Introduction
This cookbook provides examples of the code used to produce various chart types using the afcharts R package. There are also examples to demonstrate how to apply further customisation to afcharts charts.
theme_af sets a variety of chart properties to meet the
Analysis Function accessibility guidance for charts. This includes
modifications to legends, chart spacing, the size of text, and the use
of a sans serif font. By default, axes and gridlines are light grey and
text is black. The afcharts package also provides the
Analysis Function accessible
colour palettes for data visualisation.
If there is a chart type or task which you think would be useful to include here, please submit a suggestion.
use_afcharts()
The examples in this cookbook use the afcharts theme and colour
functions explicitly, however it may be easier to make use of the
use_afcharts() function if your charts all require a
similar style. More information on use_afcharts can be
found on the afcharts
homepage.
Note on use of titles, subtitles and captions
For accessibility reasons it is usually preferable to provide titles and alt-text in the body of the page rather than embedded in charts. This has been implemented throughout this cookbook, aside from a limited number of examples demonstrating functionality with embedded text.
Line charts
Line chart with one line
gapminder |>
filter(country == "United Kingdom") |>
ggplot(aes(x = year, y = lifeExp)) +
geom_line(linewidth = 1, colour = af_colour_values["dark-blue"]) +
theme_af() +
scale_y_continuous(
limits = c(0, 82),
breaks = seq(0, 80, 20),
expand = expansion(mult = c(0, 0.1))
) +
scale_x_continuous(breaks = seq(1952, 2007, 5)) +
labs(
x = "Year",
y = NULL
)
This line chart uses the afcharts theme. There are pale grey grid lines extending from the y axis, and there is a thicker dark blue line representing the data.
Line chart with multiple lines
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() +
scale_y_continuous(
limits = c(0, 82),
breaks = seq(0, 80, 20),
expand = expansion(mult = c(0, 0.1))
) +
scale_x_continuous(breaks = seq(1952, 2007, 5)) +
labs(
x = "Year",
y = NULL,
colour = NULL
)
This line chart uses the afcharts theme and there are thin pale grey lines extending from the y axis. There are two thicker lines showing the life expectancy in the UK and China over time. The line colours are from the main Analysis Function palette - dark blue for China and orange for the UK, denoted by a legend at the bottom of the chart.
Legends should be avoided unless absolutely necessary, as these usually rely on using colour to match labels to data. More information can be found in the Analysis Function charts guidance. It is best practice to label lines directly, and an example of this can be found in the annotations section.
Bar charts
pop_bar_data <- gapminder |>
filter(year == 2007 & continent == "Americas") |>
slice_max(order_by = pop, n = 5)
ggplot(pop_bar_data, aes(x = reorder(country, -pop), y = pop)) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af() +
scale_y_continuous(
limits = c(0, 350E6),
labels = scales::label_number(scale = 1E-6),
expand = expansion(mult = c(0, 0.1)),
) +
labs(
x = NULL,
y = NULL,
)
This bar chart uses the afcharts theme, and shows the populations of the five most populous countries in the Americas. Each bar is dark blue and labelled by country underneath. All text is black in a sans serif font. Pale grey grid lines extend out from the y axis.
A bar chart can sometimes be easier to interpret with horizontal
bars. This can also be a good option if your bar labels are long and
difficult to display 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(pop_bar_data, aes(x = pop, y = reorder(country, pop))) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af(grid = "x", axis = "y") +
scale_x_continuous(
limits = c(0, 350E6),
labels = scales::label_number(scale = 1E-6),
expand = expansion(mult = c(0, 0.1))
) +
labs(
x = NULL,
y = NULL,
)
This bar chart uses the afcharts theme and displays the populations of the five most populous countries in the Americas. The country names are displayed on the y axis, with the bars extending from left to right. Each bar is dark blue, and pale grey grid lines extend up from the x axis.
