06 — Extend your Vocabulary

# .kjh-yellow[Extend your] .kjh-lblue[`ggplot`] .kjh-yellow[vocabulary]

**Week 06**

]

---

---

# Load our libraries

.SMALL[

```r
library(here)      # manage file paths
library(socviz)    # data and some useful functions
library(tidyverse) # your friend and mine
```
]

---

# Tidyverse components, again

.pull-left.w45[

- .kjh-green[**`library`**]`(tidyverse)`
- `Loading tidyverse: ggplot2`
- `Loading tidyverse: tibble`
- `Loading tidyverse: tidyr`
- `Loading tidyverse: readr`
- `Loading tidyverse: purrr`
- `Loading tidyverse: dplyr`
]

]

---

# Other tidyverse components

--
.left.bottom[.footnote[Not all of these are attached when we do `library(tidyverse)`]]

---
layout: false
class: main-title main-title-inv center middle

---

---

---

---

---

# .huge[.kjh-yellow[Feeding data] .kjh-lblue[to `ggplot`]]

---

## .middle.huge.squish4[.kjh-orange[Transform and summarize first.] .kjh-lblue[Then send your clean tables to ggplot.]]

---

---

# .huge[.kjh-lblue[Extend your] .kjh-yellow[`ggplot` vocabulary]]

---

# We'll move forward in three ways

## .kjh-lblue[Learn more geoms]

- .kjh-green[`geom_point()`], .kjh-green[`geom_line()`], .kjh-green[`geom_col()`],  .kjh-green[`geom_histogram()`], .kjh-green[`geom_density()`], .kjh-green[`geom_jitter()`], 
.kjh-green[`geom_boxplot()`], .kjh-green[`geom_pointrange()`],...

## .kjh-lblue[Learn more about scales, guides, and themes]

- Functions that control the details of representing data and styling our plots.

## .kjh-lblue[Learn more about extensions to ggplot]

- Packages that enhance .kjh-lblue[`ggplot`]'s capabilities, usually by adding support for new kinds of plot (i.e., new geoms), or new functionality (e.g., the .kjh-lblue[`scales`] package).

---

## .huge.right.bottom.squish4[.kjh-yellow[Some data on] .kjh-lblue[Organ Donation]]

---
# .kjh-pink[`organdata`] is in the .kjh-lblue[`socviz`] package

```r
organdata
```

```
## # A tibble: 238 × 21
## country year donors pop pop_d…¹ gdp gdp_lag health healt…² pubhe…³
## <chr> <date> <dbl> <int> <dbl> <int> <int> <dbl> <dbl> <dbl>
## 1 Austral… NA NA 17065 0.220 16774 16591 1300 1224 4.8
## 2 Austral… 1991-01-01 12.1 17284 0.223 17171 16774 1379 1300 5.4
## 3 Austral… 1992-01-01 12.4 17495 0.226 17914 17171 1455 1379 5.4
## 4 Austral… 1993-01-01 12.5 17667 0.228 18883 17914 1540 1455 5.4
## 5 Austral… 1994-01-01 10.2 17855 0.231 19849 18883 1626 1540 5.4
## 6 Austral… 1995-01-01 10.2 18072 0.233 21079 19849 1737 1626 5.5
## 7 Austral… 1996-01-01 10.6 18311 0.237 21923 21079 1846 1737 5.6
## 8 Austral… 1997-01-01 10.3 18518 0.239 22961 21923 1948 1846 5.7
## 9 Austral… 1998-01-01 10.5 18711 0.242 24148 22961 2077 1948 5.9
## 10 Austral… 1999-01-01 8.67 18926 0.244 25445 24148 2231 2077 6.1
## # … with 228 more rows, 11 more variables: roads <dbl>, cerebvas <int>,
## # assault <int>, external <int>, txp_pop <dbl>, world <chr>, opt <chr>,
## # consent_law <chr>, consent_practice <chr>, consistent <chr>, ccode <chr>,
## # and abbreviated variable names ¹pop_dens, ²health_lag, ³pubhealth
```

