Amounts & Proportions

.leftcol30[
<center>
<img src="https://raw.githubusercontent.com/emse-eda-gwu/2021-Spring/master/images/eda_hex_sticker.png" width=250>
</center>
]
.rightcol70[
# Week 6: .fancy[Amounts & Proportions]

### <svg style="height:0.8em;top:.04em;position:relative;fill:white;" viewBox="0 0 512 512"><path d="M496 128v16a8 8 0 0 1-8 8h-24v12c0 6.627-5.373 12-12 12H60c-6.627 0-12-5.373-12-12v-12H24a8 8 0 0 1-8-8v-16a8 8 0 0 1 4.941-7.392l232-88a7.996 7.996 0 0 1 6.118 0l232 88A8 8 0 0 1 496 128zm-24 304H40c-13.255 0-24 10.745-24 24v16a8 8 0 0 0 8 8h464a8 8 0 0 0 8-8v-16c0-13.255-10.745-24-24-24zM96 192v192H60c-6.627 0-12 5.373-12 12v20h416v-20c0-6.627-5.373-12-12-12h-36V192h-64v192h-64V192h-64v192h-64V192H96z"/></svg> EMSE 4575: Exploratory Data Analysis
### <svg style="height:0.8em;top:.04em;position:relative;fill:white;" viewBox="0 0 448 512"><path d="M224 256c70.7 0 128-57.3 128-128S294.7 0 224 0 96 57.3 96 128s57.3 128 128 128zm89.6 32h-16.7c-22.2 10.2-46.9 16-72.9 16s-50.6-5.8-72.9-16h-16.7C60.2 288 0 348.2 0 422.4V464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48v-41.6c0-74.2-60.2-134.4-134.4-134.4z"/></svg> John Paul Helveston
### <svg style="height:0.8em;top:.04em;position:relative;fill:white;" viewBox="0 0 448 512"><path d="M0 464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48V192H0v272zm320-196c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zM192 268c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zM64 268c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12H76c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12H76c-6.6 0-12-5.4-12-12v-40zM400 64h-48V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48H160V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48H48C21.5 64 0 85.5 0 112v48h448v-48c0-26.5-21.5-48-48-48z"/></svg> February 17, 2021
]

---
class: center, middle, inverse

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

# The `fcuk` package

---

## 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

```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

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

Automatically load:

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

---
class: inverse, middle, center

# 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**

---
class: inverse, middle

# Next projects due:

## - [Mini project 2](https://eda.seas.gwu.edu/2021-Spring/a-mini-project-2.html): Redesign (Due 03/09)
## - [Project proposal](https://eda.seas.gwu.edu/2021-Spring/a-project.html#Proposal) (Due 03/12)

---

## 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

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

---

# Week 6: .fancy[Amounts & Proportions]

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

## BREAK

## 3. Graphing proportions

---

# Week 6: .fancy[Amounts & Proportions]

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

## BREAK

## 3. Graphing proportions

---
class: center

# Sorting in ggplot is done by reordering factors

.leftcol[
<center>
<img src="images/check-bad.png" width=75>
<img src="figs/federal_spending_bars_unsorted.png">
<center>
]

.rightcol[
<center>
<img src="images/check-good.png" width=100>
<img src="figs/federal_spending_bars.png">
<center>
]

---

## .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()
```
]]

---
class: middle

Loaded with `library(tidyverse)`
]

---
class: inverse, middle

## 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

---

### 2. Reorder factors manually

```r
# Format the data frame
lotr_words %>%
  gather(key = 'gender', value = 'wordCount',
         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

```r
# Format the data frame
lotr_words %>%
  gather(key = 'gender', value = 'wordCount',
         Female:Male) %>%

.rightcol[
.center[The film names here are too long]
<center>
<img src="figs/lotr_bars.png">
<center>
]

---

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

`"new label" = "old label"`

```r
# Format the data frame
lotr_words %>%
  gather(key = 'gender', value = 'wordCount',
         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 %>%
  gather(key = 'gender', value = 'wordCount',
         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')) %>%

---

### 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) %>%

---

### 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()
```
]]

---
class: inverse

## Your turn - practice manipulating factors

Use the `wildlife_impacts` data to create the following plot

---

# Week 6: .fancy[Amounts & Proportions]

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

## BREAK

## 3. Graphing proportions

---
class: inverse, middle, center

# Show amounts with:

<center>
<img src="images/dots.png" width=280>
<center>
]

