Data visualization with ggplot2

# Data visualization<br>with ggplot2

.class-info[
<figure>
  <img src="img/ggplot-logo.png" alt="ggplot" title="ggplot" width="15%">
</figure>
]

---

---

# Just show me the data!

.pull-left-3.small-code[

```r
head(my_data, 10)
```

```
## # A tibble: 10 × 2
##        x     y
##    <dbl> <dbl>
##  1  55.4  97.2
##  2  51.5  96.0
##  3  46.2  94.5
##  4  42.8  91.4
##  5  40.8  88.3
##  6  38.7  84.9
##  7  35.6  79.9
##  8  33.1  77.6
##  9  29.0  74.5
## 10  26.2  71.4
```
]

.pull-middle-3.small-code[

```r
mean(my_data$x)
```

```
*## [1] 54.26327
```

```r
mean(my_data$y)
```

```
*## [1] 47.83225
```

```r
cor(my_data$x, my_data$y)
```

```
*## [1] -0.06447185
```
]

&nbsp;

&nbsp;

---

# oh no

.center[
<figure>
  <img src="img/DinoSequentialSmaller.gif" alt="Datasaurus Dozen" title="Datasaurus Dozen" width="100%">
  <figcaption><a href="https://www.autodeskresearch.com/publications/samestats" target="_blank">The Datasaurus Dozen</a></figcaption>
</figure>
]

---

# Raw data is not enough

.box-inv-5.small[Each of these has the same mean, standard deviation, variance, and correlation]

---

# BMI and daily steps

> Consider the following (alternative, not null) hypotheses:
>
> 1. There is a difference in the mean number of steps between women and men
> 2. The correlation coefficient between steps and BMI is negative for women
> 3. The correlation coefficient between steps and BMI is positive for men
>
> Think about which test to use and calculate the corresponding p-value.
>
> What conclusions can you draw from the data?

]

---

.left-code.wide[

```r
library(tidyverse)
bmi_data <- read_csv("data/bmi_data.csv")

head(bmi_data)
```

```
## # A tibble: 6 × 3
##     bmi   steps sex   
##   <dbl>   <dbl> <chr> 
## 1  27.9   401.  Male  
## 2  28.4  6204.  Male  
## 3  12.4  8723.  Female
## 4  24.5 11241.  Male  
## 5  17.5  5109.  Female
## 6  23.5    73.0 Female
```
]

.right-code.wide.small[

```r
t.test(steps ~ sex, data = bmi_data)
```

```
## 
## 	Welch Two Sample t-test
## 
## data:  steps by sex
## t = -6.5215, df = 1759.9, p-value = 9.069e-11
## alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
## 95 percent confidence interval:
##  -1408.8005  -757.3441
## sample estimates:
## mean in group Female   mean in group Male 
##             6769.378             7852.450
```

```r
bmi_data %>% 
  group_by(sex) %>% 
  summarize(correlation = cor(bmi, steps))
```

```
## # A tibble: 2 × 2
##   sex    correlation
##   <chr>        <dbl>
## 1 Female      -0.306
## 2 Male        -0.192
```
]

???

- https://www.biorxiv.org/content/10.1101/2020.07.30.228916
- https://ivelasq.rbind.io/blog/reticulate-data-recreation/

---

---

# Raw numbers are not enough!

> Examine the data appropriately!

> What do you notice?

> What conclusions can you draw from the data?

]]

.pull-right[
<img src="02_grammar-of-graphics_files/figure-html/gorilla-bmi-1.png" width="100%" style="display: block; margin: auto;" />

]

---

# Beauty is necessary to see patterns

]

.pull-right.center[

]

---

.box-5.large[Beauty is necessary<br>for finding truth]

---

---

# Applied data science

.center[
<figure>
  <img src="img/cover.png" alt="R for Data Science" title="R for Data Science" width="28%">
  <figcaption><a href="">R for Data Science, free online!</a></figcaption>
</figure>
]

---

# Applied data science

.center[
<figure>
  <img src="img/data-science-workflow.png" alt="Data science workflow" title="Data science workflow" width="100%">
</figure>
]

---

layout: false
name: grammar-of-graphics
class: center middle section-title section-title-5 animated fadeIn

# The Grammar of Graphics

---

---

# Mapping data to aesthetics

.pull-left.center[

]

.box-inv-5.medium[Aesthetic]

.box-5.sp-after[Position, shape, color, etc.]

