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.
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 homepage.
Note on use of titles, subtitles and captions
Titles, subtitles and captions have been embedded in the charts in this
cookbook for demonstration purposes. However, for accessibility reasons,
it is usually preferable to provide titles in the body of the page
rather than embedded within the image of the plot.
The following packages are required to produce the example charts in this cookbook:
library(afcharts)
library(ggplot2)
library(dplyr)
library(ggtext)
library(scales)
# Use gapminder data for cookbook charts
library(gapminder)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,
title = "Living Longer",
subtitle = "Life Expectancy in the United Kingdom 1952-2007",
caption = "Source: Gapminder"
)
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,
title = "Living Longer",
subtitle = "Life Expectancy in the United Kingdom and China 1952-2007",
caption = "Source: Gapminder",
colour = NULL
)
An example with line labels and no legend can be found in the Adding 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,
title = stringr::str_wrap(
"The U.S.A. is the most populous country in the Americas",
40
),
subtitle = "Population of countries in the Americas (millions), 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(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,
title = stringr::str_wrap(
"The U.S.A. is the most populous country in the Americas",
40
),
subtitle = "Population of countries in the Americas (millions), 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(
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,
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(
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",
title = "How life expectancy varies across continents",
subtitle = "Percentage of countries by life expectancy band, 2007",
caption = "Source: Gapminder"
)
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",
title = "How life expectancy varies",
subtitle = "Distribution of life expectancy, 2007",
caption = "Source: Gapminder"
)
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",
title = stringr::str_wrap(
"The relationship between GDP and Life Expectancy is complex",
40
),
subtitle = "GDP and Life Expectancy for all countires, 2007",
caption = "Source: Gapminder"
)
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,
title = "Asia's rapid growth",
subtitle = "Population growth by continent, 1952-2007",
caption = "Source: Gapminder"
)
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,
title = "How life expectancy varies in Europe",
subtitle = "Percentage of countries by life expectancy band, 2007",
caption = "Source: Gapminder"
)
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,
title = stringr::str_wrap(
"Brazil has the second highest population in the Americas",
40
),
subtitle = "Population of countries in the Americas (millions), 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
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. This example therefore has no title, subtitle or caption.
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 = "Population (millions)"
)
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. 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,
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)) +
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,
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(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,
title = stringr::str_wrap(
"The U.S.A. is the most populous country in the Americas",
40
),
subtitle = "Population of countries in the Americas (millions), 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
population. 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")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.
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"
)
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:
population_chart +
scale_x_continuous(expand = expansion(mult = c(0, 0.1)))
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.
population_chart +
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)"
)
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 with a custom limits_pretty function ensures the
highest gridline value is above the maximum value of the data.
limits_pretty <- function(x) range(pretty(x))
population_chart +
scale_x_continuous(
breaks = pretty,
labels = label_number(scale = 1E-6),
limits = limits_pretty,
expand = expansion(mult = c(0, 0.2))
) +
labs(
x = "Population (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",
title = "How life expectancy varies across continents",
subtitle = "Percentage of countries by life expectancy band, 2007",
caption = "Source: Gapminder"
)
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 = expansion(mult = c(0, 0.1))
) +
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(pop_bar_data, aes(x = reorder(country, -pop), y = pop)) +
geom_col(fill = af_colour_values["dark-blue"]) +
theme_af() +
scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
labs(
x = NULL,
subtitle = "Population of countries in the Americas (millions), 2007",
caption = "Source: Gapminder"
)
plot +
labs(
y = "Population of country",
title = paste("The U.S.A. is the most populous country in ",
"the Americas")
)
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\n of country",
title = stringr::str_wrap(
paste("The U.S.A. is the most populous country in ",
"the Americas"),
width = 40
)
)
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,
title = stringr::str_wrap(
"The U.S.A. is the most populous country in the Americas",
40
),
subtitle = "Population of countries in the Americas (millions), 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 = 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 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.
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.