examples/gallery/gallery.R
In houseofcommonslibrary/clcharts: Themes, Colors and Tools for Making Charts in the House of Commons Library style
### Functions to create a gallery of examples using the theme and scales
# Imports ---------------------------------------------------------------------
library(tidyverse)
library(scales)
devtools::load_all(".")
# Constants -------------------------------------------------------------------
DATASET_DIR <- "datasets"
DATASET_CON_FILE <- file.path(DATASET_DIR, "constituencies.csv")
DATASET_MIGRATION_FILE <- file.path(DATASET_DIR, "migration.csv")
DATASET_MIGRATION_NATIONALITY_FILE <- file.path(DATASET_DIR, "migration-nationality.csv")
DATASET_NURSES_FILE <- file.path(DATASET_DIR, "nurses.csv")
GALLERY_DIR <- "."
GALLERY_FILE <- "gallery.html"
GALLERY_BAR_CONS_REGION_NAME <- file.path("bar-cons-region")
GALLERY_LINE_MIGRATION_NAME <- file.path("line-migration")
GALLERY_BAR_MIGRATION_NAME <- file.path("bar-migration")
GALLERY_AREA_NURSES_NAME <- file.path("area-nurses")
GALLERY_SCATTER_CONS_TYPE_NAME <- file.path("scatter-cons-type")
GALLERY_SCATTER_CONS_FACET_NAME <- file.path("scatter-cons-facet")
SVG_WIDTH <- 8
SVG_HEIGHT <- 5
# Font setup: Change these paths for your local setup -------------------------
sysfonts::font_add(
family = "National-LFSN Semibd",
regular = "/Users/oli/Library/Fonts/National-LFSN-Semibold.ttf")
sysfonts::font_add(
family = "National-LFSN Book",
regular = "/Users/oli/Library/Fonts/National-LFSN-Book.ttf")
sysfonts::font_add(
family = "Open Sans",
regular = "/Users/oli/Library/Fonts/OpenSans-Regular.ttf",
bold = "/Users/oli/Library/Fonts/OpenSans-Bold.ttf")
showtext::showtext_auto()
# Build gallery ---------------------------------------------------------------
build_example <- function(example_name, example_func) {
plot <- example_func()
svgfile <- file.path(GALLERY_DIR, str_glue("{example_name}.svg"))
save_svg(plot, svgfile, SVG_WIDTH, SVG_HEIGHT)
pngfile <- file.path(GALLERY_DIR, str_glue("{example_name}.png"))
save_png(plot, pngfile, SVG_WIDTH, SVG_HEIGHT)
}
build_gallery <- function() {
examples <- list()
examples[[GALLERY_BAR_CONS_REGION_NAME]] = example_bar_cons_region
examples[[GALLERY_LINE_MIGRATION_NAME]] = example_line_migration
examples[[GALLERY_BAR_MIGRATION_NAME]] = example_bar_migration
examples[[GALLERY_AREA_NURSES_NAME]] = example_area_nurses
examples[[GALLERY_SCATTER_CONS_TYPE_NAME]] = example_scatter_cons_type
examples[[GALLERY_SCATTER_CONS_FACET_NAME]] = example_scatter_cons_facet
elements <- map_chr(names(examples), function(example_name) {
build_example(example_name, examples[[example_name]])
element <- str_glue(
"<p>
<img src=\"{example_name}.svg\" height=\"500\" width=\"800\" />
</p>")
})
elements <- str_c(elements, collapse = "")
html <- str_glue(
"<html><head></head><body>{elements}</body></html>")
htmlfile <- file.path(GALLERY_DIR, GALLERY_FILE)
write_file(html, htmlfile)
}
# Examples --------------------------------------------------------------------
example_bar_cons_region <- function() {
df <- read_csv(DATASET_CON_FILE)
df$region <- factor(df$region)
df$region <- fct_rev(reorder(df$region, df$region, length))
plot <- ggplot(data = df) +
geom_bar(
mapping = aes(x = region),
fill = commonslib_color("commons_green"),
width = 0.8) +
geom_text_commonslib(
stat = "count",
mapping = aes(x = region, label = ..count..),
vjust = "top",
nudge_y = -3) +
labs(
x = "Country or region",
y = "Number of constituencies",
caption = "Source: House of Commons Library") +
scale_x_discrete(
expand = expansion(add = c(0.5, 0.5))) +
scale_y_continuous(expand = c(0,0)) +
theme_commonslib(axes = "b", grid = "h", caption_position = "left") +
theme(legend.position = "none")