.box-inv-5.medium[Data]

]

---

# Your turn #1

.box-5.medium[Watch this video]

.box-5.medium[andhs.co/rosling]

.box-5[Make a list of how those variables are shown in the graph<br>(think about the graph's aesthetics and geometries)]

---

???

Source: [New York Times](https://www.nytimes.com/2017/02/09/world/europe/hans-rosling-dead-statistician.html)

---

---

# Mapping data to aesthetics

<table>
  <tr>
    <th class="cell-left">Data</th>
    <th class="cell-left">Aesthetic</th>
    <th class="cell-left">Geometry</th>
  </tr>
  <tr>
    <td class="cell-left">Wealth (GDP/capita)</td>
    <td class="cell-left">Position (x-axis)</td>
    <td class="cell-left">Point</td>
  </tr>
  <tr>
    <td class="cell-left">Health (Life expectancy)&emsp;</td>
    <td class="cell-left">Position (y-axis)&emsp;</td>
    <td class="cell-left">Point</td>
  </tr>
  <tr>
    <td class="cell-left">Continent</td>
    <td class="cell-left">Color</td>
    <td class="cell-left">Point</td>
  </tr>
  <tr>
    <td class="cell-left">Population</td>
    <td class="cell-left">Size&emsp;</td>
    <td class="cell-left">Point</td>
  </tr>
  <tr>
    <td class="cell-left">Year</td>
    <td class="cell-left">Time</td>
    <td class="cell-left">Animation&emsp;</td>
  </tr>
</table>

---

# Mapping data to aesthetics

<table>
  <tr>
    <th class="cell-left">Data</th>
    <th class="cell-left"><code class="remark-inline-code">aes()</code></th>
    <th class="cell-left"><code class="remark-inline-code">geom</code></th>
  </tr>
  <tr>
    <td class="cell-left">Wealth (GDP/capita)</td>
    <td class="cell-left"><code class="remark-inline-code">x</code></td>
    <td class="cell-left"><code class="remark-inline-code">geom_point()</code></td>
  </tr>
  <tr>
    <td class="cell-left">Health (Life expectancy)&emsp;</td>
    <td class="cell-left"><code class="remark-inline-code">y</code></td>
    <td class="cell-left"><code class="remark-inline-code">geom_point()</code></td>
  </tr>
  <tr>
    <td class="cell-left">Continent</td>
    <td class="cell-left"><code class="remark-inline-code">color</code></td>
    <td class="cell-left"><code class="remark-inline-code">geom_point()</code></td>
  </tr>
  <tr>
    <td class="cell-left">Population</td>
    <td class="cell-left"><code class="remark-inline-code">size</code>&emsp;</td>
    <td class="cell-left"><code class="remark-inline-code">geom_point()</code></td>
  </tr>
  <tr>
    <td class="cell-left">Year</td>
    <td class="cell-left"><code class="remark-inline-code">transition</code>&emsp;</td>
    <td class="cell-left"><code class="remark-inline-code">transition_time()</code></td>
  </tr>
</table>

---

# `ggplot()` template

<code class ='r hljs remark-code'>ggplot(data = <b><span style="background-color:#CBB5FF">DATA</span></b>) +<br>&nbsp;&nbsp;<b><span style="background-color:#FFDFD1">GEOM_FUNCTION</span></b>(mapping = aes(<b><span style="background-color:#FFD0CF">AESTHETIC MAPPINGS</span></b>))</code>

<code class ='r hljs remark-code'>ggplot(data = <b><span style="background-color:#CBB5FF">gapminder_2007</span></b>) +<br>&nbsp;&nbsp;<b><span style="background-color:#FFDFD1">geom_point</span></b>(mapping = aes(<b><span style="background-color:#FFD0CF">x = gdpPercap</span></b>, <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<b><span style="background-color:#FFD0CF">y = lifeExp</span></b>,<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<b><span style="background-color:#FFD0CF">color = continent</span></b>, <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<b><span style="background-color:#FFD0CF">size = pop</span></b>)))</code>

