# NOT RUN {
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: http://bit.ly/2016votecount",
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: http://bit.ly/2016votecount",
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: http://bit.ly/2016votecount",
x = NULL,
y = "Votes (millions)") +
theme(strip.text.x = element_text(size = 6))
# }
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