Taking your plot to the next level - themes, axes, and annotation

class: center, middle, inverse, title-slide

.title[
# Taking your plot to the next level - themes, axes, and annotation
]
.subtitle[
## <br><br> College of the Atlantic
]
.author[
### Laurie Baker
]

---

---
# Recap: How do we express visuals in words?

- **Data** to be visualized

- **`Aes`thetic mappings** from data to visual component

- **`Geom`etric objects** that appear on the plot

- **`Stat`istics** transform data on the way to visualization

- **`Coord`inates** organize location of geometric objects

- **`Scale`s** define the range of values for aesthetics

- **`Facet`s** group into subplots

--
- **`Theme`s** the visual elements of the plot not linked to the data

---
# Today's Focus

- **Theme**s: the visual elements of the plot not linked to the data
- **Geom**etric objects: `geom_vline` and `geom_hline`
- **Scale**s: define the range of values for aesthetics
- **Annotations:** text, highlight, etc. 
---
## Packages

```r
library(tidyverse)
library(ggthemes)
library(gapminder)
```

---
# head(gapminder)

```
## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.
```

---
# glimpse(gapminder)

```
Rows: 1,704
Columns: 6
$ country   <fct> "Afghanistan", "Afghanistan", "Afghanistan", …
$ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asi…
$ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 198…
$ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.43…
$ pop       <int> 8425333, 9240934, 10267083, 11537966, 1307946…
$ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9…
```

---
# Life Expectancy

.panelset[
<div class="panel">
<div class="panel-name">Code</div>

```r
(p1 <- gapminder |>
  filter(continent == "Asia") |>
ggplot(aes(x = year,
      y = lifeExp, 
      group = country)) +
  geom_line() +
  labs(x = "Year",
       y = "Life Expectancy (years)",
       title = "Life Expectancy in Asia"))
```

</div>

</div>
]
---
# themes - black and white

```r
p1 + theme_bw()
```

---
# themes - dark

```r
p1 + theme_dark()
```

---
# themes - minimal

```r
p1 + theme_minimal()
```

---
# themes - build your own

.right-column[
Huge number of parameters, grouped by plot area:

- Global options: `line`, `rect`, `text`, `title`
- `axis`: x-, y- or other axis title, ticks, lines
- `legend`: Plot legends
- `panel`: Actual plot area
- `plot`: Whole image
- `strip`: Facet labels
]
---
# themes - build your own

.right-column[
Theme options are supported by helper functions:

- `element_blank()` removes the element
- `element_line()`
- `element_rect()`
- `element_text()`

```r
my_theme <- theme_bw() + theme(text = element_text(color = "hotpink", size = 20), axis.ticks.x = element_blank())
```
```

]

---

.right-column[

```r
p1 + my_theme
```

<img src="u5-d10-axes-themes_files/figure-html/unnamed-chunk-8-1.png" width="90%" style="display: block; margin: auto;" />
]

---
# ggthemes - Economist

```r
p1 + theme_economist()
```

---
# Life Expectancy - highlight
.panelset[
<div class="panel">
<div class="panel-name">Code</div>

```r
(p1 <- p1 +
  geom_line(data = gapminder |> filter(country == "Cambodia"),
  color = "orange", 
  lwd = 2, 
  aes(x = year, 
  y = lifeExp, 
  group = country)) +
   theme_bw())
```

</div>

</div>
]
- [Cambodian Genocide](https://sfi.usc.edu/collections/cambodian-genocide)

---
# Add annotations with `geom_vline`

.panelset[
<div class="panel">
<div class="panel-name">Code</div>

```r
(p1 <- p1 +
  geom_vline(xintercept = 1975, linetype = "dotted", color = "red") +
  geom_vline(xintercept = 1979, linetype = "dotted", color = "red"))
```

</div>

</div>
]

---
# Add annotations with `annotate`

.panelset[
<div class="panel">
<div class="panel-name">Code</div>