---

<table>
 <thead>
  <tr>
   <th style="text-align:center;"> country </th>
   <th style="text-align:center;"> continent </th>
   <th style="text-align:center;"> gdpPercap </th>
   <th style="text-align:center;"> lifeExp </th>
   <th style="text-align:center;"> pop </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:center;"> Afghanistan </td>
   <td style="text-align:center;"> Asia </td>
   <td style="text-align:center;"> 974.5803384 </td>
   <td style="text-align:center;"> 43.828 </td>
   <td style="text-align:center;"> 31889923 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Albania </td>
   <td style="text-align:center;"> Europe </td>
   <td style="text-align:center;"> 5937.029526 </td>
   <td style="text-align:center;"> 76.423 </td>
   <td style="text-align:center;"> 3600523 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> … </td>
   <td style="text-align:center;"> … </td>
   <td style="text-align:center;"> … </td>
   <td style="text-align:center;"> … </td>
   <td style="text-align:center;"> … </td>
  </tr>
</tbody>
</table>

]

<code class ='r hljs remark-code'>ggplot(data = <b><span style="background-color:#CBB5FF">gapminder_2007</span></b>,<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;mapping = aes(<b><span style="background-color:#FFD0CF">x = gdpPercap</span></b>, <b><span style="background-color:#FFD0CF">y = lifeExp</span></b>,<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<b><span style="background-color:#FFD0CF">color = continent</span></b>, <b><span style="background-color:#FFD0CF">size = pop</span></b>)) +<br>&nbsp;&nbsp;<b><span style="background-color:#FFDFD1">geom_point</span></b>() +<br>&nbsp;&nbsp;scale_x_log10()</code>

---

---

# Health and wealth

---

# Aesthetics

.box-inv-5.small[`color` (discrete)]

.box-inv-5.small[`color` (continuous)]

<img src="02_grammar-of-graphics_files/figure-html/aes-color-continuous-1.png" width="100%" style="display: block; margin: auto;" />
]

.box-inv-5.small[`size`]

.box-inv-5.small[`fill`]

<img src="02_grammar-of-graphics_files/figure-html/aes-fill-1.png" width="100%" style="display: block; margin: auto;" />
]

.box-inv-5.small[`shape`]

.box-inv-5.small[`alpha`]

<img src="02_grammar-of-graphics_files/figure-html/aes-alpha-1.png" width="100%" style="display: block; margin: auto;" />
]

---

???

Artwork by @allison_horst

https://github.com/allisonhorst/palmerpenguins

---

???

Artwork by @allison_horst

https://github.com/allisonhorst/palmerpenguins

---

```r
ggplot(data = penguins) +
  geom_point(mapping = aes(x = flipper_length_mm, 
                           y = body_mass_g, color = species))
```

---

# Your turn #2

---

# How would you make this plot?

---

```r
ggplot(penguins) +
  geom_point(aes(x = body_mass_g, 
                 y = bill_length_mm, 
                 color = species))
```
]

---

```r
ggplot(penguins) +
  geom_point(aes(x = body_mass_g, 
                 y = bill_length_mm), 
             color = "blue")
```
]

---

```r
ggplot(penguins) +
  geom_point(aes(x = body_mass_g, 
                 y = bill_length_mm, 
                 color = "blue"))
```

<img src="02_grammar-of-graphics_files/figure-html/unnamed-chunk-7-1.png" width="100%" style="display: block; margin: auto;" />
]
]

```r
ggplot(penguins) +
  geom_point(aes(x = body_mass_g, 
                 y = bill_length_mm), 
             color = "blue")
```

<img src="02_grammar-of-graphics_files/figure-html/unnamed-chunk-8-1.png" width="100%" style="display: block; margin: auto;" />
]
]

---

---

# Same aesthetics, different geoms

.pull-left[
<img src="02_grammar-of-graphics_files/figure-html/unnamed-chunk-9-1.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="02_grammar-of-graphics_files/figure-html/unnamed-chunk-10-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Geoms