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(
limits = c(0, 100),
breaks = c(seq(0, 100, 20)),
labels = c(seq(0, 100, 20)),
expand = expansion(mult = c(0, 0.1))
) +
theme_af(legend = "bottom") +
scale_fill_discrete_af() +
labs(
x = "Country",
y = NULL,
fill = NULL
)
This grouped bar chart uses the afcharts theme. It shows the life expectancy in 1967 and 2007 for four countries, which are displayed on the x axis. For each country there are two bars. The bar colours are from the main Analysis Function palette - dark blue for 1967 and orange for 2007, denoted by a legend at the bottom of the chart.
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(
labels = scales::percent,
expand = expansion(mult = c(0, 0.1))
) +
coord_cartesian(clip = "off") +
scale_fill_discrete_af() +
labs(
x = NULL,
y = NULL,
fill = "Life Expectancy",
)
This stacked bar chart uses the afcharts theme and shows the proportions of countries with a life expectancy over and under 75 by continent. The continents are listed along the x axis, with the y axis labelled between 0% and 100% in breaks of 25%. The colours for the bar segments are from the main Analysis Function palette - dark blue for under 75 and orange for over 75, denoted by a legend at the bottom of the chart. There is whitespace between each bar.
Histograms
gapminder |>
filter(year == 2007) |>
ggplot(aes(x = lifeExp)) +
geom_histogram(
binwidth = 5,
colour = "white",
fill = af_colour_values["dark-blue"]
) +
theme_af() +
scale_y_continuous(
limits = c(0, 35),
breaks = c(seq(0, 35, 5)),
expand = expansion(mult = c(0, 0.1))
) +
labs(
x = NULL,
y = "Number of \ncountries",
)
This histogram uses the afcharts theme, and shows the distribution of life expectancy by number of countries. There are pale grey grid lines extending out from the y axis. The bars are dark blue with white space between each.
Scatterplots
gapminder |>
filter(year == 2007) |>
ggplot(aes(x = gdpPercap, y = lifeExp)) +
geom_point(colour = af_colour_values["dark-blue"]) +
theme_af(axis = "none", grid = "xy") +
scale_x_continuous(labels = scales::label_comma()) +
labs(
x = "GDP (US$, inflation-adjusted)",
y = "Life\nExpectancy",
)
This scatterplot uses the afcharts theme, and shows life expectancy against GDP for all countries. Thin pale grey lines extend out from the x and y axis labels, forming a grid. The data points are plotted as dark blue circles. Both axes are labeled in black using a sans serif font.
Small multiples
gapminder |>
filter(continent != "Oceania") |>
group_by(continent, year) |>
summarise(pop = sum(as.numeric(pop)), .groups = "drop") |>
ggplot(aes(x = year, y = pop, fill = continent)) +
geom_area() +
theme_af(axis = "none", ticks = "none", legend = "none") +
scale_fill_discrete_af() +
facet_wrap(~ continent, ncol = 2) +
scale_y_continuous(
breaks = c(0, 2e9, 4e9),
labels = c(0, "2bn", "4bn")
) +
coord_cartesian(clip = "off") +
theme(axis.text.x = element_blank()) +
labs(
x = NULL,
y = NULL,
)
This chart uses the afcharts theme. It contains four subplots in a two by two grid showing how the populations of four continents have changed over time. Each subplot is labelled with the continent. The subplots have a common y axis, with no values on the x axis to facilitate for a simple comparison of the relative values. Each subplot is filled with a different colour from the Analysis Function categorical colour palette to be distinct from other subplots.
Pie charts
stacked_bar_data |>
filter(continent == "Europe") |>
ggplot(aes(x = "", y = n_countries, fill = lifeExpGrouped)) +
geom_col(colour = "white", position = "fill") +
coord_polar(theta = "y") +
theme_af(grid = "none", axis = "none", ticks = "none") +
theme(axis.text = element_blank()) +
scale_fill_discrete_af() +
labs(
x = NULL,
y = NULL,
fill = NULL,
)
This pie chart uses the afcharts theme, showing the proportions of European countries with a life expectancy under and over 75. The segment colours are from the Analysis Function main palette, with the smaller under 75 segment in dark blue, and the larger over 75 segment in orange. This is indicated by a legend to the right of the pie chart. There is whitespace separating the segments from each other.
