---
title: Communication
author: David Kane
tutorial:
  id: communication
output:
  learnr::tutorial:
    progressive: yes
    allow_skip: yes
runtime: shiny_prerendered
description: 'Tutorial for Chapter 11: Communication'
---

```{r setup, include = FALSE}
library(learnr)
library(tutorial.helpers)
library(tidyverse)
library(scales)
library(ggrepel)
library(patchwork)

knitr::opts_chunk$set(echo = FALSE)
options(tutorial.exercise.timelimit = 60, 
        tutorial.storage = "local") 

labels_tib <- tibble(
  start = 1:10,
  end = cumsum(start^2)
)

label_info <- mpg |>
  arrange(desc(displ)) |>
  slice_head(n = 1, by = drv) |>
  mutate(
    drive_type = case_when(
      drv == "f" ~ "front-wheel drive",
      drv == "r" ~ "rear-wheel drive",
      drv == "4" ~ "4-wheel drive"
    )
  ) |>
  select(displ, hwy, drv, drive_type)

potential_outliers <- mpg |>
  filter(hwy > 40 | (hwy > 20 & displ > 5))

trend_text <- "Larger engine sizes tend to have lower fuel economy." |>
  str_wrap(width = 30)

# For Legend layout section

base <- ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = class))

random_vals <- tibble(
  random_x = rnorm(10000),
  random_y = rnorm(10000)
)

suv <- mpg |> filter(class == "suv")
compact <- mpg |> filter(class == "compact")

## Layout section

p1 <- ggplot(mpg, aes(x = drv, y = cty, color = drv)) + 
  geom_boxplot(show.legend = FALSE) + 
  labs(title = "Plot 1")

p2 <- ggplot(mpg, aes(x = drv, y = hwy, color = drv)) + 
  geom_boxplot(show.legend = FALSE) + 
  labs(title = "Plot 2")

p3 <- ggplot(mpg, aes(x = cty, color = drv, fill = drv)) + 
  geom_density(alpha = 0.5) + 
  labs(title = "Plot 3")

p4 <- ggplot(mpg, aes(x = hwy, color = drv, fill = drv)) + 
  geom_density(alpha = 0.5) + 
  labs(title = "Plot 4")

p5 <- ggplot(mpg, aes(x = cty, y = hwy, color = drv)) + 
  geom_point(show.legend = FALSE) + 
  facet_wrap(~drv) +
  labs(title = "Plot 5")
```

```{r copy-code-chunk, child = system.file("child_documents/copy_button.Rmd", package = "tutorial.helpers")}
```

```{r info-section, child = system.file("child_documents/info_section.Rmd", package = "tutorial.helpers")}
```

<!-- This took 90 minutes in June 2024, but we have no removed the major OECD plot at the end, so the timing should be OK. -->


<!-- This is a tough chapter to deal with. There are so many examples, and so much information, that it is impossible (?) to include them all. But surely we can do a better of including more knowledge drops? Either way, no other tutorial in the first 11 is more worthy of being revisted than this one. -->

<!-- I am also not sure if all the objects which are needed are actually created. Better test coverage should catch these things, but that still hasn't been done. -->

<!-- Note that there is an error in R4DS. Send a PR. -->


<!-- Clark feedback -->

<!-- Legend Layout ex 4 extraneous "." in directions for typing in left -->

<!-- More Zooming ex 19 on "object 'x_scale' not found" for each scale when you just add them -->

<!-- Themes before ex 1 "we will be creating this graph" then no graph displayed -->

<!-- Themes right at end reminder this is what plot should look like and nothing is there -->



## Introduction
### 

This tutorial covers [Chapter 11: Communication](https://r4ds.hadley.nz/communication.html) from [*R for Data Science (2e)*](https://r4ds.hadley.nz/) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. In this tutorial we will be making use of three packages associated with [**ggplot2**](https://ggplot2.tidyverse.org/): [**scales**](https://scales.r-lib.org/), [**ggrepel**](https://ggrepel.slowkow.com), and [**patchwork**](https://patchwork.data-imaginist.com). Key commands include [`quote()`](https://rdrr.io/r/base/substitute.html) which simply returns its argument and [`geom_label_repel()`](https://rdrr.io/cran/ggrepel/man/geom_text_repel.html) which adds text directly to the plot.

## Labels
### 

The easiest place to start when turning an exploratory graphic into an expository graphic is with good labels. You add labels with the `labs()` function. We will create this plot:

```{r}
p_1 <- mpg |> 
  ggplot(aes(x = displ, 
             y = hwy)) +
    geom_point(aes(color = class)) +
    geom_smooth(se = FALSE) +
    labs(
      x = "Engine displacement (L)",
      y = "Highway fuel economy (mpg)",
      color = "Car type",
      title = "Fuel efficiency generally decreases with engine size",
      subtitle = "Two seaters (sports cars) are an exception because of their light weight",
      caption = "Data from fueleconomy.gov"
    )

p_1
```

### Exercise 1

Load the **tidyverse** package using the `library()` function. 

```{r labels-1, exercise = TRUE}

```

```{r labels-1-hint-1, eval = FALSE}
library(...)
```

```{r labels-1-test, include = FALSE}
library(tidyverse)
```

### 

In previous tutorials, you learned how to use plots as tools for exploration. When you make exploratory plots, you know—even before looking—which variables the plot will display. You made each plot for a purpose, could quickly look at it, and then move on to the next plot. In the course of most analyses, you’ll produce tens or hundreds of plots, most of which are immediately thrown away.

### Exercise 2

Load the [**scales**](https://scales.r-lib.org/) package using the `library()` function. 

```{r labels-2, exercise = TRUE}

```

```{r labels-2-hint-1, eval = FALSE}
library(...)
```

```{r labels-2-test, include = FALSE}
library(scales)
```

### 

The [**scales**](https://scales.r-lib.org/) package is used to override the default breaks, labels, transformations and palettes.

You need to communicate your understanding to others. Your audience will likely not share your background knowledge and will not be deeply invested in the data. 

### Exercise 3

Load the [**ggrepel**](https://ggrepel.slowkow.com) package using `library()`. 

```{r labels-3, exercise = TRUE}

```

```{r labels-3-hint-1, eval = FALSE}
library(...)
```

```{r labels-3-test, include = FALSE}
library(ggrepel)
```

### 

The [**ggrepel**](https://ggrepel.slowkow.com) package will automatically adjust labels so that they don’t overlap. To help others quickly build up a good mental model of the data, you will need to invest considerable effort in making your plots as self-explanatory as possible.



### Exercise 4

Load the [**patchwork**](https://patchwork.data-imaginist.com) package using `library()`. 

```{r labels-4, exercise = TRUE}

```

```{r labels-4-hint-1, eval = FALSE}
library(...)
```

```{r labels-4-test, include = FALSE}
library(patchwork)
```

### 

The [**patchwork**](https://patchwork.data-imaginist.com) package allows you to combine separate plots into the same graphic.

We recommend pairing this tutorial with a good general visualization book. We particularly like [*The Truthful Art*](https://www.amazon.com/gp/product/0321934075/), by Albert Cairo. It doesn’t teach the mechanics of creating visualizations, but instead focuses on what you need to think about in order to create effective graphics.


### Exercise 5

Now, let's explore the dataset that we want to create a plot on. Type in `mpg` and hit "Run Code". 

```{r labels-5, exercise = TRUE}

```

```{r labels-5-hint-1, eval = FALSE}
mpg
```

```{r labels-5-test, include = FALSE}
mpg
```

### 

The `mpg` dataset provides fuel economy data from 1999 and 2008 for 38 popular models of cars. The dataset is shipped with [**ggplot2**](https://ggplot2.tidyverse.org/) package.

### Exercise 6

Pipe `mpg` to `ggplot()`. 

```{r labels-6, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r labels-6-hint-1, eval = FALSE}
mpg |> 
  ...
```

```{r labels-6-test, include = FALSE}
mpg |> 
  ggplot()
```

As always, `ggplot()` alone, without the use of the `aes()` function as an argument to `mapping`, produces an empty rectangle.

### Exercise 7

Within `ggplot()`, using `aes()`, set the `x` argument to the `displ` variable and the `y` argument to the `hwy` variable


```{r labels-7, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r labels-7-hint-1, eval = FALSE}
mpg |> 
  ggplot(aes(x = ..., y = ...))
```

```{r labels-7-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy))
```

### Exercise 8

Add `geom_point()` to the pipeline.

```{r labels-8, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r labels-8-hint-1, eval = FALSE}
... +
  geom_point()
```

```{r labels-8-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
  geom_point()
```

We can finally see some data. Although there is more plotting to do, it is not too early to start thinking about the labels we will be using.

### Exercise 9

Within `geom_point()`, using `aes()`, set the `color` argument to the `class` variable. 

```{r labels-9, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r labels-9-hint-1, eval = FALSE}
... +
  geom_point(aes(color = ...))
```

```{r labels-9-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
  geom_point(aes(color = class)) 
```

### 

The purpose of a plot title is to summarize the main finding. Avoid titles that just describe what the plot is, e.g., “A scatterplot of engine displacement vs. fuel economy”.

### Exercise 10

Add `geom_smooth()` to the pipeline.

```{r labels-10, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r labels-10-hint-1, eval = FALSE}
... +
  geom_smooth()
```

```{r labels-10-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth()
```

### 

The `subtitle` adds additional detail in a smaller font beneath the title. If there is one key conclusion which readers should come away with, spell it out in the subtitle.


