Amounts & Proportions

]

# Week 7: .fancy[Amounts & Proportions]

### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M243.4 2.587C251.4-.8625 260.6-.8625 268.6 2.587L492.6 98.59C506.6 104.6 514.4 119.6 511.3 134.4C508.3 149.3 495.2 159.1 479.1 160V168C479.1 181.3 469.3 192 455.1 192H55.1C42.74 192 31.1 181.3 31.1 168V160C16.81 159.1 3.708 149.3 .6528 134.4C-2.402 119.6 5.429 104.6 19.39 98.59L243.4 2.587zM256 128C273.7 128 288 113.7 288 96C288 78.33 273.7 64 256 64C238.3 64 224 78.33 224 96C224 113.7 238.3 128 256 128zM127.1 416H167.1V224H231.1V416H280V224H344V416H384V224H448V420.3C448.6 420.6 449.2 420.1 449.8 421.4L497.8 453.4C509.5 461.2 514.7 475.8 510.6 489.3C506.5 502.8 494.1 512 480 512H31.1C17.9 512 5.458 502.8 1.372 489.3C-2.715 475.8 2.515 461.2 14.25 453.4L62.25 421.4C62.82 420.1 63.41 420.6 63.1 420.3V224H127.1V416z"/></svg> EMSE 4572: Exploratory Data Analysis
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M224 256c70.7 0 128-57.31 128-128s-57.3-128-128-128C153.3 0 96 57.31 96 128S153.3 256 224 256zM274.7 304H173.3C77.61 304 0 381.6 0 477.3c0 19.14 15.52 34.67 34.66 34.67h378.7C432.5 512 448 496.5 448 477.3C448 381.6 370.4 304 274.7 304z"/></svg> John Paul Helveston
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M96 32C96 14.33 110.3 0 128 0C145.7 0 160 14.33 160 32V64H288V32C288 14.33 302.3 0 320 0C337.7 0 352 14.33 352 32V64H400C426.5 64 448 85.49 448 112V160H0V112C0 85.49 21.49 64 48 64H96V32zM448 464C448 490.5 426.5 512 400 512H48C21.49 512 0 490.5 0 464V192H448V464z"/></svg> October 12, 2022

]

---

# .fancy[.blue[Tip of the week]]

# The `fcuk` package

---

## The `fcuk` package

Error message **without** the `fcuk` package:

```r
maen(c(1, 2, 3, 4, 5))
```

```
Error in maen(c(1, 2, 3, 4, 5)) : could not find function "maen"
```

]

]

---

## The `fcuk` package

Error message **without** the `fcuk` package:

```r
maen(c(1, 2, 3, 4, 5))
```

```
Error in maen(c(1, 2, 3, 4, 5)) : could not find function "maen"
```

Error message **with** the `fcuk` package:

```r
library(fcuk)

maen(c(1, 2, 3, 4, 5))
```

```
Error in maen(c(1, 2, 3, 4, 5)) : could not find function "maen"

Did you mean : mean or rename ?
```

]

]

---

## The `fcuk` package

Install:

```r
install.packages("fcuk")
```

Automatically load:

```r
fcuk::add_fcuk_to_rprofile()
```

]

]

---

# Tidy data review

---

# Tidy data

Tidy data follows the following three rules:

- Each **variable** has its own **column**
- Each **observation** has its own **row**
- Each **value** has its own **cell**

---

# Next projects due:

## - [Mini project 2](https://eda.seas.gwu.edu/2022-Fall/project-mini/2-exploring-data.html): Exploring Data (Due 10/18)
## - [Project progress report](https://eda.seas.gwu.edu/2022-Fall/project-final/2-progress-report.html) (Due 11/01)