Focus charts
pop_bar_data |>
ggplot(aes(x = reorder(country, -pop), y = pop, fill = country == "Brazil")) +
geom_col() +
theme_af(legend = "none") +
scale_y_continuous(
limits = c(0, 350E6),
labels = scales::label_number(scale = 1E-6),
expand = expansion(mult = c(0, 0.1))
) +
scale_fill_discrete_af("focus", reverse = TRUE) +
labs(
x = NULL,
y = NULL,
)
This bar chart uses the afcharts theme, and shows the populations of five countries of the Americas in descending order. The country names are given on the x axis, with all chart text in black in a sans serif font. Four of the bars on the chart are light grey, and the bar for Brazil is filled in dark blue to highlight it.
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
plotlydoes not recognise this font. To work around this you should add a further call tothemeto 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.
p <-
pop_bar_data |>
# Format text for tooltips
mutate(
tooltip = paste0(
"Country: ", country, "\n",
"Population (millions): ", round(pop / 10 ^ 6, 1)
)
) |>
ggplot(aes(x = reorder(country, -pop), y = pop, text = tooltip)) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af(ticks = "x") +
theme(text = element_text(family = "")) +
scale_y_continuous(
limits = c(0, 350E6),
labels = scales::label_number(scale = 1E-6),
expand = expansion(mult = c(0, 0.1))
) +
labs(
x = NULL,
y = NULL
)
plotly::ggplotly(p, tooltip = "text") |>
plotly::config(
modeBarButtons = list(list("resetViews")),
displaylogo = FALSE
)This is an interactive bar chart using the afcharts theme, showing the populations of the five most populous countries in the Americas. The bars are dark blue. A tooltip appears when hovering over each bar with the cursor, which shows a matching dark blue box with the country and population displayed in white text.
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. Note that black text has been used for the labels, as this ensures sufficient contrast against the white background.
ann_data |>
ggplot(aes(x = year, y = lifeExp)) +
geom_line(
aes(colour = country),
linewidth = 1
) +
theme_af(legend = "none") +
scale_colour_discrete_af() +
scale_y_continuous(
limits = c(0, 82),
breaks = seq(0, 80, 20),
expand = expansion(mult = c(0, 0.1))
) +
scale_x_continuous(
limits = c(1952, 2017),
breaks = seq(1952, 2017, 5)
) +
annotate(
geom = "label",
x = 2008, y = 73,
label = "China",
label.size = NA,
hjust = 0,
vjust = 0.5
) +
annotate(
geom = "label",
x = 2008,
y = 79.4,
label = "United Kingdom",
label.size = NA,
hjust = 0,
vjust = 0.5
) +
labs(
x = "Year",
y = NULL,
)
The line chart uses the afcharts theme, and has one blue and one orange line. These are each labelled with a sans serif font in black at the end of the line.
However, this code is difficult to reuse as the annotation position 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)) +
geom_line(
aes(colour = country),
linewidth = 1
) +
theme_af(legend = "none") +
scale_colour_discrete_af() +
scale_y_continuous(
limits = c(0, 82),
breaks = seq(0, 80, 20),
expand = expansion(mult = c(0, 0.1))
) +
scale_x_continuous(
limits = c(1952, 2017),
breaks = seq(1952, 2017, 5)
) +
geom_label(
data = ann_labs,
aes(x = year, y = lifeExp, label = country),
hjust = 0,
vjust = 0.5,
nudge_x = 0.5,
label.size = NA
) +
labs(
x = "Year",
y = NULL,
)
The line chart uses the afcharts theme and appears highly similar to the previous plot. It has one blue and one orange line with each labelled with a sans serif font in black at the end of the line.
