36-315-Lec7

# 36-315: Statistical Graphics and Visualization
## Lecture 7

Meghan Hall <br> Department of Statistics & Data Science <br> Carnegie Mellon University <br> June 7, 2021

---
layout: true
<div class="my-footer"><span>cmu-36315.netlify.app</span></div>
---

# From last time

<br>

<br>

---

# Updates

<br>
.large[Homework]
<br>
.medium[due tomorrow!]
<br>
<br>
<br>
.large[Lab 5 tomorrow]
<br>
<br>
<br>
.large[Schedule going forward]

---

# Today

<br>

<br>

---

# Today's agenda

<br>

1. heat maps

2. dot plots

3. pie charts & parts of a whole

4. maps

]

---

# Today's data

<br>
<br>
<br>
.center[`tx_housing`]<br>
.center[`lincoln_weather`]<br>
.center[`storms`]<br>
.center[`penguins`]<br>
.center[`tx_data`]<br>
.center[`us_rent_income`]<br>

---

# Today's agenda

<br>

1. **heat maps**

2. dot plots

3. pie charts & parts of a whole

4. maps

]

---

# Heat maps

<br>
<br>
.large[Mapping data values onto color]
<br>
<br>
--
<br>
.large[Can be difficult to determine exact values]
<br>
.medium[but very useful for identifying broader trends]
<br>
<br>
--
<br>
.large[Order data in a way that's useful for your data/question]
<br>
.medium[if ordering is appropriate]
<br>
<br>
--
<br>
.large[Can also be used for chronological/time series data]

---

# Heat maps

---

# Today's data

<br>
.center[`txhousing`]<br>

<table class="table" style="font-size: 16px; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> city </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> year </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> month </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> sales </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> volume </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> median </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> listings </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> inventory </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> date </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Abilene </td>
   <td style="text-align:right;"> 2000 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 72 </td>
   <td style="text-align:right;"> 5380000 </td>
   <td style="text-align:right;"> 71400 </td>
   <td style="text-align:right;"> 701 </td>
   <td style="text-align:right;"> 6.3 </td>
   <td style="text-align:right;"> 2000.000 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Abilene </td>
   <td style="text-align:right;"> 2000 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 98 </td>
   <td style="text-align:right;"> 6505000 </td>
   <td style="text-align:right;"> 58700 </td>
   <td style="text-align:right;"> 746 </td>
   <td style="text-align:right;"> 6.6 </td>
   <td style="text-align:right;"> 2000.083 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Abilene </td>
   <td style="text-align:right;"> 2000 </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:right;"> 130 </td>
   <td style="text-align:right;"> 9285000 </td>
   <td style="text-align:right;"> 58100 </td>
   <td style="text-align:right;"> 784 </td>
   <td style="text-align:right;"> 6.8 </td>
   <td style="text-align:right;"> 2000.167 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Abilene </td>
   <td style="text-align:right;"> 2000 </td>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:right;"> 98 </td>
   <td style="text-align:right;"> 9730000 </td>
   <td style="text-align:right;"> 68600 </td>
   <td style="text-align:right;"> 785 </td>
   <td style="text-align:right;"> 6.9 </td>
   <td style="text-align:right;"> 2000.250 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Abilene </td>
   <td style="text-align:right;"> 2000 </td>
   <td style="text-align:right;"> 5 </td>
   <td style="text-align:right;"> 141 </td>
   <td style="text-align:right;"> 10590000 </td>
   <td style="text-align:right;"> 67300 </td>
   <td style="text-align:right;"> 794 </td>
   <td style="text-align:right;"> 6.8 </td>
   <td style="text-align:right;"> 2000.333 </td>
  </tr>
</tbody>
</table>

--
<br>
<br>
.center[**how has the median listing price changed over time in Texas cities?**]

---

# Aggregating data

**two methods, depending on needs**

```r
txhousing %>% 
  group_by(city, year) %>% 
  summarize(mean_price = mean(median, na.rm = TRUE))
```

---

# Aggregating data

**two methods, depending on needs**

```r
txhousing %>% 
  group_by(city, year) %>% 
  mutate(mean_price = mean(median, na.rm = TRUE))
```

