This cookbook provides examples of the code used to produce various
chart types using afcharts. There are also examples to demonstrate how
to apply further customisation to afcharts charts.
If there is a chart type or task which you think would be useful to
include here, please submit
a suggestion.
The following packages are required to produce the example charts in
this cookbook:
Bar charts
bar_data <-
gapminder |>
filter(year == 2007 & continent == "Europe") |>
slice_max(order_by = lifeExp, n = 5)
ggplot(bar_data, aes(x = reorder(country, -lifeExp), y = lifeExp)) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af() +
scale_y_continuous(expand = c(0, 0)) +
labs(
x = NULL,
y = NULL,
title = "Iceland has the highest life expectancy in Europe",
subtitle = "Life expectancy in European countries, 2007",
caption = "Source: Gapminder"
)
A bar chart can sometimes look better with horizontal bars. This can
also be a good option if your bar labels are long and difficult to
display horizontally on the x axis. To produce a horizontal bar chart,
swap the variables defined for x and y in aes() and make a
few tweaks to theme_af(); draw grid lines for the x axis
only by setting the grid argument, and draw an axis line
for the y axis only by setting the axis argument.
ggplot(bar_data, aes(x = lifeExp, y = reorder(country, lifeExp))) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af(grid = "x", axis = "y") +
scale_x_continuous(expand = c(0, 0)) +
labs(
x = NULL,
y = NULL,
title = "Iceland has the highest life expectancy in Europe",
subtitle = "Life expectancy in European countries, 2007",
caption = "Source: Gapminder"
)
Grouped bar chart
To create a grouped bar chart, set stat = "identity" and
position = "dodge" in the call to geom_bar().
Also assign a variable to fill within aes() to
determine what variable is used to create bars within groups. The
legend argument in theme_af() can be used to
set the position of the legend.
grouped_bar_data <-
gapminder |>
filter(year %in% c(1967, 2007) &
country %in% c("United Kingdom", "Ireland", "France", "Belgium"))
ggplot(grouped_bar_data,
aes(x = country, y = lifeExp, fill = as.factor(year))) +
geom_bar(stat = "identity", position = "dodge") +
scale_y_continuous(expand = c(0, 0)) +
theme_af(legend = "bottom") +
scale_fill_discrete_af() +
labs(
x = "Country",
y = NULL,
fill = NULL,
title = "Living longer",
subtitle = "Difference in life expectancy, 1967-2007",
caption = "Source: Gapminder"
)
Stacked bar chart
To create a stacked bar chart, set stat = "identity and
position = "fill" in the call to geom_bar()
and assign a variable to fill as before. This will plot
your data as part-to-whole. To plot counts, set
position = "identity".
Caution should be taken when producing stacked bar charts. They can
quickly become difficult to interpret if plotting non part-to-whole
data, and/or if plotting more than two categories per stack. First and
last categories in the stack will always be easier to compare across
bars than those in the middle. Think carefully about the story you are
trying to tell with your chart.
stacked_bar_data <-
gapminder |>
filter(year == 2007) |>
mutate(lifeExpGrouped = cut(lifeExp,
breaks = c(0, 75, Inf),
labels = c("Under 75", "75+"))) |>
group_by(continent, lifeExpGrouped) |>
summarise(n_countries = n(), .groups = "drop")
ggplot(stacked_bar_data,
aes(x = continent, y = n_countries, fill = lifeExpGrouped)) +
geom_bar(stat = "identity", position = "fill") +
theme_af(legend = "bottom") +
scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
coord_cartesian(clip = "off") +
scale_fill_discrete_af() +
labs(
x = NULL,
y = NULL,
fill = "Life Expectancy",
title = "How life expectancy varies across continents",
subtitle = "Percentage of countries by life expectancy band, 2007",
caption = "Source: Gapminder"
)
Interactive charts
To make a ggplot2 chart interactive, use
ggplotly() from the plotly package. Note
however that ggplotly() has a number of ‘quirks’, including
the following:
afcharts uses the ‘sans’ font family, however plotly
does not recognise this font. To work around this you should add a
further call to theme to set the font family for text to
"".
Subtitles and captions are not supported in
ggplotly(). As stated elsewhere in this guidance, titles
and subtitles should ideally be included in the body of text surrounding
a chart rather than embedded in the chart itself, and so this is
hopefully not a big issue. This example therefore has no title, subtitle
or caption.
p <-
bar_data |>
# Format text for tooltips
mutate(tooltip = paste0(
"Country: ", country, "\n",
"Life Expectancy: ", round(lifeExp, 1)
)) |>
ggplot(aes(x = reorder(country, -lifeExp), y = lifeExp, text = tooltip)) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af(ticks = "x") +
theme(text = element_text(family = "")) +
scale_y_continuous(expand = c(0, 0)) +
labs(
x = NULL,
y = NULL
)
plotly::ggplotly(p, tooltip = "text") |>
plotly::config(
modeBarButtons = list(list("resetViews")),
displaylogo = FALSE
)
afcharts currently only works with ggplot2 charts,
however there are plans to develop the package further to support
interactive Highcharts produced using the highcharter
package.