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(pop_bar_data, aes(x = reorder(country, -pop), y = pop)) +
geom_col(fill = af_colour_values["dark-blue"]) +
geom_text(
aes(label = round(pop / 1E6, 1)),
vjust = 1.2,
colour = "white"
) +
theme_af() +
scale_y_continuous(
limits = c(0, 350E6),
labels = scales::label_number(scale = 1E-6),
expand = expansion(mult = c(0, 0.1))
) +
labs(
x = NULL,
y = NULL
)
This bar chart uses the afcharts theme. All the bars are dark blue and have white text labels at the top of each bar showing the y axis value.
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
population from the bottom of the chart up. To sort in descending order,
you would change this to reorder(country, desc(pop)).
population_chart <- pop_bar_data |>
ggplot(aes(x = pop, y = reorder(country, pop))) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af(axis = "y", grid = "x") +
labs(
x = NULL,
y = NULL
)
print(population_chart)
This bar chart uses the afcharts theme and shows the population of five countries. The country names are given on the y axis, with the bars extending from left to right, sorted by values decreasing from the top down. All the bars are in dark blue, and there is a small white gap between the bars and the y axis. The x axis values are given in exponential notation.
Examples in the following sections iterate on this chart.
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 = expansion(mult = c(0, 0.1)))
This horizontal bar chart uses the afcharts theme. It shows the populations of five countries, with country names given on the y axis. All the bars are in dark blue and against the y axis. The x axis values are given in exponential notation.
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(
breaks = seq(0, 400E6, 100E6),
labels = seq(0, 400, 100),
limits = c(0, 420E6),
expand = expansion(mult = c(0, 0.1))
) +
labs(
x = "Population (millions)"
)
This horizonal bar chart uses the afcharts theme and shows the populations of five countries. The country names are given on the y axis, and all the bars are in blue. The x axis is labeled every 100 from zero to 400. The x axis label indicates that population is reported in millions.
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. Setting
the limits manually based on the range of the calculated breaks ensures
the highest gridline value is above the maximum value of the data.
breaks_pretty <- pretty(pop_bar_data$pop)
limits_pretty <- range(breaks_pretty)
last_plot() +
scale_x_continuous(
breaks = breaks_pretty,
labels = label_number(scale = 1E-6),
limits = limits_pretty,
expand = expansion(mult = c(0, 0.2))
) +
labs(
x = "Population (millions)"
)
This horizonal bar chart uses the afcharts theme and shows the populations of five countries. The country names are given on the y axis, and all the bars are in blue. The x axis is labeled every 50 from zero to 350. The x axis label indicates that population is reported in millions.
Formatting labels
Formatting axis labels or legend labels is easily handled using the
scales package. The following example formats y axis labels
as percentages, however scales can also handle currency and
thousands separators.
stacked_bar_data |>
ggplot(aes(x = continent, y = n_countries, fill = lifeExpGrouped)) +
geom_bar(stat = "identity", position = "fill") +
theme_af(legend = "bottom") +
scale_y_continuous(
expand = expansion(mult = c(0, 0.1)),
labels = scales::percent
) +
scale_fill_discrete_af() +
labs(
x = NULL,
y = NULL,
fill = "Life Expectancy"
)
This stacked bar uses the afcharts theme. The y axis is labelled every 25 from 0 to 100 percent, with light grey grid lines and tick marks going horizontally from the y axis.
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. In the previous plot, 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")
This is a stacked bar chart with the afcharts theme, where the top gridline is the same width as the adjoining tick mark and other grid lines.
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 = expansion(mult = c(0, 0.1))
) +
scale_x_continuous(breaks = seq(1952, 2007, 5)) +
labs(
x = "Year",
y = NULL
)
This line chart uses the afcharts theme. There is a solid dark blue line trending upwards, and a dashed teal line horizontal to 75 on the y-axis. There are also pale grey grid lines extending from regular intervals on the y axis.
Embedding chart titles
As mentioned previously, chart titles, subtitles, and captions should
be included as titles or the main body of the text where possible for
accessibility purposes. However, these can embedded into the chart image
with labs() if required. A title can be removed using
NULL.
population_chart +
labs(
x = NULL,
y = NULL,
title = stringr::str_wrap(
paste("The U.S.A. has the highest population in the Americas"),
width = 40
),
subtitle = "Population of countries of the Americas (millions), 2007",
caption = "Source: Gapminder"
)
In the bar chart image, the title gives a data insight and is presented in large bold text at the top. This is followed by smaller text giving a brief description of the data. In the bottom left of the image is a data citation as a caption.