<table class="table" style="font-size: 16px; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> city </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> year </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> month </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> sales </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> volume </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> median </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> listings </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> inventory </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> date </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> mean_price </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Abilene </td>
   <td style="text-align:right;"> 2000 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 72 </td>
   <td style="text-align:right;"> 5380000 </td>
   <td style="text-align:right;"> 71400 </td>
   <td style="text-align:right;"> 701 </td>
   <td style="text-align:right;"> 6.3 </td>
   <td style="text-align:right;"> 2000.000 </td>
   <td style="text-align:right;"> 66600 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Abilene </td>
   <td style="text-align:right;"> 2000 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 98 </td>
   <td style="text-align:right;"> 6505000 </td>
   <td style="text-align:right;"> 58700 </td>
   <td style="text-align:right;"> 746 </td>
   <td style="text-align:right;"> 6.6 </td>
   <td style="text-align:right;"> 2000.083 </td>
   <td style="text-align:right;"> 66600 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Abilene </td>
   <td style="text-align:right;"> 2000 </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:right;"> 130 </td>
   <td style="text-align:right;"> 9285000 </td>
   <td style="text-align:right;"> 58100 </td>
   <td style="text-align:right;"> 784 </td>
   <td style="text-align:right;"> 6.8 </td>
   <td style="text-align:right;"> 2000.167 </td>
   <td style="text-align:right;"> 66600 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Abilene </td>
   <td style="text-align:right;"> 2000 </td>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:right;"> 98 </td>
   <td style="text-align:right;"> 9730000 </td>
   <td style="text-align:right;"> 68600 </td>
   <td style="text-align:right;"> 785 </td>
   <td style="text-align:right;"> 6.9 </td>
   <td style="text-align:right;"> 2000.250 </td>
   <td style="text-align:right;"> 66600 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Abilene </td>
   <td style="text-align:right;"> 2000 </td>
   <td style="text-align:right;"> 5 </td>
   <td style="text-align:right;"> 141 </td>
   <td style="text-align:right;"> 10590000 </td>
   <td style="text-align:right;"> 67300 </td>
   <td style="text-align:right;"> 794 </td>
   <td style="text-align:right;"> 6.8 </td>
   <td style="text-align:right;"> 2000.333 </td>
   <td style="text-align:right;"> 66600 </td>
  </tr>
</tbody>
</table>

---

# Heat maps

```r
txhousing_plot %>% 
  ggplot(aes(x = year, y = city, fill = mean_price)) +
  geom_tile()
```

---

# Heat maps

```r
txhousing_plot %>% 
* ggplot(aes(x = year, y = reorder(city, rank_2015),
             fill = mean_price)) +
  geom_tile() +
* scale_fill_gradient(low = "#fafafa", high = "#191970",
*                     labels = dollar, name = "")
```

---

# Heat maps

```r
txhousing_plot %>% 
  ggplot(aes(x = year, y = reorder(city, rank_2015), 
             fill = mean_price)) +
  geom_tile() +
  scale_fill_gradient(low = "#fafafa", high = "#191970",
                      labels = dollar, name = "") +
* coord_cartesian(expand = FALSE) +
  labs(title = "Listings Price in Texas Cities") +
  theme(axis.ticks = element_blank(),
        axis.title = element_blank()) 
```

---

# Heat maps

---

# Today's data

<br>
.center[`lincoln_weather`]<br>

--
<br>
<br>
.center[**how did the max temperature change by day and month?**]

---

# Practice with `lubridate`

```r
lincoln_weather %>% 
  select(CST, temp = `Max Temperature [F]`) %>% 
  mutate(date = ymd(CST), 
         month = month(date), 
         day = day(date))
```