### Exercise 11

Within `geom_smooth()`, add the `se` argument and set it to `FALSE`. 

```{r labels-11, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r labels-11-hint-1, eval = FALSE}
... +
  geom_smooth(se = ...)
```

```{r labels-11-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE)
```

### 

The `caption` adds text at the bottom right of the plot, often used to describe the source of the data. Any plot you make should have a caption since your readers will always want to know where the data come from.

### Exercise 12

Add a `title`, `subtitle`, `x` axis title, `y` axis title, legend title(`color`), and a `caption` by adding `labs()` to the pipeline. 


```{r labels-12, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r labels-12-hint-1, eval = FALSE}
... +
  labs(
    title = ...,
    subtitle = ...,
    x = ...,
    y = ...,
    color = ...,
    caption = ...
  )
```

```{r labels-12-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) + 
  labs(
    x = "Engine displacement (L)",
    y = "Highway fuel economy (mpg)",
    color = "Car type",
    title = "Fuel efficiency generally decreases with engine size",
    subtitle = "Two seaters (sports cars) are an exception because of their light weight",
    caption = "Data from fueleconomy.gov"
  )
```

Reminder: The graphic should look something like this

```{r}
p_1
```

### Exercise 13

Let's move on to a new plot. Type in `labels_tib` and hit "Run Code". 

```{r labels-13, exercise = TRUE}

```

```{r labels-13-hint-1, eval = FALSE}
labels_tib
```

```{r labels-13-test, include = FALSE}
labels_tib
```

### 

The tibble, `labels_tib`, is a premade tibble that we will be using to show that it is also possible to use mathematical equations instead of `textstrings` for the labels if we switch `""` for the function `quote()`

### Exercise 14

Pipe `labels_tib` to the function `ggplot()`. 

```{r labels-14, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r labels-14-hint-1, eval = FALSE}
labels_tib |> 
  ...()
```

```{r labels-14-test, include = FALSE}
labels_tib |> 
  ggplot()
```

### Exercise 15

Within this function, using `aes()`, set the `x` argument to the `start` variable and set the `y` argument to the `end` variable.


```{r labels-15, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r labels-15-hint-1, eval = FALSE}
labels_tib |> 
  ggplot(aes(x = start, y = ...))
```

```{r labels-15-test, include = FALSE}
labels_tib |> 
  ggplot(aes(x = start, y = end))
```

### Exercise 16

Add the `geom_point()` function to the pipeline.

```{r labels-16, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r labels-16-hint-1, eval = FALSE}
... +
  geom_point()
```

```{r labels-16-test, include = FALSE}
labels_tib |> 
  ggplot(aes(x = start, y = end)) + 
  geom_point()
```

### Exercise 17

Using `labs()`, set the `x` axis title to `quote(x[i])`.

```{r labels-17, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r labels-17-hint-1, eval = FALSE}
... +
  labs(
    x = ...
  )
```

### 

```{r labels-17-test, include = FALSE}
labels_tib |> 
  ggplot(aes(x = start, y = end)) + 
  geom_point() +
  labs(
    x = quote(x[i])
  )
```

### Exercise 18

Within `labs()`, set the `y` axis title to `quote(sum(x[i] ^ 2, i == 1, n))`. 

```{r labels-18, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r labels-18-hint-1, eval = FALSE}
... +
  labs(
    x = quote(x[i]),
    y = ...
  )
```

```{r labels-18-test, include = FALSE}
labels_tib |> 
  ggplot(aes(x = start, y = end)) + 
  geom_point() +
  labs(
    x = quote(x[i]),
    y = quote(sum(x[i] ^ 2, i == 1, n))
  )
```

### Exercise 19

Type in `?plotmath` in the Console and learn more about what kind of syntax you can input into the `quote()` function. Copy/paste an interesting syntax and a meaning to the textbox below and hit "Submit".

```{r labels-19}
question_text(NULL,
	answer(NULL, correct = TRUE),
	allow_retry = TRUE,
	try_again_button = "Edit Answer",
	incorrect = NULL,
	rows = 6)
```

## Annotations
### 

<!-- DK: The Annotation section still needs cleaning. There is space for some knowledge drops, and there are several items in R4DS which are not currently mentioned, but should be. -->

In addition to labeling major components of your plot, it’s often useful to label individual observations or groups of observations. The first tool you have at your disposal is `geom_text()`, a function similar to `geom_point()`, but with an additional aesthetic: `label`. This makes it possible to add text labels to your plots.

In this section, we will make plots which look like this:

```{r}
p_2 <- 
  mpg |>   
    ggplot(aes(x = displ, 
               y = hwy, 
               color = drv)) +
      geom_point(alpha = 0.3) +
      geom_smooth(method = "loess", formula = y ~ x, se = FALSE) +
      geom_text(
        data = label_info, 
        mapping = aes(x = displ, 
                      y = hwy, 
                      label = drive_type),
        fontface = "bold", 
        size = 5, 
        hjust = "right", 
        vjust = "bottom"
      ) +
      theme(legend.position = "none")

p_2
```

### Exercise 1

There are two possible sources of labels. First, you might have a tibble that provides labels. Type `label_info` and hit "Run Code".

```{r annotations-1, exercise = TRUE}

```

```{r annotations-1-hint-1, eval = FALSE}
label_info
```

```{r annotations-1-test, include = FALSE}
label_info
```

### 

`label_info` is a tibble with information about three specific cars from `mpg`. We can use this tibble to directly label the three groups and replace the legend by placing labels directly on the plot. 

### Exercise 2

Create a new pipeline. Pipe `mpg` to the `ggplot()`. Using `aes()`, map `x` to `displ` and `y` to `hwy`. Add `geom_point()`.


```{r annotations-2, exercise = TRUE}

```

```{r annotations-2-hint-1, eval = FALSE}
mpg |> 
  ggplot(aes(x = ..., 
             y = ...)) +
    ...()
```

```{r annotations-2-test, include = FALSE}
mpg |>   
  ggplot(aes(x = displ, 
             y = hwy)) +
    geom_point()
```

This is not a bad plot, but it fails to use all the data we have about individual cars. 


### Exercise 3

Within `aes()`, map `color` to `drv`.

```{r annotations-3, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-3-hint-1, eval = FALSE}
mpg |>   
  ggplot(aes(x = displ, 
             y = hwy,
             ... = ...)) +
    geom_point()
```

```{r annotations-3-test, include = FALSE}
mpg |>   
  ggplot(aes(x = displ, 
             y = hwy,
             color = drv)) +
    geom_point()
```

Using color as another "dimension" allows us to see another pattern. Front-wheel drive cars (`f`) have smaller engines and get better mileage. But notice how hard this is to read for a new viewer. How are they to know what `f` means?


### Exercise 4

Within `geom_point()`, add the `alpha` argument and set it equal to `0.3`. 

```{r annotations-4, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-4-hint-1, eval = FALSE}
... + 
  geom_point(alpha = ...)
```

```{r annotations-4-test, include = FALSE}
mpg |>   
  ggplot(aes(x = displ, 
             y = hwy,
             color = drv)) +
    geom_point(alpha = 0.3)
```

### 

Using `alpha` helps, first, to highlight where the data is densest and, second, to lessen the business of the plot, the better to allow space for labels.

<!-- Using the `fontface` and `size` arguments we can customize the look of the text labels. They’re larger than the rest of the text on the plot and bolded. In addition, the `theme()` function with the argument `legend.position` set to `"none"` turns all the legends off. -->

### Exercise 5

Add `geom_smooth()` with `method` set to `"loess"`, `formula` to `y ~ x`, and `se` to `FALSE`.

```{r annotations-5, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-5-hint-1, eval = FALSE}
... +
  geom_smooth(method = ..., ... = y ~ x, se = ...)
```

```{r annotations-5-test, include = FALSE}
mpg |>   
  ggplot(aes(x = displ, 
             y = hwy,
             color = drv)) +
    geom_point(alpha = 0.3) +
    geom_smooth(method = "loess", formula = y ~ x, se = FALSE)
```

### 

The lines cover different regions because, for example, there are no front-wheel drive (`f`) cars with engines much larger than 5 liters.

### Exercise 6

This plot is not bad, but reading it would be easier if the labels were included in the graphic itself. 

Add `geom_text()` to the plot. Within `geom_text()`, add the `data` argument and set it equal to  `label_info`. Set the `mapping` argument equal to `aes(label = drive_type)`. 

```{r annotations-6, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-6-hint-1, eval = FALSE}
... +
  geom_text(
    data = ...,
    mapping = ...
  )
```

```{r annotations-6-test, include = FALSE}
mpg |>   
  ggplot(aes(x = displ, 
             y = hwy,
             color = drv)) +
    geom_point(alpha = 0.3) +
    geom_smooth(method = "loess", formula = y ~ x, se = FALSE) +
    geom_text(
      data = label_info,
      mapping = aes(label = drive_type)
    )
```

### 

There are two improvements. First, instead of opaque abbreviations like `4` or `f`, we are now using words, like "4-wheel drive" and "front-wheel drive" that most viewers will understand. Second, that information is in the plot itself, rather than off to the side in the legend.