---

# First looks

```r
p <- ggplot(data = organdata,
 mapping = aes(x = year, y = donors))
p + geom_point()
```

---

# First looks

```r
p <- ggplot(data = organdata,
 mapping = aes(x = year, y = donors))
p + geom_line() 
```

---

# First looks

```r
p <- ggplot(data = organdata,
 mapping = aes(x = year, y = donors))
p + geom_line(aes(group = country)) 
```

---

# First looks

```r
p <- ggplot(data = organdata,
 mapping = aes(x = year, y = donors))
p + geom_line() + 
 facet_wrap(~ country, nrow = 3)
```

---

# First looks

```r
p <- ggplot(data = organdata,
 mapping = aes(x = year, y = donors))
p + geom_line() + 
 facet_wrap(~ reorder(country, donors, na.rm = TRUE), nrow = 3)
```

---

# First looks

```r
p <- ggplot(data = organdata,
 mapping = aes(x = year, y = donors))
p + geom_line() + 
 facet_wrap(~ reorder(country, -donors, na.rm = TRUE), nrow = 3)
```

---

## .huge.right.bottom.squish4[.kjh-yellow[Showing continuous measures] .kjh-lblue[by category]]

---

# Boxplots: .kjh-green[`geom_boxplot()`]

```r
## Pipeline the data directly; then it's implicitly the first argument to `ggplot()`
organdata |> 
  ggplot(mapping = aes(x = country, y = donors)) + 
  geom_boxplot()
```

---

# Put categories on the y-axis!

```r
organdata |> 
* ggplot(mapping = aes(x = donors, y = country)) +
  geom_boxplot() +
  labs(y = NULL)
```

---

# Reorder y by the mean of x

```r
organdata |> 
* ggplot(mapping = aes(x = donors, y = reorder(country, donors, na.rm = TRUE))) +
  geom_boxplot() +
  labs(y = NULL)
```

---

# (Reorder y by any statistic you like)

```r
organdata |> 
* ggplot(mapping = aes(x = donors, y = reorder(country, donors, sd, na.rm = TRUE))) +
  geom_boxplot() +
  labs(y = NULL)
```

---

# .kjh-green[geom_boxplot()] knows .kjh-orange[`color`] and .kjh-orange[`fill`]

```r
organdata |> 
* ggplot(mapping = aes(x = donors, y = reorder(country, donors, na.rm = TRUE), fill = world)) +
  geom_boxplot() +
  labs(y = NULL)
```

---

# These strategies are quite general

```r
organdata |> 
  ggplot(mapping = aes(x = donors, y = reorder(country, donors, na.rm = TRUE), color = world)) + 
* geom_point(size = rel(3)) +
  labs(y = NULL)
```

---

# .kjh-green[geom-jitter()] can help with overplotting

```r
organdata |> 
  ggplot(mapping = aes(x = donors, y = reorder(country, donors, na.rm = TRUE), color = world)) + 
* geom_jitter(size = rel(3)) +
  labs(y = NULL)
```

---

# Adjust with a .kjh-orange[`position`] argument

```r
organdata |> 
  ggplot(mapping = aes(x = donors, y = reorder(country, donors, na.rm = TRUE),
                       color = world)) + 
* geom_jitter(size = rel(3), position = position_jitter(height = 0.1)) +
  labs(y = NULL)
```