Annotations
Labelling your chart is often preferable to using a legend, as often
this relies on a user matching the legend to the data using colour
alone. The legend can be removed from a chart by setting
legend = "none" in theme_af().
The easiest way to add an annotation is to manually define the
co-ordinates of the required position.
ann_data <- gapminder |>
filter(country %in% c("United Kingdom", "China"))
ann_data |>
ggplot(aes(x = year, y = lifeExp, colour = country)) +
geom_line(linewidth = 1) +
theme_af(legend = "none") +
scale_colour_discrete_af() +
scale_y_continuous(limits = c(0, 82),
breaks = seq(0, 80, 20),
expand = c(0, 0)) +
scale_x_continuous(limits = c(1952, 2017),
breaks = seq(1952, 2017, 5)) +
annotate(geom = "label", x = 2008, y = 73, label = "China",
colour = af_colour_values[1],
label.size = NA,
hjust = 0, vjust = 0.5) +
annotate(geom = "label", x = 2008, y = 79.4, label = "United Kingdom",
colour = af_colour_values[2],
label.size = NA,
hjust = 0, vjust = 0.5) +
labs(
x = "Year",
y = NULL,
title = "Living Longer",
subtitle = "Life Expectancy in the United Kingdom and China 1952-2007",
caption = "Source: Gapminder"
)
However, this makes the code difficult to reuse as values are hard
coded and not automatically generated from the data. Automating the
position of annotations is possible, but more fiddly.
The following examples use geom_label() to use values
from the data to position annotations. geom_label() draws a
rectangle behind the text (white by default) and a border the same
colour as the text (label_size = NA can be used to remove
the border). geom_text() is also an option for annotations,
but this does not include a background and so can be harder for text to
read if it overlaps with other chart elements. These functions also have
nudge arguments that can be used to displace text to
improve the positioning.
Note that in the previous examples, annotate() also
requires a geom (label or text). These operate
in the same way as geom_label() and
geom_text(), but as discussed, annotate() is
only able to deal with fixed values.
ann_labs <- ann_data |>
group_by(country) |>
mutate(min_year = min(year)) |>
filter(year == max(year)) |>
ungroup()
ann_data |>
ggplot(aes(x = year, y = lifeExp, colour = country)) +
geom_line(linewidth = 1) +
theme_af(legend = "none") +
scale_colour_discrete_af() +
scale_y_continuous(limits = c(0, 82),
breaks = seq(0, 80, 20),
expand = c(0, 0)) +
scale_x_continuous(limits = c(1952, 2017),
breaks = seq(1952, 2017, 5)) +
geom_label(data = ann_labs,
aes(x = year, y = lifeExp, label = country, colour = country),
hjust = 0,
vjust = 0.5,
nudge_x = 0.5,
label.size = NA) +
labs(
x = "Year",
y = NULL,
title = "Living Longer",
subtitle = "Life Expectancy in the United Kingdom and China 1952-2007",
caption = "Source: Gapminder"
)
Annotations may also be used to add value labels to a bar chart. Note
that geom_text() is used here as a background is not
required.
ggplot(bar_data, aes(x = reorder(country, -lifeExp), y = lifeExp)) +
geom_col(fill = af_colour_values["dark-blue"]) +
geom_text(aes(label = round(lifeExp, 1)),
nudge_y = -5, colour = "white") +
theme_af() +
scale_y_continuous(expand = c(0, 0)) +
labs(
x = NULL,
y = NULL,
title = "Iceland has the highest life expectancy in Europe",
subtitle = "Life expectancy in European countries, 2007",
caption = "Source: Gapminder"
)
Note: The annotate() function should be
used to add annotations with manually defined positioning co-ordinates,
whereas geom_label() and geom_text() should be
used when using co-ordinates defined in a data frame. Although the
reverse may work, text can appear blurry.
Other customisations
theme_af() has arguments to control the legend position
and appearance of grid lines, axis lines and axis ticks. More
information on accepted values can be found in the help
file.
