R/facet_geo.R
facet_geo.RdArrange a sequence of geographical panels into a grid that preserves some geographical orientation
facet_geo(facets, ..., grid = "us_state_grid1", label = NULL, move_axes = TRUE)passed to facet_wrap
character vector of the grid layout to use (currently only "us_state_grid1" and "us_state_grid2" are available)
an optional string denoting the name of a column in grid to use for facet labels. If NULL, the variable that best matches that in the data specified with facets will be used for the facet labels.
should axis labels and ticks be moved to the closest panel along the margins?
additional parameters passed to facet_wrap
if (FALSE) { # \dontrun{
library(ggplot2)
# barchart of state rankings in various categories
ggplot(state_ranks, aes(variable, rank, fill = variable)) +
geom_col() +
coord_flip() +
facet_geo(~ state) +
theme_bw()
# use an alternative US state grid and place
ggplot(state_ranks, aes(variable, rank, fill = variable)) +
geom_col() +
coord_flip() +
facet_geo(~ state, grid = "us_state_grid2") +
theme(panel.spacing = unit(0.1, "lines"))
# custom grid (move Wisconsin above Michigan)
my_grid <- us_state_grid1
my_grid$col[my_grid$code == "WI"] <- 7
ggplot(state_ranks, aes(variable, rank, fill = variable)) +
geom_col() +
coord_flip() +
facet_geo(~ state, grid = my_grid)
# plot unemployment rate time series for each state
ggplot(state_unemp, aes(year, rate)) +
geom_line() +
facet_geo(~ state) +
scale_x_continuous(labels = function(x) paste0("'", substr(x, 3, 4))) +
ylab("Unemployment Rate (%)") +
theme_bw()
# plot the 2016 unemployment rate
ggplot(subset(state_unemp, year == 2016), aes(factor(year), rate)) +
geom_col(fill = "steelblue") +
facet_geo(~ state) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
ylab("Unemployment Rate (%)") +
xlab("Year")
# plot European Union GDP
ggplot(eu_gdp, aes(year, gdp_pc)) +
geom_line(color = "steelblue") +
geom_hline(yintercept = 100, linetype = 2) +
facet_geo(~ name, grid = "eu_grid1") +
scale_x_continuous(labels = function(x) paste0("'", substr(x, 3, 4))) +
ylab("GDP Per Capita") +
theme_bw()
# use a free x-axis to look at just change
ggplot(eu_gdp, aes(year, gdp_pc)) +
geom_line(color = "steelblue") +
facet_geo(~ name, grid = "eu_grid1", scales = "free_y") +
scale_x_continuous(labels = function(x) paste0("'", substr(x, 3, 4))) +
ylab("GDP Per Capita in Relation to EU Index (100)") +
theme_bw()
# would be nice if ggplot2 had a "sliced" option...
# (for example, there's not much going on with Denmark but it looks like there is)
# plot European Union annual # of resettled persons
ggplot(eu_imm, aes(year, persons)) +
geom_line() +
facet_geo(~ name, grid = "eu_grid1") +
scale_x_continuous(labels = function(x) paste0("'", substr(x, 3, 4))) +
scale_y_sqrt(minor_breaks = NULL) +
ylab("# Resettled Persons") +
theme_bw()
# plot just for 2016
ggplot(subset(eu_imm, year == 2016), aes(factor(year), persons)) +
geom_col(fill = "steelblue") +
geom_text(aes(factor(year), 3000, label = persons), color = "gray") +
facet_geo(~ name, grid = "eu_grid1") +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
ylab("# Resettled Persons in 2016") +
xlab("Year") +
theme_bw()
# plot Australian population
ggplot(aus_pop, aes(age_group, pop / 1e6, fill = age_group)) +
geom_col() +
facet_geo(~ code, grid = "aus_grid1") +
coord_flip() +
labs(
title = "Australian Population Breakdown",
caption = "Data Source: ABS Labour Force Survey, 12 month average",
y = "Population [Millions]") +
theme_bw()