### Exercise 7

We have control over how our labels look. Within your call to `geom_text()`, set the `fontface` argument to `"bold"` and the `size` argument to `5`.

```{r annotations-7, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-7-hint-1, eval = FALSE}
... + 
  geom_text(
    data = label_info, 
    aes(label = drive_type), 
    fontface = ...,
    ... = 5,
  )
```

```{r annotations-7-test, include = FALSE}
mpg |>   
  ggplot(aes(x = displ, 
             y = hwy,
             color = drv)) +
    geom_point(alpha = 0.3) +
    geom_smooth(method = "loess", formula = y ~ x, se = FALSE) +
    geom_text(
      data = label_info,
      mapping = aes(label = drive_type),
      fontface = "bold",
      size = 5
    )
```

### 

The `fontface` and `size` arguments we can customize the look of the text labels. In this case, these arguments make the labels larger than the rest of the text on the plot and bolded.

### Exercise 8

The labels are easier to read, but they are still misplaced. Within `geom_text()`, set the `hjust` to `"right"` and the `vjust` argument to `"bottom"`. 

```{r annotations-8, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-8-hint-1, eval = FALSE}
... +
  geom_text(
      data = label_info,
      mapping = aes(label = drive_type),
      fontface = "bold",
      size = 5, 
      hjust = ..., 
      ... = "bottom"
    )
```

```{r annotations-8-test, include = FALSE}
mpg |>   
  ggplot(aes(x = displ, 
             y = hwy,
             color = drv)) +
    geom_point(alpha = 0.3) +
    geom_smooth(method = "loess", formula = y ~ x, se = FALSE) +
    geom_text(
      data = label_info,
      mapping = aes(label = drive_type),
      fontface = "bold",
      size = 5, 
      hjust = "right", 
      vjust = "bottom"
    )
```

### 

We use `hjust` (horizontal justification) and `vjust` (vertical justification) to control the alignment of the label.

### Exercise 9

Add `theme()` to the pipeline. Within `theme()`, add the argument `legend.position` and set it to `"none"`. 

```{r annotations-9, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-9-hint-1, eval = FALSE}
... + 
  theme(legend.position = ...)
```

```{r annotations-9-test, include = FALSE}
mpg |>   
  ggplot(aes(x = displ, 
             y = hwy,
             color = drv)) +
    geom_point(alpha = 0.3) +
    geom_smooth(method = "loess", formula = y ~ x, se = FALSE) +
    geom_text(
      data = label_info,
      mapping = aes(label = drive_type),
      fontface = "bold",
      size = 5, 
      hjust = "right", 
      vjust = "bottom"
    ) +
    theme(legend.position = "none")
```

### 

### Exercise 10

The annotated plot we made is hard to read because the labels overlap with each other and with the points. We can use the `geom_label_repel()` function from the **ggrepel** package to address both of these issues. This useful package will automatically adjust labels so that they don’t overlap as you can see above.

Change `geom_text()` to `geom_label_repel()`. Remove the `hjust` and `vjust` arguments. Also, set the `nudge_y` argument to `2`.

```{r annotations-10, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-10-hint-1, eval = FALSE}
... + 
    geom_..._repel(
      data = label_info,
      mapping = aes(label = drive_type),
      fontface = "bold",
      size = 5,
      nudge_y = 2
    ) +
  ...
```

```{r annotations-10-test, include = FALSE}
mpg |>   
  ggplot(aes(x = displ, 
             y = hwy,
             color = drv)) +
    geom_point(alpha = 0.3) +
    geom_smooth(method = "loess", formula = y ~ x, se = FALSE) +
    geom_label_repel(
      data = label_info,
      mapping = aes(label = drive_type),
      fontface = "bold",
      size = 5,
      nudge_y = 2
    ) +
    theme(legend.position = "none")
```

### 

You can also use the same idea to highlight certain points on a plot with `geom_text_repel()` from the **ggrepel** package. 

### Exercise 11

Type `potential_outliers` and hit "Run Code". 

```{r annotations-11, exercise = TRUE}

```

```{r annotations-11-hint-1, eval = FALSE}
potential_outliers
```

```{r annotations-11-test, include = FALSE}

```

### 

This is a tibble of cars with either `hwy > 40` or `hwy > 20 & displ > 5`. In other words, they are outside of the typical relationship between `hwy` and `displ`. 

### Exercise 12

Before identifying these outliers in the graphic, we need to set up our initial plot. With a new pipeline, pipe `mpg` to `ggplot()`. Within this `ggplot()`, using `aes()`, map `x` to `displ` and `y` to `hwy`. Add `geom_point()` so that the data is displayed. 

```{r annotations-12, exercise = TRUE}

```

```{r annotations-12-hint-1, eval = FALSE}
... |> 
  ggplot(aes(x = ..., y = hwy)) +
    ...()
```

```{r annotations-12-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point()
```

### 

In addition to `geom_text()` and `geom_label()`, you have many other geoms in **ggplot2** available to help annotate your plot. For example, you can use `geom_hline()` and `geom_vline()` to add reference lines. We often make them thick (`linewidth = 2`) and white (`color = "white"`), and draw them underneath the primary data layer. That makes them easy to see, without drawing attention away from the data.

<!-- DK: Error in R4DS. Submit a PR. Should be "white". Use geom_hline() and geom_vline() to add reference lines. We often make them thick (linewidth = 2) and white (color = white), and draw them underneath the primary data layer. That makes them easy to see, without drawing attention away from the data. -->

### Exercise 13

Add the `geom_text_repel()` to the pipeline. Within `geom_text_repel()`, set the `data` argument equal to `potential_outliers` and the `mapping` argument to `aes(label = model)`.

```{r annotations-13, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-13-hint-1, eval = FALSE}
... +
  geom_text_repel(data = ...,
                  mapping = aes(... = model))
```

```{r annotations-13-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point() +
    geom_text_repel(data = potential_outliers, 
                    mapping = aes(label = model))
```

### 

Our graphic is combining information from two different data sets: `mpg` and `potential_outliers`. `ggplot()` is, implicitly, assigning `mpg` as the value of `data`. `geom_text_repel()` is, instead, using `potential_outliers`.


### Exercise 14

Using your previous code, add the `geom_point()` function to the pipeline. Within this function, set `data` equal to `potential_outliers` and `color` equal to `"red"`. 

```{r annotations-14, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-14-hint-1, eval = FALSE}
... +
  geom_point(data = ...,
             color = "...")
```

```{r annotations-14-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point() +
    geom_text_repel(data = potential_outliers, 
                    mapping = aes(label = model)) +
    geom_point(data = potential_outliers,
               color = "red")
```

### 

You can use `geom_segment()` with the arrow argument to draw attention to a point with an arrow. Use aesthetics `x` and `y` to define the starting location, and `xend` and `yend` to define the end location. 

### Exercise 15

If we want to modify the outlier points further, it is often easier, or even necessary, to add another call to `geom_point()`. It may seem strange to have three separate calls to `geom_point()` in a single graphic, but making the graphic look exactly as we want often requires such gymnastics.

Add another call to `geom_point()`. Set `data` equal to `potential_outliers`, `color` equal to `"red"`, `size` equal to `3`, and `shape` equal to `"circle open"`.


```{r annotations-15, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-15-hint-1, eval = FALSE}

```

```{r annotations-15-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point() +
    geom_text_repel(data = potential_outliers, 
                    mapping = aes(label = model)) +
    geom_point(data = potential_outliers,
               color = "red") +
    geom_point(data = potential_outliers,
               color = "red",
               size = 3,
               shape = "circle open")
```

We added a second layer of large, hollow points to further highlight the labelled points.


Another geom that can help annotate your plot is `geom_rect()`. You can use `geom_rect()` to draw a rectangle around points of interest. The boundaries of the rectangle are defined by aesthetics `xmin`, `xmax`, `ymin`, `ymax`. Alternatively, look into the [**ggforce**](https://ggforce.data-imaginist.com/) package, specifically [`geom_mark_hull()`](https://ggforce.data-imaginist.com/reference/geom_mark_hull.html), which allows you to annotate subsets of points with hulls.


### Exercise 16

Another handy function for adding annotations to plots is `annotate()`. As a rule of thumb, geoms are generally useful for highlighting a subset of the data while `annotate()` is useful for adding one or few annotation elements to a plot.

### 

Create a new variable called `trend_text` and set it to `"Larger engine sizes tend to have lower fuel economy."`

```{r annotations-16, exercise = TRUE}
trend_text <- "Larger engine sizes tend to have lower fuel economy."
```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-16-hint-1, eval = FALSE}
trend_text <- ...
```

```{r annotations-16-test, include = FALSE}
trend_text <- "Larger engine sizes tend to have lower fuel economy."
```

### 

To demonstrate using `annotate()`, let’s create some text to add to our plot. The text is a bit long, so we’ll use `stringr::str_wrap()` to automatically add line breaks to it given the number of characters you want per line.

### Exercise 17

Using your previous code, create a new pipeline and pipe `trend_text` with the `str_wrap()` function. Within this function add an argument called `width` and set it to `30`. 

```{r annotations-17, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-17-hint-1, eval = FALSE}
trend_text <- ... |> 
  str_wrap(width = ...)
```

```{r annotations-17-test, include = FALSE}
trend_text <- "Larger engine sizes tend to have lower fuel economy." |> 
  str_wrap(width = 30)
```

### Exercise 18

Now let's make use of this annotation within a plot. Create a new pipeline and pipe `mpg` to the `ggplot()` function. Within this function map `x` to `displ` and `y` to `hwy` using `aes()`. Add the `geom_point()` function to the pipeline.