---

# Heat maps

---

# Heat maps

---

# Heat maps

---

# Heat maps

---

# Heat maps

```r
lincoln_weather %>% 
  select(CST, temp = `Max Temperature [F]`) %>% 
  mutate(date = ymd(CST), 
         month = month(date), 
         day = day(date)) %>% 
  ggplot(aes(x = month, y = day, fill = temp)) +
  geom_tile(color = "white") +
  scale_y_continuous(trans = "reverse", 
                     breaks = seq(1, 31, 5)) + 
  scale_x_continuous(breaks = seq(1, 12, 1)) +
* labs(title = "Max Temp in Lincoln, NE in 2016") +
* scale_fill_gradient(low = "blue", high = "yellow",
*                     breaks = seq(20, 90, 10),
*                     name = " °F") +
* theme(axis.ticks = element_blank()) +
* coord_cartesian(expand = FALSE)
```

---

# Heat maps

---

# Today's agenda

<br>

1. heat maps

2. **dot plots**

3. pie charts & parts of a whole

4. maps

]

---

# Dot plots

<br>
<br>
.large[Combines the advantages of a **lollipop chart**]
<br>
.medium[narrow profile means you can show a variable with many values]
<br>
.medium[dots draw the eye to the end point, making for useful comparisons]
<br>
<br>
--
<br>
.large[With the benefits of encoding data by position]
<br>
.medium[axis doesn't need to start at zero]
<br>
.medium[useful when zero isn't particularly relevant to your data]
<br>
<br>
--
<br>
.large[Values must be ordered for this to be effective]

---

# Today's data

<br>
.center[`storms`]<br>

<table class="table" style="font-size: 16px; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> name </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> year </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> month </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> day </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> hour </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> lat </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> long </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> status </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> category </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> wind </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> pressure </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Amy </td>
   <td style="text-align:right;"> 1975 </td>
   <td style="text-align:right;"> 6 </td>
   <td style="text-align:right;"> 27 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 27.5 </td>
   <td style="text-align:right;"> -79.0 </td>
   <td style="text-align:left;"> tropical depression </td>
   <td style="text-align:left;"> -1 </td>
   <td style="text-align:right;"> 25 </td>
   <td style="text-align:right;"> 1013 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Amy </td>
   <td style="text-align:right;"> 1975 </td>
   <td style="text-align:right;"> 6 </td>
   <td style="text-align:right;"> 27 </td>
   <td style="text-align:right;"> 6 </td>
   <td style="text-align:right;"> 28.5 </td>
   <td style="text-align:right;"> -79.0 </td>
   <td style="text-align:left;"> tropical depression </td>
   <td style="text-align:left;"> -1 </td>
   <td style="text-align:right;"> 25 </td>
   <td style="text-align:right;"> 1013 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Amy </td>
   <td style="text-align:right;"> 1975 </td>
   <td style="text-align:right;"> 6 </td>
   <td style="text-align:right;"> 27 </td>
   <td style="text-align:right;"> 12 </td>
   <td style="text-align:right;"> 29.5 </td>
   <td style="text-align:right;"> -79.0 </td>
   <td style="text-align:left;"> tropical depression </td>
   <td style="text-align:left;"> -1 </td>
   <td style="text-align:right;"> 25 </td>
   <td style="text-align:right;"> 1013 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Amy </td>
   <td style="text-align:right;"> 1975 </td>
   <td style="text-align:right;"> 6 </td>
   <td style="text-align:right;"> 27 </td>
   <td style="text-align:right;"> 18 </td>
   <td style="text-align:right;"> 30.5 </td>
   <td style="text-align:right;"> -79.0 </td>
   <td style="text-align:left;"> tropical depression </td>
   <td style="text-align:left;"> -1 </td>
   <td style="text-align:right;"> 25 </td>
   <td style="text-align:right;"> 1013 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Amy </td>
   <td style="text-align:right;"> 1975 </td>
   <td style="text-align:right;"> 6 </td>
   <td style="text-align:right;"> 28 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 31.5 </td>
   <td style="text-align:right;"> -78.8 </td>
   <td style="text-align:left;"> tropical depression </td>
   <td style="text-align:left;"> -1 </td>
   <td style="text-align:right;"> 25 </td>
   <td style="text-align:right;"> 1012 </td>
  </tr>
</tbody>
</table>

--
<br>
<br>
.center[**what's the max wind speed of each hurricane from 2011 onward?**]