---

# Using .kjh-green[`across()`] and .kjh-green[`where()`]

```r
by_country <- organdata |> 
 group_by(consent_law, country) |>
 summarize(across(where(is.numeric),
 list(mean = ~ mean(.x, na.rm = TRUE), 
 sd = ~ sd(.x, na.rm = TRUE))), 
* .groups = "drop")
head(by_country) 
```

```
## # A tibble: 6 × 28
## consen…¹ country donor…² donor…³ pop_m…⁴ pop_sd pop_d…⁵ pop_d…⁶ gdp_m…⁷ gdp_sd
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Informed Austra… 10.6 1.14 18318. 831. 0.237 0.0107 22179. 3959.
## 2 Informed Canada 14.0 0.751 29608. 1193. 0.297 0.0120 23711. 3966.
## 3 Informed Denmark 13.1 1.47 5257. 80.6 12.2 0.187 23722. 3896.
## 4 Informed Germany 13.0 0.611 80255. 5158. 22.5 1.44 22163. 2501.
## 5 Informed Ireland 19.8 2.48 3674. 132. 5.23 0.187 20824. 6670.
## 6 Informed Nether… 13.7 1.55 15548. 373. 37.4 0.898 23013. 3770.
## # … with 18 more variables: gdp_lag_mean <dbl>, gdp_lag_sd <dbl>,
## # health_mean <dbl>, health_sd <dbl>, health_lag_mean <dbl>,
## # health_lag_sd <dbl>, pubhealth_mean <dbl>, pubhealth_sd <dbl>,
## # roads_mean <dbl>, roads_sd <dbl>, cerebvas_mean <dbl>, cerebvas_sd <dbl>,
## # assault_mean <dbl>, assault_sd <dbl>, external_mean <dbl>,
## # external_sd <dbl>, txp_pop_mean <dbl>, txp_pop_sd <dbl>, and abbreviated
## # variable names ¹consent_law, ²donors_mean, ³donors_sd, ⁴pop_mean, …
```

---

# Plot our summary data

.pull-left.w45[

```r
by_country |> 
  ggplot(mapping = 
           aes(x = donors_mean, 
               y = reorder(country, donors_mean),
               color = consent_law)) + 
  geom_point(size=3) +
  labs(x = "Donor Procurement Rate",
       y = NULL, 
       color = "Consent Law")
```
]

.pull-right.w55[
<img src="06-slides_files/figure-html/codefig-consent1-1.png" width="768" style="display: block; margin: auto;" />

]

---

# What about faceting it instead?

.pull-left.w45[

```r
by_country |> 
  ggplot(mapping = 
           aes(x = donors_mean, 
               y = reorder(country, donors_mean),
               color = consent_law)) + 
  geom_point(size=3) +
  guides(color = "none") +
* facet_wrap(~ consent_law) +
  labs(x = "Donor Procurement Rate",
       y = NULL, 
       color = "Consent Law")
```

.pull-left.w80[The problem is that countries can only be in one Consent Law category.]

]

.pull-right.w55[
<img src="06-slides_files/figure-html/codefig-consent2-1.png" width="768" style="display: block; margin: auto;" />
]

---

# What about faceting it instead?

.pull-left.w45[

```r
by_country |> 
  ggplot(mapping = 
           aes(x = donors_mean, 
               y = reorder(country, donors_mean),
               color = consent_law)) + 
  geom_point(size=3) +
  guides(color = "none") +
* facet_wrap(~ consent_law, ncol = 1) +
  labs(x = "Donor Procurement Rate",
       y = NULL, 
       color = "Consent Law")
```

.pull-left.w80[Restricting to one column doesn't fix it.]

]

.pull-right.w55[
<img src="06-slides_files/figure-html/codefig-consent2a-1.png" width="480" style="display: block; margin: auto;" />
]

---

# Allow the y-scale to vary

.pull-left.w45[

```r
by_country |> 
  ggplot(mapping = 
           aes(x = donors_mean, 
               y = reorder(country, donors_mean),
               color = consent_law)) + 
  geom_point(size=3) +
  guides(color = "none") +
  facet_wrap(~ consent_law, 
             ncol = 1,
*            scales = "free_y") +
  labs(x = "Donor Procurement Rate",
       y = NULL, 
       color = "Consent Law")
```

.pull.left.w90[Normally the point of a facet is to preserve comparability between panels by not allowing the scales to vary. But for categorical measures it can be useful to allow this.]

]

.pull-right.w55[
<img src="06-slides_files/figure-html/codefig-consent3-1.png" width="768" style="display: block; margin: auto;" />
]

---

# Again, these methods are general

.pull-left.w50[

```r
by_country |> 
  ggplot(mapping = 
           aes(x = donors_mean, 
               y = reorder(country, donors_mean),
               color = consent_law)) + 
* geom_pointrange(mapping =
*                   aes(xmin = donors_mean - donors_sd,
*                       xmax = donors_mean + donors_sd)) +
  guides(color = "none") +
  facet_wrap(~ consent_law, 
             ncol = 1,
             scales = "free_y") +  
  labs(x = "Donor Procurement Rate",
       y = NULL, 
       color = "Consent Law")
```
]

