themes.qmd

# Custom Data Visualization Themes {#sec-themes-chapter}

```{r}
i <- 1
chapter_number <- 3
source("_common.R")
```

A *custom theme* is nothing more than a chunk of code that applies a set of small tweaks to all plots. So much of the work involved in making a professional chart consists of these kinds of adjustments. What font should you use? Where should the legend go? Should axes have titles? Should charts have grid lines? These questions may seem minor, but they have a major impact on the final product.

In 2018, BBC data journalists Nassos Stylianou and Clara Guibourg, along with their team, developed a custom ggplot theme that matches the BBC’s style. By introducing this bbplot package for others to use, they changed their organization’s culture, removed bottlenecks, and allowed the BBC to visualize data more creatively.

Rather than forcing everyone to copy the long code to tweak each plot they make, custom themes enable everyone who uses them to follow style guidelines and ensure that all data visualizations meet a brand’s standards. For example, to understand the significance of the custom theme introduced at the BBC, it’s helpful to know how things worked before bbplot.

In the mid-2010s, journalists who wanted to make data visualization had two choices:

- Use an internal tool that could create data visualizations but was limited to the predefined charts it had been designed to generate. 

- Use Excel to create mockups and then work with a graphic designer to finalize the charts. This approach led to better results and was much more flexible, but it required extensive, time-consuming back-and-forth with a designer.

Neither of these choices was ideal, and the BBC’s data visualization output was limited. R freed the journalists from having to work with a designer. It wasn’t that the designers were bad (they weren’t), but ggplot allowed the journalists to explore different visualizations on their own. As the team improved their ggplot skills, they realized that it might be possible to produce more than just exploratory data visualizations and to create production-ready charts in R that could go straight onto the BBC website.

This chapter discusses the power of custom ggplot themes, then walks through the code in the bbplot package to demonstrate how custom themes work. You’ll learn how to consolidate your styling code into a reusable function and how to consistently modify your plots’ text, axes, grid lines, background, and other elements.

## Styling a Plot with a Custom Theme

The bbplot package has two functions: `bbc_style()` and `finalise_plot()`. The latter deals with tasks like adding the BBC logo and saving plots in the correct dimensions. For now, let’s look at the `bbc_style()` function, which applies a custom ggplot theme to make all the plots look consistent and follow BBC style guidelines.

### An Example Plot

To see how this function works, you’ll create a plot showing population data about several penguin species. You’ll be using the `palmerpenguins` package, which contains data about penguins living on three islands in Antarctica. For a sense of what this data looks like, load the `palmerpenguins` and `tidyverse` packages:

```{r}
#| echo: true
library(palmerpenguins)
library(tidyverse)
```

Now you have data you can work with in an object called `penguins`. Here’s what the first 10 rows look like:

```{r}
penguins
```

To get the data in a more usable format, you’ll count how many penguins live on each island with the `count()` function from the `dplyr` package (one of several packages that are loaded with the `tidyverse`):

```{r}
#| echo: true
#| eval: false
penguins %>%
  count(island)
```

This gives you some simple data that you can use for plotting:

```{r}
penguins %>%
  count(island)
```

You’ll use this data multiple times in the chapter, so save it as an object called `penguins_summary` like so:

```{r}
#| echo: true
penguins_summary <- penguins %>%
  count(island)
```

Now you’re ready to create a plot. Before you see what `bbplot` does, make a plot with the ggplot defaults:

```{r}
#| label: fig-basic-penguins-plot
#| echo: true
penguins_plot <- ggplot(
  data = penguins_summary,
  aes(
    x = island,
    y = n,
    fill = island
  )
) +
  geom_col() +
  labs(
    title = "Number of Penguins",
    subtitle = "Islands are in Antarctica",
    caption = "Data from palmerpenguins package"
  )
```

This code tells R to use the `penguins_summary` data frame, putting the island on the x-axis and the count of the number of penguins (`n`) on the y-axis, and making each bar a different color with the `fill` aesthetic property. Since you’ll modify this plot multiple times, saving it as an object called `penguins_plot` simplifies the process. @fig-basic-penguins-plot-plot shows the resulting plot.

```{r}
#| results: asis
print_nostarch_file_name()
```

```{r}
#| label: fig-basic-penguins-plot-plot
#| fig-cap: "A chart with the default theme"
penguins_plot
```

```{r}
#| results: asis
save_figure_for_nostarch(figure_height = 5)
```

This isn’t the most aesthetically pleasing chart. The gray background is ugly, the y-axis title is hard to read because it’s angled, and the text size overall is quite small. But don’t worry, you’ll be improving it soon.