```{r annotations-18, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-18-hint-1, eval = FALSE}
mpg |> 
  ggplot(aes(x = ..., y = ...)) +
    geom_point()
```

```{r annotations-18-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point()
```

### Exercise 19

Using your previous code, add the `annotate()` function to the pipeline. Within this function, set the argument `label` to `trend_text`, `geom` to `"label"`, `x` to `3.5`, and `y` to `38`. 
 
```{r annotations-19, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-19-hint-1, eval = FALSE}
mpg |> 
  ggplot(aes(x = ..., y = ...)) +
  geom_point() +
  annotate(
    label = ...,
    geom = ...,
    x = ..., 
    y = ...
  )
```

```{r annotations-19-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point() +
    annotate(label = trend_text,
             geom = "label",
             x = 3.5,
             y = 38)
```

### 

Now, let's align it horizontally using the argument `hjust`. 

### Exercise 20

Using your previous code, within your most recent call to the `annotate()` function, add the arguments `hjust` and set it to `"left"` and `color` and set it to `"red"`.

```{r annotations-20, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r annotations-20-hint-1, eval = FALSE}
... +
  annotate(
    geom = ...,
    x = ..., 
    y = ...,
    label = ..., 
    hjust = ...,
    color = ...
  )
```

```{r annotations-20-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point() +
    annotate(label = trend_text,
             geom = "label",
             x = 3.5,
             y = 38,
             hjust = "left",
             color = "red")
```

### 

Annotation is a powerful tool for communicating main takeaways and interesting features of your visualizations. The only limit is your imagination (and your patience with positioning annotations to be aesthetically pleasing)!

## Scales
### 

In this section, we will learn about how to display plots more appropriately by using scales. The most common use of the **scales** package is to customize the appearance of axis and legend labels. Use a `break_*` function to control how breaks are generated from the limits, and a `label_*` function to control how breaks are turned in to labels.

### Exercise 1

When you make a regular plot, **ggplot2* automatically adds scales for you. Let's make a simple plot to demonstrate this.

### 

Pipe `mpg` to the `ggplot()` function. Within this function map `x` to `displ` and `y` to `hwy` within `aes()`. Finish the pipeline with `geom_point()` with `aes(color = class)` inside of it.

```{r scales-1, exercise = TRUE}

```

```{r scales-1-hint-1, eval = FALSE}
mpg |> 
  ggplot(aes(x = ..., y = ...)) +
    geom_point(aes(... = class))
```

```{r scales-1-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point(aes(color = class))
```

### 

**ggplot2** automatically adds default scales behind the scenes such as `scale_x_continuous()`, `scale_y_continuous()`, and `scale_color_discrete()`.

### Exercise 2

We can test this previous statement, by adding those functions at the end of our previous pipeline. 

### 

Using your previous code, add the `scale_x_continuous()`, `scale_y_continuous()`, and the `scale_color_discrete()` functions to the pipeline. 

```{r scales-2, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r scales-2-hint-1, eval = FALSE}
... +
    scale_x_continuous() +
    ... +
    scale_color_discrete()
```

```{r scales-2-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point(aes(color = class)) +
    scale_x_continuous() +
    scale_y_continuous() +
    scale_color_discrete()
```

### 

We can see that the two plots are the same. Note the naming scheme for scales: `scale_` followed by the name of the aesthetic, then `_`, then the name of the scale. The default scales are named according to the type of variable they align with: `continuous`, `discrete`, `datetime`, or `date`.

### Exercise 3

Create a new pipeline and pipe `mpg` to the `ggplot()` function. Within this function, map `x` to `displ`, `y` to `hwy`, and `color` to `drv` using `aes()`. Finish with `geom_point()`.

```{r scales-3, exercise = TRUE}

```

```{r scales-3-hint-1, eval = FALSE}
mpg |> 
  ggplot(aes(x = ..., y = ..., color = ...)) +
    ...
```

```{r scales-3-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy, color = drv)) +
    geom_point()
```

### 

There are two primary arguments that affect the appearance of the ticks on the axes and the keys on the legend: `breaks` and `labels`. Breaks controls the position of the ticks, or the values associated with the keys.

### Exercise 4

Using your previous code, add the `scale_y_continuous()` function to the pipeline. Within this function add the `breaks` argument and set it to `seq(15, 40, by = 5)`.

```{r scales-4, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r scales-4-hint-1, eval = FALSE}
... +
    scale_y_continuous(breaks = ...)
```

```{r scales-4-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy, color = drv)) +
    geom_point() +
    scale_y_continuous(breaks = seq(15, 40, by = 5))
```

### 

Labels controls the text label associated with each tick/key. The most common use of `breaks` is to override the default choice.

### Exercise 5

Using your previous code, add the `scale_x_continuous()` function to the pipeline. Within this function, add the `labels` argument and set it to `NULL`. 

```{r scales-5, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r scales-5-hint-1, eval = FALSE}
... +
    scale_x_continuous(labels = ...)
```

```{r scales-5-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy, color = drv)) +
    geom_point() +
    scale_y_continuous(breaks = seq(15, 40, by = 5)) +
    scale_x_continuous(labels = NULL)
```

You can use labels in the same way (a character vector the same length as breaks), but you can also set it to NULL to suppress the labels altogether. This can be useful for maps, or for publishing plots where you do want to share the absolute numbers.

### Exercise 6

Using your previous code, add the `scale_color_discrete()` function to the pipeline. Within this function, add the `labels` argument and set it to `c("4" = "4-wheel", "f" = "front", "r" = "rear")`. 

```{r scales-6, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r scales-6-hint-1, eval = FALSE}
... +
    scale_color_discrete(labels = ...)
```

```{r scales-6-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy, color = drv)) +
    geom_point() +
    scale_y_continuous(breaks = seq(15, 40, by = 5)) +
    scale_x_continuous(labels = NULL) +
    scale_color_discrete(labels = c("4" = "4-wheel", "f" = "front", "r" = "rear"))
```

### 

You can also use breaks and labels to control the appearance of legends. For discrete scales for categorical variables, labels can be a named list of the existing levels names and the desired labels for them.

### Exercise 7

This time we will be using the **scales** package to create more efficient axis labels. Create a new pipeline and pipe `diamonds` to the `ggplot()` function. Within this function, pipe map `x` to `price` and `y` to `cut`. Add the `geom_boxplot()` function to the pipeline. Within this function add the `alpha` argument and set it to `0.05`. 

```{r scales-7, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r scales-7-hint-1, eval = FALSE}
diamonds |> 
  ggplot(aes(x = ..., y = ...)) +
    geom_boxplot()
    
```

```{r scales-7-test, include = FALSE}
diamonds |> 
  ggplot(aes(x = price, y = cut)) +
    geom_boxplot(alpha = 0.05)
```

### 

Using `alpha` to adjust the transparency of individual data points is almost always a good idea if you have thousands of obervations.

### Exercise 8

Using your previous code, add the `scale_x_continuous()` function. Within this function add the `labels` argument and set it equal to the `scales::label_dollar()` function.

```{r scales-8, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r scales-8-hint-1, eval = FALSE}
... +
    scale_x_continuous(labels = ...)
```

```{r scales-8-test, include = FALSE}
diamonds |> 
  ggplot(aes(x = price, y = cut)) +
    geom_boxplot(alpha = 0.05) +
    scale_x_continuous(labels = scales::label_dollar())
```

### 

The `labels` argument coupled with labeling functions from the **scales** package is also useful for formatting numbers as currency, percent, etc. The plot shows default labeling with `label_dollar()`, which adds a dollar sign as well as a thousand separator comma. 

If the **scales** package were already loaded with `library()`, then it would not have been necessary to use the double colon notation, as in `scales::label_dollar()`. Just `label_dollar()` would have produced the same result.

<!-- The next question does not work! The y-axis never shows percentages. Don't know why. -->

<!-- ### Exercise 9 -->

<!-- Another handy label function is `label_percent()`. Create a new pipeline and pipe `diamonds` to the `ggplot()` function. Within this function, map `x` to `cut` and `fill` to `clarity`. Add the `geom_bar()` function. Within this function add the `position` argument and set it equal to `"fill"`.  -->

<!-- ```{r scales-10, exercise = TRUE} -->

<!-- ``` -->

<!-- <button onclick = "transfer_code(this)">Copy previous code</button> -->

<!-- ```{r scales-10-hint-1, eval = FALSE} -->
<!-- diamonds |>  -->
<!--   ggplot(aes(x = ..., fill = ...)) + -->
<!--     geom_bar(... = "fill") -->
<!-- ``` -->

<!-- ```{r scales-10-test, include = FALSE} -->
<!-- diamonds |>  -->
<!--   ggplot(aes(x = cut, fill = clarity)) + -->
<!--     geom_bar(position = "fill") -->
<!-- ``` -->

<!-- ###  -->

<!-- The "fill" value for position standardizes all the categories of cut to 1 and shows the percentage of the `clarity` categories for each value of `cut`. -->

<!-- ### Exercise 11 -->

<!-- Using your previous code, add the `scale_y_continuous()` function to the pipeline. Within this function, add the `name` argument and set equal to `"Percentage"` and add the `labels` argument and set it equal to `scales::label_percent()`.  -->

<!-- ```{r scales-11, exercise = TRUE} -->

<!-- ``` -->

<!-- <button onclick = "transfer_code(this)">Copy previous code</button> -->

<!-- ```{r scales-11-hint-1, eval = FALSE} -->
<!-- diamonds |>  -->
<!--   ggplot(aes(x = ..., fill = ...)) + -->
<!--   geom_bar(position = ...) + -->
<!--   scale_y_continuous(name = ..., labels = ...) -->
<!-- ``` -->

<!-- ```{r scales-11-test, include = FALSE} -->
<!-- diamonds |>  -->
<!--   ggplot(aes(x = cut, fill = clarity)) + -->
<!--     geom_bar(position = "fill") -->
<!--     scale_y_continuous(name = "Percentage", labels = scales::label_percent) -->
<!-- ``` -->

<!-- ###  -->

<!-- Either `label_percent()` or `label_percent`, without the parantheses, will work. `labels` is looking for a function, so only the function name is needed. You don't need to explicitly call the function yourself. -->

### Exercise 9

Type in `presidential` and hit "Run Code".