Sorting a bar chart
To control the order of bars in a chart, wrap the variable you want
to arrange with reorder() and specify what variable you
want to sort by. The following example sorts bars in ascending order of
life expectancy. To sort in descending order, you would change this to
reorder(country, desc(lifeExp)).
bar_data |>
ggplot(aes(x = lifeExp, y = reorder(country, lifeExp))) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af(axis = "y", grid = "x")
Examples in the following sections build on this chart.
Changing chart titles
Chart titles such as the main title, subtitle, caption, axis titles
and legend titles, can be controlled using labs(). A title
can be removed using NULL.
last_plot() +
labs(
x = NULL,
y = NULL,
title = "Iceland has the highest life expectancy in Europe",
subtitle = "Life expectancy in European countries, 2007",
caption = "Source: Gapminder"
)
Reducing space between chart and axis
By default, a bar chart will have a gap between the bottom of the
bars and the axis. This can be removed as follows:
last_plot() + scale_x_continuous(expand = c(0, 0))
The equivalent adjustment can be made for the y axis using
scale_y_continuous.
Changing axis limits, breaks and labels
Axis limits, breaks and labels for continuous variables can be
controlled using scale_x/y_continuous(). For discrete
variables, labels can be changed using scale_x/y_discrete()
or alternatively by recoding the variable in the data before creating a
chart.
Limits, breaks and labels can be defined with custom values.
last_plot() +
scale_x_continuous(expand = c(0, 0),
limits = c(0, 85),
breaks = seq(0, 80, 20),
labels = c(seq(0, 70, 20), "80 years"))
Note that further calls to scale_x/y_continuous will
overwrite previous calls, hence why expand = c(0, 0) has
been included again in this example.
Adaptive axis limits and break for
scale_x/y_continuous() can be defined using the
pretty function. This defines breaks that are equally
spaced ‘round’ values which cover the range of the data and limits that
are the next ‘round’ value just exceeding the range of the data.
limits_pretty <- function(x, ...) range(pretty(x, ...))
last_plot() +
scale_x_continuous(expand = expansion(mult = c(0, .1)),
breaks = pretty,
limits = limits_pretty)
Avoiding axis/grid lines being cut off
Axis lines and grid lines can sometimes appear ‘cut off’ if they are
drawn at the limits of the chart range. You can see in the example in
the previous section that the top grid line is slightly narrower than
the adjacent tick mark on the y axis. This is because the y axis limit
is 100%. As the grid line is centred at 100%, the top half of the line
is ‘cut off’. This can be corrected as follows:
last_plot() + coord_cartesian(clip = "off")
Adding a line
To add a horizontal or vertical line across the whole plot, use
geom_hline() or geom_vline(). This can be
useful to highlight a threshold or average level.
gapminder |>
filter(country == "United Kingdom") |>
ggplot(aes(x = year, y = lifeExp)) +
geom_line(linewidth = 1, colour = af_colour_values[1]) +
geom_hline(yintercept = 75, colour = af_colour_values[2],
linewidth = 1, linetype = "dashed") +
annotate(geom = "text", x = 2007, y = 70, label = "Age 70") +
theme_af() +
scale_y_continuous(limits = c(0, 82),
breaks = seq(0, 80, 20),
expand = c(0, 0)) +
scale_x_continuous(breaks = seq(1952, 2007, 5)) +
labs(
x = "Year",
y = NULL,
title = "Living Longer",
subtitle = "Life Expectancy in the United Kingdom 1952-2007",
caption = "Source: Gapminder"
)
Wrapping text
If text is too long, it may be cut off or distort the dimensions of
the chart.
plot <-
ggplot(bar_data, aes(x = reorder(country, -lifeExp), y = lifeExp)) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af() +
scale_y_continuous(expand = c(0, 0)) +
labs(
x = NULL,
subtitle = "Life expectancy in European countries, 2007",
caption = "Source: Gapminder"
)
plot +
labs(
y = "Percentage of countries",
title = paste("Iceland has the highest life expectancy in Europe",
"followed closely by Switzerland")
)
There are two suggested ways to solve this issue; Insert
\n within a string to force a line break; Use
stringr::str_wrap() to set a maximum character width of the
string. See examples of both of these methods as follows:
plot +
labs(
y = "Percentage\nof countries",
title = stringr::str_wrap(
paste("Iceland has the highest life expectancy in Europe",
"followed closely by Switzerland"),
width = 50
)
)
Adjusting theme elements
If you find you need to adjust theme elements for your chart, this
can be done using theme(). Note that this should be done
after the call to theme_af(), otherwise
theme_af() may overwrite the specifications you’ve
made.