### The BBC’s Custom Theme

Now that you have a basic plot to work with, you’ll start making it look like a BBC chart. To do this, you need to install the `bbplot` package. First, install the `remotes` package using `install.packages("remotes")` so that you can access packages from remote sources. Then, run the following code to install bbplot from the GitHub repository at <https://github.com/bbc/bbplot>:

```{r}
#| echo: true
#| eval: false
library(remotes)
install_github("bbc/bbplot")
```

Once you’ve installed the bbplot package, load it and apply the `bbc_style()` function to the penguins_plot as follows:

```{r penguins-bbc-style}
#| echo: true
#| eval: false
library(bbplot)

penguins_plot +
  bbc_style()
```

@fig-penguins-bbc-style-plot shows the result.

```{r}
#| results: asis
print_nostarch_file_name()
```

```{r}
#| label: fig-penguins-bbc-style-plot
#| ref-label: penguins-bbc-style
#| fig-cap: "The same chart with BBC style"
```

```{r}
#| results: asis
save_figure_for_nostarch(figure_height = 5)
```

Vastly different, right? The font size is larger, the legend is on top, there are no axis titles, the grid lines are stripped down, and the background is white. Let’s look at these changes one by one.

## The BBC Theme Components

You’ve just seen the difference that the bbc_style() function makes to a basic chart. This section walks you through the function’s code, with some minor tweaks for readability. Functions are discussed further in @sec-packages-chapter.

### Function Definition

The first line gives the function a name and indicates that what follows is, in fact, a function definition:

```{r}
#| echo: true
#| eval: false
bbc_style <- function() {
  font <- "Helvetica"
  
  ggplot2::theme(
```

```{r}
font <- "Helvetica"
```

The code then defines a variable called font and assigns it the value `Helvetica`. This allows later sections to simply use font rather than repeating `Helvetica` multiple times. If the BBC team ever wanted to use a different font, they could change `Helvetica` here to, say, `Comic Sans` and it would update the font for all of the BBC plots (though I suspect higher-ups at the BBC might not be on board with that choice).

Historically, working with custom fonts in R was notoriously tricky, but recent changes have made the process much simpler. To ensure that custom fonts such as Helvetica work in ggplot, first install the `systemfonts` and `ragg` packages by running this code in the console:

```{r}
#| echo: true
#| eval: false
install.packages(c("systemfonts", "ragg"))
```

The `systemfonts` package allows R to directly access fonts you’ve installed on your computer, and `ragg` allows ggplot to use those fonts when generating plots.

Next, select **Tools > Global Options** from RStudio’s main menu bar. Click the **Graphics** menu at the top of the interface and, under the Backend option, select **AGG**. This change should ensure that RStudio renders the previews of any plots with the `ragg` package. With these changes in place, you should be able to use any fonts you’d like (assuming you have them installed) in the same way that the b`bc_style()` function uses Helvetica.

After specifying the font to use, the code calls ggplot’s `theme()` function. Rather than first loading ggplot with `library(ggplot2)` and then calling its `theme()` function, the `ggplot2::theme(`) syntax indicates in one step that the `theme()` function comes from the ggplot2 package. You’ll write code in this way when making an R package in @sec-packages-chapter.

Nearly all of the code in `bbc_style()` exists within this `theme()` function. Remember from @sec-data-viz-chapter that `theme()` makes additional tweaks to an existing theme; it isn’t a complete theme like `theme_light()`, which will change the whole look and feel of your plot. In other words, by jumping straight into the `theme()` function, `bbc_style()` makes adjustments to the ggplot defaults. As you’ll see, the `bbc_style()` function does a lot of tweaking.

