border[

]

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### Inside `aes()`

.border[

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

]

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### Outside `aes()`

.border[

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

]

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## What happened?

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

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

.border[

]

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# Geoms (geometric objects)

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## 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

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## 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 = ___))
```

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```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

.border[

]

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## Your Turn

### 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))
```

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

.border[

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

]

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

.border[

*Fill is better*

]

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# 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

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

.border[

]

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```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") 
```

.border[

]

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```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") 
```

.border[

]

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# Facets

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## Faceting

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

| 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           |

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## Facet Single Variable

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

.border[

]

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## Facet Two Variables

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

.border[

]

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## Facet: Set Columns

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

.border[

]

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## Facet: Set Rows

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

.border[

]

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# 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)