.pull-right.w50[
<img src="06-slides_files/figure-html/codefig-consent4-1.png" width="768" style="display: block; margin: auto;" />
]

---

## .huge.right.bottom.squish4.kjh-yellow[Plot text directly]

---

# .kjh-green[`geom_text()`] for basic labels

.pull-left.w45[

```r
by_country |> 
  ggplot(mapping = aes(x = roads_mean, 
                       y = donors_mean)) + 
  geom_text(mapping = aes(label = country))
```
]

.pull-right.w55[
<img src="06-slides_files/figure-html/codefig-geomtext-1.png" width="460" style="display: block; margin: auto;" />
]

---

# It's not very flexible

.pull-left.w45[

```r
by_country |> 
  ggplot(mapping = aes(x = roads_mean, 
                       y = donors_mean)) + 
  geom_point() + 
  geom_text(mapping = aes(label = country),
            hjust = 0)
```
]

.pull-right.w55[
<img src="06-slides_files/figure-html/codefig-geomtext2-1.png" width="460" style="display: block; margin: auto;" />
]

---

# There are tricks, but they're limited

.pull-left.w45[

```r
by_country |> 
  ggplot(mapping = aes(x = roads_mean, 
                       y = donors_mean)) + 
  geom_point() + 
  geom_text(mapping = aes(x = roads_mean + 2, 
                          label = country),
            hjust = 0)
```
]

.pull-right.w55[
<img src="06-slides_files/figure-html/codefig-geomtext3-1.png" width="460" style="display: block; margin: auto;" />
]

---

# We'll use .kjh-lblue[`ggrepel`] instead

### The .kjh-lblue[`ggrepel`] package provides .kjh-green[`geom_text_repel()`] and .kjh-green[`geom_label_repel()`]

---

## .huge.right.bottom.squish4.kjh-yellow[U.S. Historic Presidential Elections]

---

# .kjh-pink[`elections_historic`] is in .kjh-orange[`socviz`]

```r
elections_historic
```

```
## # A tibble: 49 × 19
## election year winner win_p…¹ ec_pct popul…² popul…³ votes margin runne…⁴
## <int> <int> <chr> <chr> <dbl> <dbl> <dbl> <int> <int> <chr> 
## 1 10 1824 John Qui… D.-R. 0.322 0.309 -0.104 1.13e5 -38221 Andrew…
## 2 11 1828 Andrew J… Dem. 0.682 0.559 0.122 6.43e5 140839 John Q…
## 3 12 1832 Andrew J… Dem. 0.766 0.547 0.178 7.03e5 228628 Henry …
## 4 13 1836 Martin V… Dem. 0.578 0.508 0.142 7.63e5 213384 Willia…
## 5 14 1840 William … Whig 0.796 0.529 0.0605 1.28e6 145938 Martin…
## 6 15 1844 James Po… Dem. 0.618 0.495 0.0145 1.34e6 39413 Henry …
## 7 16 1848 Zachary … Whig 0.562 0.473 0.0479 1.36e6 137882 Lewis …
## 8 17 1852 Franklin… Dem. 0.858 0.508 0.0695 1.61e6 219525 Winfie…
## 9 18 1856 James Bu… Dem. 0.588 0.453 0.122 1.84e6 494472 John F…
## 10 19 1860 Abraham … Rep. 0.594 0.396 0.101 1.86e6 474049 John B…
## # … with 39 more rows, 9 more variables: ru_part <chr>, turnout_pct <dbl>,
## # winner_lname <chr>, winner_label <chr>, ru_lname <chr>, ru_label <chr>,
## # two_term <lgl>, ec_votes <dbl>, ec_denom <dbl>, and abbreviated variable
## # names ¹win_party, ²popular_pct, ³popular_margin, ⁴runner_up
```

---

# We'll draw a plot like this

---

# Keep things neat

```r
## The packages we'll use in addition to ggplot
*library(ggrepel)
*library(scales)

p_title <- "Presidential Elections: Popular & Electoral College Margins"
p_subtitle <- "1824-2016"
p_caption <- "Data for 2016 are provisional."
x_label <- "Winner's share of Popular Vote"
y_label <- "Winner's share of Electoral College Votes"
```