---

# Dot plots

```r
storms_agg <- storms %>% 
  filter(status == "hurricane" & year >= 2011) %>% 
  group_by(name, year) %>% 
  summarize(max_wind = max(wind))
```

---

# Dot plots

```r
storms_agg <- storms %>% 
  filter(status == "hurricane" & year >= 2011) %>% 
  group_by(name, year) %>% 
  summarize(max_wind = max(wind)) %>% 
* mutate(name_date = paste0(name, " (", year, ")"),
*        wind_mph = max_wind * 1.151)
```

---

# Dot plots

```r
storms_agg %>% 
* ggplot(aes(x = wind_mph, y = reorder(name_date, wind_mph))) +
  geom_point(color = "dark blue", size = 2) +
  labs(x = "Max Wind Speed (mph)", y = "",
       title = "Max wind speed of hurricanes, 2011 to 2015")
```

---

# Today's agenda

<br>

1. heat maps

2. dot plots

3. **pie charts & parts of a whole**

4. maps

]

---

# Parts of a whole

---

# Pie charts

<br>
.large[Use with caution!]
<br>
<br>
--
<br>
.large[**Only when parts add up to 100%**]
<br>
<br>
--
<br>
.large[Only when number of categories is small]
<br>
.medium[otherwise too difficult to determine differences]
<br>
<br>
--
<br>
.large[Never show multiple pie charts over time]
<br>
.medium[generally another viz type would be better]

---

# Today's data

<br>
.center[`penguins`]<br>

<table class="table" style="font-size: 16px; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> species </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> island </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> bill_length_mm </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> bill_depth_mm </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> flipper_length_mm </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> body_mass_g </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> sex </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> year </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 39.1 </td>
   <td style="text-align:right;"> 18.7 </td>
   <td style="text-align:right;"> 181 </td>
   <td style="text-align:right;"> 3750 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 39.5 </td>
   <td style="text-align:right;"> 17.4 </td>
   <td style="text-align:right;"> 186 </td>
   <td style="text-align:right;"> 3800 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 40.3 </td>
   <td style="text-align:right;"> 18.0 </td>
   <td style="text-align:right;"> 195 </td>
   <td style="text-align:right;"> 3250 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;">  </td>
   <td style="text-align:right;">  </td>
   <td style="text-align:right;">  </td>
   <td style="text-align:right;">  </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 36.7 </td>
   <td style="text-align:right;"> 19.3 </td>
   <td style="text-align:right;"> 193 </td>
   <td style="text-align:right;"> 3450 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
</tbody>
</table>

--
<br>
<br>
.center[**how many penguin species are represented in this data?**]

---

# Pie charts

```r
penguins %>% 
  count(species) %>% 
  ggplot(aes(x="", y = n, fill = species)) +
* geom_bar(stat = "identity", width = 1, color = "black") +
* coord_polar("y", start = 0)
```

---

# Pie charts

```r
penguins %>% 
  count(species) %>% 
  ggplot(aes(x="", y = n, fill = species)) +
  geom_bar(stat = "identity", width = 1, color = "black") + 
  coord_polar("y", start = 0) +
* theme_void()
```

---

# Pie charts

```r
penguins %>% 
  count(species) %>% 
* mutate(label = paste0(species, "\nn = ", n)) %>%
  ggplot(aes(x="", y = n, fill = species)) +
  geom_bar(stat = "identity", width = 1, color = "black") + 
  coord_polar("y", start = 0) +
  geom_text(aes(label = label), 
            position = position_stack(vjust = 0.5)) + 
  theme_void() 
```

---

# Pie charts

```r
penguins %>% 
  count(species) %>% 
* mutate(label = paste0(species, "\nn = ", n)) %>%
  ggplot(aes(x="", y = n, fill = species)) +
  geom_bar(stat = "identity", width = 1, color = "black") + 
  coord_polar("y", start = 0) +
* geom_text(aes(label = label),
*           position = position_stack(vjust = 0.5)) +
  theme_void() 
```