```r
p1 +
  annotate("rect", 
  xmin = 1975, 
  xmax = 1979, 
  ymin = 0, 
  ymax = 100, 
  alpha = 0.2, 
  fill = "red")
```

</div>

</div>
]
---
# Scales

.left-column[

```r
+ scale_*_*()
```
]

.right-column[ 
`scale` + `_` + `<aes>` + `_` + `<type>` + `()`

`<aes>` = parameter to adjust; `<type>` = Parameter Type
]

---
# Rescale y-axis as log `scale_y_log10()`

.panelset[
<div class="panel">
<div class="panel-name">Code</div>

```r
p1 +
  annotate("rect", xmin = 1975, xmax = 1979, ymin = 0, ymax = 100, alpha = 0.2, fill = "red") +
  scale_y_log10()
```

</div>

```
## Warning: Transformation introduced infinite values in continuous
## y-axis
```

</div>

]

---
# Change x-axis `scale_x_continuous()`

.panelset[
<div class="panel">
<div class="panel-name">Code</div>

```r
p1 +
  annotate("rect", xmin = 1975, xmax = 1979, ymin = 0, ymax = 100, alpha = 0.2, fill = "red") +
  scale_x_continuous(breaks = seq(from = 1952, to = 2007, by = 5))
```

</div>

</div>

]

---
# Annotation - geom_text

.panelset[
<div class="panel">
<div class="panel-name">Code</div>

```r
p1 +
  annotate("rect", 
  xmin = 1975, 
  xmax = 1979, 
  ymin = 0, 
  ymax = 100, 
  alpha = 0.2, 
  fill = "red") +
  geom_text(aes(x = 1985, y = 25), 
  label = "Khmer Rouge")
```

</div>

</div>
]
---
# Annotation - geom_text

.panelset[
<div class="panel">
<div class="panel-name">Code</div>

```r
(p2 <- gapminder |>
  filter(country == "Portugal") |>
ggplot(aes(x = year,
      y = lifeExp)) +
  geom_point() +
  labs(x = "Year",
       y = "Life Expectancy (years)")) +
  geom_text(aes(label = year))
```

</div>

</div>

]

---
# Annotation - geom_text

.panelset[
<div class="panel">
<div class="panel-name">Code</div>

```r
(p2 <- gapminder |>
  filter(country == "Portugal") |>
ggplot(aes(x = year,
      y = lifeExp)) +
  geom_point() +
  labs(x = "Year",
       y = "Life Expectancy (years)",
       title = "Life Expectancy in Portugal over time") +
  geom_text(aes(label = year), nudge_x = 3.75, nudge_y = -0.3))
```

</div>

</div>

]

---
# Annotation - gghighlight

.panelset[
<div class="panel">
<div class="panel-name">Code</div>

```r
gapminder |>
ggplot(aes(x = year,
      y = lifeExp, 
      group = country, 
      color = continent)) +
  geom_line() +
  labs(x = "Year",
       y = "Life Expectancy (years)") +
  gghighlight(mean(lifeExp) > 75) 
```

</div>

</div>
]
---
# Annotation - gghighlight

.panelset[
<div class="panel">
<div class="panel-name">Code</div>

```r
gapminder |>
ggplot(aes(x = year,
      y = lifeExp, 
      group = country, 
      color = continent)) +
  geom_line() +
  labs(x = "Year",
       y = "Life Expectancy (years)") +
  facet_wrap(~continent) +
  gghighlight(mean(lifeExp) > 74.5) 
```

</div>

</div>
]

---
# Annotation - gghighlight

.panelset[
<div class="panel">
<div class="panel-name">Code</div>

```r
gapminder |>
ggplot(aes(x = year,
      y = lifeExp, 
      group = country, 
      color = continent)) +
  geom_line() +
  labs(x = "Year",
       y = "Life Expectancy (years)") +
  gghighlight(min(lifeExp) < 40 & max(lifeExp) > 70)
```

</div>

</div>
]

---
# Your Turn

- Create your own plot highlighting a county of interest and a labelled historical event.