### Text

The first code section within the `theme()` function formats the text: 

```{r}
#| eval: false
#| echo: true
plot.title = ggplot2::element_text(
  family = font,
  size = 28,
  face = "bold",
  color = "#222222"
),
plot.subtitle = ggplot2::element_text(
  family = font,
  size = 22,
  margin = ggplot2::margin(9, 0, 9, 0)
),
plot.caption = ggplot2::element_blank(),
```

To make changes to the title, subtitle, and caption, it follows this pattern:

```{r}
#| eval: false
#| echo: true
AREA_OF_CHART = ELEMENT_TYPE(
  PROPERTY = VALUE
)
```

For each area, this code specifies the element type: `element_text()`, `element_line()`, `element_rect()`, or `element_blank()`. Within the element type is where you assign values to properties—for example, setting the font family (the property) to Helvetica (the value). The `bbc_style()` function uses the various `element_` functions to make tweaks, as you’ll see later in this chapter.

::: {.callout-tip}

For additional ways to customize pieces of your plots, see the ggplot2 package documentation (<https://ggplot2.tidyverse.org/reference/element.html>), which provides a comprehensive list.

:::

One of the main adjustments the `bbc_style()` function makes is bumping up the font size to help with legibility, especially when plots made with the bbplot package are viewed on smaller mobile devices. The code first formats the title (with `plot.title`) using Helvetica 28-point bold font in a nearly black color (the hex code #222222). The subtitle (`plot.subtitle`) is 22-point Helvetica.

The `bbc_style()` code also adds some spacing between the title and subtitle with the `margin()` function, specifying the value in points for the top (9), right (0), bottom (9), and left (0) sides. Finally, the `element_blank()` function removes the default caption (set through the `caption` argument in the labs() function), “Data from palmer penguins package.” (As mentioned earlier, the `finalise_plot()` function in the `bbplot` package adds elements, including an updated caption and the BBC logo, to the bottom of the plots.) 

@fig-penguins-plot-text-formatting-plot shows these changes.

```{r}
#| results: asis
print_nostarch_file_name()
```

```{r}
#| label: fig-penguins-plot-text-formatting-plot
#| fig-cap: "The penguin chart with only the text formatting changed"
penguins_plot +
  theme(
    plot.title = element_text(
      family = font,
      size = 28,
      face = "bold",
      color = "#222222"
    ),
    plot.subtitle = element_text(
      family = font,
      size = 22,
      margin = margin(9, 0, 9, 0)
    ),
    plot.caption = element_blank()
  )
```

```{r}
#| results: asis
save_figure_for_nostarch(figure_height = 5)
```

With these changes in place, you’re on your way to the BBC look.

### Legend

Next up is formatting the legend, positioning it above the plot and leftaligning its text:

```{r penguins-plot-legend}
#| echo: true
#| eval: false
legend.position = "top",
legend.text.align = 0,
legend.background = element_blank(),
legend.title = element_blank(),
legend.key = element_blank(),
legend.text = element_text(
  family = font,
  size = 18,
  color = "#222222"
),
```


This code removes the legend background (which would show up only if the background color of the entire plot weren’t white), the title, and the legend key (the borders on the boxes that show the island names, just barely visible in @fig-penguins-plot-text-formatting-plot). Finally, the code sets the legend’s text to 18-point Helvetica with the same nearly black color. @fig-penguins-plot-legend-plot shows the result.

```{r}
#| results: asis
print_nostarch_file_name()
```

```{r}
#| label: fig-penguins-plot-legend-plot
#| fig-cap: "The penguin chart with changes to the legend"
penguins_plot +
  theme(
    plot.title = element_text(
      family = font,
      size = 28,
      face = "bold",
      color = "#222222"
    ),
    plot.subtitle = element_text(
      family = font,
      size = 22,
      margin = margin(9, 0, 9, 0)
    ),
    plot.caption = element_blank(),
    legend.position = "top",
    legend.text.align = 0,
    legend.background = element_blank(),
    legend.title = element_blank(),
    legend.key = element_blank(),
    legend.text = element_text(
      family = font,
      size = 18,
      color = "#222222"
    )
  )
```

```{r}
#| results: asis
save_figure_for_nostarch(figure_height = 5)
```

The legend is looking better, but now it’s time to format the rest of the chart so it matches.