---

# Possible geoms

<table>
  <tr>
    <th class="cell-left"></th>
    <th class="cell-left">Example geom</th>
    <th class="cell-left">What it makes</th>
  </tr>
  <tr>
    <td class="cell-left"><img src="img/geom_bar.png"></td>
    <td class="cell-left"><code class="remark-inline-code">geom_col()</code></td>
    <td class="cell-left">Bar charts</td>
  </tr>
  <tr>
    <td class="cell-left"><img src="img/geom_text.png"></td>
    <td class="cell-left"><code class="remark-inline-code">geom_text()</code></td>
    <td class="cell-left">Text</td>
  </tr>
  <tr>
    <td class="cell-left"><img src="img/geom_point.png"></td>
    <td class="cell-left"><code class="remark-inline-code">geom_point()</code></td>
    <td class="cell-left">Points</td>
  </tr>
  <tr>
    <td class="cell-left"><img src="img/geom_boxplot.png"></td>
    <td class="cell-left"><code class="remark-inline-code">geom_boxplot()</code>&emsp;</td>
    <td class="cell-left">Boxplots</td>
  </tr>
  <tr>
    <td class="cell-left"><img src="img/geom_sf.png"></td>
    <td class="cell-left"><code class="remark-inline-code">geom_sf()</code></td>
    <td class="cell-left">Maps</td>
  </tr>
</table>

---

# Possible geoms

.box-5[See [the **ggplot2** documentation](https://ggplot2.tidyverse.org/reference/index.html#section-layer-geoms) for<br>complete examples of all the different geom layers]

---

# Your turn #3

.pull-left[
<img src="02_grammar-of-graphics_files/figure-html/unnamed-chunk-11-1.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="02_grammar-of-graphics_files/figure-html/unnamed-chunk-12-1.png" width="100%" style="display: block; margin: auto;" />
]

---

```r
ggplot(penguins) +
  geom_boxplot(aes(x = species, 
                   y = body_mass_g))
```
]

---

# Your turn #4

.box-5[Make a histogram of `bill_length_mm`. Use the cheetsheet.<br>Hint: don't supply a `y` variable.]

---

```r
ggplot(penguins) +
  geom_histogram(aes(x = bill_length_mm),
                 binwidth = 1, 
                 color = "white")
```
]

---

# Your turn #5

.box-5[Make this density plot of `bill_length_mm` filled by `species`.<br>Use the cheatsheet. Hint: don't supply a `y` variable.]

---

```r
ggplot(penguins) +
  geom_density(aes(x = bill_length_mm, 
                   fill = species),
               alpha = 0.75)
```
]

---

# Complex graphs!

---

# Your turn #6

```r
ggplot(data = penguins) +
  geom_point(mapping = aes(x = body_mass_g, 
                           y = bill_depth_mm, 
                           color = species)) +
  geom_smooth(mapping = aes(x = body_mass_g, 
                            y = bill_depth_mm, 
                            color = species))
```

---

```r
ggplot(data = penguins) +
  geom_point(aes(x = body_mass_g, 
                 y = bill_depth_mm, 
                 color = species)) +
  geom_smooth(aes(x = body_mass_g, 
                  y = bill_depth_mm, 
                  color = species))
```
]

---

# Global vs. local

```r
ggplot(penguins, aes(x = body_mass_g, y = bill_depth_mm, color = species)) +
  geom_point() + 
  geom_smooth()
```

<img src="02_grammar-of-graphics_files/figure-html/unnamed-chunk-21-1.png" width="60%" style="display: block; margin: auto;" />
]

---

# Global vs. local

```r
ggplot(penguins, mapping = aes(x = body_mass_g, y = bill_depth_mm)) +
  geom_point(mapping = aes(color = species)) +
  geom_smooth(method = "lm")
```

<img src="02_grammar-of-graphics_files/figure-html/unnamed-chunk-22-1.png" width="60%" style="display: block; margin: auto;" />
]

---

---

# Gammar components as layers

&nbsp;

![](img/ggplot-layers-short@4x.png)
]

???

Layer analogy borrowed from [Thomas Lin Pedersen](https://www.data-imaginist.com/) and his ["Drawing Anything with ggplot2" workshop](https://github.com/thomasp85/ggplot2_workshop).