---

# Base Layer, Lines, Points

.pull-left.w45[

```r
p <- ggplot(data = elections_historic, 
 mapping = aes(x = popular_pct, 
 y = ec_pct,
 label = winner_label))

p + geom_hline(yintercept = 0.5, 
               linewidth = 1.4, 
               color = "gray80") +
    geom_vline(xintercept = 0.5, 
               linewidth = 1.4, 
               color = "gray80") +
    geom_point()
```
]

.pull-right.w55[
<img src="06-slides_files/figure-html/codefig-presplot1-1.png" width="480" style="display: block; margin: auto;" />
]

---

# Add the labels

.pull-left.w45[

```r
p <- ggplot(data = elections_historic, 
 mapping = aes(x = popular_pct, 
 y = ec_pct,
 label = winner_label))

p + geom_hline(yintercept = 0.5, 
               linewidth = 1.4, color = "gray80") +
  geom_vline(xintercept = 0.5, 
             linewidth = 1.4, color = "gray80") +
  geom_point() + 
  geom_text_repel()
```

.pull-left.w85[This looks messy because .kjh-green[`geom_text_repel()`] uses the dimensions of the available graphics device to iteratively figure out the labels. Let's allow it to draw on the whole slide.]

]

.pull-right.w55[
<img src="06-slides_files/figure-html/codefig-presplot2-1.png" width="480" style="display: block; margin: auto;" />
]

---

# The labeling is with respect to the plot size

```r
p <- ggplot(data = elections_historic, 
 mapping = aes(x = popular_pct, 
 y = ec_pct,
 label = winner_label))

p_out <- p + 
 geom_hline(yintercept = 0.5, 
 linewidth = 1.4, 
 color = "gray80") +
 geom_vline(xintercept = 0.5, 
 linewidth = 1.4, 
 color = "gray80") +
 geom_point() + 
* geom_text_repel()
```

---

layout:false
class: middle center

---

---

# Adjust the Scales

```r
p <- ggplot(data = elections_historic, 
 mapping = aes(x = popular_pct, 
 y = ec_pct,
 label = winner_label))
p_out <- p + geom_hline(yintercept = 0.5, 
 linewidth = 1.4, 
 color = "gray80") +
 geom_vline(xintercept = 0.5, 
 linewidth = 1.4, 
 color = "gray80") +
 geom_point() +
 geom_text_repel() +
* scale_x_continuous(labels = label_percent()) +
* scale_y_continuous(labels = label_percent())
```

---

layout:false
class: middle center

---

---

# Add the labels

```r
p <- ggplot(data = elections_historic, 
 mapping = aes(x = popular_pct, 
 y = ec_pct,
 label = winner_label))
p_out <- p + geom_hline(yintercept = 0.5, 
 linewidth = 1.4, 
 color = "gray80") +
 geom_vline(xintercept = 0.5, 
 linewidth = 1.4, 
 color = "gray80") +
 geom_point() +
* geom_text_repel(mapping = aes(family = "Tenso Slide")) +
 scale_x_continuous(labels = label_percent()) +
 scale_y_continuous(labels = label_percent()) +
* labs(x = x_label, y = y_label,
 title = p_title, 
 subtitle = p_subtitle,
 caption = p_caption) 
```

---

layout:false
class: middle center

---

## .huge.right.bottom.squish4[.kjh-yellow[Labeling points of interest]]

---

---

# Option 1: On the fly inside .kjh-lblue[`ggplot`]

.pull-left.w50[

```r
by_country |> 
  ggplot(mapping = aes(x = gdp_mean,
                       y = health_mean)) +
  geom_point() + 
  geom_text_repel(data = subset(by_country, gdp_mean > 25000), 
                  mapping = aes(label = country))
```
]

.pull-right.w50[
<img src="06-slides_files/figure-html/codefig-subset1-1.png" width="460" style="display: block; margin: auto;" />
]

---

# Option 1: On the fly inside .kjh-lblue[`ggplot`]

.pull-left.w50[

```r
by_country |> 
 ggplot(mapping = aes(x = gdp_mean,
 y = health_mean)) +
 geom_point() + 
 geom_text_repel(data = subset(by_country, 
 gdp_mean > 25000 |
 health_mean < 1500 |
 country %in% "Belgium"), 
 mapping = aes(label = country))
```

.pull-left.w90[Stuffing everything into the .kjh-green[`subset()`] call might get messy]

]

.pull-right.w50[
<img src="06-slides_files/figure-html/codefig-subset2-1.png" width="460" style="display: block; margin: auto;" />
]

---

# Option 2: Use .kjh-lblue[`dplyr`] to subset first

```r
df_hl <- by_country |> 
 filter(gdp_mean > 25000 | 
 health_mean < 1500 | 
 country %in% "Belgium")

df_hl
```

```
## # A tibble: 6 × 28
## consen…¹ country donor…² donor…³ pop_m…⁴ pop_sd pop_d…⁵ pop_d…⁶ gdp_m…⁷ gdp_sd
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Informed Ireland 19.8 2.48 3674. 1.32e2 5.23 0.187 20824. 6670.
## 2 Informed United… 20.0 1.33 269330. 1.25e4 2.80 0.130 29212. 4571.
## 3 Presumed Belgium 21.9 1.94 10153. 1.09e2 30.7 0.330 22500. 3171.
## 4 Presumed Norway 15.4 1.11 4386. 9.73e1 1.35 0.0300 26448. 6492.
## 5 Presumed Spain 28.1 4.96 39666. 9.51e2 7.84 0.188 16933 2888.
## 6 Presumed Switze… 14.2 1.71 7037. 1.70e2 17.0 0.411 27233 2153.
## # … with 18 more variables: gdp_lag_mean <dbl>, gdp_lag_sd <dbl>,
## # health_mean <dbl>, health_sd <dbl>, health_lag_mean <dbl>,
## # health_lag_sd <dbl>, pubhealth_mean <dbl>, pubhealth_sd <dbl>,
## # roads_mean <dbl>, roads_sd <dbl>, cerebvas_mean <dbl>, cerebvas_sd <dbl>,
## # assault_mean <dbl>, assault_sd <dbl>, external_mean <dbl>,
## # external_sd <dbl>, txp_pop_mean <dbl>, txp_pop_sd <dbl>, and abbreviated
## # variable names ¹consent_law, ²donors_mean, ³donors_sd, ⁴pop_mean, …
```

---

# Option 2: Use .kjh-lblue[`dplyr`] to subset first

```r
df_hl <- by_country |> 
 filter(gdp_mean > 25000 | 
 health_mean < 1500 | 
 country %in% "Belgium")

df_hl
```

```
## # A tibble: 6 × 28
## consen…¹ country donor…² donor…³ pop_m…⁴ pop_sd pop_d…⁵ pop_d…⁶ gdp_m…⁷ gdp_sd
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Informed Ireland 19.8 2.48 3674. 1.32e2 5.23 0.187 20824. 6670.
## 2 Informed United… 20.0 1.33 269330. 1.25e4 2.80 0.130 29212. 4571.
## 3 Presumed Belgium 21.9 1.94 10153. 1.09e2 30.7 0.330 22500. 3171.
## 4 Presumed Norway 15.4 1.11 4386. 9.73e1 1.35 0.0300 26448. 6492.
## 5 Presumed Spain 28.1 4.96 39666. 9.51e2 7.84 0.188 16933 2888.
## 6 Presumed Switze… 14.2 1.71 7037. 1.70e2 17.0 0.411 27233 2153.
## # … with 18 more variables: gdp_lag_mean <dbl>, gdp_lag_sd <dbl>,
## # health_mean <dbl>, health_sd <dbl>, health_lag_mean <dbl>,
## # health_lag_sd <dbl>, pubhealth_mean <dbl>, pubhealth_sd <dbl>,
## # roads_mean <dbl>, roads_sd <dbl>, cerebvas_mean <dbl>, cerebvas_sd <dbl>,
## # assault_mean <dbl>, assault_sd <dbl>, external_mean <dbl>,
## # external_sd <dbl>, txp_pop_mean <dbl>, txp_pop_sd <dbl>, and abbreviated
## # variable names ¹consent_law, ²donors_mean, ³donors_sd, ⁴pop_mean, …
```

---

# Option 2: Use .kjh-lblue[`dplyr`] to subset first

.pull-left.w50[

```r
by_country |> 
  ggplot(mapping = aes(x = gdp_mean,
                       y = health_mean)) +
  geom_point() + 
  geom_text_repel(data = df_hl, 
                  mapping = aes(label = country))
```

.pull-left.w90[This makes things a little neater.

