Arrange 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)

Arguments

facets

passed to facet_wrap

grid

character vector of the grid layout to use (currently only "us_state_grid1" and "us_state_grid2" are available)

label

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.

move_axes

should axis labels and ticks be moved to the closest panel along the margins?

...

additional parameters passed to facet_wrap

Examples

if (FALSE) { 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)) }