Fundamentals of R: Data Visualization

class: center, middle, inverse, title-slide

.title[
# Fundamentals of R: Data Visualization
]
.subtitle[
## <br><br> STA35A: Statistical Data Science 1
]
.author[
### Xiao Hui Tai
]
.date[
### October 11, 2024
]

---

layout: true

---

## Today
- Data visualization using `ggplot2`

---
## Data visualization using `ggplot2`

.pull-left[
<img src="img/ggplot2-part-of-tidyverse.png" width="80%" style="display: block; margin: auto;" />
] 
.pull-right[ 
- ggplot2 is the tidyverse's **data visualization package**

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

- Inspired by the book Grammar of Graphics by Leland Wilkinson

- We will also look at some **plotting functions in base R**  
]

---
## `ggplot2` vs. base R
Mass vs. height from Star Wars data set that we've seen

.tiny[
.pull-left[

``` r
ggplot(data = starwars, 
       mapping = aes(x = height, y = mass)) +
  geom_point() +
  labs(title = "Mass vs. height of Starwars characters",
       x = "Height (cm)", 
       y = "Weight (kg)")
```

<img src="lecture8_files/figure-html/unnamed-chunk-4-1.png" width="100%" style="display: block; margin: auto;" />
]
.pull-right[

``` r
plot(starwars$height, starwars$mass, 
     main = "Mass vs. height of Starwars characters",
     xlab = "Height (cm)", 
     ylab = "Weight (kg)")
```

<img src="lecture8_files/figure-html/unnamed-chunk-5-1.png" width="100%" style="display: block; margin: auto;" />
]
]

---

## Grammar of Graphics

.pull-left-narrow[
A grammar of graphics is a tool that enables us to concisely describe the **components of a graphic**
]
.pull-right-wide[
<img src="img/grammar-of-graphics.png" width="75%" style="display: block; margin: auto;" />
]

How these are implemented in `ggplot2`: https://ggplot2.tidyverse.org/reference/

.footnote[ Source: [BloggoType](http://bloggotype.blogspot.com/2016/08/holiday-notes2-grammar-of-graphics.html)]

---

## Mass vs. height from Star Wars data set

``` r
ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
  geom_point() +
  labs(title = "Mass vs. height of Starwars characters",
       x = "Height (cm)", y = "Weight (kg)")
```

```
## Warning: Removed 28 rows containing missing values or values outside the
## scale range (`geom_point()`).
```

---

## Mass vs. height from Star Wars data set

``` r
ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
  geom_point() +
  labs(title = "Mass vs. height of Starwars characters",
       x = "Height (cm)", y = "Weight (kg)")
```

```
## Warning: Removed 28 rows containing missing values or values outside the
## scale range (`geom_point()`).
```

- What are the functions doing the plotting?

- What is the data set being plotted?

- Which variables map to which features (aesthetics) of the plot?

- What does the warning mean?

---

## ggplot2

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

- Plots are constructed in **layers**

- **Structure of the code** for plots can be summarized as

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

---

## More involved example: Palmer Penguins

Data contains information on 344 penguins, including: penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), and sex.

---

``` r
library(palmerpenguins)
dplyr::glimpse(penguins)
```

```
## Rows: 344
## Columns: 8
## $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adeli…
## $ island            <fct> Torgersen, Torgersen, Torgersen, Torg…
## $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.…
## $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.…
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195…
## $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 362…
## $ sex               <fct> male, female, female, NA, female, mal…
## $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2…
```

---
.panelset[
.panel[.panel-name[Plot]
<img src="lecture8_files/figure-html/unnamed-chunk-11-1.png" width="70%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

.small[

``` r
ggplot(data = penguins, 
       mapping = aes(x = bill_depth_mm, 
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", 
       y = "Bill length (mm)",
       color = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package") +
  scale_color_viridis_d()
```

```
## Warning: Removed 2 rows containing missing values or values outside the
## scale range (`geom_point()`).
```
]
]
]

---
.midi[
> **Start with the `penguins` data frame**
]