```{r scales-9, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r scales-9-hint-1, eval = FALSE}
presidential
```

```{r scales-9-test, include = FALSE}
presidential
```

### 

The dataset, `presidential` gives the names of each president, the start and end date of their term, and their party,  for the 12 US presidents from Eisenhower to Trump. 

### Exercise 10

Create a new pipeline and pipe `presidential` to the `mutate()` function. Within this function, create a new variable `id` and set it equal to `33` plus `row_number()`. Add the `ggplot()` function to the pipeline. Within this function, map `x` to `start` and `y` to `id` using `aes()`. Add the `geom_point()` function to the pipeline


```{r scales-10, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r scales-10-hint-1, eval = FALSE}
presidential |> 
  mutate(id = ...) |> 
  ggplot(aes(x = ..., ... = id)) +
    geom_point()
```

```{r scales-10-test, include = FALSE}
presidential |> 
  mutate(id = row_number()) |> 
  ggplot(aes(x = start, y = id)) +
    geom_point()
```

### 

`id` is simply the number of the presidency is that row. Eisenhower is the first president, Kennedy the second, and so on.

### Exercise 11

Using your previous code, add the `geom_segment()` function. Within this function, map `xend` to `end` and `yend` to `id`.

```{r scales-11, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r scales-11-hint-1, eval = FALSE}
... +
  geom_segment(aes(xend = ..., yend = ...))
```

```{r scales-11-test, include = FALSE}
presidential |> 
  mutate(id = row_number()) |> 
  ggplot(aes(x = start, y = id)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id))
```

### 

Another use of breaks is when you have relatively few data points and want to highlight exactly where the observations occur.

### Exercise 12

Using your previous code, add the `scale_x_date()` function. Within this function add the argument `name` and set it equal to `NULL`.

```{r scales-12, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r scales-12-hint-1, eval = FALSE}
... +
  scale_x_date(name = ...)
```

```{r scales-12-test, include = FALSE}
presidential |> 
  mutate(id = row_number()) |> 
  ggplot(aes(x = start, y = id)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_x_date(name = NULL)
```

### 

We don't need to explicitly label the x-axis since the context makes it clear that these are years.

### Exercise 13

Using your previous code, within your call to the function `scale_x_date()`, add the argument `breaks` and set it equal to `presidential$start`.

```{r scales-13, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r scales-13-hint-1, eval = FALSE}
... +
  scale_x_date(name = NULL, breaks = ...)
```

```{r scales-13-test, include = FALSE}
presidential |> 
  mutate(id = row_number()) |> 
  ggplot(aes(x = start, y = id)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_x_date(name = NULL, breaks = presidential$start)
```

### 

Note that for the `breaks` argument we pulled out the start variable as a vector with `presidential$start` because we can’t do an aesthetic mapping for this argument. Also note that the specification of `breaks` and `labels` for date and datetime scales is a little different.

### Exercise 14

Using your previous code, within your call to the function `scale_x_date()`, add the argument `date_labels` and set it equal to `"'%y"`.

```{r scales-14, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r scales-14-hint-1, eval = FALSE}
... +
      scale_x_date(name = NULL, 
                   breaks = presidential$start,
                   ... = "'%y")
```

```{r scales-14-test, include = FALSE}
presidential |> 
  mutate(id = row_number()) |> 
  ggplot(aes(x = start, y = id)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_x_date(name = NULL, 
                 breaks = presidential$start,
                 date_labels = "'%y")
```

### 

The argument `date_labels` takes a format specification, in the same form as `parse_datetime()`. The plot shows when each US president started and ended their term.

## Legend Layout
### 

In this section we will learn how to adjust the placement of the legend and how to customize the legend of your plot.

### Exercise 1

First create a new variable called `base`. Set `base` equal to the dataset `mpg` piped to the `ggplot()` function. Within the `aes()` for this function map `x` to `displ` and `y` to `hwy`.  Add `geom_point()` with `aes(color = class)`

```{r legend-layout-1, exercise = TRUE}


```

```{r legend-layout-1-hint-1, eval = FALSE}
base <- mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point(aes(color = class))
```

```{r legend-layout-1-test, include = FALSE}
base <- mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point(aes(color = class))
```

### 

To control the overall position of the legend, you need to use a `theme()` setting. The theme setting `legend.position` controls where the legend is drawn. The default is `legend.position = "right"`. 

### Exercise 2

Connect `base`, which is the **ggplot2** object you have created, to `theme(legend.position = "left")` using a `+`.

Note that, colloquially, we will often use the verb "pipe" in this context. That is, we "pipe" each component of a plot together, just as we "pipe" data cleaning steps together, even though, in the former, we use a `+` to connect the statements.

```{r legend-layout-2, exercise = TRUE}

```

```{r legend-layout-2-hint-1, eval = FALSE}
base +
  theme(... = "left")
```

```{r legend-layout-2-test, include = FALSE}
base +
  theme(legend.position = "left")
```

### 

The legend's position is now to the left of the plot.

### Exercise 3

Using your previous code, within your call to the function `theme()` change the argument's definition from `"left"` to `"top"`. 

```{r legend-layout-3, exercise = TRUE}


```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r legend-layout-3-hint-1, eval = FALSE}
base +
  theme(legend.position = ...)
```

```{r legend-layout-3-test, include = FALSE}
base +
  theme(legend.position = "top")
```

### 

We can see that the legend is taking too much space at the top of the plot. So let's adjust the way the legend is displayed.

### Exercise 4

Using your previous code, add the `guides()` function to the pipeline. Within this function, add the `color` argument and set it equal to `guide_legend(nrow = 3)`. 

```{r legend-layout-4, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r legend-layout-4-hint-1, eval = FALSE}
... +
  guides(color = ...(nrow = ...))
```

```{r legend-layout-4-test, include = FALSE}
base +
  theme(legend.position = "top") +
  guides(color = guide_legend(nrow = 3))
```

### 

This fixes our previous problem. If your plot is short and wide, place the legend at the top or bottom, and if it’s tall and narrow, place the legend at the left or right. You can also use `legend.position = "none"` to suppress the display of the legend altogether.

## Replacing a Scale
### 

Instead of just tweaking the details a little, you can instead replace the scale altogether. There are two types of scales you’re mostly likely to want to switch out: continuous position scales and color scales. Fortunately, the same principles apply to all the other aesthetics, so once you’ve mastered position and color, you’ll be able to quickly pick up other scale replacements.

### Exercise 1

We will be working with a dataset called `diamonds`. Type in `diamonds` and hit "Run Code".

```{r replacing-a-scale-1, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-1-hint-1, eval = FALSE}
diamonds
```

```{r replacing-a-scale-1-test, include = FALSE}
diamonds
```

### 

The dataset `diamonds` us a dataset containing the prices and other attributes of almost 54,000 diamonds. The variables are as follows:

### Exercise 2

Create a new pipeline. Pipe `diamonds` to the `ggplot()` function. Within this function map `x` to `carat` and `y` to `price`.

```{r replacing-a-scale-2, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-2-hint-1, eval = FALSE}
diamonds |> 
  ggplot(aes(x = ..., y = ...))
```

```{r replacing-a-scale-2-test, include = FALSE}
diamonds |> 
  ggplot(aes(x = carat, y = price))
```

### Exercise 3

Using your previous code, add the `geom_bin2d()` function to the pipeline.

```{r replacing-a-scale-3, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-3-hint-1, eval = FALSE}
... +
  geom_bin2d()
```

```{r replacing-a-scale-3-test, include = FALSE}
diamonds |> 
  ggplot(aes(x = carat, y = price)) +
    geom_bin_2d()
```

### 

It’s very useful to plot transformations of your variable. For example, it’s easier to see the precise relationship between carat and price if we log transform them.

### Exercise 4

Using your previous code, within the `ggplot()` function, add the `log10()` function to `carat` and `price`. 

```{r replacing-a-scale-4, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-4-hint-1, eval = FALSE}
diamonds |> 
  ggplot(aes(x = log10(...), y = ...)) +
  geom_bin2d()
```

```{r replacing-a-scale-4-test, include = FALSE}
diamonds |> 
  ggplot(aes(x = log10(carat), y = log10(price))) +
    geom_bin_2d()
```

### 

However, the disadvantage of this transformation is that the axes are now labelled with the transformed values, making it hard to interpret the plot. Instead of doing the transformation in the aesthetic mapping, we can instead do it with the scale. 