---

## Today's data

```r
avengers         <- read_csv(here('data', 'avengers.csv'))
bears            <- read_csv(here('data', 'north_america_bear_killings.csv'))
federal_spending <- read_csv(here('data', 'fed_spend_long.csv'))
gapminder        <- read_csv(here('data', 'gapminder.csv'))
lotr_words       <- read_csv(here('data', 'lotr_words.csv'))
milk_production  <- read_csv(here('data', 'milk_production.csv'))
wildlife_impacts <- read_csv(here('data', 'wildlife_impacts.csv'))
```

## New packages

The [{waffle} package](https://github.com/hrbrmstr/waffle)

```r
install.packages("waffle", repos = "https://cinc.rud.is")
```

---

# Week 7: .fancy[Amounts & Proportions]

## 1. Manipulating factors
## 2. Graphing amounts

## BREAK

## 3. Graphing proportions

---

# Week 7: .fancy[Amounts & Proportions]

## 1. .orange[Manipulating factors]
## 2. Graphing amounts

## BREAK

## 3. Graphing proportions

---

# Sorting in ggplot is done by reordering factors

]

]

---

## .center[Two ways to sort]

**Method 1**: Use `reorder()` inside aesthetic mapping

```r
# Format the data frame
federal_spending %>%
  group_by(department) %>%
  summarise(
    rd_budget_bil = sum(rd_budget_mil) / 10^3) %>%

# Make the chart
  ggplot() +
  geom_col(
    aes(
      x = rd_budget_bil,
*     y = reorder(department, rd_budget_bil)
    ), 
    width = 0.7, alpha = 0.8,
    fill = "steelblue"
  ) +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid()
```

]]

]

---

## .center[Two ways to sort]

**Method 2**: Use `fct_reorder()` when formatting the data frame

```r
# Format the data frame
federal_spending %>%
  group_by(department) %>%
  summarise(
    rd_budget_bil = sum(rd_budget_mil) / 10^3) %>%
  mutate(
*   department = fct_reorder(
*       department, rd_budget_bil)) %>%

# Make the chart
  ggplot() +
  geom_col(
    aes(x = rd_budget_bil, y = department),
    width = 0.7, alpha = 0.8,
    fill = "steelblue"
  ) +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid()
```

]]

]

---

## Reorder & modify factors with<br>the **forcats** library

Loaded with `library(tidyverse)`

]

]

---

## Common situations for modifying / reording factors:

### 1. Reorder factors based on another numerical variable
### 2. Reorder factors manually
### 3. Modify factors manually
### 4. What if there are too many factor levels?

---

### 1. Reorder factors based on another **numerical variable**

Use `fct_reorder()`

]]

]

---

### 2. Reorder factors **manually**

```r
# Format the data frame
lotr_words %>%
  pivot_longer(
      names_to = 'gender', 
      values_to = 'wordCount',
      cols = Female:Male) %>%

# Make the chart
  ggplot() +
  geom_col(
    aes(x = wordCount, y = Film),
    width = 0.7, alpha = 0.8
  ) +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid()
```

]]

]

---

### 2. Reorder factors **manually** with `fct_relevel()`

]]

]

---

### 3. Modify factors manually

<br>

```r
# Format the data frame
lotr_words %>%
  pivot_longer(
      names_to = 'gender', 
      values_to = 'wordCount',
      cols = Female:Male) %>%

]]

]

---

### 3. Modify factors manually with `fct_recode()`

`"new label" = "old label"`

```r
# Format the data frame
lotr_words %>%
  pivot_longer(
      names_to = 'gender', 
      values_to = 'wordCount',
      cols = Female:Male) %>%
* mutate(
*   Film = fct_recode(Film,
*     'The Fellowship\nof the Ring' = 'The Fellowship Of The Ring',
*     'The Return\nof the King' = 'The Return Of The King')) %>%

]]

]

