Package 'geofacet' reference manual

Title:	'ggplot2' Faceting Utilities for Geographical Data
Description:	Provides geographical faceting functionality for 'ggplot2'. Geographical faceting arranges a sequence of plots of data for different geographical entities into a grid that preserves some of the geographical orientation.
Authors:	Ryan Hafen [aut, cre], Barret Schloerke [ctb]
Maintainer:	Ryan Hafen <[email protected]>
License:	MIT + file LICENSE
Version:	0.2.2
Built:	2024-11-22 02:51:45 UTC
Source:	https://github.com/hafen/geofacet

Attach a SpatialPolygonsDataFrame object to a grid

Description

Attach a SpatialPolygonsDataFrame object to a grid

Usage

attach_spdf(x, spdf)
attach_spdf(x, spdf)

Arguments

`x`	object to attach SpatialPolygonsDataFrame object to
`spdf`	a SpatialPolygonsDataFrame object to attach

aus_pop

Description

March 2017 population data for Australian states and territories by age group. Source: http://lmip.gov.au/default.aspx?LMIP/Downloads/ABSLabourForceRegion.

Usage

aus_pop
aus_pop

auto_states

Description

List of valid values for countries for fetching rnaturalearth data when used with grid_auto to create a grid of states.

List of valid values for continents for fetching rnaturalearth data when used with grid_auto to create a grid of countries.

election

Description

2016 US presidential election results, obtained from https://docs.google.com/spreadsheets/d/133Eb4qQmOxNvtesw2hdVns073R68EZx4SfCnP4IGQf8/htmlview?sle=true.

Usage

election
election

GDP per capita in PPS - Index (EU28 = 100). "Gross domestic product (GDP) is a measure for the economic activity. It is defined as the value of all goods and services produced less the value of any goods or services used in their creation. The volume index of GDP per capita in Purchasing Power Standards (PPS) is expressed in relation to the European Union (EU28) average set to equal 100. If the index of a country is higher than 100, this country's level of GDP per head is higher than the EU average and vice versa. Basic figures are expressed in PPS, i.e. a common currency that eliminates the differences in price levels between countries allowing meaningful volume comparisons of GDP between countries. Please note that the index, calculated from PPS figures and expressed with respect to EU28 = 100, is intended for cross-country comparisons rather than for temporal comparisons." Source is no longer available (previously at http://ec.europa.eu/eurostat/web/national-accounts/data/main-tables). Dataset ID: tec00114.

Usage

eu_gdp
eu_gdp

eu_imm

Description

Annual number of resettled persons for each EU country. "Resettled refugees means persons who have been granted an authorization to reside in a Member State within the framework of a national or Community resettlement scheme.". Source: https://ec.europa.eu/eurostat/cache/metadata/en/migr_asydec_esms.htm. Dataset ID: tps00195.

Usage

eu_imm
eu_imm

Arrange a sequence of geographical panels into a grid that preserves some geographical orientation

Description

Arrange a sequence of geographical panels into a grid that preserves some geographical orientation

Usage

facet_geo(facets, ..., grid = "us_state_grid1", label = NULL, move_axes = TRUE)
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

## 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: 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))

## End(Not run)
## 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: 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))

## End(Not run)

geofacet

Description

For examples, see facet_geo.

Author(s)

Maintainer: Ryan Hafen [email protected]

Other contributors:

Barret Schloerke [email protected] [contributor]

Perform post-processing on a facet_geo ggplot object

Description

Perform post-processing on a facet_geo ggplot object

Usage

get_geofacet_grob(x)
get_geofacet_grob(x)

Arguments

`x`	object of class 'facet_geo'

Get a list of valid grid names

Description

Get a list of valid grid names

Usage

get_grid_names()
get_grid_names()

Get rnaturalearth data

Description

Get rnaturalearth data

Usage

get_ne_data(code)
get_ne_data(code)

Arguments

code

A country/continent name to get rnaturalearth data from (see auto_countries or auto_states).

Examples

## Not run: 
dat <- get_ne_data("brazil")

## End(Not run)
## Not run: 
dat <- get_ne_data("brazil")

## End(Not run)

Generate a grid automatically from a country/continent name or a SpatialPolygonsDataFrame or 'sf' polygons

Description

Generate a grid automatically from a country/continent name or a SpatialPolygonsDataFrame or 'sf' polygons

Usage

grid_auto(x, names = NULL, codes = NULL, seed = NULL)
grid_auto(x, names = NULL, codes = NULL, seed = NULL)

Arguments

`x`	A country/continent name, a SpatialPolygonsDataFrame or 'sf' polygons to build a grid for.
`names`	An optional vector of variable names in `x@data` to use as "name_" columns in the resulting grid.
`codes`	An optional vector of variable names in `x@data` to use as "code_" columns in the resulting grid.
`seed`	An optional random seed sent to `calculate_grid`.