---

# Additional layers

.box-inv-5[We sequentially add layers onto the foundational `ggplot()` plot to create complex figures]
]

---

# Putting it all together

.box-inv-5.medium[We can build a plot sequentially<br>to see how each grammatical layer<br>changes the appearance]

---

```r
*ggplot(data = penguins,
*      mapping = aes(x = body_mass_g,
*                    y = bill_depth_mm,
*                    color = species))
```
]

---

```r
ggplot(data = penguins, 
       mapping = aes(x = body_mass_g,
                     y = bill_depth_mm,
                     color = species)) +
* geom_point()
```
]

---

```r
ggplot(data = penguins, 
       mapping = aes(x = body_mass_g,
                     y = bill_depth_mm,
                     color = species)) +
  geom_point() +
* geom_smooth()
```
]

---

```r
ggplot(data = penguins, 
       mapping = aes(x = body_mass_g,
                     y = bill_depth_mm,
                     color = species)) +
  geom_point() +
* geom_smooth(method = "lm")
```
]

---

```r
ggplot(data = penguins, 
       mapping = aes(x = body_mass_g,
                     y = bill_depth_mm,
                     color = species)) +
  geom_point() +
  geom_smooth(method = "lm") + 
* scale_color_viridis_d()
```
]

---

```r
ggplot(data = penguins, 
       mapping = aes(x = body_mass_g,
                     y = bill_depth_mm,
                     color = species)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  scale_color_viridis_d() +
* facet_wrap(vars(island), ncol = 1)
```
]

---

```r
ggplot(data = penguins, 
       mapping = aes(x = body_mass_g,
                     y = bill_depth_mm,
                     color = species)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  scale_color_viridis_d() +
  facet_wrap(vars(island), ncol = 1) +
* labs(x = "Body mass (g)", y = "Bill depth (mm)",
*      color = "Species",
*      title = "Heavier penguins have taller bills",
*      subtitle = "And penguins live on different islands!",
*      caption = "Penguins!")
```
]

---

```r
ggplot(data = penguins, 
       mapping = aes(x = body_mass_g,
                     y = bill_depth_mm,
                     color = species)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  scale_color_viridis_d() +
  facet_wrap(vars(island), ncol = 1) +
  labs(x = "Body mass (g)", y = "Bill depth (mm)",
       color = "Species", 
       title = "Heavier penguins have taller bills",
       subtitle = "And penguins live on different islands!",
       caption = "Penguins!") +
* theme_bw()
```
]

---

```r
ggplot(data = penguins, 
       mapping = aes(x = body_mass_g,
                     y = bill_depth_mm,
                     color = species)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  scale_color_viridis_d() +
  facet_wrap(vars(island), ncol = 1) +
  labs(x = "Body mass (g)", y = "Bill depth (mm)",
       color = "Species", 
       title = "Heavier penguins have taller bills",
       subtitle = "And penguins live on different islands!",
       caption = "Penguins!") +
  theme_bw() +
* theme(legend.position = "bottom",
*       plot.title = element_text(face = "bold"))
```
]

---

```r
ggplot(data = penguins, 
       mapping = aes(x = body_mass_g,
                     y = bill_depth_mm,
                     color = species)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  scale_color_viridis_d() +
  facet_wrap(vars(island), ncol = 1) +
  labs(x = "Body mass (g)", y = "Bill depth (mm)",
       color = "Species", 
       title = "Heavier penguins have taller bills",
       subtitle = "And penguins live on different islands!",
       caption = "Penguins!") +
  theme_bw() +
  theme(legend.position = "bottom",
        plot.title = element_text(face = "bold"))
```
]

---

---

# So many possibilities!

.box-5[See [the **ggplot2** documentation](https://ggplot2.tidyverse.org/reference/index.html) for<br>complete examples of everything you can do]
]

---

# A true grammar

.box-5.small[Hunt through Excel menus for a stacked bar chart and manually reshape your data to work with it]
]

.pull-right[
.center[
<figure>
  <img src="img/excel-chart-types.png" alt="Excel chart types" title="Excel chart types" width="78%">
</figure>
]
]