---

### 2 & 3. Modify and reorder factors manually

```r
# Format the data frame
lotr_words %>%
  pivot_longer(
      names_to = 'gender', 
      values_to = 'wordCount',
      cols = Female:Male) %>% 
* mutate(
*   Film = fct_relevel(Film, levels = c(
*     'The Fellowship Of The Ring',
*     'The Two Towers',
*     'The Return Of The King')),
*   Film = fct_recode(Film,
*     'The Fellowship\nof the Ring' = 'The Fellowship Of The Ring',
*     'The Return\nof the King' = 'The Return Of The King')) %>%

# Make the chart
  ggplot() +
  geom_col(
      aes(x = wordCount, y = Film),
      width = 0.7, alpha = 0.8
  ) +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid()
```

]]

]

---

### 4. What if there are too many factor levels?

]]

]

---

### 4. What if there are too many factor levels?

**Strategy**: Merge smaller factors into "Other" with `fct_other()`

```r
# Format the data frame
federal_spending %>%
* mutate(
*   department = fct_other(department,
*     keep = c('DOD', 'HHS', 'NIH', 'NASA', 'DOE'))) %>%
  group_by(department) %>%
  summarise(
    rd_budget_bil = sum(rd_budget_mil) / 10^3) %>%
  mutate(
    department = fct_reorder(department, rd_budget_bil)) %>%

]]

]

---

### 4. What if there are _really_ too many factor levels?

```r
# Format the data frame
avengers %>%
  mutate(
*   name_alias = fct_reorder(name_alias, appearances)) %>%

# Make the chart
  ggplot() +
  geom_col(
    aes(x = appearances,y = name_alias),
    width = 0.7, alpha = 0.8
  ) +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid()
```

]]

]

---

### 4. What if there are _really_ too many factor levels?

**Strategy**: Keep top N, drop the rest with `slice()`

```r
# Format the data frame
avengers %>%
  mutate(
    name_alias = fct_reorder(name_alias, appearances)) %>%
* arrange(desc(appearances)) %>%
* slice(1:10) %>%

# Make the chart
  ggplot() +
  geom_col(
    aes(x = appearances, y = name_alias),
    width = 0.7, alpha = 0.8
  ) +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid()
```

]]

]

---

### 4. What if there are _really_ too many factor levels?

`slice()` works with grouping too!

```r
# Format the data frame
avengers %>%
  mutate(
    name_alias = fct_reorder(name_alias, appearances)) %>%
* arrange(desc(appearances)) %>%
* group_by(gender) %>%
* slice(1:10) %>%

# Make the chart
  ggplot() +
  geom_col(
    aes(
      x = appearances, 
      y = name_alias,
      fill = gender
    ),
    width = 0.7, alpha = 0.8
  ) +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid()
```

]]

]

---

## Your turn - practice manipulating factors

Use the `wildlife_impacts` data to create the following plot

---

# Week 7: .fancy[Amounts & Proportions]

## 1. Manipulating factors
## 2. .orange[Graphing amounts]

## BREAK

## 3. Graphing proportions

---

# Show amounts with:

]

]

]

---

## Bar chart

]

## Dot chart

]

## Lollipop chart

]

---

## Bars are good for highlighting specific categories

---

## Use lollipops when:

### - The bars are overwhelming<br> - You're not highlighting categories

]

]

---

## Or use dots and don't set axis to 0

]

]

---

## How to make a **Bar chart**

```r
# Summarize the data
federal_spending %>%
  group_by(department) %>%
  summarise(rd_budget_bil = sum(rd_budget_mil) / 10^3) %>%
* mutate(department = fct_reorder(department, rd_budget_bil)) %>%

# Make chart
  ggplot() +
* geom_col(
*   aes(x = rd_budget_bil, y = department),
    width = 0.7, alpha = 0.8,
    fill = 'steelblue') +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid()
```

]]

]

---

## Filling the bars with color

```r
# Summarize the data
federal_spending %>%
  group_by(department) %>%
  summarise(rd_budget_bil = sum(rd_budget_mil) / 10^3) %>%
  mutate(
    department = fct_reorder(department, rd_budget_bil),
*   is_dod = if_else(
*     department == 'DOD', TRUE, FALSE)) %>%

