examples/recipes/pyramid-chart/pyramid-chart.R

In houseofcommonslibrary/clcharts: Themes, Colors and Tools for Making Charts in the House of Commons Library style

# This is an example of how to make a pyramid chart using the clcharts package.
#
# This script is self-contained: running it will create a PNG and an SVG of the
# example chart in the same directory. To run the script will need to ensure:
#
# 1. You have installed all of the packages that the script imports
# 2. You have the theme fonts set up on your computer
# 3. You have the dataset "pyramid-chart.csv" in the same directory
#
# See the readme on GitHub to find out how to install the package and setup
# the fonts.
#
# To run the script, type the following code in your R console:
#
# source("pyramid-chart.R")

# Imports ---------------------------------------------------------------------

library(tidyverse)
library(scales)
library(clcharts)

# Read in and prepare the data ------------------------------------------------

# Load the data from the csv as a dataframe
df <- read_csv("pyramid-chart.csv")

# Create age range groups and cut age variable to groups
age_breaks <- c(seq(from = -1, to = 90, by = 5), 90)
age_labels <- c("0-4", "5-9", "10-14", "15-19", "20-24",
    "25-29", "30-34", "35-39", "40-44", "45-49", "50-54",
    "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90+")
df$age_group <- cut(df$age, breaks = age_breaks, labels = age_labels)

# Multiply female by -1 so that columns will split left and right
df$female <- df$female * -1

# Privot dataframe from wide to long and sum population for each age group
df <- df %>%
    pivot_longer(-c(age, age_group), names_to = "sex", values_to = "count") %>%
    group_by(sex, age_group) %>%
    summarise(count = sum(count))

# Divide count by one thound to get values in thousands
df$count <- df$count / 1000

# Create plot -----------------------------------------------------------------

# Use ggplot to create a plot with data and mappings
plot <- ggplot(
        data = df,
        mapping = aes(x = age_group, y = count, fill = sex)) +
    # Add a col geometry for columns: use width = 0.8 to match house style;
    # geom_col will plot the values for each category
    geom_col(width = 0.8) +
    # Configure the x and y axes: we set the y axis breaks and limits, and
    # we turn off the y-axis expansion
    scale_x_discrete() +
    scale_y_continuous(
        limits = c(-2500, 2500),
        breaks = seq(-2500, 2500, 500),
        labels = comma(c(
            seq(from = 2500, to = 500, by = -500),
            seq(from = 0, to = 2500, by = 500)),
        expand = c(0,0))) +
    # Use the coord_flip function to flip the axes: this will turn a vertical
    # column chart into a horizontal bar chart
    coord_flip() +
    # Set labels for the axes, legend, and caption: DON'T set titles here
    labs(
        x = NULL,
        y = NULL,
        fill = NULL,
        caption = "Source: ONS, Mid-year population 2016") +
    # Use annotate_commonslib to add annotations to a plot: this function does
    # the same thing as annotate but it automatically sets the fonts to match
    # the house style; position each annotation using values on the axis scales
    annotate_commonslib(
        x = 19,
        y = 1750,
        label = "Male",
        size = 3.5) +
    annotate_commonslib(
        x = 19,
        y = -1750,
        label = "Female",
        size = 3.5) +
    # Add the Commons Library theme: we don't specify settings for the axes and
    # grid which means we are using the defaults; we set the legend and caption
    # positions
    theme_commonslib(
        legend_position = "none",
        caption_position = "right",
        axes = "h",
        grid = "v") +
    scale_fill_manual(values = c(
        commonslib_color("commons_green"),
        commonslib_color("ocean_green")))

# After creating the plot, add a title and subtitle with add_commonslib_titles
plot <- add_commonslib_titles(
    plot,
    title = "Women outnumber men over the age of 80",
    subtitle = "UK mid-year 2016 population by gender and age, Millions")

# Save the plot in different formats ------------------------------------------

# Save a high resolution export of the plot as a png
save_png(
    "pyramid-chart.png",
    plot = plot,
    width = 8,
    height = 8)

# Save an editable verson of the plot as an svg
save_svg(
    "pyramid-chart.svg",
    plot = plot,
    width = 8,
    height = 8)