Layers of plots

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# Layers of plots
### Heike Hofmann

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# Layers in `ggplot2`

---

# Outline

- grammar of graphics, again

- layer specifications

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# Grammar of Graphics

A graphical representation (plot) consists of:

1. **mappings** (`aes`):  data variables are mapped to graphical elements

2. **layers**:  geometric elements (`geoms`, such as points, lines, rectangles, text, ...) and statistical transformations (`stats`, are identity, counts, bins, ...)

3. **scales**: map values in the data space to values in an aesthetic space (e.g. color, size, shape, but also position)

4. **coordinate system** (`coord`): normally Cartesian, but pie charts use e.g. polar coordinates

5. **facetting**: for small multiples (subsets) and their arrangement

6. **theme**: fine-tune display items, such as font and its size, color of background, margins, ...

---

# Layers

each layer has several parts, the two most important are:

- mapping: using the `aes()` function, we specify mappings between variables and aesthetics of the chart

- `data`: the dataset used in the layer

`ggplot` is the layer that sets the defaults, <br>

`geom_XXX` creates a layer: for each aspect it either uses the default or has to specify its own

---

# Wallabies

```r
wallabies <- read.table("http://www.statsci.org/data/oz/wallaby.txt", sep="\t", header=TRUE)
head(wallabies)
```

```
##   Anim Sex Loca Leng Head Ear Arm Leg Pes Tail Weight Age
## 1   45   1    G   NA  123  NA  59  69  59   93     NA  14
## 2   45   1    G   NA  178  54  90 120  92  185     NA  28
## 3   45   1    G   NA  250  92 130 210 142  307     NA  49
## 4   45   1    G   NA  324 108 174 284 205  454    290  69
## 5   45   1    G   NA  369 129 198 340 257  568    410  83
## 6   45   1    G   NA  408 155 237 411 308  648    570  97
```

```r
wallabies <- wallabies %>% mutate(
  Anim = as.factor(Anim),
  Sex = as.factor(Sex)
)
```

---

# Plot

```r
wallabies %>% 
  ggplot(aes(x = Age, y = Weight, colour=Sex)) +
  geom_point()
```

```
## Warning: Removed 260 rows containing missing values (geom_point).
```

![](03_layers_files/figure-html/unnamed-chunk-2-1.png)

---

# Adding additional text

```r
wallabies %>% 
  ggplot(aes(x = Age, y = Weight, colour=Sex)) +
  geom_point() +
  geom_text(aes(label = Anim), 
            hjust = 0, nudge_x = 5,
            data = wallabies %>% filter(Age > 450, Weight < 30000))
```

```
## Warning: Removed 260 rows containing missing values (geom_point).
```

![](03_layers_files/figure-html/unnamed-chunk-3-1.png)

---

# Default versus Layer Specification

```r
wallabies %>% 
  ggplot(aes(x = Age, y = Weight)) +
  geom_point(aes(colour=Sex)) +
  geom_text(aes(label = Anim), 
            hjust = 0, nudge_x = 5,
            data = wallabies %>% filter(Age > 450, Weight < 30000))
```

```
## Warning: Removed 260 rows containing missing values (geom_point).
```

![](03_layers_files/figure-html/unnamed-chunk-4-1.png)

---
class: inverse
# Your Turn (6 mins)

- Load the `mojo` data from the package `classdata`,

- Plot total gross by week that the movie is out (restrict the x-axis to a maximum of 30 weeks).

- Label the three movies with the highest total gross.

- Color the label of these three movies with a color of your choice.