---
class: center, middle

## Bar chart

<center>
<img src="figs/federal_spending_bars.png">
<center>
]

## Dot chart

<center>
<img src="figs/federal_spending_dots.png">
<center>
]

## Lollipop chart

<center>
<img src="figs/federal_spending_lollipop.png">
<center>
]

---
class: center

## Bars are good for highlighting specific categories

---

## Use lollipops when:

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

---
class: center

## 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')
```
]]

<center>
<img src="figs/federal_spending_bars_highlight_badcolor.png">
<center>
]]

---

## 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')
```
]]

<center>
<img src="figs/federal_spending_bars_highlight.png">
<center>
]]

---

## How to make a **Dot 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 the chart
  ggplot() +
  geom_point(
*   aes(x = rd_budget_bil, y = department),
    size = 2.5, color = 'steelblue') +
  theme_minimal_vgrid()
```
]]

<center>
<img src="figs/federal_spending_dots.png">
<center>
]]

---

## How to make a **Lollipop 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 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()
```
]]

<center>
<img src="figs/federal_spending_lollipop.png">
<center>
]]

---
class: inverse, middle

## Your turn - practice plotting amounts

Create the following charts:

---
class: inverse, center

# Break!

## Stand up, Move around, Stretch!

---

# Week 6: .fancy[Amounts & Proportions]

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

## BREAK

## 3. .orange[Graphing proportions]

---
class: inverse, middle, center

# Show proportions with:

---
class: center, middle

## Bar charts

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

## Pie charts

<center>
<img src="figs/milk_2017_pie-1.png" width=300>
<center>
]

## Waffle charts

<center>
<img src="figs/milk_waffle_2017.png" width=300>
<center>
]

---

## 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')
```
]]

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')
```
]]

.rightcol45[
Better for comparing **total**:
<center>
<img src="figs/milk_compare_bars_stacked.png">
</center>

Better for comparing **parts**:
<center>
<img src="figs/milk_compare_bars_dodged.png">
</center>
]

---

## .center[Where stacking is useful]

.leftcol60[
<center>
<img src="images/bechdel-stacked.png" width="550">
</center>
.font70[https://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/]
]

---

## .center[Where stacking is useful]

.leftcol60[
<center>
<img src="images/Coles-Graph.png" width="600">
</center>
.font80[https://www.perceptualedge.com/blog/?p=2239]
]

---

## 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')
```
]]

.rightcol40[
<img src="figs/unnamed-chunk-35-1.png" width="288" style="display: block; margin: auto;" />
]

---

## 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')
```
]]

.rightcol45[
<img src="figs/unnamed-chunk-36-1.png" width="504" style="display: block; margin: auto;" />
]

---

```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')
```
]]

Final chart with labels & `theme_map()`

<img src="figs/unnamed-chunk-37-1.png" width="504" style="display: block; margin: auto;" />
]

---
class: center

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

---
class: center

## 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)')
```
]]

(You don't want 1,000,000,000 boxes!)

```
#> # A tibble: 3 x 2
#>   state      milk_produced
#>   <fct>              <dbl>
#> 1 California          39.8
#> 2 Wisconsin           30.3
#> 3 Other              145.
```
<img src="figs/unnamed-chunk-39-1.png" width="360" style="display: block; margin: auto;" />
]

---

### 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)')
```
]]

(You don't want 1,000,000,000 boxes!)

```
#> # A tibble: 3 x 2
#>   state      milk_produced
#>   <fct>              <dbl>
#> 1 California          39.8
#> 2 Wisconsin           30.3
#> 3 Other              145.
```
<img src="figs/unnamed-chunk-41-1.png" width="360" style="display: block; margin: auto;" />
]

---

```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)')
```
]]

```
#> # A tibble: 3 x 2
#>   state      milk_produced
#>   <fct>              <dbl>
#> 1 California          39.8
#> 2 Wisconsin           30.3
#> 3 Other              145.
```
<img src="figs/unnamed-chunk-43-1.png" width="360" style="display: block; margin: auto;" />
]

---
class: center

Dodged bars
<center>
<img src="figs/milk_compare_bars_dodged.png">
</center>
]
.rightcol[

Pie chart
<img src="figs/milk_compare_pie-1.png" width="360" style="display: block; margin: auto;" />

Waffle chart
<img src="figs/unnamed-chunk-44-1.png" width="360" style="display: block; margin: auto;" />
]

---
class: inverse

## Your turn

.leftcol[
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
]

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

wildlife_summary
```

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