# Make the chart
  ggplot() +
  geom_col(
    aes(x = rd_budget_bil, y = department,
*       fill = is_dod),
    width = 0.7, alpha = 0.8) +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid() +
  theme(legend.position = 'none')
```

]]

The DOD's R&D budget is nearly the same as all other departments combined

]]

---

## Filling the bars with color

```r
# Summarize the data
federal_spending %>%
  group_by(department) %>%
  summarise(rd_budget_bil = sum(rd_budget_mil) / 10^3) %>%
  mutate(
    department = fct_reorder(department, rd_budget_bil),
    is_dod = if_else(
      department == 'DOD', TRUE, FALSE)) %>%

# Make the chart
  ggplot() +
  geom_col(
    aes(x = rd_budget_bil, y = department, 
        fill = is_dod),
    width = 0.7, alpha = 0.8) +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
* scale_fill_manual(values = c('grey', 'steelblue')) +
  theme_minimal_vgrid() +
  theme(legend.position = 'none')
```

]]

The DOD's R&D budget is nearly the same as all other departments combined

]]

---

## How to make a **Dot chart**

Summarize data frame:

```r
# Summarize the data
federal_spending %>%
  group_by(department) %>%
  summarise(
    rd_budget_bil = sum(rd_budget_mil) / 10^3) %>%
  mutate(
    department = fct_reorder(department, rd_budget_bil)) %>%

# Make the chart
  ggplot() +
* geom_point(
*   aes(x = rd_budget_bil, y = department),
    size = 2.5, color = 'steelblue') +
  theme_minimal_vgrid()
```

]]

**Dot chart** of federal R&D spending by department

]]

---

## How to make a **Lollipop chart**

Summarize data frame:

```r
# Summarize the data
federal_spending %>%
  group_by(department) %>%
  summarise(
    rd_budget_bil = sum(rd_budget_mil) / 10^3) %>%
  mutate(
    department = fct_reorder(department, rd_budget_bil)) %>%

# Make the chart
  ggplot() +
* geom_segment(
*   aes(x = 0, xend = rd_budget_bil,
*       y = department, yend = department),
    color = 'grey') +
* geom_point(
*   aes(x = rd_budget_bil, y = department),
    size = 2.5, color = 'steelblue') +
  theme_minimal_vgrid()
```

]]

**Lollipop chart** of federal R&D spending by department

]]

---

## Your turn - practice plotting amounts

Create the following charts:

Data: `bears`
<center>
<img src="figs/bear_bars.png" width=500>
</center>

]

Data: `milk_production`
<center>
<img src="figs/milk_dots_top10-1.png">
</center>

]

---

# Break!

## Stand up, Move around, Stretch!

---

# Week 7: .fancy[Amounts & Proportions]

## 1. Manipulating factors
## 2. Graphing amounts

## BREAK

## 3. .orange[Graphing proportions]

---

# Show proportions with:

]

]

]

---

## Bar charts

]

## Pie charts

]

## Waffle charts

]

---

## Stacked bars

# Make the chart
  ggplot() +
* geom_col(
*   aes(x = "", y = milk_produced, fill = state),
    width = 0.7, alpha = 0.8) +
  scale_y_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_hgrid() +
  labs(x = NULL,
       y = 'Milk produced (lbs)',
       fill = 'State',
       title = '2017 Milk Production\nby State')
```

]]

]

---

## Stacked bars - Rotated also looks good

# Make the chart
  ggplot() +
* geom_col(
*   aes(x = milk_produced, y = "", fill = state),
    width = 0.7, alpha = 0.8) +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_hgrid() +
  labs(y = NULL,
       x = 'Milk produced (lbs)',
       fill = 'State',
       title = '2017 Milk Production by State')
```

]]

]

---

## Stacked bars - not great for more than a few categories

```r
# Format the data
milk_production %>%
  filter(year == 2017) %>%
  mutate(state = fct_other(state,
*   keep = c('California', 'Wisconsin',
*            'New York', 'Idaho'))) %>%
  group_by(state) %>%
  summarise(milk_produced = sum(milk_produced))