plot <- add_commonslib_titles(
plot,
title = "Countries and regions vary in representation",
subtitle = "Constituencies by country or region, United Kingdom")
plot
}
example_line_migration <- function() {
df <- read_csv(DATASET_MIGRATION_FILE)
plot <- ggplot(
data = df,
mapping = aes(x = quarter, y = estimate, color = flow)) +
geom_line(size = 1.1) +
labs(
color = NULL,
x = NULL,
y = "Thousands of people",
caption = "Source: ONS, Provisional LTIM estimates") +
scale_y_continuous(
breaks = seq(0, 800, 200),
limits = c(0, 800)) +
coord_cartesian(expand = FALSE) +
theme_commonslib(axes = "b", grid = "h") +
scale_color_manual(values = c(
"Immigration" = commonslib_color("commons_green"),
"Net migration" = commonslib_color("ocean_green"))) +
#theme(legend.position = "top", legend.direction = "horizontal")
theme(
legend.position = c(1, 0.99),
legend.justification = c(1, 1),
legend.direction = "horizontal")
plot <- add_commonslib_titles(
plot,
title = "Net migration has fallen since the EU referendum",
subtitle = "International migration in the year ending each quarter")
}
example_bar_migration <- function() {
df <- read_csv(DATASET_MIGRATION_NATIONALITY_FILE)
df$year <- as.character(df$year)
df$nationality <- factor(df$nationality, levels = c("Non-EU", "EU", "British"))
plot <- ggplot(
data = df,
mapping = aes(x = year, y = estimate, fill = nationality)) +
geom_col(width = 0.8) +
labs(
x = NULL,
y = NULL,
fill = NULL,
caption = "Source: ONS, Provisional LTIM estimates") +
scale_x_discrete() +
scale_y_continuous(
limits = c(0, 700),
breaks = seq(0, 700, 100),
expand = c(0,0)) +
theme_commonslib(
legend_position = "top-right",
caption_position = "left") +
scale_fill_manual(values = c(
"British" = commonslib_color("tangerine"),
"EU" = commonslib_color("commons_green"),
"Non-EU" = commonslib_color("ocean_green")))
plot <- add_commonslib_titles(
plot,
title = "Immigration is stable but the composition has changed",
subtitle = "Immigration by nationality in each year ending September, Thousands")
}
example_area_nurses <- function() {
df <- read_csv(DATASET_NURSES_FILE) %>%
pivot_longer(
cols = -date,
names_to = "care_setting",
values_to = "number")
df$care_setting <- factor(
df$care_setting,
levels = c("neonatal", "maternity"))
plot <- ggplot(
data = df,
mapping = aes(x = date, y = number, fill = care_setting)) +
geom_area() +
labs(
x = NULL,
y = NULL) +
scale_y_continuous(
label = comma,
limits = c(0, 9000),
breaks = seq(0, 9000, 3000)) +
scale_x_date() +
coord_cartesian(expand = FALSE) +
annotate_commonslib(
x = as.Date("2015-01-01"),
y = 5500,
label = "Neonatal nurses",
color = "#202020",
hjust = 0) +
annotate_commonslib(
x = as.Date("2015-01-01"),
y = 1500,
label = "Maternity nurses",
color = "#ffffff",
hjust = 0) +
theme_commonslib(grid = "h") +
scale_fill_manual(values = c(
"neonatal" = commonslib_color("ocean_green"),
"maternity" = commonslib_color("commons_green"))) +
theme(legend.position = "none")
plot <- add_commonslib_titles(
plot,
title = "Neonatal nurses have overtaken maternity nurses",
subtitle = "Maternity and neonatal nurses in England")
}
example_scatter_cons_type <- function() {
types <- c("London", "Other city", "Large town",
"Medium town", "Small town", "Village")
df <- read_csv(DATASET_CON_FILE)
df <- df %>% filter(! is.na(classification))
df$type <- factor(df$classification, levels = types)
plot <- ggplot(
data = df,
mapping = aes(x = median_age, y = turnout, color = type)) +
geom_point(
shape = 16,
size = 2) +
labs(
x = "Median age",
y = "Turnout",
color = "Settlement class",
caption = "Source: House of Commons Library") +
scale_x_continuous(
limits = c(25, 55)) +
scale_y_continuous(
limits = c(0.5, 0.8),