---

# Pie charts

---

# Pie charts

```r
penguins %>% 
  count(species) %>% 
  mutate(label = paste0(species, "\nn = ", n)) %>% 
  ggplot(aes(x="", y = n, fill = species)) +
  geom_bar(stat = "identity", width = 1, color = "black") + 
  coord_polar("y", start = 0) +
* scale_fill_manual(values = c("#496185","#965641","#D7B377")) +
  geom_text(aes(label = label), 
            position = position_stack(vjust = 0.5)) + 
* labs(title = "The distribution of penguin species") +
  theme_void() +
* theme(legend.position = "none",
*       plot.title = element_text(hjust = 0.5))
```

---

# Pie charts

---

# Today's data

<br>
.center[`penguins`]<br>

<table class="table" style="font-size: 16px; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> species </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> island </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> bill_length_mm </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> bill_depth_mm </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> flipper_length_mm </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> body_mass_g </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> sex </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> year </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 39.1 </td>
   <td style="text-align:right;"> 18.7 </td>
   <td style="text-align:right;"> 181 </td>
   <td style="text-align:right;"> 3750 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 39.5 </td>
   <td style="text-align:right;"> 17.4 </td>
   <td style="text-align:right;"> 186 </td>
   <td style="text-align:right;"> 3800 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 40.3 </td>
   <td style="text-align:right;"> 18.0 </td>
   <td style="text-align:right;"> 195 </td>
   <td style="text-align:right;"> 3250 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;">  </td>
   <td style="text-align:right;">  </td>
   <td style="text-align:right;">  </td>
   <td style="text-align:right;">  </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 36.7 </td>
   <td style="text-align:right;"> 19.3 </td>
   <td style="text-align:right;"> 193 </td>
   <td style="text-align:right;"> 3450 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
</tbody>
</table>

--
<br>
<br>
.center[**how does the distribution of body mass differ by penguin?**]

---

# Comparing dist. to a whole

**standard distribution with `geom_density`**

---

# Comparing dist. to a whole

**distribution comparison with `facet_wrap`**

---

# Comparing dist. to a whole

**distribution comparison with `geom_density`**

---

# Comparing dist. to a whole

**distribution comparison with `ggridges`**

---

# Comparing dist. to a whole

```r
penguins %>% 
  ggplot(aes(x = body_mass_g, y = ..count..)) +
* geom_density_line(data = select(penguins, -species),
*               aes(fill = "all penguins"), color = "transparent")
```

---

# Comparing dist. to a whole

```r
penguins %>% 
  ggplot(aes(x = body_mass_g, y = ..count..)) +
  geom_density_line(data = select(penguins, -species), 
                aes(fill = "all penguins"), color = "transparent") +
* geom_density_line(aes(fill = "species"), color = "transparent") +
* facet_wrap(~species, nrow = 1)
```

---

# Comparing dist. to a whole

```r
penguins %>% 
  ggplot(aes(x = body_mass_g, y = ..count..)) +
  geom_density_line(data = select(penguins, -species), 
                aes(fill = "all penguins"), color = "transparent") +
  geom_density_line(aes(fill = "species"), color = "transparent") +
  facet_wrap(~species, nrow = 1) +
* scale_fill_manual(values = c("grey","#0C8346"), name = NULL,
*                   guide = guide_legend(direction = "horizontal")) +
* labs(x = "Body Mass (g)") +
* theme(legend.position = "top")
```

---

# Comparing dist. to a whole

---

# Today's agenda

<br>

1. heat maps

2. dot plots

3. pie charts & parts of a whole

4. **maps**

]

---

# Maps

<br>
.large[Various methods to make maps with R/`ggplot2`]
<br>
.medium[an easy option is the `maps` package]
<br>
<br>
--
<br>
.large[Choropleths & cartograms]
<br>
<br>
--
<br>
.large[Be careful with continuous vs. discrete color scales]
<br>
.medium[continuous allows for more nuance but makes it hard to mark a specific value]
<br>
<br>
--
<br>
.large[For choropleth maps, watch out for areas drawing disproportionate attention due to their size]
<br>
.medium[counts are almost never appropriate]