# Make the chart
  ggplot() +
  geom_col(
    aes(x = "", y = milk_produced, fill = state),
    width = 0.7, alpha = 0.8) +
  scale_y_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid() +
  labs(x = NULL,
       y = 'Milk produced (lbs)',
       fill = 'State',
       title = '2017 Milk Production\nby State')
```

]]

]

---

## Dodged bars

Better for **part-to-whole comparison**

```r
# Format the data
milk_production %>%
  filter(year == 2017) %>%
  mutate(state = fct_other(state,
    keep = c('California', 'Wisconsin'))) %>%
  group_by(state) %>%
  summarise(milk_produced = sum(milk_produced)) %>%
  mutate(state = fct_reorder(state, milk_produced)) %>%

# Make the chart
  ggplot() +
  geom_col(
    aes(x = milk_produced, y = state),
    width = 0.7, alpha = 0.8) +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid() +
  labs(x = 'Milk produced (lbs)',
       y = 'State',
       title = '2017 Milk Production by State')
```

]]

Okay:
<center>
<img src="figs/milk_2017_bars_stacked_rotated.png">
</center>

Better:
<center>
<img src="figs/milk_2017_bars_dodged.png">
</center>

]

---

## Dodged bars

```r
milk_production %>%
  filter(year %in% c(1970, 2017)) %>%
  mutate(state = fct_other(state,
    keep = c('California', 'Wisconsin'))) %>%
  group_by(year, state) %>%
  summarise(milk_produced = sum(milk_produced)) %>%

# Make the chart
  ggplot() +
  geom_col(
    aes(x = milk_produced, 
        y = as.factor(year),
        fill = state),
*   position = 'dodge',
    width = 0.7, alpha = 0.8) +
  scale_x_continuous(
    expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid() +
  labs(x = 'Milk produced (lbs)',
       y = 'Year',
       fill = 'State',
       title = '1970 & 2017 Milk Production by State')
```

]]

Better for comparing **total**:

Better for comparing **parts**:

]

---

## .center[Where stacking is useful]

.font70[https://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/]

]

### - **2 to 3 groups**
### - Proportions over time

]

---

## .center[Where stacking is useful]

]

### - 2 to 3 groups
### - **Proportions over time**

]

---

## The Notorious P-I-E

Start with a bar chart

# Make the chart
  ggplot() +
* geom_col(
*   aes(x = "", y = milk_produced, fill = state),
    width = 0.7, alpha = 0.8) +
  theme_minimal_hgrid() +
  labs(x = NULL,
       y = 'Milk produced (lbs)',
       fill = 'State',
       title = '2017 Milk Production\nby State')
```

]]

]

---

## The Notorious P-I-E

Convert bar to pie with `coord_polar()`

# Make the chart
  ggplot() +
  geom_col(
    aes(x = "", y = milk_produced, fill = state),
    width = 0.7, alpha = 0.8) +
* coord_polar(theta = "y") +
  theme_minimal_hgrid() +
  labs(x = NULL,
       y = 'Milk produced (lbs)',
       fill = 'State',
       title = '2017 Milk Production by State')
```

]]

]

---

```r
# Format the data
milk_production %>%
  filter(year == 2017) %>%
  mutate(state = fct_other(state,
    keep = c('California', 'Wisconsin'))) %>%
  group_by(state) %>%
  summarise(milk_produced = sum(milk_produced)) %>%
* arrange(desc(state)) %>%
* mutate(p = 100*(milk_produced / sum(milk_produced)),
*        label = str_c(round(p), '%')) %>%

# Make the chart
  ggplot() +
  geom_col(
    aes(x = "", y = milk_produced, fill = state),
    width = 0.7, alpha = 0.8) +
* geom_text(
*   aes(x = "", y = milk_produced, label = label),
*   color = "white", size = 6,
*   position = position_stack(vjust = 0.5)) +
  coord_polar(theta = "y") +
* theme_map() +
  labs(x = NULL,
       y = NULL,
       fill = 'State',
       title = '2017 Milk Production by State')
```

]]

### The Notorious P-I-E

Final chart with labels & `theme_map()`

]

---

## Pies are still useful if the sum of components matters

]

]

]

---

## The best pies are **square pies**

---

### Waffle plots