label = percent_format(accuracy = 1)) +
coord_cartesian(expand = FALSE) +
theme_commonslib(grid = "hv") +
scale_color_manual(values = c(
"London" = commonslib_color("commons_green"),
"Other city" = commonslib_color("ocean_green"),
"Large town" = commonslib_color("grape"),
"Medium town" = commonslib_color("lilac"),
"Small town" = commonslib_color("tangerine"),
"Village" = commonslib_color("burnt_orange")))
plot <- add_commonslib_titles(
plot,
title = "Turnout was higher in older, less urban constituencies",
subtitle = "Constituencies by age, turnout and settlement class, 2017")
}
example_scatter_cons_facet <- function() {
types <- c("London", "Other city", "Large town",
"Medium town", "Small town", "Village")
df <- read_csv(DATASET_CON_FILE)
df <- df %>% filter(! is.na(classification))
df$type <- factor(df$classification, levels = types)
plot <- ggplot(
data = df,
mapping = aes(x = median_age, y = turnout, color = type)) +
geom_point(
shape = 16,
size = 2,
alpha = 0.6) +
facet_wrap(~ type) +
labs(
x = "Median age",
y = "Turnout",
color = "Settlement class",
caption = "Source: House of Commons Library") +
scale_x_continuous(
limits = c(25, 55)) +
scale_y_continuous(
limits = c(0.5, 0.85),
label = percent_format(accuracy = 1)) +
coord_cartesian(expand = FALSE) +
theme_commonslib(axes = "", grid = "hv") +
scale_color_manual(values = c(
"London" = commonslib_color("commons_green"),
"Other city" = commonslib_color("ocean_green"),
"Large town" = commonslib_color("grape"),
"Medium town" = commonslib_color("lilac"),
"Small town" = commonslib_color("tangerine"),
"Village" = commonslib_color("burnt_orange"))) +
theme(legend.position ="None")
plot <- add_commonslib_titles(
plot,
title = "Turnout was higher in older, less urban constituencies",
subtitle = "Constituencies by age, turnout and settlement class, 2017")
}
houseofcommonslibrary/clcharts documentation built on June 10, 2025, 9:16 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
### Functions to create a gallery of examples using the theme and scales
# Imports ---------------------------------------------------------------------
library(tidyverse)
library(scales)
devtools::load_all(".")
# Constants -------------------------------------------------------------------
DATASET_DIR <- "datasets"
DATASET_CON_FILE <- file.path(DATASET_DIR, "constituencies.csv")
DATASET_MIGRATION_FILE <- file.path(DATASET_DIR, "migration.csv")
DATASET_MIGRATION_NATIONALITY_FILE <- file.path(DATASET_DIR, "migration-nationality.csv")
DATASET_NURSES_FILE <- file.path(DATASET_DIR, "nurses.csv")
GALLERY_DIR <- "."
GALLERY_FILE <- "gallery.html"
GALLERY_BAR_CONS_REGION_NAME <- file.path("bar-cons-region")
GALLERY_LINE_MIGRATION_NAME <- file.path("line-migration")
GALLERY_BAR_MIGRATION_NAME <- file.path("bar-migration")
GALLERY_AREA_NURSES_NAME <- file.path("area-nurses")
GALLERY_SCATTER_CONS_TYPE_NAME <- file.path("scatter-cons-type")
GALLERY_SCATTER_CONS_FACET_NAME <- file.path("scatter-cons-facet")
SVG_WIDTH <- 8
SVG_HEIGHT <- 5
# Font setup: Change these paths for your local setup -------------------------
sysfonts::font_add(
family = "National-LFSN Semibd",
regular = "/Users/oli/Library/Fonts/National-LFSN-Semibold.ttf")
sysfonts::font_add(
family = "National-LFSN Book",
regular = "/Users/oli/Library/Fonts/National-LFSN-Book.ttf")
sysfonts::font_add(
family = "Open Sans",
regular = "/Users/oli/Library/Fonts/OpenSans-Regular.ttf",
bold = "/Users/oli/Library/Fonts/OpenSans-Bold.ttf")
showtext::showtext_auto()
# Build gallery ---------------------------------------------------------------
build_example <- function(example_name, example_func) {
plot <- example_func()
svgfile <- file.path(GALLERY_DIR, str_glue("{example_name}.svg"))