.tiny[
.pull-left[

``` r
ggplot(data = penguins) #<<
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-12-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.midi[
> Start with the `penguins` data frame,
> **map bill depth to the x-axis**
]

.tiny[
.pull-left[

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm)) #<<
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-13-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.midi[
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> **and map bill length to the y-axis.**
]

.tiny[
.pull-left[

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm)) #<<
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-14-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.midi[
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis. 
> **Represent each observation with a point**
]

.tiny[
.pull-left[

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm)) + 
  geom_point() #<<
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-15-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.midi[
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis. 
> Represent each observation with a point
> **and map species to the color of each point.**
]

.tiny[
.pull-left[

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) + #<<
  geom_point()
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-16-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.tiny[
.pull-left[

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length") #<<
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-17-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.tiny[
.pull-left[

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins") #<<
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-18-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.tiny[
.pull-left[

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", 
       y = "Bill length (mm)") #<<
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-19-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.midi[
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis. 
> Represent each observation with a point
> and map species to the color of each point.
> Title the plot "Bill depth and length", 
> add the subtitle "Dimensions for Adelie, Chinstrap, and Gentoo Penguins", 
> label the x and y axes as "Bill depth (mm)" and "Bill length (mm)", respectively,
> **label the legend "Species"**
]

.tiny[
.pull-left[

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", 
       y = "Bill length (mm)",
       color = "Species") #<<
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-20-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.tiny[
.pull-left[

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", 
       y = "Bill length (mm)",
       color = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package") #<<
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-21-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.tiny[
.pull-left[

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", 
       y = "Bill length (mm)",
       color = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package") +
  scale_color_viridis_d() #<<
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-22-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Argument names

We can omit the names of first two arguments when building plots with `ggplot()`.

.tiny[
.pull-left[

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  scale_color_viridis_d()
```
]
]
.tiny[
.pull-right[

``` r
ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           color = species)) +
  geom_point() +
  scale_color_viridis_d()
```
]
]

<br><br><br><br>You will also often see code that omits the data argument using a pipe, and adds lines for data manipulation:

``` r
penguins %>%
  # other data manipulation functions
  ggplot(aes(x = bill_depth_mm,
           y = bill_length_mm,
           color = species)) +
  geom_point() +
  scale_color_viridis_d()
```

---

## Aesthetics options

- Variables in the data are **mapped** to visual properties (aesthetics) of geoms.

- Commonly used aesthetics are

- `color`

- `shape`

- `size`

- `alpha` (transparency)

---

## Color

.tiny[
.pull-left[

``` r
ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species)) + #<<
  geom_point() +
  scale_color_viridis_d()
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-24-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Shape

Different variables mapped to `color` and `shape`

.tiny[
.pull-left[

``` r
ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species,
           shape = island)) + #<<
  geom_point() +
  scale_color_viridis_d()
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-25-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Shape

Same variable matched to `color` and `shape`

.tiny[
.pull-left[

``` r
ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species,
           shape = species)) + #<<
  geom_point() +
  scale_color_viridis_d()
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-26-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Size

.tiny[
.pull-left[

``` r
ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species,
           shape = species,
           size = body_mass_g)) + #<<
  geom_point() +
  scale_color_viridis_d()
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-27-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Alpha

.tiny[
.pull-left[

``` r
ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species,
           shape = species,
           size = body_mass_g,
           alpha = flipper_length_mm)) + #<<
  geom_point() +
  scale_color_viridis_d()
```
]
]
.pull-right[
<img src="lecture8_files/figure-html/unnamed-chunk-28-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Mapping vs. setting

.pull-left[
**Mapping**

.tiny[

``` r
ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           size = body_mass_g, #<<
           alpha = flipper_length_mm)) + #<<
  geom_point()
```

<img src="lecture8_files/figure-html/unnamed-chunk-29-1.png" width="100%" style="display: block; margin: auto;" />
]
]
.pull-right[
**Setting**
.tiny[

``` r
ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm)) + 
  geom_point(size = 2, alpha = 0.5) #<<
```

<img src="lecture8_files/figure-html/unnamed-chunk-30-1.png" width="100%" style="display: block; margin: auto;" />
]
]
---

## Mapping vs. setting

- **Mapping:** Determine the size, alpha, etc. of points based on the values of a variable in the data
  - Goes into `aes()`

- **Setting:** Determine the size, alpha, etc. of points **not** based on the values of a variable in the data
  - Goes into `geom_*()` 
  
<small>this was `geom_point()` in the previous example, but we'll learn about other geoms soon</small>

---

## Faceting

- Smaller plots that display different subsets of the data
- Useful for exploring conditional relationships and large data

.panelset[
.panel[.panel-name[Plot]
<img src="lecture8_files/figure-html/unnamed-chunk-31-1.png" width="70%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_grid(species ~ island) #<<
```

```
## Warning: Removed 2 rows containing missing values or values outside the
## scale range (`geom_point()`).
```
]
]

---

## `facet_wrap()` with one variable

- `facet_wrap()`: creates a 1d ribbon wrapped according to number of rows and columns specified or available plotting area