---

# A true grammar

.box-5.small[Map a column to the x-axis, fill by a different variable, and `geom_col()` to get stacked bars]

.box-5.small[Geoms can be interchangable<br>(e.g. switch `geom_violin()` to `geom_boxplot()`)]
]

.pull-right[
.center[
<figure>
  <img src="img/ggplot-layers@4x.png" alt="Grammar of graphics layers" title="Grammar of graphics layers" width="100%">
</figure>
]
]

---

# Describing graphs with the grammar

.left-code[
.box-5.small[Map wealth to the x-axis, health to the y-axis, add points, color by continent, size by population, scale the y-axis with a log, and facet by year]

```r
ggplot(filter(gapminder, 
              year %in% c(2002, 2007)), 
       aes(x = gdpPercap,
           y = lifeExp,
           color = continent,
           size = pop)) +
  geom_point() +
  scale_x_log10() + 
  facet_wrap(vars(year), ncol = 1)
```
]

---

# Describing graphs with the grammar

.left-code[
.box-5.small[Map health to the x-axis, add a histogram with bins for every 5 years, fill and facet by continent]

```r
ggplot(gapminder_2007, 
       aes(x = lifeExp,
           fill = continent)) +
  geom_histogram(binwidth = 5, 
                 color = "white") +
  guides(fill = FALSE) +  # Turn off legend
  facet_wrap(vars(continent))
```
]

---

# Describing graphs with the grammar

.left-code[
.box-5.small[Map continent to the x-axis, health to the y-axis, add violin plots and semi-transparent boxplots, fill by continent]

```r
ggplot(gapminder, 
       aes(x = continent,
           y = lifeExp,
           fill = continent)) +
  geom_violin() +
  geom_boxplot(alpha = 0.5) +
  guides(fill = FALSE)  # Turn off legend
```
]

---

# Scales

<table>
  <tr>
    <th class="cell-left">Example layer</th>
    <th class="cell-left">What it does</th>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">scale_x_continuous()</code></td>
    <td class="cell-left">Make the x-axis continuous</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">scale_x_continuous(breaks = 1:5)&ensp;</code></td>
    <td class="cell-left">Manually specify axis ticks</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">scale_x_log10()</code></td>
    <td class="cell-left">Log the x-axis</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">scale_color_gradient()</code></td>
    <td class="cell-left">Use a gradient</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">scale_fill_viridis_d()</code></td>
    <td class="cell-left">Fill with discrete viridis colors</td>
  </tr>
</table>

---

# Scales

.box-inv-5.small[`scale_x_log10()`]

<img src="02_grammar-of-graphics_files/figure-html/scale-example-1-1.png" width="100%" style="display: block; margin: auto;" />
]

.box-inv-5.small[`scale_color_viridis_d()`]

<img src="02_grammar-of-graphics_files/figure-html/scale-example-2-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Your turn #7

.box-5[Make this density plot of `bill_length_mm` filled by `species`.<br>Use the viridis fill scale.]

.box-5.small[For bonus fun, try a different viridis option like `plasma` or `inferno`.]

---

```r
ggplot(penguins, 
       aes(x = bill_length_mm, 
           fill = species)) +
  geom_density(alpha = 0.75) +
  scale_fill_viridis_d(option = "plasma")
```
]

---

---

# Facets

<table>
  <tr>
    <th class="cell-left">Example layer</th>
    <th class="cell-left">What it does</th>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">facet_wrap(vars(continent))</code></td>
    <td class="cell-left">Plot for each continent</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">facet_wrap(vars(continent, year))</code>&emsp;</td>
    <td class="cell-left">Plot for each continent/year</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">facet_wrap(..., ncol = 1)</code></td>
    <td class="cell-left">Put all facets in one column</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">facet_wrap(..., nrow = 1)</code></td>
    <td class="cell-left">Put all facets in one row</td>
  </tr>
</table>

---

# Facets

.box-inv-5.small[`facet_wrap(vars(continent))`]

<img src="02_grammar-of-graphics_files/figure-html/facet-example-1-1.png" width="100%" style="display: block; margin: auto;" />
]

