r4ds/communicate-plots.Rmd

# Communication with plots

## Introduction

In [EDA], you learned how to use plots as tools for _exploration_. When you made these plots, you knew---even before you looked at them---which variables the plot would display and which datasets the variables would come from. You might have even known what to look for in the completed plots, assuming that you made each plot with a goal in mind. As a result, it was not very important to put a title or a useful set of labels on your plots.

The importance of titles and labels changes once you use your plots for _communication_. Your audience will not share your background knowledge. In fact, they may not know anything about your plots except what the plots themselves display. If you want your plots to communicate your findings effectively, you will need to make them as self-explanatory as possible.

Luckily, `ggplot2` provides some features that can help you.

### Prerequisites

In this chapter, we'll focus once again on ggplot2.

```{r}
library(ggplot2)
library(dplyr)
```

## Titles

One of the most helpful things you can do to an exploratory graphic into an expository graphic is to add good titles.

You can add a title to any `ggplot2` plot by adding the command `labs()` to your plot call. Set the `title` argument of `labs()` to the character string that you would like to appear as the title of your plot. `ggplot2` will place the title at the top of your plot.

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) + 
  labs(title = "Fuel efficiency decreases with engine size")
```

Generally, titles should be written in sentence case, and should describe the main finding in the plot, not just what the plot displays. In ggplot2 2.2.0, which should be available by the time you're reading this book, you can also set `subtitle` and `caption` to add either a subtitle beneath the main title, or a caption at the bottom right of the plot.

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) + 
  labs(
    title = "Fuel efficiency decreases with engine size",
    subtitle = "Two seaters don't follow the rule because they are light weight",
    caption = "Data from fueleconomy.gov"
  )
```

### Axes and legend labels

You can also use `labs()` to replace the axis and legend labels in your plot, which might be a good idea if your data uses ambiguous or abbreviated variable names. To replace either of the axis labels, set the `x` or `y` arguments to a character string. `ggplot2` will replace the associated axis label with your character string. To replace a legend label, set the name of the aesthetic displayed in the legend to the character string that should appear as the title of the legend. For example, the legend in our plot corresponds to the color aesthetic. We can change its title with the command, `labs(color = "New Title")`, or, more usefully:

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) + 
  labs(
    title = "Fuel efficiency decreases with engine size",
    x = "Displacement (L)", 
    y = "Highway mpg",
    colour = "Car type"
  ) 
```

### Legend layout

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) + 
  theme(legend.position = "bottom")
```

For even finer control, use `guides()` and `guide_legend()` (or `guide_colourbar()`). The following example shows two important settings: controlling the number of rows with `nrow`, and override one of the aesthetics to make the points bigger. This is particularly useful if you hae

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(nrow = 1, override.aes = list(size = 4)))
```

### Exercises

1.  Low alpha - use `override.aes` to make legend more useful.

## Annotations

`labs()` help you better label your plot, but often you will want to label components of the data too. The first tool you have at your disposal is `geom_text()`. `geom_text()` is similar to `geom_point()`, but it has an additional aesthetic: `label`. This makes it possible to add textual labels to your plots.

There are two possible sources of labels. First, you might have a data set that you want to label. The plot below isn't terribly useful, but I first pull out the most efficient car in each class using a little dplyr, and then add it to the plot.

```{r}
best_in_class <- mpg %>% 
  group_by(class) %>% 
  filter(row_number(desc(hwy)) == 1)

ggplot(mpg, aes(displ, hwy)) + 
  geom_point(aes(colour = class)) + 
  geom_text(aes(label = model), data = best_in_class)
```

This plot illustrates some common problems when labelling text: it's hard to read the labels because they overlap on top of the points. We can make things a little easier by switching to `geom_label()` which draws a rectangle behind the text.  We also use the `nudge_y` parameter to move the labels slightly about the corresponding points:

```{r}
ggplot(mpg, aes(displ, hwy)) + 
  geom_point(aes(colour = class)) + 
  geom_label(aes(label = model), data = best_in_class, nudge_y = 2, alpha = 0.5)
```

That helps a bit, but if you look closely in the top-left hand corner, you'll notice that there are two labels practically on top of each other. There's no way that we can fix these by applying the same transformation for every label.

Instead, we can use the __ggrepel__ package by Kamil Slowikowski. This useful package will automatically adjust labels so that they don't overlap:

```{r}
ggplot(mpg, aes(displ, hwy)) + 
  geom_point(aes(colour = class)) + 
  ggrepel::geom_label_repel(aes(label = model), data = best_in_class)
```

You can sometimes use the same idea to replace the legend with labels directly on the same graph. I'm not sure it's terribly effective here, but it isn't too bad. (We'll turn out `legend.position = "none"` very shortly).

```{r}
class_avg <- mpg %>% 
  group_by(class) %>% 
  summarise(
    displ = median(displ),
    hwy = median(hwy)
  )

ggplot(mpg, aes(displ, hwy, colour = class)) + 
  ggrepel::geom_label_repel(aes(label = class), 
    data = class_avg, 
    size = 6, 
    label.size = 0, 
    segment.color = NA
  ) + 
  geom_point() + 
  theme(legend.position = "none")
```

If you want to add a single label, you'll still need to create a data frame. Often you want to the label in the corner of the plot, so it's convenient to create a new data frame using `summarise()`. (If you want to add it at an arbitrary location just use `tibble()` to create the data frame.)

```{r}
label <- mpg %>% 
  summarise(
    displ = max(displ),
    hwy = max(hwy),
    label = "Increasing engine size is \nrelated to decreasing fuel economy."
  )
label

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  geom_text(aes(label = label), data = label, vjust = "top", hjust = "right")
```

If you want to place the text in the absolute top-right corner, you can use inf:

```{r}
label <- tibble(
    displ = Inf,
    hwy = Inf,
    label = "Increasing engine size is \nrelated to decreasing fuel economy."
  )

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  geom_text(aes(label = label), data = label, vjust = "top", hjust = "right")
```

Here I manually broke the label up in lines using `"\n"`. Alternatively, you could use `stringr::str_wrap()` to automatically wrap it, given the number of characters you want per line:

```{r}
"Increasing engine size is related to decreasing fuel economy." %>% 
  stringr::str_wrap(width = 40) %>% 
  writeLines()
```

Note the use of `hjust` and `vjust` to control the the alignment of the label. \@ref(fig:just) shows all nine possible combinations.

```{r just, echo = FALSE, fig.cap = "All nine combinations of `hjust` and `vjust`."}
vjust <- c(bottom = 0, center = 0.5, top = 1)
hjust <- c(left = 0, center = 0.5, right = 1)

df <- tidyr::crossing(hj = names(hjust), vj = names(vjust)) %>% 
  mutate(
    y = vjust[vj],
    x = hjust[hj],
    label = paste0("hjust = '", hj, "'\n", "vjust = '", vj, "'")
  )

ggplot(df, aes(x, y)) + 
  geom_point(colour = "grey60", size = 5) + 
  geom_text(aes(label = label, hjust = hj, vjust = vj), size = 4)
```

### Exercises

## Scales

The third way you can make your plot better for communication is to adjust the scales. Scales control the mapping from data values to things that you can perceive. 
Normally, ggplot2 automatically adds scales for you. That means behind the scenes when you type:

```{r default-scales, fig.show = "hide"}
ggplot(mpg, aes(displ, hwy)) + 
  geom_point(aes(colour = class))
```

ggplot2 automatically fills in the default scales for you:

```{r, fig.show = "hide"}
ggplot(mpg, aes(displ, hwy)) + 
  geom_point(aes(colour = class)) +
  scale_x_continuous() + 
  scale_y_continuous() + 
  scale_colour_discrete()
```

You need to know this for two reasons:

*   You might want to tweak some of the parameters of the default scale. 
    This allows you to do things like change the breaks on the legend.
    
*   You might want to replace the scale altogether. The defaults have been 
    tuned to be widely useful, but often you can do even better with a little 
    hand tuning.

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 with: continuous, discrete, datetime, or date. There are lots of non-default scales which you'll learn about below.

### Axis breaks and legend keys

`date_format` and `date_labels`. Uses the same format specification as `parse_datetime()`.

```{r}
presidential %>% 
  mutate(id = 33 + row_number()) %>% 
  ggplot(aes(start, id)) + 
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) + 
    scale_x_date(NULL, breaks = presidential$start, date_labels = "'%y")
```

### Replacing a scale

We'll focus on colour scales because those are most likely.

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) + 
  scale_colour_brewer(palette = "Set1")
```

Figure \@ref(fig-brewer) shows the complete list of all palettes.

```{r brewer, fig.asp = 2.5, echo = FALSE, fig.cap = "All ColourBrewer scales."}
par(mar = c(0, 3, 0, 0))
RColorBrewer::display.brewer.all()
```

When you have a predefined mapping between values and colours use `scale_colour_manual()`. For example, if we map Presidential party to colour, we want to use the standard mapping of red for Republicans and blue for Democrats:

```{r}
presidential %>% 
  mutate(id = 33 + row_number()) %>% 
  ggplot(aes(start, id, colour = party)) + 
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) + 
    scale_colour_manual(values = c(Republican = "Red", Democratic = "Blue"))
```

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

viridis

### Exercises

1. Example where you set colour scale instead of fill. Why doesn't it work?


## Zooming

Often, it can be helpful to zoom in on a specific region of your plot. In `ggplot2` you can do this by adding `coord_cartesian()` to your plot and setting it's `xlim` and `ylim` arguments. Pass each argument a vector of two numbers, the minimum value to display on that axis and the maximum value, e.g.

```{r out.width = "50%", fig.align = "default"}
ggplot(mpg, mapping = aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth() + 
  coord_cartesian(xlim = c(5, 7), ylim = c(10, 30)) 

mpg %>% 
  filter(displ >= 5, displ <= 7, hwy >= 10, hwy <= 30) %>% 
  ggplot(aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth() + 
  coord_cartesian(xlim = c(5, 7), ylim = c(10, 30)) 
```

`coord_cartesian()` adds a cartesian coordinate system to your plot (which is the default coordinate system). However, the new coordinate system will use the zoomed in limits. 

`filter()`

Nowdays, I'd generally avoid the `xlim()` and `ylim()` helpers.

## Themes

Finally, you can also quickly customize the "look" of your plot by adding a theme function to your plot call. This is useful if you have a corporate style that youcan be a useful thing to do, for example, if you'd like to save ink when you print your plots, or if you wish to ensure that the plots photocopy well. 

The theme is designed to put the data forward while supporting comparisons, following the advice of Edward Tufte, Cynthia Brewer, and Dan Carr. We can still see the gridlines, which are important aid to the judgement of position,  but they have little visual impact and we can easily 'tune' them out. The grey background gives the plot a similar typographic colour 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 grey background creates a continuous field of colour which ensures that the plot is perceived as a single visual entity.

ggplot2 contains eight theme functions, listed in the table below. Each applies a different visual theme to your finished plot. You can think of the themes as "skins" for the plot. The themes change how the plot looks without changing the information that the plot displays.

To use a theme, add it to your plot:

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) + 
  theme_bw()
```

```{r, echo = FALSE}
knitr::include_graphics("images/visualization-themes.png")
```

You can also control the individual components of the plot using `theme()`. There are a lot of options and .  You'll need to refer to the ggplot2 book for the full details.

Finally, if you have a corporate style or you're trying to match a specific journal, you might want to create your own theme. Once you've figured out the    . This is an increasingly common trend: for example, both AirBnB and 538 have custom ggplot2 styles that they use internally.

## Learning more

The absolute best place to learn more is the ggplot2 book: [_ggplot2: elegant graphics for data analysis_](https://amzn.com/331924275X).  Unfortunately it is not available online for free, but you can find the source code for the book at <https://github.com/hadley/ggplot2-book>.

Another great resource is the ggplot2 extensions guide at  <http://www.ggplot2-exts.org/>. This lists many of the packages that extend ggplot2 with new geoms and scales. It's a great place to start if you're trying to do something that seems really hard with ggplot2.