---

# Choropleth maps

**use `map_data` from the `maps` package**

```r
tx_map <- 
  map_data("county", region = "texas")
```

---

# Choropleth maps

```r
tx_map %>% 
  ggplot(aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "gray", color = "white") +
  theme_void()
```

---

# Today's data

<br>
.center[`tx_data`]<br>

--
<br>
<br>
.center[**how does the share of Hispanic people vary by county?**]

---

# Choropleth map

```r
tx_data_agg <- tx_data %>% 
  filter(Age == "All Ages" & County != "STATE OF TEXAS") %>% 
  mutate(hispanic_perc = Hispanic_Total / Total) %>% 
  select(County, hispanic_perc) %>% 
* mutate(county_lower = str_replace_all(County, " COUNTY", ""),
*        county_lower = str_to_lower(county_lower))
```

---

# Choropleth map

```r
tx_map <- tx_map %>%  
  left_join(tx_data_agg, by = c("subregion" = "county_lower"))
```

<table class="table" style="font-size: 16px; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> long </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> lat </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> group </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> order </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> region </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> subregion </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> County </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> hispanic_perc </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> -95.75271 </td>
   <td style="text-align:right;"> 31.53560 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:left;"> texas </td>
   <td style="text-align:left;"> anderson </td>
   <td style="text-align:left;"> ANDERSON COUNTY </td>
   <td style="text-align:right;"> 0.1814147 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> -95.76989 </td>
   <td style="text-align:right;"> 31.55852 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:left;"> texas </td>
   <td style="text-align:left;"> anderson </td>
   <td style="text-align:left;"> ANDERSON COUNTY </td>
   <td style="text-align:right;"> 0.1814147 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> -95.76416 </td>
   <td style="text-align:right;"> 31.58143 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:left;"> texas </td>
   <td style="text-align:left;"> anderson </td>
   <td style="text-align:left;"> ANDERSON COUNTY </td>
   <td style="text-align:right;"> 0.1814147 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> -95.72979 </td>
   <td style="text-align:right;"> 31.58143 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:left;"> texas </td>
   <td style="text-align:left;"> anderson </td>
   <td style="text-align:left;"> ANDERSON COUNTY </td>
   <td style="text-align:right;"> 0.1814147 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> -95.74698 </td>
   <td style="text-align:right;"> 31.61008 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 5 </td>
   <td style="text-align:left;"> texas </td>
   <td style="text-align:left;"> anderson </td>
   <td style="text-align:left;"> ANDERSON COUNTY </td>
   <td style="text-align:right;"> 0.1814147 </td>
  </tr>
</tbody>
</table>

---

# Choropleth map

```r
tx_map %>% 
  ggplot(aes(x = long, y = lat, group = group, fill = hispanic_perc)) +
  geom_polygon(color = "white")
```

---

# Choropleth map

---

# Choropleth map

```r
tx_map %>% 
  ggplot(aes(x = long, y = lat, group = group, fill = hispanic_perc)) +
  geom_polygon(color = "white") +
  scale_fill_gradient(low = "#fafafa", high = "#191970", 
                      breaks = seq(0, 0.75, 0.25),
                      name = NULL, labels = percent) +
  labs(title = "Percentage of Hispanic people by county in Texas", 
       subtitle = "2019 population estimate") +
  theme_void() +
  theme(legend.position = c(0.2, 0.8))
```