### Exercise 5

Using your previous code, within the `ggplot()` function remove the `log10()` function around `carat` and `price`. Then add the function `scale_x_log10()` to the pipeline. 

```{r replacing-a-scale-5, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-5-hint-1, eval = FALSE}
diamonds |> 
  ggplot(aes(x = ..., y = ...)) +
    geom_bin2d() +
    scale_x_log10()
```

```{r replacing-a-scale-5-test, include = FALSE}
diamonds |> 
  ggplot(aes(x = carat, y = price)) +
    geom_bin2d() +
    scale_x_log10()
```

Look at the x-axis labels. Note how they are no longer linearly spaced. The distance from the second labelled point to the third is almost three times bigger than the distance from the first labelled point to the second.

### Exercise 6

Using your previous code, add the `scale_y_log10()` function to the pipeline.

```{r replacing-a-scale-6, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-6-hint-1, eval = FALSE}
... + 
  scale_y_log10()
```

```{r replacing-a-scale-6-test, include = FALSE}
diamonds |> 
  ggplot(aes(x = carat, y = price)) +
    geom_bin2d() +
    scale_x_log10() +
    scale_y_log10()
```

### 

This plot is much easier on the eyes due to the axes being labeled in units which make sense to the viewer.

### Exercise 7

Create a new pipeline. Pipe `mpg` to the `ggplot()` function. Within this function map `x` to `displ` and `y` to `hwy`. Add `geom_point()` function to the pipeline with `aes()` and `color` equal to `drv`.

```{r replacing-a-scale-7, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-7-hint-1, eval = FALSE}
,,, +
  geom_point(aes(color = ...))
```

```{r replacing-a-scale-7-test, include = FALSE}
mpg |> 
  ggplot((aes(x = displ, y = hwy))) +
    geom_point(aes(color = drv))
```

### 

Another scale that is frequently customized is color. The default categorical scale picks colors that are evenly spaced around the color wheel. Useful alternatives are the ColorBrewer scales which have been hand-tuned to work better for people with common types of color blindness.

### Exercise 8

Using your previous code, add a scale function, `scale_color_brewer()` to the pipeline. Within this function, add the argument `palette` and set it equal to `"Set1"`. 

```{r replacing-a-scale-8, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-8-hint-1, eval = FALSE}
... +
  scale_color_brewer(palette = ...)
```

```{r replacing-a-scale-8-test, include = FALSE}
mpg |> 
  ggplot((aes(x = displ, y = hwy))) +
    geom_point(aes(color = drv)) +
    scale_color_brewer(palette = "Set1")
```

### 

Don’t forget simpler techniques for improving accessibility. If there are just a few colors, you can add a redundant shape mapping. This will also help ensure your plot is interpretable in black and white.

### Exercise 9

Using your previous code, within your call to the `geom_point()` function, add to your mapping by setting `shape` equal to `drv`. 

```{r replacing-a-scale-9, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-9-hint-1, eval = FALSE}
... +
    geom_point(aes(color = drv, shape = ...)) +
    scale_color_brewer(palette = "Set1")
```

```{r replacing-a-scale-9-test, include = FALSE}
mpg |> 
  ggplot((aes(x = displ, y = hwy))) +
    geom_point(aes(color = drv, shape = drv)) +
    scale_color_brewer(palette = "Set1")
```

### 

The ColorBrewer scales are documented online at https://colorbrewer2.org/ and made available in R via the RColorBrewer package, by Erich Neuwirth.

### Exercise 10

Create a new pipeline and pipe the dataset `presidential` to the `mutate()` function. Within the `mutate()` function, create a new variable called `id` and set it equal to `33` plus `row_number()`. Add the `ggplot()` function to the pipeline and map `x` to `start`, `y` to `id`, and `color` to `party`. Add `geom_point()`.

```{r replacing-a-scale-10, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-10-hint-1, eval = FALSE}
presidential |> 
  mutate(id = 33 + ...)
```

```{r replacing-a-scale-10-test, include = FALSE}
presidential |> 
  mutate(id = 33 + row_number()) |> 
  ggplot(aes(x = start, y = id, color = party)) +
    geom_point()
```

### 

For continuous color, you can use the built-in `scale_color_gradient()` or `scale_fill_gradient()`. 

### Exercise 11

Add `geom_segment()` to the pipe, use `aes()`, setting the `xend` argument set to `end` and the `yend` argument set to `id`.

```{r replacing-a-scale-11, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-11-hint-1, eval = FALSE}
... +
  geom_segment(aes(xend = ..., yend = ...))
```

```{r replacing-a-scale-11-test, include = FALSE}
presidential |> 
  mutate(id = 33 + row_number()) |> 
  ggplot(aes(x = start, y = id, color = party)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id))
```

### 

If you have a diverging scale, you can use `scale_color_gradient2()`. That allows you to give, for example, positive and negative values different colors. That’s sometimes also useful if you want to distinguish points above or below the mean.

### Exercise 12

Using your previous code, add the `scale_color_manual()` function to the pipeline. Within this function add the argument `values` and set it to equal `c(Republican = "#E81B23", Democratic = "#00AEF3")`. 

```{r replacing-a-scale-12, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-12-hint-1, eval = FALSE}
... +
  scale_color_manual(values = ...)
```

```{r replacing-a-scale-12-test, include = FALSE}
presidential |> 
  mutate(id = 33 + row_number()) |> 
  ggplot(aes(x = start, y = id, color = party)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_color_manual(values = c(Republican = "#E81B23", Democratic = "#00AEF3"))
```

### 

If we wanted to map presidential party to color, we want to use the standard mapping of red for Republicans and blue for Democrats. One approach for assigning these colors is using hex color codes as shown above. 

### Exercise 13

For our next plot, we will create a plot with the dataset `random_vals`. Type in `random_vals` and hit "Run Code".

```{r replacing-a-scale-13, exercise = TRUE}

```

```{r replacing-a-scale-13-hint-1, eval = FALSE}
random_vals
```

```{r replacing-a-scale-13-test, include = FALSE}
random_vals
```

### 

Another option is to use the viridis color scales. The designers, Nathaniel Smith and Stéfan van der Walt, carefully tailored continuous color schemes that are perceptible to people with various forms of color blindness as well as perceptually uniform in both color and black and white. 

### Exercise 14

Pipe `random_vals` to `ggplot()`. Within `ggplot()`, map `x` to `random_x` and `y` to `random_y`. Add the `geom_hex()` and `coord_fixed()` to the pipeline.

```{r replacing-a-scale-14, exercise = TRUE}

```

```{r replacing-a-scale-14-hint-1, eval = FALSE}
random_vals |> 
  ggplot(aes(x = ..., ... = random_y)) +
    ... +
    coord_fixed()
```

```{r replacing-a-scale-14-test, include = FALSE}
random_vals |> 
  ggplot(aes(x = random_x, y = random_y)) +
    geom_hex() +
    coord_fixed()
```

### 

viridis scales are available as continuous (c), discrete (d), and binned (b) palettes in **ggplot2**.

### Exercise 15

Using your previous code, add the `scale_fill_viridis_c()` function to the pipeline. 

```{r replacing-a-scale-15, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-15-hint-1, eval = FALSE}
... +
  scale_fill_viridis_c()
```

```{r replacing-a-scale-15-test, include = FALSE}
random_vals |> 
  ggplot(aes(x = random_x, y = random_y)) +
    geom_hex() +
    coord_fixed() +
    scale_fill_viridis_c()
```

### 

Note that all color scales come in two varieties: scale_color_*() and scale_fill_*() for the color and fill aesthetics respectively (the color scales are available in both UK and US spellings).

### Exercise 16

Using your previous code, change the `scale_fill_viridis_c()` to `scale_fill_viridis_b()`.

```{r replacing-a-scale-16, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r replacing-a-scale-16-hint-1, eval = FALSE}
... +
  scale_fill_viridis_b()
```

```{r replacing-a-scale-16-test, include = FALSE}
random_vals |> 
  ggplot(aes(x = random_x, y = random_y)) +
    geom_hex() +
    coord_fixed() +
    scale_fill_viridis_b()
```

### 

As you can see the color gradient looks chunky and unlike the continuous plot before. This is because it is a binned plot on the viridis color scale. 



## Zooming
### 

There are three ways to control the plot limits:

* Adjusting what data are plotted.
* Setting the limits in each scale.
* Setting xlim and ylim in `coord_cartesian()`.

The last of these is probably what you want to use in most cases because it ensures that any modelling is performed on the entire data set.


### Exercise 1

Pipe `mpg` to `ggplot()`, using `aes()`, map `x` to `displ` and `y` to `hwy`. Add `geom_point()` to the pipeline. Within your call to `geom_point()`, using `aes()`, map `color` to `drv`. Add `geom_smooth()` to the pipeline.

```{r zooming-1, exercise = TRUE}

```

```{r zooming-1-hint-1, eval = FALSE}
... |> 
  ggplot(aes(... = displ, y = ...)) +
    geom_point(...(color = drv)) +
    geom_smooth()
```

```{r zooming-1-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point(aes(color = drv)) +
    geom_smooth()
```

### 

This plot shows the relationship between engine size (`displ`) and fuel efficiency (`hwy`), colored by type of drive train (`drv`).