As you can see, a `geom` can be fully "autonomous". Each one can have its own .kjh-orange[`mapping`] call _and_ its own .kjh-orange[`data`] source. This can be very useful when building up plots overlaying several sources or subsets of data.
]

]

.pull-right.w50[
<img src="06-slides_files/figure-html/codefig-subset3-1.png" width="460" style="display: block; margin: auto;" />
]

---

## .huge.right.bottom.squish4[.kjh-yellow[Write and draw] .kjh-lblue[inside the plot area]]

---

---

# .kjh-green[`annotate()`] can imitate geoms

.pull-left.w50[

```r
organdata |> 
  ggplot(mapping = aes(x = roads, 
                       y = donors)) + 
  geom_point() + 
  annotate(geom = "text", 
           family = "Tenso Slide",
           x = 157, 
           y = 33,
           label = "A surprisingly high \n recovery rate.",
           hjust = 0)
```
]

.pull-right.w50[
<img src="06-slides_files/figure-html/codefig-annotate1-1.png" width="460" style="display: block; margin: auto;" />
]

---

# .kjh-green[`annotate()`] can imitate geoms

.pull-left.w50[

```r
organdata |> 
  ggplot(mapping = aes(x = roads, 
                       y = donors)) + 
  geom_point() +
  annotate(geom = "rect", 
           xmin = 125, xmax = 155,
           ymin = 30, ymax = 35,
           fill = "red", 
           alpha = 0.2) + 
  annotate(geom = "text", 
           x = 157, y = 33,
           family = "Tenso Slide",
           label = "A surprisingly high \n recovery rate.", 
           hjust = 0)
```
]

.pull-right.w50[
<img src="06-slides_files/figure-html/codefig-annotate2-1.png" width="460" style="display: block; margin: auto;" />
]

---

# .huge[.kjh-lblue[Scales, Guides, Themes]]

---

---

# Every .kjh-lblue[mapped variable] has a .kjh-orange[scale]

### Aesthetic mappings link quantities or categories in your data to things you can see on the graph. Thus, they have a  scale associated with that representation.

### Scale functions manage this relationship. Remember: not just `x` and `y` but also `color`, `fill`, `shape`, `size`, and `alpha` are scales.

- If it can represent your data, it has a scale, and a _scale function_ to manage it.

### This means you control things like color schemes _for data mappings_ through scale functions

- Because those colors are representing features of your data.

---

# Naming conventions for scale functions

- In general, scale functions are named like this:

- .center.large[.kjh-green[`scale\\\_`].kjh-orange[`<MAPPING>`].kjh-green[`\\\_`].kjh-lblue[`<KIND>`].kjh-green[`()`]]

- .large[We already know there are a lot of .kjh-orange[**mappings**]]. 
- .right[_.kjh-orange[`x`], .kjh-orange[`y`], .kjh-orange[`color`], .kjh-orange[`size`], .kjh-orange[`shape`], and so on._]

- .large[And there are many .kjh-lblue[**kinds**] of scale as well.] 
- .right[_.kjh-lblue[discrete], .kjh-lblue[continuous], .kjh-lblue[log10], .kjh-lblue[date], .kjh-lblue[binned], and many others._]

- .large[So there's a whole zoo of scale functions.]
- .right[_The naming convention helps us keep track._]

---

# Naming conventions for scale functions

- .large.center[.kjh-green[`scale\\\_`].kjh-orange[`mapping`].kjh-green[`\\\_`].kjh-lblue[`kind`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`x`].kjh-green[`\\\_`].kjh-lblue[`continuous`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`y`].kjh-green[`\\\_`].kjh-lblue[`continous`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`x`].kjh-green[`\\\_`].kjh-lblue[`discrete`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`y`].kjh-green[`\\\_`].kjh-lblue[`discrete`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`x`].kjh-green[`\\\_`].kjh-lblue[`log10`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`x`].kjh-green[`\\\_`].kjh-lblue[`sqrt`].kjh-green[`()`]]