### Axes

The code first removes the axis titles because they tend to take up a lot of chart real estate, and you can use the title and subtitle to clarify what the axes show:

```{r}
#| echo: true
#| eval: false
axis.title = ggplot2::element_blank(),
axis.text = ggplot2::element_text(
  family = font,
  size = 18,
  color = "#222222"
),
axis.text.x = ggplot2::element_text(margin = ggplot2::margin(5, b = 10)),
axis.ticks = ggplot2::element_blank(),
axis.line = ggplot2::element_blank(),
```

All text on the axes becomes 18-point Helvetica and nearly black. The text on the x-axis (Biscoe, Dream, and Torgersen) gets a bit of spacing around it. Finally, both axes’ ticks and lines are removed. @fig-penguins-plot-axes-plot shows these changes, although the removal of the axis lines doesn’t make a difference to the display here.

```{r}
#| results: asis
print_nostarch_file_name()
```

```{r}
#| label: fig-penguins-plot-axes-plot
#| fig-cap: "The penguin chart with axis formatting changes"
penguins_plot +
  theme(
    plot.title = element_text(
      family = font,
      size = 28,
      face = "bold",
      color = "#222222"
    ),
    plot.subtitle = element_text(
      family = font,
      size = 22,
      margin = margin(9, 0, 9, 0)
    ),
    plot.caption = element_blank(),
    legend.position = "top",
    legend.text.align = 0,
    legend.background = element_blank(),
    legend.title = element_blank(),
    legend.key = element_blank(),
    legend.text = element_text(
      family = font,
      size = 18,
      color = "#222222"
    ),
    axis.title = ggplot2::element_blank(),
    axis.text = ggplot2::element_text(
      family = font,
      size = 18,
      color = "#222222"
    ),
    axis.text.x = ggplot2::element_text(margin = ggplot2::margin(5, b = 10)),
    axis.ticks = ggplot2::element_blank(),
    axis.line = ggplot2::element_blank()
  )
```

```{r}
#| results: asis
save_figure_for_nostarch(figure_height = 5)
```

The axis text matches the legend text, and the axis tick marks and lines are gone.

### Grid Lines

Now for the grid lines:

```{r}
#| echo: true
#| eval: false
panel.grid.minor = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_line(color = "#cbcbcb"),
panel.grid.major.x = ggplot2::element_blank(),
```

The approach here is fairly straightforward: this code removes minor grid lines for both axes, removes major grid lines on the x-axis, and keeps major grid lines on the y-axis but makes them a light gray (the #cbcbcb hex code). @fig-penguins-plot-gridlines-plot shows the result.

```{r}
#| results: asis
print_nostarch_file_name()
```

```{r}
#| label: fig-penguins-plot-gridlines-plot
#| fig-cap: "Our chart with tweaks to the grid lines"
penguins_plot +
  theme(
    plot.title = element_text(
      family = font,
      size = 28,
      face = "bold",
      color = "#222222"
    ),
    plot.subtitle = element_text(
      family = font,
      size = 22,
      margin = margin(9, 0, 9, 0)
    ),
    plot.caption = element_blank(),
    legend.position = "top",
    legend.text.align = 0,
    legend.background = element_blank(),
    legend.title = element_blank(),
    legend.key = element_blank(),
    legend.text = element_text(
      family = font,
      size = 18,
      color = "#222222"
    ),
    axis.title = ggplot2::element_blank(),
    axis.text = ggplot2::element_text(
      family = font,
      size = 18,
      color = "#222222"
    ),
    axis.text.x = ggplot2::element_text(margin = ggplot2::margin(5, b = 10)),
    axis.ticks = ggplot2::element_blank(),
    axis.line = ggplot2::element_blank(),
    panel.grid.minor = ggplot2::element_blank(),
    panel.grid.major.y = ggplot2::element_line(color = "#cbcbcb"),
    panel.grid.major.x = ggplot2::element_blank()
  )
```

```{r}
#| results: asis
save_figure_for_nostarch(figure_height = 5)
```

### Background

The previous iteration of our plot still had a gray background. The `bbc_style()` function removes this with the following code.

```{r}
#| echo: true
#| eval: false
panel.background = ggplot2::element_blank(),
```