### Exercise 2

Using the same pipe, add `filter()` to the pipe in between the beginning of the pipe and your call to `ggplot()`. Within `filter()`, include `displ` greater than or equal to `5` *and* `displ` less than or equal to `6`.

```{r zooming-2, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r zooming-2-hint-1, eval = FALSE}
mpg |> 
  filter(displ >= ... & displ <= ...) |> 
  ...
```

```{r zooming-2-test, include = FALSE}
mpg |> 
  filter(displ >= 5 & displ <= 6) |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point(aes(color = drv)) +
    geom_smooth()
```

One way to zoom in on a plot is to decrease the range of the data which is plotted. The "problem" with this approach is that only this data is used in geom's like `geom_smooth()`. This is probably not what you want. Subsetting the data has affected the `x` and `y` scales as well as the smooth curve.

### Exercise 3

Remove the `filter()` line in your pipeline.

There exists a `limits` argument on individual scales like `scale_x_continuous()` and `scale_y_continuous()`. Reducing the limits is equivalent to subsetting the data. To see this, add `scale_x_continuous()` with `limits` argument to `c(5, 6)` and `scale_y_continuous()` with `limits` argument to `c(10, 25)` to the pipeline.

```{r zooming-3, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r zooming-3-hint-1, eval = FALSE}
... +
    scale_x_continuous(limits = ...) +
    scale_y_continuous(... = c(10, 25)) 
```

```{r zooming-3-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point(aes(color = drv)) +
    geom_smooth() +
    scale_x_continuous(limits = c(5, 6)) +
    scale_y_continuous(limits = c(10, 25))
```

### 

As you can see, this is another way we can control plot limits which is by setting the limits in each scale. Since this is probably not what we want to do, R insists on generating a warning, reminding us about all the data which we are ignoring.

### Exercise 4

Using your previous pipeline, delete `scale_x_continuous()` and `scale_y_continuous()`. 

Add `coord_cartesian()`, setting the `xlim` argument to `c(5, 6)` and the `ylim` argument to `c(10, 25)`.

```{r zooming-4, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r zooming-4-hint-1, eval = FALSE}
... +
    coord_cartesian(xlim = c(5, 6), 
                    ylim = c(10, 25))
```

```{r zooming-4-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point(aes(color = drv)) +
    geom_smooth() +
    coord_cartesian(xlim = c(5, 6), 
                    ylim = c(10, 25))
```

### 

To zoom in on a region of the plot, it’s generally best to use `coord_cartesian()`.

On the other hand, setting the limits on individual scales is generally more useful if you want to expand the limits, e.g., to match scales across different plots.

<!-- DK: The chapter has an example of this, but it was just too long to bother with in this draft. Maybe add it later? -->



## Themes
### 

In this section, we will learn about how to customize the non-data elements of your plot with a theme.

The **ggplot2** package includes eight themes, with `theme_gray()` as the default. Many more are included in add-on packages such as **ggthemes** (https://jrnold.github.io/ggthemes), by Jeffrey Arnold. You can also create your own themes, if you are trying to match a particular corporate or journal style.

### Exercise 1

Pipe `mpg` to `ggplot()`. Within `aes()`, set `x = displ` and `y = hwy`. Follow with `geom_point(aes(color = class))`. Last line is `geom_smooth(se = FALSE)`.

```{r themes-1, exercise = TRUE}

```

```{r themes-1-hint-1, eval = FALSE}
... |> 
  ggplot(aes(x = ..., ... = hwy)) +
    geom_point(...(color = class)) +
    geom_smooth(se = ...)
```

```{r themes-1-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point(aes(color = class)) +
    geom_smooth(se = FALSE)
```

### 

This plot show the `theme_gray()` default. 

Many people wonder why the default theme has a gray background. This was a deliberate choice because it puts the data forward while still making the grid lines visible. The white grid lines are visible (which is important because they significantly aid position judgments), but they have little visual impact and we can easily tune them out. The gray background gives the plot a similar typographic color to the text, ensuring that the graphics fit in with the flow of a document without jumping out with a bright white background. Finally, the gray background creates a continuous field of color which ensures that the plot is perceived as a single visual entity


### Exercise 2

Add `theme_bw()` to the pipeline.

```{r themes-2, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r themes-2-hint-1, eval = FALSE}
... +
    theme_bw()
```

```{r themes-2-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point(aes(color = class)) +
    geom_smooth(se = FALSE) +
    theme_bw()
```

### 

This is the classic dark-on-light **ggplot2** theme. This theme may work better for presentations displayed with a projector.

### Exercise 3

Replace `theme_bw()` with `theme_linedraw()`.


```{r themes-3, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r themes-3-hint-1, eval = FALSE}
... +
    theme_linedraw()
```

```{r themes-3-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point(aes(color = class)) +
    geom_smooth(se = FALSE) +
    theme_linedraw()
```

### 

A theme with only black lines of various widths on white backgrounds, reminiscent of a line drawing. Serves a purpose similar to `theme_bw()`.

### Exercise 4

Replace `theme_linedraw()` with `theme_light()`.


```{r themes-4, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r themes-4-hint-1, eval = FALSE}
... +
    theme_light()
```

```{r themes-4-test, include = FALSE}
mpg |> 
  ggplot(aes(x = displ, y = hwy)) +
    geom_point(aes(color = class)) +
    geom_smooth(se = FALSE) +
    theme_light()
```

### 

A theme similar to `theme_linedraw()` but with light grey lines and axes, to direct more attention towards the data. There are four other themes built into **ggplot2**.



### Exercise 5

Having learned about the **ggplot2** themes, we will now create this graphic:

<!-- DK: Don't necessarily need to use the exact same plot as in the book. Why not make it nicer with, for example, a subtitle and caption.  -->


```{r}
themes_plot <- mpg |> 
  ggplot(aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  labs(
    title = "Larger engine sizes tend to have lower fuel economy",
    caption = "Source: https://fueleconomy.gov."
  ) +
  theme(
    legend.position = c(0.6, 0.7),
    legend.direction = "horizontal",
    legend.box.background = element_rect(color = "black"),
    plot.title = element_text(face = "bold"),
    plot.title.position = "plot",
    plot.caption.position = "plot",
    plot.caption = element_text(hjust = 0)
  )

themes_plot
```

Start a new pipeline with 

````
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point()
````


```{r themes-5, exercise = TRUE}

```

```{r themes-5-hint-1, eval = FALSE}
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point()
```

```{r themes-5-test, include = FALSE}
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point()
```

### 

It’s possible to control individual components of each theme, like the size and color of the font used for the `y` axis. We’ve already seen that `legend.position` controls where the legend is drawn. There are many other aspects of the legend that can be customized with `theme()`.

### Exercise 6

Using `labs()`, set `title` to `"Larger engine sizes tend to have lower fuel economy"`and `caption` to `"Source: https://fueleconomy.gov"`.

```{r themes-6, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r themes-6-hint-1, eval = FALSE}
... +
  labs(title = ...,
       caption = ...)
```

```{r themes-6-test, include = FALSE}
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  labs(
    title = "Larger engine sizes tend to have lower fuel economy",
    caption = "Source: https://fueleconomy.gov."
  )
```

### 

In the plot which we are trying to create, we need to change the direction of the legend as well as put a black border around it. The `theme()` function provides us with the ability to make very detailed changes in plot.

### Exercise 7

Add `theme()` to the pipeline. Within `theme()`, add the `legend.position` argument and set it equal to `c(0.6, 0.7)`.

```{r themes-7, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r themes-7-hint-1, eval = FALSE}
... +
  theme(
    legend.position = ...
    )
```

```{r themes-7-test, include = FALSE}
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  labs(
    title = "Larger engine sizes tend to have lower fuel economy",
    caption = "Source: https://fueleconomy.gov."
  ) +
  theme(
    legend.position = c(0.6, 0.7)
  )
```

### 

The `legend.position` argument sets the position of legends ("none", "left", "right", "bottom", "top", or a two-element numeric vector).

### Exercise 8

Within `theme()`, add the `legend.direction` argument and set it equal to `"horizontal"`. 

```{r themes-8, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r themes-8-hint-1, eval = FALSE}
... +
  theme(
    legend.position = c(0.6, 0.7),
    legend.direction = ...
    )
```

```{r themes-8-test, include = FALSE}
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  labs(
    title = "Larger engine sizes tend to have lower fuel economy",
    caption = "Source: https://fueleconomy.gov."
  ) +
  theme(
    legend.position = c(0.6, 0.7),
    legend.direction = "horizontal"
  )
```

### 

The `legend.direction` argument sets the layout of items in legends ("horizontal" or "vertical"). Note the change in the legend in this plot relative to the previous one.

### Exercise 9

Within `theme()`, add the `legend.box.background` argument and set it equal to `element_rect(color = "black")`. 

```{r themes-9, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r themes-9-hint-1, eval = FALSE}
... +
  theme(
    legend.position = ...,
    legend.direction = ...,
    legend.box.background = ...,
    )
```

```{r themes-9-test, include = FALSE}
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  labs(
    title = "Larger engine sizes tend to have lower fuel economy",
    caption = "Source: https://fueleconomy.gov."
  ) +
  theme(
    legend.position = c(0.6, 0.7),
    legend.direction = "horizontal",
    legend.box.background = element_rect(color = "black")
  )
```

### 

Note that customization of the legend box and plot title elements of the theme are done with `element_*()` functions. These functions specify the styling of non-data components, e.g., the legend border color is defined in the color argument of `element_rect()`. 