ggplot(bar_data, aes(x = reorder(country, -lifeExp), y = lifeExp)) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af(axis = "xy") +
theme(axis.line = element_line(colour = "black"),
axis.ticks = element_line(colour = "black")) +
scale_y_continuous(expand = c(0, 0)) +
labs(
x = NULL,
y = NULL,
title = "Iceland has the highest life expectancy in Europe",
subtitle = "Life expectancy in European countries, 2007",
caption = "Source: Gapminder"
)
You may also consider using markdown or HTML formatted text within
your charts. This can be readily achieved with
ggtext::element_markdown(). Please refer to Analysis
Function guidance in considering the accessibility of custom formatting,
such as when using colours.
ann_data <- gapminder |>
filter(country %in% c("United Kingdom", "China"))
ann_labs <- ann_data |>
group_by(country) |>
mutate(min_year = min(year)) |>
filter(year == max(year)) |>
ungroup()
ann_data |>
ggplot(aes(x = year, y = lifeExp, colour = country)) +
geom_line(linewidth = 1) +
theme_af(legend = "none") +
scale_colour_discrete_af() +
scale_y_continuous(limits = c(0, 82),
breaks = seq(0, 80, 20),
expand = c(0, 0)) +
scale_x_continuous(limits = c(1952, 2017),
breaks = seq(1952, 2017, 5)) +
geom_label(data = ann_labs,
aes(x = year, y = lifeExp, label = country, colour = country),
hjust = 0,
vjust = 0.5,
nudge_x = 0.5,
label.size = NA) +
labs(
x = "Year",
y = NULL,
title = "Living Longer",
subtitle = "Life Expectancy in the
<span style='color:darkorange;'>United Kingdom</span> and
<span style='color:navy;'>China</span> 1952-2007",
caption = "Source: Gapminder"
) +
theme(plot.subtitle = element_markdown())
Using different colour palettes
afcharts provides colour palettes as set out by the Government
Analysis Function suggested colour palettes. These palettes have
been developed to meet the Web
Content Accessibility Guidelines 2.1 for graphical objects.
The main
palette is the default for discrete colour/fill functions, and the
sequential
palette for continuous colour/fill functions.
More information on the colours used in afcharts can be found at
vignette("colours").
Using afcharts colour palettes
The full list of available palettes can be found by running
afcharts::af_colour_palettes.
For example, to use the Analysis Function main2
palette:
gapminder |>
filter(country %in% c("United Kingdom", "China")) |>
ggplot(aes(x = year, y = lifeExp, colour = country)) +
geom_line(linewidth = 1) +
theme_af(legend = "bottom") +
scale_colour_discrete_af("main2") +
scale_y_continuous(limits = c(0, 82),
breaks = seq(0, 80, 20),
expand = c(0, 0)) +
scale_x_continuous(breaks = seq(1952, 2007, 5)) +
labs(
x = "Year",
y = NULL,
title = "Living Longer",
subtitle = "Life Expectancy in the United Kingdom and China 1952-2007",
caption = "Source: Gapminder",
colour = NULL
)
Using your own colour palette
There may be instances where you’d like to use a colour palette that
is not available in afcharts. If so, this should be carefully considered
to ensure it meets accessibility requirements. The Government Analysis
Function guidance outlines appropriate
steps for choosing your own accessible colour palette and should be
used.
my_palette <- c("#0F820D", "#000000")
gapminder |>
filter(country == "United Kingdom") |>
ggplot(aes(x = year, y = lifeExp)) +
geom_line(linewidth = 1, colour = my_palette[1]) +
theme_af() +
scale_y_continuous(limits = c(0, 82),
breaks = seq(0, 80, 20),
expand = c(0, 0)) +
scale_x_continuous(breaks = seq(1952, 2007, 5)) +
labs(
x = "Year",
y = NULL,
title = "Living Longer",
subtitle = "Life Expectancy in the United Kingdom 1952-2007",
caption = "Source: Gapminder"
)
gapminder |>
filter(country %in% c("United Kingdom", "China")) |>
ggplot(aes(x = year, y = lifeExp, colour = country)) +
geom_line(linewidth = 1) +
theme_af(legend = "bottom") +
scale_colour_manual(values = my_palette) +
scale_y_continuous(limits = c(0, 82),
breaks = seq(0, 80, 20),
expand = c(0, 0)) +
scale_x_continuous(breaks = seq(1952, 2007, 5)) +
labs(
x = "Year",
y = NULL,
title = "Living Longer",
subtitle = "Life Expectancy in the United Kingdom and China 1952-2007",
caption = "Source: Gapminder",
colour = NULL
)
Adding a new colour palette to afcharts
If you use a different palette regularly and feel it would be useful
for this to be added to afcharts, please make a suggestion as per the contributing
guidance.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