save_svg(plot, svgfile, SVG_WIDTH, SVG_HEIGHT)
pngfile <- file.path(GALLERY_DIR, str_glue("{example_name}.png"))
save_png(plot, pngfile, SVG_WIDTH, SVG_HEIGHT)
}
build_gallery <- function() {
examples <- list()
examples[[GALLERY_BAR_CONS_REGION_NAME]] = example_bar_cons_region
examples[[GALLERY_LINE_MIGRATION_NAME]] = example_line_migration
examples[[GALLERY_BAR_MIGRATION_NAME]] = example_bar_migration
examples[[GALLERY_AREA_NURSES_NAME]] = example_area_nurses
examples[[GALLERY_SCATTER_CONS_TYPE_NAME]] = example_scatter_cons_type
examples[[GALLERY_SCATTER_CONS_FACET_NAME]] = example_scatter_cons_facet
elements <- map_chr(names(examples), function(example_name) {
build_example(example_name, examples[[example_name]])
element <- str_glue(
"<p>
<img src=\"{example_name}.svg\" height=\"500\" width=\"800\" />
</p>")
})
elements <- str_c(elements, collapse = "")
html <- str_glue(
"<html><head></head><body>{elements}</body></html>")
htmlfile <- file.path(GALLERY_DIR, GALLERY_FILE)
write_file(html, htmlfile)
}
# Examples --------------------------------------------------------------------
example_bar_cons_region <- function() {
df <- read_csv(DATASET_CON_FILE)
df$region <- factor(df$region)
df$region <- fct_rev(reorder(df$region, df$region, length))
plot <- ggplot(data = df) +
geom_bar(
mapping = aes(x = region),
fill = commonslib_color("commons_green"),
width = 0.8) +
geom_text_commonslib(
stat = "count",
mapping = aes(x = region, label = ..count..),
vjust = "top",
nudge_y = -3) +
labs(
x = "Country or region",
y = "Number of constituencies",
caption = "Source: House of Commons Library") +
scale_x_discrete(
expand = expansion(add = c(0.5, 0.5))) +
scale_y_continuous(expand = c(0,0)) +
theme_commonslib(axes = "b", grid = "h", caption_position = "left") +
theme(legend.position = "none")
plot <- add_commonslib_titles(
plot,
title = "Countries and regions vary in representation",
subtitle = "Constituencies by country or region, United Kingdom")
plot
}
example_line_migration <- function() {
df <- read_csv(DATASET_MIGRATION_FILE)
plot <- ggplot(
data = df,
mapping = aes(x = quarter, y = estimate, color = flow)) +
geom_line(size = 1.1) +
labs(
color = NULL,
x = NULL,
y = "Thousands of people",
caption = "Source: ONS, Provisional LTIM estimates") +
scale_y_continuous(
breaks = seq(0, 800, 200),
limits = c(0, 800)) +
coord_cartesian(expand = FALSE) +
theme_commonslib(axes = "b", grid = "h") +
scale_color_manual(values = c(
"Immigration" = commonslib_color("commons_green"),
"Net migration" = commonslib_color("ocean_green"))) +
#theme(legend.position = "top", legend.direction = "horizontal")
theme(
legend.position = c(1, 0.99),
legend.justification = c(1, 1),
legend.direction = "horizontal")
plot <- add_commonslib_titles(
plot,
title = "Net migration has fallen since the EU referendum",
subtitle = "International migration in the year ending each quarter")
}
example_bar_migration <- function() {
df <- read_csv(DATASET_MIGRATION_NATIONALITY_FILE)
df$year <- as.character(df$year)
df$nationality <- factor(df$nationality, levels = c("Non-EU", "EU", "British"))
plot <- ggplot(
data = df,
mapping = aes(x = year, y = estimate, fill = nationality)) +
geom_col(width = 0.8) +
labs(
x = NULL,
y = NULL,
fill = NULL,
caption = "Source: ONS, Provisional LTIM estimates") +
scale_x_discrete() +
scale_y_continuous(
limits = c(0, 700),
breaks = seq(0, 700, 100),
expand = c(0,0)) +
theme_commonslib(
legend_position = "top-right",
caption_position = "left") +
scale_fill_manual(values = c(
"British" = commonslib_color("tangerine"),
"EU" = commonslib_color("commons_green"),
"Non-EU" = commonslib_color("ocean_green")))
plot <- add_commonslib_titles(
plot,