The y-axis title should be horizontal, not vertical. The position can
sometimes look odd, especially for longer titles. You can use the plot
subtitle as a description of the y-axis and set the y-axis title to
NULL.
ggplot(pop_bar_data, aes(x = reorder(country, -pop), y = pop)) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af() +
scale_y_continuous(
limits = c(0, 350E6),
labels = scales::label_number(scale = 1E-6),
expand = expansion(mult = c(0, 0.1)),
) +
labs(
x = NULL,
y = "Population (millions)",
title = stringr::str_wrap(
"The U.S.A. is the most populous country in the Americas",
35
),
caption = "Source: Gapminder"
)
In this bar chart image, the y axis title, “Population (millions)”, is to the left of the top of the y axis.
ggplot(pop_bar_data, aes(x = reorder(country, -pop), y = pop)) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af() +
scale_y_continuous(
limits = c(0, 350E6),
labels = scales::label_number(scale = 1E-6),
expand = expansion(mult = c(0, 0.1)),
) +
labs(
x = NULL,
y = NULL,
title = stringr::str_wrap(
"The U.S.A. is the most populous country in the Americas",
40
),
subtitle = "Population (millions)",
caption = "Source: Gapminder"
)
In this bar chart image, the y axis title, “Population (millions)”, is above and to the right of the y axis.
Wrapping text
If text is too long, it may be cut off or distort the dimensions of the chart.
plot <-
ggplot(pop_bar_data, aes(x = reorder(country, -pop), y = pop)) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af() +
scale_y_continuous(labels = label_number(scale = 1E-6),
expand = expansion(mult = c(0, 0.1))) +
labs(
x = NULL,
subtitle = "Population of countries in the Americas, 2007",
caption = "Source: Gapminder"
)
plot +
labs(
y = "Population in millions",
title = paste("The U.S.A. is the most populous country in ",
"the Americas")
)
In this bar chart image, the title text is being cut off with only part of the text appearing in the image.
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 = "Population\nin millions",
title = stringr::str_wrap(
paste("The U.S.A. is the most populous country in ",
"the Americas"),
width = 40
)
)
In this bar chart image, the y axis label and chart title text have been wrapped onto two lines so that all the text is visible.
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(pop_bar_data, aes(x = reorder(country, -pop), y = pop)) +
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 = expansion(mult = c(0, 0.1)),
limits = c(0, 350E6),
labels = scales::label_number(scale = 1E-6)
) +
labs(
x = NULL,
y = NULL
)
This bar chart uses the afcharts theme. The bars are dark blue, with
axis lines and ticks coloured black as opposed to the default
theme_af grey.
You may also consider using markdown or HTML formatted text within
your charts. This can be readily achieved with
ggtext::element_markdown().
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 = expansion(mult = c(0, 0.1))
) +
scale_x_continuous(
limits = c(1952, 2017),
breaks = seq(1952, 2017, 5)
) +
geom_label(
data = ann_labs,
aes(x = year, y = lifeExp, label = 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 years 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())
In this line chart image, the word ‘China’ and ‘United Kingdom’ in the subtitle and label text have been set to correspond to their line colours. Please refer to Analysis Function guidance in considering the accessibility of custom formatting.
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,
colour = NULL
)
This line chart uses the afcharts theme and the Analysis Function main palette, with one line in dark blue and one in orange.
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,
)
This line chart uses the afcharts theme with a single green line.
Alternatively, you may wish to use multiple custom colours:
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,
colour = NULL
)
This line chart uses the afcharts theme. One line is green and the other is black.
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.
Acknowledgments
The afcharts package is based on the sgplot package, written by Alice Hannah.
This cookbook and the examples it contains have been inspired by the BBC Visual and Data Journalism cookbook for R graphics and their bbplot package.
The data used throughout the cookbook is from gapminder.