.box-inv-5.small[`facet_wrap(vars(continent, year))`]

<img src="02_grammar-of-graphics_files/figure-html/facet-example-2-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Your turn #8

---

```r
ggplot(penguins, 
       aes(x = body_mass_g, 
           y = bill_length_mm, 
           color = species)) +
  geom_point() +
  facet_wrap(vars(island))
```
]

---

---

# Coordinates

<table>
  <tr>
    <th class="cell-left">Example layer</th>
    <th class="cell-left">What it does</th>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">coord_cartesian()</code></td>
    <td class="cell-left">Standard x-y coordinate system</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">coord_cartesian(ylim = c(1, 10))</code>&emsp;</td>
    <td class="cell-left">Zoom in where y is 1–10</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">coord_flip()</code></td>
    <td class="cell-left">Switch x and y</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">coord_polar()</code></td>
    <td class="cell-left">Use circular polar system</td>
  </tr>
</table>

---

# Coordinates

.box-inv-5.small[`coord_cartesian(ylim = c(70, 80), xlim = c(10000, 30000))`]

<img src="02_grammar-of-graphics_files/figure-html/coord-example-1-1.png" width="100%" style="display: block; margin: auto;" />
]

.box-inv-5.small[`coord_flip()`]

<img src="02_grammar-of-graphics_files/figure-html/coord-example-2-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Labels

<table>
  <tr>
    <th class="cell-left">Example layer</th>
    <th class="cell-left">What it does</th>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">labs(title = "Neat title")</code></td>
    <td class="cell-left">Title</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">labs(caption = "Something")</td>
    <td class="cell-left">Caption</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">labs(y = "Something")</td>
    <td class="cell-left">y-axis</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">labs(size = "Population")</code></td>
    <td class="cell-left">Title of size legend</td>
  </tr>
</table>

---

# Labels

```r
ggplot(gapminder_2007, 
       aes(x = gdpPercap, y = lifeExp, 
           color = continent, size = pop)) +
  geom_point() +
  scale_x_log10() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from 2007",
       x = "Wealth (GDP per capita)",
       y = "Health (life expectancy)",
       color = "Continent",
       size = "Population",
       caption = "Source: The Gapminder Project")
```
]

---

# Theme

<table>
  <tr>
    <th class="cell-left">Example layer</th>
    <th class="cell-left">What it does</th>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">theme_grey()</code></td>
    <td class="cell-left">Default grey background</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">theme_bw()</td>
    <td class="cell-left">Black and white</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">theme_dark()</td>
    <td class="cell-left">Dark</td>
  </tr>
  <tr>
    <td class="cell-left"><code class="remark-inline-code">theme_minimal()</code></td>
    <td class="cell-left">Minimal</td>
  </tr>
</table>

---

# Theme

.box-inv-5.small[`theme_dark()`]

<img src="02_grammar-of-graphics_files/figure-html/theme-example-1-1.png" width="100%" style="display: block; margin: auto;" />
]

.box-inv-5.small[`theme_minimal()`]

<img src="02_grammar-of-graphics_files/figure-html/theme-example-2-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Theme

.box-inv-5[There are collections of pre-built themes online,<br>like [the **ggthemes** package](https://jrnold.github.io/ggthemes/)]

.center[
<figure>
  <img src="img/ggthemes.png" alt="ggthemes" title="ggthemes" width="45%">
</figure>
]

---

# Theme

.box-inv-5[Organizations often make their own custom themes, [like the BBC](https://bbc.github.io/rcookbook/)]

.center[
<figure>
  <img src="img/bbc-cookbook.png" alt="ggthemes" title="ggthemes" width="80%">
</figure>
]

---

# Theme options

```r
theme_bw() + 
theme(legend.position = "bottom",
      plot.title = element_text(face = "bold"),
      panel.grid = element_blank(),
      axis.title.y = element_text(face = "italic"))
```

---

# Saving graphs

.box-inv-5.medium[Use `ggsave()` to save a plot to your computer]

```r
my_plot <- ggplot(...)

ggsave("plot_name.pdf", my_plot, width = 5, height = 3.5)
ggsave("plot_name.png", my_plot, width = 5, height = 3.5)
```