---

# Naming conventions for scale functions

- .large.center[.kjh-green[`scale\\\_`].kjh-orange[`mapping`].kjh-green[`\\\_`].kjh-lblue[`kind`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`color`].kjh-green[`\\\_`].kjh-lblue[`discrete`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`color`].kjh-green[`\\\_`].kjh-lblue[`gradient`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`color`].kjh-green[`\\\_`].kjh-lblue[`gradient2`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`color`].kjh-green[`\\\_`].kjh-lblue[`brewer`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`fill`].kjh-green[`\\\_`].kjh-lblue[`discrete`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`fill`].kjh-green[`\\\_`].kjh-lblue[`gradient`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`fill`].kjh-green[`\\\_`].kjh-lblue[`gradient2`].kjh-green[`()`]]

- .center[.kjh-green[`scale\\\_`].kjh-orange[`fill`].kjh-green[`\\\_`].kjh-lblue[`brewer`].kjh-green[`()`]]

---

# Scale functions in practice

- Scale functions take arguments appropriate to their mapping and kind

.pull-left.w50[

```r
organdata |> 
  ggplot(mapping = aes(x = roads,
                       y = donors,
                       color = world)) + 
  geom_point() +
  scale_y_continuous(breaks = c(5, 15, 25),
                     labels = c("Five", 
                                "Fifteen", 
                                "Twenty Five"))
```
]

.pull-right.w50[
<img src="06-slides_files/figure-html/codefig-scalefn1-1.png" width="460" style="display: block; margin: auto;" />
]

---

# More usefully ...

.pull-left.w50[

```r
organdata |> 
  ggplot(mapping = aes(x = roads,
                       y = donors,
                       color = world)) + 
  geom_point() +
  scale_color_discrete(labels =
                         c("Corporatist", 
                           "Liberal",
                           "Social Democratic", 
                           "Unclassified")) +
  labs(x = "Road Deaths",
       y = "Donor Procurement",
       color = "Welfare State")
```
]

.pull-right.w50[
<img src="06-slides_files/figure-html/codefig-scalecolordiscrete-1.png" width="460" style="display: block; margin: auto;" />
]

---

# The .kjh-green[`guides()`] function

.pull-left.w45[

```r
organdata |> 
  ggplot(mapping = aes(x = roads,
                       y = donors,
                       color = consent_law)) + 
  geom_point() +
  facet_wrap(~ consent_law, ncol = 1) +
  guides(color = "none") + 
  labs(x = "Road Deaths",
       y = "Donor Procurement")
```

.pull-left.w90[- Control overall properties of the guide labels.
- Common use: turning it off.
- We'll see more advanced uses later.]

]

.pull-right.w50[
<img src="06-slides_files/figure-html/codefig-guidesfn-1.png" width="460" style="display: block; margin: auto;" />
]

---

# The .kjh-green[`theme()`] function

.pull-left.w45[

```r
## Using the "classic" ggplot theme here
organdata |> 
  ggplot(mapping = aes(x = roads,
                       y = donors,
                       color = consent_law)) + 
  geom_point() +
  labs(title = "By Consent Law",
    x = "Road Deaths",
    y = "Donor Procurement", 
    color = "Legal Regime:") + 
  theme(legend.position = "bottom", 
        plot.title = element_text(color = "darkred",
                                  face = "bold"))
```

.pull-left.w95[
.kjh-green[`theme()`] styles parts of your plot that are _not_ directly representing your data. Often the first thing people want to adjust; but logically it's the _last_ thing. 
]

]

.pull-right.w50[
<img src="06-slides_files/figure-html/codefig-themefn-1.png" width="480" style="display: block; margin: auto;" />
]

---

# Sidenote: Smoothers

---

# Sidenote: Smoothers

---

# Sidenote: Smoothers

---

# Sidenote: Smoothers

---

# Sidenote: Smoothers