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< section id="examples" class="level2">
Examples
Yes/No map of London administrative areas:
if (!require(remotes)) install.packages("remotes")
if (!require(ukmaps)) remotes::install_github("pachadotdev/ukmaps")
── R CMD build ─────────────────────────────────────────────────────────────────
* checking for file ‘/tmp/RtmpAfJKmO/remotes1bf76f146e9df9/pachadotdev-ukmaps-458273a/DESCRIPTION’ ... OK
* preparing ‘ukmaps’:
* checking DESCRIPTION meta-information ... OK
* checking for LF line-endings in source and make files and shell scripts
* checking for empty or unneeded directories
* building ‘ukmaps_0.0.4.tar.gz’
if (!require(dplyr)) install.packages("dplyr")
if (!require(ggplot2)) install.packages("ggplot2")
library(ukmaps)
library(dplyr)
library(ggplot2)
d <- boundaries %>%
mutate(
region_name = if_else(is.na(region_name), "Notr Available", region_name),
is_london = if_else(region_name == "London", "Yes", "No")
)
pal <- c("#165976", "#d04e66")
ggplot(d) +
geom_sf(aes(fill = is_london, geometry = geometry), color = "white", linewidth = 0) +
scale_fill_manual(values = pal, name = "Is this London?") +
labs(title = "Map of England with Administrative Boundaries") +
theme_minimal(base_size = 13)
Which part of London is Barnet?
d <- boundaries %>%
filter(region_name == "London") %>%
mutate(is_barnet = if_else(lad_name == "Barnet", "Yes", "No"))
pal <- c("#165976", "#d04e66")
ggplot(d) +
geom_sf(aes(fill = is_barnet, geometry = geometry), color = "white") +
scale_fill_manual(values = pal, name = "Is this Barnet?") +
labs(title = "Which part of London is Barnet?") +
theme_minimal(base_size = 13)
Which part of London is Golders Green?
d <- boundaries %>%
filter(region_name == "London") %>%
mutate(
is_golders_green = if_else(ward_name == "Golders Green", "Yes", "No")
)
pal <- c("#165976", "#d04e66")
ggplot(d) +
geom_sf(aes(fill = is_golders_green, geometry = geometry), color = "white") +
scale_fill_manual(values = pal, name = "Is this Golders Green?") +
labs(title = "Which part of London is Golders Green?") +
theme_minimal(base_size = 13)
The following maps use functions that aggregate the dataset to keep the package size small.
Country level map of the UK:
pal <- c("#165976", "#365158", "#d04e66", "#ffd613")
# country() aggregates the map to country level
ggplot(country()) +
geom_sf(aes(fill = country_name, geometry = geometry), color = "white") +
scale_fill_manual(values = pal, name = "Country") +
labs(title = "Map of England with Country Boundaries") +
theme_minimal(base_size = 13)
How many R’s in each county name?
# number of R's in county names
d <- counties() %>%
mutate(n = stringr::str_count(county_name, "[rR]"))
# region() aggregates the map to country level
ggplot(d) +
geom_sf(aes(fill = n, geometry = geometry), color = "white") +
scale_fill_gradient(low = "#165976", high = "#d04e66", name = "R's",
breaks = seq(0, max(d$n), by = 1)) +
labs(title = "How many R's in each county name?") +
theme_minimal(base_size = 13)
How many R’s in each LAD name? Local Authority Districts (LAD) (Local Government District (LGD) in Northern Ireland)
d <- lads() %>%
mutate(n = stringr::str_count(lad_name, "[rR]"))
ggplot(d) +
geom_sf(aes(fill = n, geometry = geometry), color = "white") +
scale_fill_gradient(low = "#165976", high = "#d04e66", name = "R's",
breaks = seq(0, max(d$n), by = 1)) +
labs(title = "How many R's in each LAD name?") +
theme_minimal(base_size = 13)
Now an example with real data for the election results (thanks to Dr. Catherine Moez for sharing the data):
library(sf)
library(tintin)
# filter the map to England and Wales, and aggregate to LAD level
# similar to the lads() function for the UK
d <- boundaries %>%
filter(country_name %in% c("England", "Wales")) %>%
group_by(lad_code, lad_name) %>%
summarise(geometry = st_union(geometry), .groups = "drop")
# Add the election results to the map
d <- d %>%
left_join(election_results) %>%
group_by(lad_name) %>%
filter(election_year == max(election_year, na.rm = TRUE)) %>%
ungroup()
# Expand LADs to all years, so all polygons appear in every year
all_years <- sort(unique(election_results$election_year))
all_lads <- d %>% select(lad_code, lad_name, geometry)
d <- tidyr::expand_grid(all_lads, election_year = all_years) %>%
left_join(election_results, by = c("lad_name", "election_year"))
ggplot(d) +
geom_sf(aes(fill = top_party, geometry = geometry), color = "white") +
# scale_fill_discrete(name = "Top Party") +
scale_fill_manual(values = tintin_clrs(
n = length(unique(d$top_party)),
option = "cigars of the pharaoh"),
na.value = "gray80",
name = "Top Party") +
labs(title = "UK Local Authority Districts - Top Party by Year",
subtitle = "Data: Dr. Catherine Moez") +
facet_wrap(~ election_year) +
theme_minimal(base_size = 13) +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
panel.background = element_rect(color = "black", fill = NA, linewidth = 2),
strip.background = element_rect(fill = "#012169", color = "black"),
strip.text = element_text(face = "bold", color = "white")
)
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