The plot without the gray background is seen in Figure \@ref(fig:penguins-plot-no-bg).

```{r}
#| results: asis
print_nostarch_file_name()
```

```{r}
#| label: fig-penguins-plot-no-bg
#| fig-cap: "The chart with the gray background removed"
penguins_plot +
  theme(
    plot.title = element_text(
      family = font,
      size = 28,
      face = "bold",
      color = "#222222"
    ),
    plot.subtitle = element_text(
      family = font,
      size = 22,
      margin = margin(9, 0, 9, 0)
    ),
    plot.caption = element_blank(),
    legend.position = "top",
    legend.text.align = 0,
    legend.background = element_blank(),
    legend.title = element_blank(),
    legend.key = element_blank(),
    legend.text = element_text(
      family = font,
      size = 18,
      color = "#222222"
    ),
    axis.title = ggplot2::element_blank(),
    axis.text = ggplot2::element_text(
      family = font,
      size = 18,
      color = "#222222"
    ),
    axis.text.x = ggplot2::element_text(margin = ggplot2::margin(5, b = 10)),
    axis.ticks = ggplot2::element_blank(),
    axis.line = ggplot2::element_blank(),
    panel.grid.minor = ggplot2::element_blank(),
    panel.grid.major.y = ggplot2::element_line(color = "#cbcbcb"),
    panel.grid.major.x = ggplot2::element_blank(),
    panel.background = ggplot2::element_blank()
  )
```

```{r}
#| results: asis
save_figure_for_nostarch(figure_height = 5)
```

You’ve nearly re-created the penguin plot using the bbc_style() function.

### Small Multiples

The `bbc_style()` function contains a bit more code to modify `strip.background` and `strip.text`. In ggplot, the *strip* refers to the text above faceted charts like the ones discussed in @sec-data-viz-chapter. Next, you’ll turn your penguin chart into
a faceted chart to see these components of the BBC’s theme. I’ve used the code from the `bbc_style()` function, minus the sections that deal with small multiples, to make @fig-penguin-facetted-plot.


```{r}
#| results: asis
print_nostarch_file_name()
```

```{r}
#| label: fig-penguin-facetted-plot
#| fig-cap: "The faceted chart with no changes to the strip text formatting"
penguins %>%
  group_by(island, sex) %>%
  summarize(avg_weight = mean(body_mass_g, na.rm = TRUE)) %>%
  drop_na(sex) %>%
  mutate(sex = fct_recode(sex,
                          "F" = "female",
                          "M" = "male")) %>% 
  ggplot(aes(
    x = sex,
    y = avg_weight,
    fill = island
  )) +
  geom_col() +
  labs(title = "Penguin Weight",
       subtitle = "By Island and Sex") +
  facet_wrap(~island) +
  theme(
    
    plot.title = element_text(
      family = font,
      size = 28,
      face = "bold",
      color = "#222222"
    ),
    
    plot.subtitle = element_text(
      family = font,
      size = 22,
      margin = margin(9, 0, 9, 0)
    ),
    
    plot.caption = element_blank(),
    
    legend.position = "none",
    legend.text.align = 0,
    legend.background = element_blank(),
    legend.title = element_blank(),
    legend.key = element_blank(),
    legend.text = element_text(
      family = font,
      size = 18,
      color = "#222222"
    ),
    
    axis.title = element_blank(),
    axis.text = element_text(
      family = font,
      size = 18,
      color = "#222222"
    ),
    axis.text.x = element_text(margin = margin(5, b = 10)),
    axis.ticks = element_blank(),
    axis.line = element_blank(),
    
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_line(color = "#cbcbcb"),
    panel.grid.major.x = element_blank(),
    
    panel.background = element_blank()
  )
```

```{r}
#| results: asis
save_figure_for_nostarch(figure_height = 5)
```

Using the `facet_wrap()` function to make a small multiples chart leaves you with one chart per island, but by default, the text above each small multiple is noticeably smaller than the rest of the chart. What’s more, the gray background behind the text stands out because you’ve already removed the gray background from the other parts of the chart. The consistency you’ve worked toward is now compromised, with small text that is out of proportion to the other chart text and a gray background that sticks out like a sore thumb.

The following code changes the strip text above each small multiple:

```{r}
#| eval: false
#| echo: true

strip.background = ggplot2::element_rect(fill = "white"),
strip.text = ggplot2::element_text(size = 22, hjust = 0)
```


