ggplot2

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# Data vizualisation using _ggplot2_

### Maximilian H.K. Hesselbarth

#### University of Michigan (EEB)

2022/10/24

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# Grammar of Graphics

- Based on Grammar of Graphics

.ref[Wilkinson, L., 2012. The grammar of graphics. In Handbook of computational statistics (pp. 375-414). Springer, Berlin, Heidelberg.]

- Elements of all _ggplots_:

1. **data** Information to visualize
  
  2. **aesthetic** Mappings of data to plot coordinates (position, color, size, ...)
  
  3. **geometry** Shapes to represent the data (points, lines, polygons, ...)

.ref[Wickham, H., 2016. ggplot2: Elegant graphics for data analysis. Springer, New York, USA.]  
.ref[Illustration from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst]

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# Data and mapping layer

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- Map body mass to **x** axis and bill length to **y** axis

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm))
```

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# Geometry layer

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- Adding a geometric **point** layer

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm)) + 
* geom_point()
```

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# Advanced mapping layer

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- Add *color* mapping and change **size** and **shape**

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm)) + 
* geom_point(aes(col = species),
*            size = 2.5, shape = 1)
```

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# Scaling layer

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- **Scaling** relates data values space to aesthetic values space

```r
*vc <- c("#F8C759", "#5965AC", "#619961")

ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm)) + 
  geom_point(aes(col = species), 
             size = 2.5, shape = 1) +
* scale_color_manual(values = vc) +
  scale_x_continuous(
*   limits = function(x) c(min(x), max(x)),
*   breaks = function(x) seq(quantile(x, 0.1), quantile(x, 0.9),
                             length.out = 5))
```

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# Using different themes

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- Customize layout using **themes**

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm)) + 
  geom_point(aes(col = species), 
             size = 2.5, shape = 1) +
  scale_color_manual(values = vc) +
* labs(x = "Body mass [cm]",
       y = "Bill length [mm]",
       title = "Body - Bill Relationship") + 
* theme_classic() +
* theme(legend.position = "bottom",
        plot.title = element_text(
          face = "bold", size = 16))
```

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# Adding geoms

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- Adding further **geometries**

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm)) + 
  geom_point(aes(col = species), 
             size = 2.5, shape = 1) +
* geom_smooth(aes(col = species),
*             method = "lm", se = FALSE) +
* geom_label(data = df_text,
*   aes(x = mass_mean, y = bill_mean,
*       label = species)) +
  scale_color_manual(values = vc) +
  labs(x = "Body mass [cm]", y = "Bill length [mm]",
       title = "Body - Bill Relationship") + 
  theme_classic() +
  theme(legend.position = "none",
        plot.title = element_text(
          face = "bold", size = 16))
```

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<img src="ggplot2_files/figure-html/unnamed-chunk-12-1.png" width="100%" style="display: block; margin: auto;" />
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# Facet wrapping

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- Split into **panels** using grouping

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm)) + 
  geom_point(aes(col = species), 
             size = 2.5, shape = 1) +
  geom_smooth(aes(col = species),
              method = "lm", se = FALSE) +
* facet_wrap(. ~ island) +
  scale_color_manual(values = vc) +
  labs(x = "Body mass [cm]", y = "Bill length [mm]",
       title = "Body - Bill Relationship") + 
  theme_classic() +
  theme(legend.position = "bottom",
        plot.title = element_text(
          face = "bold", size = 16))
```

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# Combining figures

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- Figures can be stored in **variables**

```r
library(cowplot)

p1 <- ggplot(data = penguins, 
  aes(x = species, body_mass_g)) + 
* geom_jitter()

p2 <- ggplot(data = penguins, 
  aes(x = species, body_mass_g)) + 
* geom_boxplot()

p3 <- ggplot(data = penguins, 
  aes(x = species, body_mass_g)) +
* geom_violin()

*cowplot::plot_grid(p1, p2, p3, nrow = 3)
```