title = "Immigration is stable but the composition has changed",
subtitle = "Immigration by nationality in each year ending September, Thousands")
}
example_area_nurses <- function() {
df <- read_csv(DATASET_NURSES_FILE) %>%
pivot_longer(
cols = -date,
names_to = "care_setting",
values_to = "number")
df$care_setting <- factor(
df$care_setting,
levels = c("neonatal", "maternity"))
plot <- ggplot(
data = df,
mapping = aes(x = date, y = number, fill = care_setting)) +
geom_area() +
labs(
x = NULL,
y = NULL) +
scale_y_continuous(
label = comma,
limits = c(0, 9000),
breaks = seq(0, 9000, 3000)) +
scale_x_date() +
coord_cartesian(expand = FALSE) +
annotate_commonslib(
x = as.Date("2015-01-01"),
y = 5500,
label = "Neonatal nurses",
color = "#202020",
hjust = 0) +
annotate_commonslib(
x = as.Date("2015-01-01"),
y = 1500,
label = "Maternity nurses",
color = "#ffffff",
hjust = 0) +
theme_commonslib(grid = "h") +
scale_fill_manual(values = c(
"neonatal" = commonslib_color("ocean_green"),
"maternity" = commonslib_color("commons_green"))) +
theme(legend.position = "none")
plot <- add_commonslib_titles(
plot,
title = "Neonatal nurses have overtaken maternity nurses",
subtitle = "Maternity and neonatal nurses in England")
}
example_scatter_cons_type <- function() {
types <- c("London", "Other city", "Large town",
"Medium town", "Small town", "Village")
df <- read_csv(DATASET_CON_FILE)
df <- df %>% filter(! is.na(classification))
df$type <- factor(df$classification, levels = types)
plot <- ggplot(
data = df,
mapping = aes(x = median_age, y = turnout, color = type)) +
geom_point(
shape = 16,
size = 2) +
labs(
x = "Median age",
y = "Turnout",
color = "Settlement class",
caption = "Source: House of Commons Library") +
scale_x_continuous(
limits = c(25, 55)) +
scale_y_continuous(
limits = c(0.5, 0.8),
label = percent_format(accuracy = 1)) +
coord_cartesian(expand = FALSE) +
theme_commonslib(grid = "hv") +
scale_color_manual(values = c(
"London" = commonslib_color("commons_green"),
"Other city" = commonslib_color("ocean_green"),
"Large town" = commonslib_color("grape"),
"Medium town" = commonslib_color("lilac"),
"Small town" = commonslib_color("tangerine"),
"Village" = commonslib_color("burnt_orange")))
plot <- add_commonslib_titles(
plot,
title = "Turnout was higher in older, less urban constituencies",
subtitle = "Constituencies by age, turnout and settlement class, 2017")
}
example_scatter_cons_facet <- function() {
types <- c("London", "Other city", "Large town",
"Medium town", "Small town", "Village")
df <- read_csv(DATASET_CON_FILE)
df <- df %>% filter(! is.na(classification))
df$type <- factor(df$classification, levels = types)
plot <- ggplot(
data = df,
mapping = aes(x = median_age, y = turnout, color = type)) +
geom_point(
shape = 16,
size = 2,
alpha = 0.6) +
facet_wrap(~ type) +
labs(
x = "Median age",
y = "Turnout",
color = "Settlement class",
caption = "Source: House of Commons Library") +
scale_x_continuous(
limits = c(25, 55)) +
scale_y_continuous(
limits = c(0.5, 0.85),
label = percent_format(accuracy = 1)) +
coord_cartesian(expand = FALSE) +
theme_commonslib(axes = "", grid = "hv") +
scale_color_manual(values = c(
"London" = commonslib_color("commons_green"),
"Other city" = commonslib_color("ocean_green"),
"Large town" = commonslib_color("grape"),
"Medium town" = commonslib_color("lilac"),
"Small town" = commonslib_color("tangerine"),
"Village" = commonslib_color("burnt_orange"))) +
theme(legend.position ="None")
plot <- add_commonslib_titles(
plot,
title = "Turnout was higher in older, less urban constituencies",
subtitle = "Constituencies by age, turnout and settlement class, 2017")
}
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