### Exercise 10

Within `theme()`, add the `plot.title` argument and set it equal to `element_text(face = "bold")`. 

```{r themes-10, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r themes-10-hint-1, eval = FALSE}
... +
  theme(
    legend.position = ...,
    legend.direction = ...,
    legend.box.background = ...,
    plot.title = ...
    )
```

```{r themes-10-test, include = FALSE}
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  labs(
    title = "Larger engine sizes tend to have lower fuel economy",
    caption = "Source: https://fueleconomy.gov."
  ) +
  theme(
    legend.position = c(0.6, 0.7),
    legend.direction = "horizontal",
    legend.box.background = element_rect(color = "black"),
    plot.title = element_text(face = "bold")
  )
```

### 

The `plot.title` argument changes the plot title (text appearance) (`element_text()`; inherits from title) left-aligned by default.




### Exercise 11

Within `theme()`, let's make three more additions: Set `plot.title.position` to `"plot"`, `plot.caption.position` to `"plot"`, and `plot.caption` to `element_text(hjust = 0)`. 

```{r themes-11, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r themes-11-hint-1, eval = FALSE}
... +
  theme(
    legend.position = ...,
    legend.direction = ...,
    legend.box.background = ...,
    plot.title = ...,
    plot.title.position = ...,
    plot.caption.position = ..., 
    plot.caption = ...
    )
```

```{r themes-11-test, include = FALSE}
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  labs(
    title = "Larger engine sizes tend to have lower fuel economy",
    caption = "Source: https://fueleconomy.gov."
  ) +
  theme(
    legend.position = c(0.6, 0.7),
    legend.direction = "horizontal",
    legend.box.background = element_rect(color = "black"),
    plot.title = element_text(face = "bold"),
    plot.title.position = "plot",
    plot.caption.position = "plot",
    plot.caption = element_text(hjust = 0)
  )
```

### 

The theme elements that control the position of the title and the caption are `plot.title.position` and `plot.caption.position`, respectively. In the this plot these are set to `"plot"` to indicate these elements are aligned to the entire plot area, instead of the plot panel (the default).

The `plot.caption` argument with value `element_text(hjust = 0)` places the caption flush left. It would otherwise be right-aligned by default. 

### 

Reminder: This is what our plot should look like

```{r}
themes_plot 
```

### 

For an overview of all `theme()` components, see help with `?theme`. The **ggplot2** [book](https://ggplot2-book.org/) is also a great place to go for the [full details on theming](https://ggplot2-book.org/themes).

## Layout
### 

So far we talked about how to create and modify a single plot. What if you have multiple plots you want to lay out in a certain way? The **patchwork** package allows you to combine separate plots into the same graphic. 


### Exercise 1

We have created several plots (`p1` through `p8`) for you to use in these exercises. Type `p1` and hit "Run Code".

```{r layout-1, exercise = TRUE}

```

```{r layout-1-hint-1, eval = FALSE}
p1
```

```{r layout-1-test, include = FALSE}
p1
```

### 

The **ggplot2** package provides a strong API for sequentially building up a plot, but does not concern itself with composition of multiple plots. **patchwork** is a package that expands the API to allow for arbitrarily complex composition of plots by, among others, providing mathematical operators for combining multiple plots.

### Exercise 2

Enter `p1` on the first line and `p2` on the second line. Hit "Run Code".


```{r layout-2, exercise = TRUE}


```

```{r layout-2-hint-1, eval = FALSE}
...
p2
```

```{r layout-2-test, include = FALSE}
p1
p2
```

### 

To place two plots next to each other, you can simply add them to each other, if you have loaded the **patchwork** package. Note that you first need to create the plots and save them as objects (in the following example they’re called `p1` and `p2`). Then, you place them next to each other with `+`.

### Exercise 3

Type `p1 + p2` and hit "Run Code".

```{r layout-3, exercise = TRUE}

```

```{r layout-3-hint-1, eval = FALSE}
p1 + ...
```

```{r layout-3-test, include = FALSE}
p1 + p2
```

### 

It’s important to note that in the above code chunk we did not use a new function from the **patchwork** package. Instead, the package added a new functionality to the `+` operator.

### Exercise 4

You can also create complex plot layouts with **patchwork**. Type `(p1 | p3) / p2` and hit "Run Code".

```{r layout-4, exercise = TRUE}

```

```{r layout-4-hint-1, eval = FALSE}
(... | p3) / ...
```

```{r layout-4-test, include = FALSE}
(p1 | p3) / p2
```

### 

`|` places the `p1` and `p3` next to each other and `/` moves `p2` to the next line.

### Exercise 5

Additionally, **patchwork** allows you to collect legends from multiple plots into one common legend, customize the placement of the legend as well as dimensions of the plots, and add a common title, subtitle, caption, etc. to your plots. Start with `(p1 + p2) / (p3 + p4) / p5`.  


```{r layout-5, exercise = TRUE}

```

```{r layout-5-hint-1, eval = FALSE}
(p1 + p2) ... (p3 + p4) / ...
```

```{r layout-5-test, include = FALSE}
(p1 + p2) / (p3 + p4) / p5
```

### 

This creates a collection of plots with two plots in the first row, two in the second, and then a single plot in the third row.

### Exercise 6

Modify the code by adding `guide_area()` to the front of the call.


```{r layout-6, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r layout-6-hint-1, eval = FALSE}
... / (p1 + p2) / (p3 + p4) / p5
```

```{r layout-6-test, include = FALSE}
guide_area() / (p1 + p2) / (p3 + p4) / p5
```

### 

By default plot guides (like legends) will be put on the side as with regular plots, but by adding a `guide_area()` to the plot you can tell **patchwork** to place the guides in that area instead. 

### Exercise 7

None of our plots, by design, include a legend because we want a single legend for the entire diplay. We also want a single title. To add this, we need to use the **patchwork** function `plot_annotation()`. Add it to our plot, setting the `title` argument to `"City and highway mileage for cars with different drive trains"` and the `caption` argument to `"Source: https://fueleconomy.gov."`. 

```{r layout-7, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r layout-7-hint-1, eval = FALSE}
guide_area() / (p1 + p2) / (p3 + p4) / p5 +
     ...(
         title = "City and highway mileage for cars with different drive trains",
         caption = ...
     ) 
```

```{r layout-7-test, include = FALSE}
guide_area() / (p1 + p2) / (p3 + p4) / p5 +
  plot_annotation(
      title = "City and highway mileage for cars with different drive trains",
      caption = "Source: https://fueleconomy.gov."
  ) 
```

### 

**patchwork** has several functions which perform similar tasks for a collection of plots which **ggplot2** provides for individual plots.

### Exercise 8

`plot_layout()` provides fine-grained control over the details of how the plots are put together. Add `plot_layout()` with the `guides` argument set to `"collect"` and the `heights` argument set to `c(1, 3, 2, 4)`.

```{r layout-8, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r layout-8-hint-1, eval = FALSE}
... +
  plot_layout(
    ... = "collect",
    heights = ...
  )
```

```{r layout-8-test, include = FALSE}
guide_area() / (p1 + p2) / (p3 + p4) / p5 +
  plot_annotation(
      title = "City and highway mileage for cars with different drive trains",
      caption = "Source: https://fueleconomy.gov."
  ) +
  plot_layout(
    guides = "collect",
    heights = c(1, 3, 2, 4)
  )
```

### 

We have customized the heights of the various components of our patchwork – the guide has a height of 1, the box plots 3, density plots 2, and the faceted scatterplot 4. **patchwork** divides up the area you have allotted for your plot using this scale and places the components accordingly.

### Exercise 9

The top of the collection is still a bit messed up. We can fix this by adding `theme(legend.position = "top")` as the last line. WARNING: Instead of using a `+` sign to connect this line to the rest of the code, you need to use an `&`. 

```{r layout-9, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r layout-9-hint-1, eval = FALSE}
... & 
    theme(legend.position = "top")
```

```{r layout-9-test, include = FALSE}
guide_area() / (p1 + p2) / (p3 + p4) / p5 +
    plot_annotation(
        title = "City and highway mileage for cars with different drive trains",
        caption = "Source: https://fueleconomy.gov."
    ) +
    plot_layout(
        guides = "collect",
        heights = c(1, 3, 2, 4)
    ) & 
    theme(legend.position = "top")
```

### 

Note the use of the & operator here instead of the usual +. This is because we’re modifying the theme for the patchwork plot as opposed to the individual ggplots. The legend is placed on top, inside the guide_area().




## Summary
### 

This tutorial covered [Chapter 11: Communication](https://r4ds.hadley.nz/communication.html) from [*R for Data Science (2e)*](https://r4ds.hadley.nz/) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. In this tutorial we made use of three packages associated with [**ggplot2**](https://ggplot2.tidyverse.org/): [**scales**](https://scales.r-lib.org/), [**ggrepel**](https://ggrepel.slowkow.com), and [**patchwork**](https://patchwork.data-imaginist.com). Key commands included [`quote()`](https://rdrr.io/r/base/substitute.html) which simply returns its argument and [`geom_label_repel()`](https://rdrr.io/cran/ggrepel/man/geom_text_repel.html) which adds text directly to the plot. 

[*ggplot2: Elegant Graphics for Data Analysis (3e)*](https://ggplot2-book.org/) by Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen is the best source for all the details about making beautiful graphics with **ggplot2**.


```{r download-answers, child = system.file("child_documents/download_answers.Rmd", package = "tutorial.helpers")}
```