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# More figures

- Prepare data set with min, max, median body mass grouped by species and sex (tidy!)

```r
penguins_range <- dplyr::group_by(penguins, species, sex) |> 
  dplyr::summarise(min = min(body_mass_g), 
                   max = max(body_mass_g), 
                   med = median(body_mass_g), 
                   .groups = "drop") |> 
  tidyr::pivot_longer(-c(species, sex)) |> 
  tidyr::drop_na(value)

head(penguins_range)
```

```
## # A tibble: 6 × 4
##   species sex    name  value
##   <fct>   <fct>  <chr> <dbl>
## 1 Adelie  female min    2850
## 2 Adelie  female max    3900
## 3 Adelie  female med    3400
## 4 Adelie  male   min    3325
## 5 Adelie  male   max    4775
## 6 Adelie  male   med    4000
```

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# Grouping variables

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- Use **group** to set interaction of all discrete variables

```r
ggplot(data = penguins_range,
  aes(x = species, y = value, colour = sex)
  ) + 
  geom_line(
*   aes(group = interaction(sex, name)),
    alpha = 0.15, size = 2.5
  ) + 
  geom_point(
    aes(shape = name, colour = sex), 
    size = 5) + 
  guides(
*   colour = guide_legend(title = "Sex", order = 2),
    shape = guide_legend( title = "Measurment", order = 1)
  )
```
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# Segments

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- `geom_segment()` allows to draw **lines** and curves

```r
ggplot(data = df_sampled, aes(x = 1:n)) + 
  geom_point(aes(
    y = bill_length_mm, col = species)) +
* geom_segment(
    aes(xend = 1:n, y = 0, 
        yend = bill_length_mm, col = species)) +
  geom_line(
    aes(y = bill_depth_mm), col = "#EEA236") +
  geom_hline(yintercept = 0) + 
* annotate(geom = "rect",
           xmin = 10.5, xmax = 14.5, 
           ymin = 10.5, ymax = 13.5, 
           fill = "black", alpha = 0.9) +
* annotate(geom = "text",
           x = 12.5, y = 12, 
           label = "Bill depth", color = "#EEA236")
```
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<img src="ggplot2_files/figure-html/unnamed-chunk-21-1.png" width="85%" style="display: block; margin: auto;" />
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# Densities

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```r
ggplot(data = penguins) + 
  geom_density(
*   aes(x = bill_depth_mm, y = ..density..),
    fill = "#69b3a2") +
  geom_histogram(
    aes(x = bill_depth_mm, y = ..density..), 
    col = "black", fill = NA) +
  annotate(
    geom = "text", x = 35, y = 0.05, label = "bill_depth_mm", 
    color = "#69b3a2") +
  geom_density(
    aes(x = bill_length_mm, y = -..density..), 
    fill = "#404080") + 
  geom_histogram(
*   aes(x = bill_length_mm, y = -..density..),
    col = "black", fill = NA) +
  annotate(geom = "text", x = 25, y = -0.05, label = "bill_length_mm", 
           color = "#404080") +
  # scale_x_continuous(breaks = seq(10, 60, 5), limits = c(10, 60)) +
  xlab("bill dimensions") + theme_classic()
```
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<img src="ggplot2_files/figure-html/unnamed-chunk-23-1.png" width="85%" style="display: block; margin: auto;" />
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.ref[Illustration from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst]

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# Animated figures

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- `gganimate` allows **dynamic** figures

```r
library(gganimate)

p <- ggplot(data = penguins, 
    aes(x = body_mass_g, 
        y = bill_length_mm)) + 
  geom_point(aes(color = species)) + 
  geom_label(data = df_text, 
    aes(x = mass_mean, y = bill_mean, 
        label = species)) +
  scale_color_viridis_d(option = "C") + 
* transition_states(states = species,
*   transition_length = 2,
*   state_length = 1) +
* enter_fade() +
* exit_fade() +
  theme_classic() + 
  theme(legend.position = "none")

gganimate::animate(p)
```

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## Thank you for your attention

### Questions?

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Further resources: [https://mhesselbarth.github.io/advanced-r-workshop/resources](https://mhesselbarth.github.io/advanced-r-workshop/resources)

Exercise: [https://mhesselbarth.github.io/advanced-r-workshop/exercise-ggplot](https://mhesselbarth.github.io/advanced-r-workshop/exercise-ggplot)

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