This code removes the background (or, more accurately, colors it white). Then it makes the text larger, bold, and left-aligned using hjust = 0. Note that I did have to make the text size slightly smaller than in the actual chart to fit the book, and I added code to make it bold. @fig-penguins-plot-facetted-bbc shows the result.

```{r}
penguins_plot_weight <- penguins %>%
  group_by(island, sex) %>%
  summarize(avg_weight = mean(body_mass_g, na.rm = TRUE)) %>%
  drop_na(sex) %>%
  mutate(sex = fct_recode(sex,
                          "F" = "female",
                          "M" = "male")) %>% 
  ggplot(aes(
    x = sex,
    y = avg_weight,
    fill = island
  )) +
  geom_col() +
  labs(title = "Penguin Weight",
       subtitle = "By Island and Sex") +
  facet_wrap(~island) +
  theme(
    
    plot.title = element_text(
      family = font,
      size = 28,
      face = "bold",
      color = "#222222"
    ),
    
    plot.subtitle = element_text(
      family = font,
      size = 22,
      margin = margin(9, 0, 9, 0)
    ),
    
    plot.caption = element_blank(),
    
    legend.position = "none",
    legend.text.align = 0,
    legend.background = element_blank(),
    legend.title = element_blank(),
    legend.key = element_blank(),
    legend.text = element_text(
      family = font,
      size = 18,
      color = "#222222"
    ),
    
    axis.title = element_blank(),
    axis.text = element_text(
      family = font,
      size = 18,
      color = "#222222"
    ),
    axis.text.x = element_text(margin = margin(5, b = 10)),
    axis.ticks = element_blank(),
    axis.line = element_blank(),
    
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_line(color = "#cbcbcb"),
    panel.grid.major.x = element_blank(),
    
    panel.background = element_blank()
  )
```

```{r}
#| results: asis
print_nostarch_file_name()
```
```{r}
#| label: fig-penguins-plot-facetted-bbc
#| fig-cap: "The small multiples chart in the BBC style"
penguins_plot_weight +
  theme(
    strip.background = element_rect(fill = "white"),
    strip.text = element_text(size = 17, hjust = 0, face = "bold")
  )
```

```{r}
#| results: asis
save_figure_for_nostarch(figure_height = 5)
```

If you look at any chart on the BBC website, you’ll see how similar it looks to your own. The tweaks in the `bbc_style()` function to the text formatting, legends, axes, grid lines, and backgrounds show up in charts viewed by millions of people worldwide.

## Color

You might be thinking, *Wait, what about the color of the bars? Doesn’t the theme change those?* This is a common point of confusion, but the answer is that it doesn’t. The documentation for the `theme()` function explains why this is the case: “Themes are a powerful way to customize the non-data components of your plots: i.e. titles, labels, fonts, background, gridlines, and legends.” In other words, ggplot themes change the elements of the chart that aren’t mapped to data. 

Plots, on the other hand, use color to communicate information about data. In the faceted chart, for instance, the `fill` property is mapped to the island (Biscoe is salmon, Dream is green, and Torgersen is blue). As you saw in @sec-data-viz-chapter, you can change the fill using the various `scale_fill_` functions. In the world of ggplot, these `scale_` functions control color, while the custom themes control the chart’s overall look and feel.

## Summary

When Stylianou and Guibourg started developing a custom theme for the BBC, they had one question: Would they be able to create graphs in R that could go directly onto the BBC website? Using ggplot, they succeeded. The `bbplot` package allowed them to make plots with a consistent look and feel that followed BBC standards and, most important, did not require a designer’s help.

You can see many of the principles of high-quality data visualization discussed in @sec-data-viz-chapter in this custom theme. In particular, the removal of extraneous elements (axis titles and grid lines, for instance) helps keep the focus on the data itself. And because applying the theme requires users to add only a single line to their ggplot code, it was easy to get others on board. They had only to append `bbc_style()` to their code to produce a BBC-style plot.

Over time, others at the BBC noticed the data journalism team’s production-ready graphs and wanted to make their own. The team members set up R trainings for their colleagues and developed a “cookbook” (<https://bbc.github.io/rcookbook/>) showing how to make various types of charts. Soon, the quality and quantity of BBC’s data visualization exploded. Stylianou told me, “I don’t think there’s been a day where someone at the BBC hasn’t used the package to produce a graphic.”

Now that you’ve seen how custom ggplot themes work, try making one of your own. After all, once you’ve written the code, you can apply it with only one line of code.

## Additional Resources

Consult the following resources to learn more about how the BBC created and used their custom theme:

- BBC Visual and Data Journalism cookbook for R graphics (2019), <https://bbc.github.io/rcookbook/>

- "How the BBC Visual and Data Journalism team works with graphics in R" by the BBC Visual and Data Journalism team (2019), <https://medium.com/bbc-visual-and-data-journalism/how-the-bbc-visual-and-data-journalism-team-works-with-graphics-in-r-ed0b35693535>