Details

If a country or continent name is specified for x, it can be any of the strings found in auto_countries or auto_states. In this case, the rnaturalearth package will be searched for the corresponding shapefiles. You can use get_ne_data to see what these shapefiles look like.

The columns of the @data component of resulting shapefile (either user-specified or fetched from rnaturalearth) are those that will be available to names and codes.

Examples

## Not run: 
# auto grid using a name to identify the country
grd <- grid_auto("brazil", seed = 1234)
grid_preview(grd, label = "name")
# open the result up in the grid designer for further refinement
grid_design(grd, label = "name")

# using a custom file (can be GeoJSON or shapefile)
ff <- system.file("extdata", "bay_counties.geojson", package = "geogrid")
bay_shp <- sf::st_read(ff)
grd <- grid_auto(bay_shp, seed = 1) # names are inferred
grid_preview(grd, label = "name_county")
grid_design(grd, label = "code_fipsstco")

# explicitly specify the names and codes variables to use
grd <- grid_auto(bay_shp, seed = 1, names = "county", codes = "fipsstco")
grid_preview(grd, label = "name_county")
grid_preview(grd, label = "code_fipsstco")

## End(Not run)
## Not run: 
# auto grid using a name to identify the country
grd <- grid_auto("brazil", seed = 1234)
grid_preview(grd, label = "name")
# open the result up in the grid designer for further refinement
grid_design(grd, label = "name")

# using a custom file (can be GeoJSON or shapefile)
ff <- system.file("extdata", "bay_counties.geojson", package = "geogrid")
bay_shp <- sf::st_read(ff)
grd <- grid_auto(bay_shp, seed = 1) # names are inferred
grid_preview(grd, label = "name_county")
grid_design(grd, label = "code_fipsstco")

# explicitly specify the names and codes variables to use
grd <- grid_auto(bay_shp, seed = 1, names = "county", codes = "fipsstco")
grid_preview(grd, label = "name_county")
grid_preview(grd, label = "code_fipsstco")

## End(Not run)

Interactively design a grid

Description

Interactively design a grid

Usage

grid_design(data = NULL, img = NULL, label = "code", auto_img = TRUE)
grid_design(data = NULL, img = NULL, label = "code", auto_img = TRUE)

Arguments

`data`	A data frame containing a grid to start from or NULL if starting from scratch.
`img`	An optional URL pointing to a reference image containing a geographic map of the entities in the grid.
`label`	An optional column name to use as the label for plotting the original geography, if attached to `data`.
`auto_img`	If the original geography is attached to `data`, should a plot of that be created and uploaded to the viewer?

Examples

# edit aus_grid1
grid_design(data = aus_grid1, img = "http://www.john.chapman.name/Austral4.gif")
# start with a clean slate
grid_design()
# arrange the alphabet
grid_design(data.frame(code = letters))
# edit aus_grid1
grid_design(data = aus_grid1, img = "http://www.john.chapman.name/Austral4.gif")
# start with a clean slate
grid_design()
# arrange the alphabet
grid_design(data.frame(code = letters))

Plot a preview of a grid

Description

Plot a preview of a grid

Usage

grid_preview(x, label = NULL, label_raw = NULL, do_plot = TRUE)
grid_preview(x, label = NULL, label_raw = NULL, do_plot = TRUE)

Arguments

`x`	a data frame containing a grid
`label`	the column name in `x` that should be used for text labels in the grid plot
`label_raw`	the column name in the optional SpatialPolygonsDataFrame attached to `x` that should be used for text labels in the raw geography plot
`do_plot`	should the grid preview be plotted?

Examples

grid_preview(us_state_grid2)
grid_preview(eu_grid1, label = "name")
grid_preview(us_state_grid2)
grid_preview(eu_grid1, label = "name")

Submit a grid to be included in the package

Description

Submit a grid to be included in the package

Usage

grid_submit(x, name = NULL, desc = NULL)
grid_submit(x, name = NULL, desc = NULL)

Arguments

`x`	a data frame containing a grid
`name`	proposed name of the grid (if not supplied, will be asked for interactively)
`desc`	a description of the grid (if not supplied, will be asked for interactively)

Details

This opens up a github issue for this package in the web browser with pre-populated content for adding a grid to the package.