``` r
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_wrap(~ species) #<<
```

---
## `facet_wrap()` with one variable

- Specifying 2 columns

``` r
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_wrap(~ species, ncol = 2) #<<
```

---
## `facet_wrap()` with two variables

``` r
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_wrap(~ species + sex) #<<
```

---
## 2D grid using `facet_grid()`

- `facet_grid()`:
    - 2D grid
    - `rows ~ cols`
    - use `.` for no split (1D)

- Uses all levels, even if there are no observations; i.e., may produce empty plots

- `facet_wrap()` only displays the plots having actual values
---

## 2D grid using `facet_grid()`

``` r
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_grid(species ~ sex) #<<
```

---
## 2D grid using `facet_grid()`

``` r
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_grid(sex ~ species) #<<
```

---
## Going back to the UN Votes example:

---

.small[

``` r
un_votes %>%
  filter(country %in% c("United States", "United Kingdom", "China", "Singapore")) %>%
  inner_join(un_roll_calls, by = "rcid") %>%
  inner_join(un_roll_call_issues, by = "rcid", relationship = "many-to-many") %>%
  mutate(year = lubridate::year(date)) %>%
  group_by(country, year, issue) %>%
  summarize(votes = n(),
            percent_yes = mean(vote == "yes")) %>%
  filter(votes > 5) %>%  # Only use records where there are more than 5 votes
  ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
  geom_point(alpha = 0.4) +
  geom_smooth(method = "loess", se = FALSE) +
  facet_wrap(~ issue) +
  scale_y_continuous(labels = scales::percent) +
  labs(
    title = "Percentage of 'Yes' votes in the UN General Assembly",
    subtitle = "1946 to 2019",
    y = "% Yes",
    x = "Year",
    color = "Country"
  ) +
  scale_color_viridis_d() +
  theme(text = element_text(size = 9))   
```
]

How can we reduce the size of all points?

What if we just want to group by country?

---
<img src="lecture8_files/figure-html/unnamed-chunk-39-1.png" width="100%" style="display: block; margin: auto;" />

---

.small[

``` r
un_votes %>%
  filter(country %in% c("United States", "United Kingdom", "China", "Singapore")) %>%
  inner_join(un_roll_calls, by = "rcid") %>%
  inner_join(un_roll_call_issues, by = "rcid", relationship = "many-to-many") %>%
  mutate(year = lubridate::year(date)) %>%
  group_by(country, year, issue) %>%
  summarize(votes = n(),
            percent_yes = mean(vote == "yes")) %>%
  filter(votes > 5) %>%  # Only use records where there are more than 5 votes
  ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
  geom_point(alpha = 0.4, size = 0.5) +
  geom_smooth(method = "loess", se = FALSE) +
  facet_wrap(~ issue) +
  scale_y_continuous(labels = scales::percent) +
  labs(
    title = "Percentage of 'Yes' votes in the UN General Assembly",
    subtitle = "1946 to 2019",
    y = "% Yes",
    x = "Year",
    color = "Country"
  ) +
  scale_color_viridis_d() +
  theme(text = element_text(size = 9))   
```
]

---
<img src="lecture8_files/figure-html/unnamed-chunk-41-1.png" width="100%" style="display: block; margin: auto;" />

---

.small[

``` r
un_votes %>%
  filter(country %in% c("United States", "United Kingdom", "China", "Singapore")) %>%
  inner_join(un_roll_calls, by = "rcid") %>%
  inner_join(un_roll_call_issues, by = "rcid", relationship = "many-to-many") %>%
  mutate(year = lubridate::year(date)) %>%
  group_by(country, year) %>%
  summarize(votes = n(),
            percent_yes = mean(vote == "yes")) %>%
  filter(votes > 5) %>%  # Only use records where there are more than 5 votes
  ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
  geom_point(alpha = 0.4) +
  geom_smooth(method = "loess", se = FALSE) +
  # facet_wrap(~ issue) +
  scale_y_continuous(labels = scales::percent) +
  labs(
    title = "Percentage of 'Yes' votes in the UN General Assembly",
    subtitle = "1946 to 2019",
    y = "% Yes",
    x = "Year",
    color = "Country"
  ) +
  scale_color_viridis_d() +
  theme(text = element_text(size = 9))   
```
]

---
## Summary
--

- Data visualization using `ggplot2`

- `ggplot2` vs. base R
  
  - `ggplot()` is the main function in the `ggplot2` package

- Plots are constructed in layers

- Basic structure of `ggplot()` code

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

- Data, mapping, geom, labels

- Mapping vs. setting
  
  - Faceting