Layers in ggplot2

class: center, middle, inverse, title-slide

# Layers in <code>ggplot2</code>

---

background-image: url(https://raw.githubusercontent.com/allisonhorst/stats-illustrations/master/rstats-artwork/ggplot2_masterpiece.png)
background-size: 500px
background-position: 50% 90%
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## Outline:

### 1. The grammar of graphics, again

### 2. Layer specifications

---

## A layered grammar of graphics

- ggplot2 works on the philosophy of adding layers to the visualization

- there are 7 layers of grammatical elements

.center[
<img src="images/layers2.png" width="500">
]

---

## A layered grammar of graphics

- only 3 of these layers are necessary to make a plot:

- **data** are the subjects & objects of the data visualization  
  
  - The **geom** is what relates the data to a visual element.  
  
  - **Aesthetic mappings** (aes) substitute visual properties (aesthetics) for the data

- these three layers allows us maximum flexibility to make subtle changes in each layer to clearly communicate our message.

.center[
 
<img src="images/layers1.png" width="500">
]

---
## A layered grammar of graphics

A graphical representation (plot) in ggplot2 consists of:

1. **default data and mappings** (`aes`):  data variables are mapped to visual properties of the graphical elements

2. **one or more layers**:  
  - geometric element (`geom`, such as point, line, rectangle, text, ...), 
  - statistical transformation (`stat`, such as identity, counts, bins, ...), 
  - position adjustment,  
  - (optional) one dataset and set of aesthetic mappings

3. **scales**: map values in the data space to values in an aesthetic space

4. **coordinate system** (`coord`): normally Cartesian, but could use polar coordinates for pie charts or different mapping 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, ...

---
## Previous example

```r
data(nasa, package = "GGally")
nasa1 <- nasa %>% 
 dplyr::filter(id== "1-1")

nasa1 %>%
  ggplot(aes(x = time, y = temperature)) + 
  geom_point()
```

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

---
## Adding a line

```r
ggplot(nasa1, aes(x = time, y = temperature)) + 
  geom_point() +
  geom_line()
```

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

---
## Adding additional lines

```r
nasa_subset <- nasa %>% 
 dplyr::filter(id %in% c("1-1", "13-1", "5-12"))

nasa_subset %>%
  ggplot(aes(x = time, y = temperature, group = id)) + 
  geom_line()
```

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

---
## Adding additional text

```r
ggplot(nasa_subset, aes(x = time, y = temperature, group = id)) + 
  geom_line() +
  geom_text(data = nasa_subset[nasa_subset$time == 72, ], 
            aes(label = id, x = time + 0.5), 
            colour = "dodgerblue3", hjust = "left", size = 5)
```

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

---
## Layers

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

- **data**: the dataset used in the layer

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

`ggplot` is the layer that sets the defaults, 
 
`geom_XXX` creates a layer:

- the default mappings will automatically be applied

- you can override the defaults by specifyng new mappings in each layer

- you can add additional mappings

---
## Default vs Layer Specification

Specifying the aesthetics as the defaults:

```r
ggplot(nasa_subset, aes(x = time, y = temperature, color = id)) + 
  geom_line() +
  geom_text(data = nasa_subset[nasa_subset$time == 72, ], 
            aes(label = id, x = time + 0.5), hjust = "left", size = 5)
```

![](03_layers_files/figure-html/unnamed-chunk-5-1.png)

---
## Default vs Layer Specification

Specifying the aesthetics in the layer:

```r
ggplot(nasa_subset, aes(x = time, y = temperature, group = id)) + 
  geom_line() +
  geom_text(data = nasa_subset[nasa_subset$time == 72, ], 
            aes(label = id, colour = id, x = time + 0.5), hjust = "left", size = 5)
```

![](03_layers_files/figure-html/unnamed-chunk-6-1.png)

---
class: yourturn
# Your Turn

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

- Plot a time line for each movie: plot total gross by the number of weeks that the movie is out.

- Label the three movies with the highest total gross.

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

---
## Resources

- reference/document: http://ggplot2.tidyverse.org/reference/

- RStudio cheat sheet for [ggplot2](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)

- Artwork by [@allison_horst](https://twitter.com/allison_horst?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor)