
```{r polishing, include = FALSE}
chapter <- "themes"
#install.packages("ggthemes")
source("common.R")
```

# ggplot Themes (Extra Supplement) {#cha:polishing}

Original source for this documents was provided by Hadley Wickham as part of the
second edition of this book.
It is available here: <https://github.com/hadley/ggplot2-book>

The material in this document is provided as a course supplement but
it will not be covered in the tests in our Statistics 9864B course.

## Introduction

In this chapter you will learn how to use the ggplot2 theme system, which allows you to exercise fine control over the non-data elements of your plot. The theme system does not affect how the data is rendered by geoms, or how it is transformed by scales. Themes don't change the perceptual properties of the plot, but they do help you make the plot aesthetically pleasing or match an existing style guide. Themes give you control over things like fonts, ticks, panel strips, and backgrounds. \index{Themes}

This separation of control into data and non-data parts is quite different from base and lattice graphics. In base and lattice graphics, most functions take a large number of arguments that specify both data and non-data appearance, which makes the functions complicated and harder to learn. ggplot2 takes a different approach: when creating the plot you determine how the data is displayed, then *after* it has been created you can edit every detail of the rendering, using the theming system. 

The theming system is composed of four main components:

* Theme __elements__ specify the non-data elements that you can control.
  For example, the `plot.title` element controls the appearance of the
  plot title; `axis.ticks.x`, the ticks on the x axis; `legend.key.height`,
  the height of the keys in the legend.

* Each element is associated with an __element function__, which describes 
  the visual properties of the element. For example, `element_text()` sets
  the font size, colour and face of text elements like `plot.title`.

* The `theme()` function which allows you to override the default theme
  elements by calling element functions, like 
  `theme(plot.title = element_text(colour = "red"))`.
  
* Complete __themes__, like `theme_grey()` set all of the theme elements to 
  values designed to work together harmoniously.

For example, imagine you've made the following plot of your data.

`r columns(1, 2/3, 0.75)`
```{r motivation-1}
base <- ggplot(mpg, aes(cty, hwy, color = factor(cyl))) +
  geom_jitter() + 
  geom_abline(colour = "grey50", size = 2)
base
```

It's served its purpose for you: you've learned that `cty` and `hwy` are highly correlated, both are tightly coupled with `cyl`, and that `hwy` is always greater than `cty` (and the difference increases as `cty` increases). Now you want to share the plot with others, perhaps by publishing it in a paper. That requires some changes. First, you need to make sure the plot can stand alone by:

* Improving the axes and legend labels.
* Adding a title for the plot.
* Tweaking the colour scale.

Fortunately you know how to do that already because you've read [the scales chapter](#cha:scales):

```{r motivation-2}
labelled <- base +
  labs(
    x = "City mileage/gallon",
    y = "Highway mileage/gallon",
    colour = "Cylinders",
    title = "Highway and city mileage are highly correlated"
  ) +
  scale_colour_brewer(type = "seq", palette = "Spectral")
labelled
```

Next, you need to make sure the plot matches the style guidelines of your journal:

* The background should be white, not pale grey.
* The legend should be placed inside the plot if there's room.
* Major gridlines should be a pale grey and minor gridlines should be removed.
* The plot title should be 12pt bold text.

In this chapter, you'll learn how to use the theming system to make those changes, as shown below:

```{r motivation-3}
styled <- labelled +
  theme_bw() + 
  theme(
    plot.title = element_text(face = "bold", size = 12),
    legend.background = element_rect(fill = "white", size = 4, colour = "white"),
    legend.justification = c(0, 1),
    legend.position = c(0, 1),
    axis.ticks = element_line(colour = "grey70", size = 0.2),
    panel.grid.major = element_line(colour = "grey70", size = 0.2),
    panel.grid.minor = element_blank()
  )
styled
```

Finally, the journal wants the figure as a 600 dpi TIFF file. You'll learn the fine details of `ggsave()` in [saving your output](#sec:saving).

## Complete themes {#sec:themes}

ggplot2 comes with a number of built in themes. The most important is `theme_grey()`, the signature ggplot2 theme with a light grey background and white gridlines. The theme is designed to put the data forward while supporting comparisons, following the advice of [@tufte:2006; @brewer:1994; @carr:2002; @carr:1994; @carr:1999]. We can still see the gridlines to aid in the judgement of position [@cleveland:1993a], 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. \index{Themes!built-in} \indexf{theme\_grey}
  
There are seven other themes built in to ggplot2 1.1.0:
  
* `theme_bw()`: a variation on `theme_grey()` that uses a white background
  and thin grey grid lines.  \indexf{theme\_bw}
  
* `theme_linedraw()`: A theme with only black lines of various widths on white 
  backgrounds, reminiscent of a line drawing.  \indexf{theme\_linedraw}
  
* `theme_light()`: similar to `theme_linedraw()` but with light grey lines and 
  axes, to direct more attention towards the data.  \indexf{theme\_light}

* `theme_dark()`: the dark cousin of `theme_light()`, with similar line sizes 
  but a dark background. Useful to make thin coloured lines pop out.
  \indexf{theme\_dark}
  
* `theme_minimal()`: A minimalistic theme with no background annotations.
   \indexf{theme\_minimal}

* `theme_classic()`: A classic-looking theme, with x and y axis lines and no 
   gridlines.  \indexf{theme\_classic}
   
* `theme_void()`: A completely empty theme.  \indexf{theme\_void}

`r columns(3, 3/4)`
```{r built-in}
df <- data.frame(x = 1:3, y = 1:3)
base <- ggplot(df, aes(x, y)) + geom_point()
base + theme_grey() + ggtitle("theme_grey()")
base + theme_bw() + ggtitle("theme_bw()")
base + theme_linedraw() + ggtitle("theme_linedraw()")
```

```{r}
base + theme_light() + ggtitle("theme_light()")
base + theme_dark() + ggtitle("theme_dark()")
base + theme_minimal()  + ggtitle("theme_minimal()")
```

```{r}
base + theme_classic() + ggtitle("theme_classic()")
base + theme_void() + ggtitle("theme_void()")
```

All themes have a `base_size` parameter which controls the base font size. The base font size is the size that the axis titles use: the plot title is usually bigger (1.2x), and the tick and strip labels are smaller (0.8x). If you want to control these sizes separately, you'll need to modify the individual elements as described below.

As well as applying themes a plot at a time, you can change the default theme with `theme_set()`. For example, if you really hate the default grey background, run `theme_set(theme_bw())` to use a white background for all plots. \indexf{theme\_set}

You're not limited to the themes built-in to ggplot2. Other packages, like ggthemes by Jeffrey Arnold, add even more. Here's a few of my favourites from ggthemes: \index{ggtheme}

```{r ggtheme, message = FALSE, warning = FALSE}
library(ggthemes)
base + theme_tufte() + ggtitle("theme_tufte()")
base + theme_solarized() + ggtitle("theme_solarized()")
base + theme_excel() + ggtitle("theme_excel()") # ;)
```

The complete themes are a great place to start but don't give you a lot of control. To modify individual elements, you need to use `theme()` to override the default setting for an element with an element function.

### Exercises

1.  Try out all the themes in ggthemes. Which do you like the best?

1.  What aspects of the default theme do you like? What don't you like?   
    What would you change?

1.  Look at the plots in your favourite scientific journal. What theme
    do they most resemble? What are the main differences?

## Modifying theme components

To modify an individual theme component you use code like `plot + theme(element.name = element_function())`. In this section you'll learn about the basic element functions, and then in the next section, you'll see all the elements that you can modify. \indexf{theme}

There are four basic types of built-in element functions: text, lines, rectangles, and blank. Each element function has a set of parameters that control the appearance:

*   `element_text()` draws labels and headings. You can control the font 
    `family`, `face`, `colour`, `size` (in points), `hjust`, `vjust`, `angle` 
    (in degrees) and `lineheight` (as ratio of `fontcase`). More details on 
    the parameters can be found in `vignette("ggplot2-specs")`.
    Setting the font face is particularly challenging.
    \index{Themes!labels} \indexf{element\_text}
  
    ```{r element_text}
    base_t <- base + labs(title = "This is a ggplot") + xlab(NULL) + ylab(NULL)
    base_t + theme(plot.title = element_text(size = 16))
    base_t + theme(plot.title = element_text(face = "bold", colour = "red"))
    base_t + theme(plot.title = element_text(hjust = 1))
    ```
    
    You can control the margins around the text with the `margin` argument and
    `margin()` function. `margin()` has four arguments: the amount of space 
    (in points) to add to the top, right, bottom and left sides of the text. 
    Any elements not specified default to 0.

    ```{r element_text-margin}
    # The margins here look asymmetric because there are also plot margins
    base_t + theme(plot.title = element_text(margin = margin()))
    base_t + theme(plot.title = element_text(margin = margin(t = 10, b = 10)))
    base_t + theme(axis.title.y = element_text(margin = margin(r = 10)))
    ```

*  `element_line()` draws lines parameterised by `colour`, `size` and
   `linetype`: \indexf{element\_line} \index{Themes!lines}

    ```{r element_line}
    base + theme(panel.grid.major = element_line(colour = "black"))
    base + theme(panel.grid.major = element_line(size = 2))
    base + theme(panel.grid.major = element_line(linetype = "dotted"))
    ```

* `element_rect()` draws rectangles, mostly used for backgrounds, parameterised
   by `fill` colour and border `colour`, `size` and `linetype`.  
   \index{Background} \index{Themes!background} \indexf{theme\_rect}
  
    ```{r element_rect}
    base + theme(plot.background = element_rect(fill = "grey80", colour = NA))
    base + theme(plot.background = element_rect(colour = "red", size = 2))
    base + theme(panel.background = element_rect(fill = "linen"))
    ```

*   `element_blank()` draws nothing. Use this if you don't want anything drawn,
    and no space allocated for that element. The following example uses
    `element_blank()` to progressively suppress the appearance of elements 
    we're not interested in. Notice how the plot automatically reclaims 
    the space previously used by these elements: if you don't want this to 
    happen (perhaps because they need to line up with other plots on the page), 
    use `colour = NA, fill = NA` to create invisible elements that 
    still take up space. \indexf{element\_blank}

    ```{r element_blank}
    base
    last_plot() + theme(panel.grid.minor = element_blank())
    last_plot() + theme(panel.grid.major = element_blank())
    ```
    ```{r element_blank-2}
    last_plot() + theme(panel.background = element_blank())
    last_plot() + theme(
      axis.title.x = element_blank(), 
      axis.title.y = element_blank()
    )
    last_plot() + theme(axis.line = element_line(colour = "grey50"))
    ```
    
* A few other settings take grid units. Create them with `unit(1, "cm")` or 
  `unit(0.25, "in")`.

To modify theme elements for all future plots, use `theme_update()`. It returns the previous theme settings, so you can easily restore the original parameters once you're done. \index{Themes!updating} \indexf{theme\_set}

`r columns(2, 3/4, 2/3)`
```{r theme-update}
old_theme <- theme_update(
  plot.background = element_rect(fill = "lightblue3", colour = NA),
  panel.background = element_rect(fill = "lightblue", colour = NA),
  axis.text = element_text(colour = "linen"),
  axis.title = element_text(colour = "linen")
)
base
theme_set(old_theme)
base
```

## Theme elements {#sec:theme-elements}

There are around 40 unique elements that control the appearance of the plot. They can be roughly grouped into five categories: plot, axis, legend, panel and facet. The following sections describe each in turn. \index{Themes!elements}

### Plot elements
\index{Themes!plot}

Some elements affect the plot as a whole:

Element               | Setter           | Description               
----------------------|------------------|------------
plot.background       | `element_rect()` | plot background
plot.title            | `element_text()` | plot title
plot.margin           | `margin()`       | margins around plot

`plot.background` draws a rectangle that underlies everything else on the plot. By default, ggplot2 uses a white background which ensures that the plot is usable wherever it might end up (e.g. even if you save as a png and put on a slide with a black background). When exporting plots to use in other systems, you might want to make the background transparent with `fill = NA`. Similarly, if you're embedding a plot in a system that already has margins you might want to eliminate the built-in margins. Note that a small margin is still necessary if you want to draw a border around the plot.

`r columns(3, 3/4)`
```{r plot}
base + theme(plot.background = element_rect(colour = "grey50", size = 2))
base + theme(
  plot.background = element_rect(colour = "grey50", size = 2),
  plot.margin = margin(2, 2, 2, 2)
)
base + theme(plot.background = element_rect(fill = "lightblue"))
```

### Axis elements {#sub:theme-axis}
\index{Themes!axis} \index{Axis!styling}

The axis elements control the apperance of the axes:

Element             | Setter            | Description               
--------------------|-------------------|---------------------------
axis.line           | `element_line()`  | line parallel to axis (hidden in default themes)
axis.text           | `element_text()`  | tick labels               
axis.text.x         | `element_text()`  | x-axis tick labels        
axis.text.y         | `element_text()`  | y-axis tick labels        
axis.title          | `element_text()`  | axis titles               
axis.title.x        | `element_text()`  | x-axis title              
axis.title.y        | `element_text()`  | y-axis title              
axis.ticks          | `element_line()`  | axis tick marks           
axis.ticks.length   | `unit()`          | length of tick marks      

Note that `axis.text` (and `axis.title`) comes in three forms: `axis.text`, `axis.text.x`, and `axis.text.y`. Use the first form if you want to modify the properties of both axes at once: any properties that you don't explicitly set in `axis.text.x` and `axis.text.y` will be inherited from `axis.text`.

```{r axis}
df <- data.frame(x = 1:3, y = 1:3)
base <- ggplot(df, aes(x, y)) + geom_point()

# Accentuate the axes
base + theme(axis.line = element_line(colour = "grey50", size = 1))
# Style both x and y axis labels
base + theme(axis.text = element_text(color = "blue", size = 12))
# Useful for long labels
base + theme(axis.text.x = element_text(angle = -90, vjust = 0.5))
```

The most common adjustment is to rotate the x-axis labels to avoid long overlapping labels. If you do this, note negative angles tend to look best and you should set `hjust = 0` and `vjust = 1`:

`r columns(2, 3/4)`
```{r axis-labels}
df <- data.frame(
  x = c("label", "a long label", "an even longer label"), 
  y = 1:3
)
base <- ggplot(df, aes(x, y)) + geom_point()
base
base + 
  theme(axis.text.x = element_text(angle = -30, vjust = 1, hjust = 0)) + 
  xlab(NULL) + 
  ylab(NULL)
```

### Legend elements
\index{Themes!legend} \index{Legend!styling}

The legend elements control the apperance of all legends. You can also modify the appearance of individual legends by modifying the same elements in `guide_legend()` or `guide_colourbar()`.

Element             | Setter                    | Description                                 |
--------------------|---------------------------|---------------------------------------------|
legend.background   |  `element_rect()`         | legend background                           |
legend.key          |  `element_rect()`         | background of legend keys                   |
legend.key.size     |  `unit()`                 | legend key size                             |
legend.key.height   |  `unit()`                 | legend key height                           |
legend.key.width    |  `unit()`                 | legend key width                            |
legend.margin       |  `unit()`                 | legend margin                               |
legend.text         |  `element_text()`         | legend labels                               |
legend.text.align   |  0--1                     | legend label alignment (0 = right, 1 = left)|
legend.title        |  `element_text()`         | legend name                                 |
legend.title.align  |  0--1                     | legend name alignment (0 = right, 1 = left) |

These options are illustrated below:

`r columns(3, 3/4)`
```{r legend}
df <- data.frame(x = 1:4, y = 1:4, z = rep(c("a", "b"), each = 2))
base <- ggplot(df, aes(x, y, colour = z)) + geom_point()

base + theme(
  legend.background = element_rect(
    fill = "lemonchiffon", 
    colour = "grey50", 
    size = 1
  )
)
base + theme(
  legend.key = element_rect(color = "grey50"),
  legend.key.width = unit(0.9, "cm"),
  legend.key.height = unit(0.75, "cm")
)
base + theme(
  legend.text = element_text(size = 15),
  legend.title = element_text(size = 15, face = "bold")
)
```

There are four other properties that control how legends are laid out in the context of the plot (`legend.position`, `legend.direction`, `legend.justification`, `legend.box`). They are described in [legend layout](#sub:legend-layout).

### Panel elements
\index{Themes!panel} \index{Aspect ratio}

Panel elements control the appearance of the plotting panels:

Element             | Setter            | Description
--------------------|-------------------|-------------------------------
panel.background    | `element_rect()`  | panel background (under data)
panel.border        | `element_rect()`  | panel border (over data)
panel.grid.major    | `element_line()`  | major grid lines
panel.grid.major.x  | `element_line()`  | vertical major grid lines
panel.grid.major.y  | `element_line()`  | horizontal major grid lines
panel.grid.minor    | `element_line()`  | minor grid lines
panel.grid.minor.x  | `element_line()`  | vertical minor grid lines
panel.grid.minor.y  | `element_line()`  | horizontal minor grid lines
aspect.ratio        | numeric           | plot aspect ratio

The main difference between `panel.background` and `panel.border` is that the background is drawn underneath the data, and the border is drawn on top of it. For that reason, you'll always need to assign `fill = NA` when overriding `panel.border`.

```{r panel}
base <- ggplot(df, aes(x, y)) + geom_point()
# Modify background
base + theme(panel.background = element_rect(fill = "lightblue"))

# Tweak major grid lines
base + theme(
  panel.grid.major = element_line(color = "gray60", size = 0.8)
)
# Just in one direction  
base + theme(
  panel.grid.major.x = element_line(color = "gray60", size = 0.8)
)
```

Note that aspect ratio controls the aspect ratio of the _panel_, not the overall plot:

```{r aspect-ratio}
base2 <- base + theme(plot.background = element_rect(colour = "grey50"))
# Wide screen
base2 + theme(aspect.ratio = 9 / 16)
# Long and skiny
base2 + theme(aspect.ratio = 2 / 1)
# Square
base2 + theme(aspect.ratio = 1)
```

### Facetting elements
\index{Themes!facets} \index{Facetting!styling}

The following theme elements are associated with faceted ggplots:

Element             | Setter            | Description                       |
--------------------|------------------|------------------------------------|
strip.background    | `element_rect()` | background of panel strips         |
strip.text          | `element_text()` | strip text                         |
strip.text.x        | `element_text()` | horizontal strip text              |
strip.text.y        | `element_text()` | vertical strip text                |
panel.margin        | `unit()`         | margin between facets              |
panel.margin.x      | `unit()`         | margin between facets (vertical)   |
panel.margin.y      | `unit()`         | margin between facets (horizontal) |

Element `strip.text.x` affects both `facet_wrap()` or `facet_grid()`; `strip.text.y` only affects `facet_grid()`.

```{r facetting}
df <- data.frame(x = 1:4, y = 1:4, z = c("a", "a", "b", "b"))
base_f <- ggplot(df, aes(x, y)) + geom_point() + facet_wrap(~z)

base_f
base_f + theme(panel.margin = unit(0.5, "in"))
base_f + theme(
  strip.background = element_rect(fill = "grey20", color = "grey80", size = 1),
  strip.text = element_text(colour = "white")
)
```

### Exercises

1.  Create the ugliest plot possible! (Contributed by Andrew D. Steen, 
    University of Tennessee - Knoxville)
    
1.  `theme_dark()` makes the inside of the plot dark, but not the 
    outside. Change the plot background to black, and then update the
    text settings so you can still read the labels.
    
1.  Make an elegant theme that uses "linen" as the background colour and
    a serif font for the text.

1.  Systematically explore the effects of `hjust` when you have a multiline
    title. Why doesn't `vjust` do anything?

## Saving your output {#sec:saving}

When saving a plot to use in another program, you have two basic choices of  output: raster or vector: \index{Exporting} \index{Saving output}

* Vector graphics describe a plot as sequence of operations: draw a line 
  from $(x_1, y_1)$ to $(x_2, y_2)$, draw a circle at $(x_3, x_4)$ with 
  radius $r$. This means that they are effectively 'infinitely' zoomable; 
  there is no loss of detail. The most useful vector graphic formats are 
  pdf and svg.
  
* Raster graphics are stored as an array of pixel colours and have a fixed 
  optimal viewing size. The most useful raster graphic format is png.
  
Figure \ref{fig:vector-raster} illustrates the basic differences in these formats for a circle. A good description is available at <http://tinyurl.com/rstrvctr>. 

\begin{figure}[htbp]
  \centering
    \includegraphics[width= 0.5\linewidth]{diagrams/vector-raster}
  \caption{The schematic difference between raster (left) and vector (right) graphics. }
  \label{fig:vector-raster}
\end{figure}

Unless there is a compelling reason not to, use vector graphics: they look better in more places. There are two main reasons to use raster graphics:

* You have a plot (e.g. a scatterplot) with thousands of graphical objects 
  (i.e. points). A vector version will be large and slow to render.

* You want to embed the graphic in MS Office. MS has poor support for vector
  graphics (except for their own DrawingXML format which is not currently
  easy to make from R), so raster graphics are easier.
  
There are two ways to save output from ggplot2. You can use the standard R approach where you open a graphics device, generate the plot, then close the device: \indexf{pdf}

```R
pdf("output.pdf", width = 6, height = 6)
ggplot(mpg, aes(displ, cty)) + geom_point()
dev.off()
```

This works for all packages, but is verbose. ggplot2 provides a convenient shorthand with `ggsave()`:

```R
ggplot(mpg, aes(displ, cty)) + geom_point()
ggsave("output.pdf")
```

`ggsave()` is optimised for interactive use: you can use it after you've drawn a plot. It has the following important arguments: \indexf{ggsave}

* The first argument, `path`, specifies the path where the image should be 
  saved. The file extension will be used to automatically select the correct 
  graphics device. `ggsave()` can produce `.eps`, `.pdf`, `.svg`, `.wmf`,
  `.png`, `.jpg`, `.bmp`, and `.tiff`.

* `width` and `height` control the output size, specified in inches. If left 
  blank, they'll use the size of the on-screen graphics device.
  
* For raster graphics (i.e. `.png`, `.jpg`), the `dpi` argument controls the 
  resolution of the plot.  It defaults to 300, which is appropriate for most 
  printers, but you may want to use 600 for particularly high-resolution output, 
  or 96 for on-screen (e.g., web) display.

See `?ggsave` for more details.

## References