```r
*library(waffle)

# Format the data
milk_production %>%
  filter(year == 2017) %>%
  mutate(state = fct_other(state,
    keep = c('California', 'Wisconsin'))) %>%
  group_by(state) %>%
  summarise(milk_produced = sum(milk_produced)) %>%
  mutate(milk_produced = milk_produced / 10^9) %>%

# Make the chart
  ggplot() +
* geom_waffle(
*   aes(fill = state, values = milk_produced),
*   color = "white", size = 1, n_rows = 15) +
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_discrete(expand = c(0, 0)) +
  theme_minimal() +
  labs(fill = 'State',
       x = NULL, y = NULL,
       title = '2017 Milk Production by State',
       subtitle = '(1 square = 1 billion lbs)')
```

]]

**Use values between 100 - 1,000**

```
#> # A tibble: 3 × 2
#>   state      milk_produced
#>   <fct>              <dbl>
#> 1 California          39.8
#> 2 Wisconsin           30.3
#> 3 Other              145.
```

]

---

### Waffle plots

```r
*library(waffle)

# Make the chart
  ggplot() +
  geom_waffle(
    aes(fill = state, values = milk_produced),
    color = "white", size = 1, n_rows = 15,
*   flip = TRUE) +
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_discrete(expand = c(0, 0)) +
  theme_minimal() +
  labs(fill = 'State',
       x = NULL, y = NULL,
       title = '2017 Milk Production by State',
       subtitle = '(1 square = 1 billion lbs)')
```

]]

**Use values between 100 - 1,000**

```
#> # A tibble: 3 × 2
#>   state      milk_produced
#>   <fct>              <dbl>
#> 1 California          39.8
#> 2 Wisconsin           30.3
#> 3 Other              145.
```

]

---

```r
library(waffle)

# Format the data
milk_production %>%
* filter(year %in% c(1970, 2017)) %>%
  mutate(state = fct_other(state,
    keep = c('California', 'Wisconsin'))) %>%
* group_by(year, state) %>%
  summarise(milk_produced = sum(milk_produced)) %>%
  mutate(milk_produced = milk_produced / 10^9) %>%

# Make the chart
  ggplot() +
  geom_waffle(
    aes(fill = state, values = milk_produced),
    color = "white", size = 1, n_rows = 10,
    flip = TRUE) +
* facet_wrap(vars(year), strip.position = 'bottom') +
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_discrete(expand = c(0, 0)) +
  theme_minimal() +
  labs(fill = 'State',
       x = NULL, y = NULL,
       title = '1970 & 2017 Milk Production by State',
       subtitle = '(1 square = 1 billion lbs)')
```

]]

### Waffle comparison

```
#> # A tibble: 3 × 2
#>   state      milk_produced
#>   <fct>              <dbl>
#> 1 California          39.8
#> 2 Wisconsin           30.3
#> 3 Other              145.
```

]

---

Stacked bars

Dodged bars

]

Pie chart

Waffle chart

]

---

## Your turn

Using the `wildlife_impacts` data, create plots that shows the proportion of incidents that occur at each different time of day.

For this exercise, you can remove `NA` values.

Try to create the following plots:

- Stacked bars
- Dodged bars
- Pie chart
- Waffle chart

]

To get started, you'll need to first summarize the data:

```r
wildlife_summary <- wildlife_impacts %>%
  filter(!is.na(time_of_day)) %>%
  count(time_of_day)

wildlife_summary
```

```
#> # A tibble: 4 × 2
#>   time_of_day     n
#>   <chr>       <int>
#> 1 Dawn         1270
#> 2 Day         25123
#> 3 Dusk         1717
#> 4 Night       12735
```

]