# South Africa population density by province
ggplot(sa_pop_dens, aes(factor(year), density, fill = factor(year))) +
geom_col() +
facet_geo(~ province, grid = "sa_prov_grid1") +
labs(title = "South Africa population density by province",
caption = "Data Source: Statistics SA Census",
y = "Population density per square km") +
theme_bw()
# use the Afrikaans name stored in the grid, "name_af", as facet labels
ggplot(sa_pop_dens, aes(factor(year), density, fill = factor(year))) +
geom_col() +
facet_geo(~ code, grid = "sa_prov_grid1", label = "name_af") +
labs(title = "South Africa population density by province",
caption = "Data Source: Statistics SA Census",
y = "Population density per square km") +
theme_bw()
# affordable housing starts by year for boroughs in London
ggplot(london_afford, aes(x = year, y = starts, fill = year)) +
geom_col(position = position_dodge()) +
facet_geo(~ code, grid = "london_boroughs_grid", label = "name") +
labs(title = "Affordable Housing Starts in London",
subtitle = "Each Borough, 2015-16 to 2016-17",
caption = "Source: London Datastore", x = "", y = "")
# dental health in Scotland
ggplot(nhs_scot_dental, aes(x = year, y = percent)) +
geom_line() +
facet_geo(~ name, grid = "nhs_scot_grid") +
scale_x_continuous(breaks = c(2004, 2007, 2010, 2013)) +
scale_y_continuous(breaks = c(40, 60, 80)) +
labs(title = "Child Dental Health in Scotland",
subtitle = "Percentage of P1 children in Scotland with no obvious decay experience.",
caption = "Source: statistics.gov.scot", x = "", y = "")
# India population breakdown
ggplot(subset(india_pop, type == "state"),
aes(pop_type, value / 1e6, fill = pop_type)) +
geom_col() +
facet_geo(~ name, grid = "india_grid1", label = "code") +
labs(title = "Indian Population Breakdown",
caption = "Data Source: Wikipedia",
x = "",
y = "Population [Millions]") +
theme_bw() +
theme(axis.text.x = element_text(angle = 40, hjust = 1))
ggplot(subset(india_pop, type == "state"),
aes(pop_type, value / 1e6, fill = pop_type)) +
geom_col() +
facet_geo(~ name, grid = "india_grid2", label = "name") +
labs(title = "Indian Population Breakdown",
caption = "Data Source: Wikipedia",
x = "",
y = "Population [Millions]") +
theme_bw() +
theme(axis.text.x = element_text(angle = 40, hjust = 1),
strip.text.x = element_text(size = 6))
# A few ways to look at the 2016 election results
ggplot(election, aes("", pct, fill = candidate)) +
geom_col(alpha = 0.8, width = 1) +
scale_fill_manual(values = c("#4e79a7", "#e15759", "#59a14f")) +
facet_geo(~ state, grid = "us_state_grid2") +
scale_y_continuous(expand = c(0, 0)) +
labs(title = "2016 Election Results",
caption = "Data Source: 2016 National Popular Vote Tracker",
x = NULL,
y = "Percentage of Voters") +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
strip.text.x = element_text(size = 6))
ggplot(election, aes(candidate, pct, fill = candidate)) +
geom_col() +
scale_fill_manual(values = c("#4e79a7", "#e15759", "#59a14f")) +
facet_geo(~ state, grid = "us_state_grid2") +
theme_bw() +
coord_flip() +
labs(title = "2016 Election Results",
caption = "Data Source: 2016 National Popular Vote Tracker",
x = NULL,
y = "Percentage of Voters") +
theme(strip.text.x = element_text(size = 6))
ggplot(election, aes(candidate, votes / 1000000, fill = candidate)) +
geom_col() +
scale_fill_manual(values = c("#4e79a7", "#e15759", "#59a14f")) +
facet_geo(~ state, grid = "us_state_grid2") +
coord_flip() +
labs(title = "2016 Election Results",
caption = "Data Source: 2016 National Popular Vote Tracker",
x = NULL,
y = "Votes (millions)") +
theme(strip.text.x = element_text(size = 6))
} # }