---

# Choropleth map

---

# Choropleth map

**binning data for a discrete scale**

<table class="table" style="font-size: 16px; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> long </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> lat </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> group </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> order </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> region </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> subregion </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> County </th>
   <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> hispanic_perc </th>
   <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> perc_bin </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> -95.75271 </td>
   <td style="text-align:right;"> 31.53560 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:left;"> texas </td>
   <td style="text-align:left;"> anderson </td>
   <td style="text-align:left;"> ANDERSON COUNTY </td>
   <td style="text-align:right;"> 0.1814147 </td>
   <td style="text-align:left;"> 0-20% </td>
  </tr>
  <tr>
   <td style="text-align:right;"> -95.76989 </td>
   <td style="text-align:right;"> 31.55852 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:left;"> texas </td>
   <td style="text-align:left;"> anderson </td>
   <td style="text-align:left;"> ANDERSON COUNTY </td>
   <td style="text-align:right;"> 0.1814147 </td>
   <td style="text-align:left;"> 0-20% </td>
  </tr>
  <tr>
   <td style="text-align:right;"> -95.76416 </td>
   <td style="text-align:right;"> 31.58143 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:left;"> texas </td>
   <td style="text-align:left;"> anderson </td>
   <td style="text-align:left;"> ANDERSON COUNTY </td>
   <td style="text-align:right;"> 0.1814147 </td>
   <td style="text-align:left;"> 0-20% </td>
  </tr>
  <tr>
   <td style="text-align:right;"> -95.72979 </td>
   <td style="text-align:right;"> 31.58143 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:left;"> texas </td>
   <td style="text-align:left;"> anderson </td>
   <td style="text-align:left;"> ANDERSON COUNTY </td>
   <td style="text-align:right;"> 0.1814147 </td>
   <td style="text-align:left;"> 0-20% </td>
  </tr>
  <tr>
   <td style="text-align:right;"> -95.74698 </td>
   <td style="text-align:right;"> 31.61008 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 5 </td>
   <td style="text-align:left;"> texas </td>
   <td style="text-align:left;"> anderson </td>
   <td style="text-align:left;"> ANDERSON COUNTY </td>
   <td style="text-align:right;"> 0.1814147 </td>
   <td style="text-align:left;"> 0-20% </td>
  </tr>
</tbody>
</table>

---

# Choropleth map

```r
tx_map %>% 
  mutate(perc_bin = case_when(hispanic_perc >= 0.8 ~ "80-100%",
                              hispanic_perc >= 0.6 ~ "60-80%",
                              hispanic_perc >= 0.4 ~ "40-60%",
                              hispanic_perc >= 0.2 ~ "20-40%",
                              TRUE ~ "0-20%")) %>% 
  ggplot(aes(x = long, y = lat, group = group, fill = perc_bin)) +
  geom_polygon(color = "white") +
* scale_fill_manual(values = c("#EEEEFB","#BDBDEF","#8C8CE3",
*                              "#4A4AD3","#191970"),
*                   name = NULL) +
  labs(title = "Percentage of Hispanic people by county in Texas",
       subtitle = "2019 population estimate") +
  theme_void() +
  theme(legend.position = c(0.17, 0.8))
```

---

# Choropleth map

---

# Cartogram

---

# Cartogram

<br>
.large[By definition:]
<br>
.medium[a map in which geographic size is altered to be directly proportional to a selected variable]
<br>
.medium[can be tricky to interpret]
<br>
<br>
<br>
.large[Cartogram heatmap is a popular variation]
<br>
.medium[shows each state as a colored square]
<br>
.medium[doesn't weigh states by shape or size, though it equalizes population]
<br>
<br>
--
<br>
.large[Use the `statebins` package]

---

# Today's data

<br>
.center[`us_rent_income`]<br>

--
<br>
<br>
.center[**how does the mean rent price vary by state?**]

---

# Cartogram

```r
us_rent_income %>% 
  select(-c(moe)) %>% 
  pivot_wider(names_from = variable, values_from = estimate)
```

---

# Cartogram

---

# Cartogram

---

# Cartogram

```r
us_rent_income %>% 
  select(-c(moe)) %>% 
  pivot_wider(names_from = variable, values_from = estimate) %>% 
  mutate(rent_bin = cut(rent, breaks = c(-Inf, 800, 1000, 1200, Inf), 
                        labels = c("< $800","$800 - $1000",
                                   "$1000 - $1200","1200+"))) %>% 
  rename(state = NAME) %>% 
* statebins(value_col = "rent_bin", name = "Average Rent",
*           round = TRUE, # creates a square w/ rounded edges
*           ggplot2_scale_function = scale_fill_manual,
*           values = c("#EEEEFB","#BDBDEF","#4A4AD3","#191970")) +
  labs(title = "Average Rent Per State (2017)") +
  theme_statebins()
```

---

# Cartogram

---

# Upcoming

<br>

<br>

<br>