Examples

## Not run: 
my_grid <- us_state_grid1
my_grid$col[my_grid$label == "WI"] <- 7
grid_submit(my_grid, name = "us_grid_tweak_wi",
  desc = "Modified us_state_grid1 to move WI over")

## End(Not run)
## Not run: 
my_grid <- us_state_grid1
my_grid$col[my_grid$label == "WI"] <- 7
grid_submit(my_grid, name = "us_grid_tweak_wi",
  desc = "Modified us_state_grid1 to move WI over")

## End(Not run)

Geo Grids

Description

There are now 141 grids available in this package and more online. To view a full list of available grids, see here. To create and submit your own grid, see here. To see several examples of grids being used to visualize data, see facet_geo.

us_state_grid1: Grid layout for US states (including DC) Image reference here.

us_state_grid2: Grid layout for US states (including DC) Image reference here.

eu_grid1: Grid layout for the 28 EU Countries Image reference here.

aus_grid1: Grid layout for the Australian States and Territories. Image reference here. Thanks to jonocarroll.

sa_prov_grid1: Grid layout for the provinces of South Africa Image reference here. Thanks to jonmcalder.

gb_london_boroughs_grid: Grid layout for the boroughs of London. Note that the column code_ons contains the codes used by UK Office for National Statistics. Image reference here. Thanks to eldenvo.

nhs_scot_grid: Grid layout for a grid of NHS Scotland Health Boards. Note that the column code contains the codes used by UK Office for National Statistics. Image reference here. Thanks to jsphdms.

india_grid1: Grid layout for India states (not including union territories). Image reference here. Thanks to meysubb.

india_grid2: Grid layout for India states (not including union territories). Image reference here.

argentina_grid1: Grid for the 23 provinces of Argentina. It includes the Malvinas/Falkland Islands and the Antarctic Territories (these are disputed, but they are included since many researchers might use data from these locations). Image reference here. Thanks to eliocamp.

br_states_grid1: Grid for the 27 states of Brazil. Image reference here. Thanks to italocegatta.

sea_grid1: Grid for South East Asian countries. Image reference here. Thanks to jasonjb82.

mys_grid1: Grid for Malaysian states and territories. Image reference here. Thanks to jasonjb82.

fr_regions_grid1: Land and overseas regions of France. Codes are INSEE codes. Image reference here. Thanks to mtmx.

de_states_grid1: Grid for the German states ('Länder') Image reference here. Thanks to DominikVogel.

us_or_counties_grid1: Grid for Oregon counties. Image reference here. Thanks to aosmith16.

us_wa_counties_grid1: Grid for Washington counties. Image reference here.

us_in_counties_grid1: Grid for Indiana counties. Image reference here. Thanks to nateapathy.

us_in_central_counties_grid1: Grid for central Indiana counties. Image reference here. Thanks to nateapathy.

se_counties_grid1: Grid for counties of Sweden. Image reference here. Thanks to duleise.

sf_bay_area_counties_grid1: Grid of the 9 San Francisco Bay Area counties. Image reference here. Thanks to Eunoia.

ua_region_grid1: Grid of administrative divisions of Ukraine (24 oblasts, one autonomous region, and two cities). Image reference here. Thanks to woldemarg.

mx_state_grid1: Grid layout for the states of Mexico. Image reference here. Thanks to ikashnitsky.

mx_state_grid2: Grid layout for the states of Mexico. Image reference here. Thanks to diegovalle.

scotland_local_authority_grid1: Grid layout for the local authorities of Scotland. Image reference here. Thanks to davidhen.

us_state_without_DC_grid1: Grid layout for US states (excluding DC) Image reference here. Thanks to ejr248.

italy_grid1: Grid layout for regions of Italy (in collaboration with Stella Cangelosi and Luciana Dalla Valle). Image reference here. Thanks to JulianStander.

italy_grid2: Grid layout for regions of Italy (in collaboration with Stella Cangelosi and Luciana Dalla Valle). Image reference here. Thanks to JulianStander.

be_province_grid1: Grid layout for provinces of Belgium plus Brussels, including names in three languages (French, Dutch, English) and Belgium internal codes (NIS). Image reference here. Thanks to ericlecoutre.

us_state_grid3: Grid layout for US states (including DC). Image reference here. Thanks to kanishkamisra.

jp_prefs_grid1: Grid layout for the prefectures of Japan. Image reference here. Thanks to uribo.

ng_state_grid1: Grid layout for the 37 Federal States of Nigeria. Image reference here. Thanks to aledemogr.

bd_upazila_grid1: Grid layout for Bangladesh 64 Upazilas. Image reference here. Thanks to aledemogr.

spain_prov_grid1: Grid layout for Provinces of Spain. Image reference here. Thanks to kintero.

ch_cantons_grid1: Grid layout for Cantons of Switzerland. Image reference here. Thanks to tinu-schneider.

ch_cantons_grid2: Grid layout for Cantons of Switzerland. Image reference here. Thanks to rastrau.

china_prov_grid1: Grid layout for Provinces of China. Image reference here. Thanks to weiyunna.

world_86countries_grid: Grid layout for 86 countries in the world. Image reference here. Thanks to akangsha.

se_counties_grid2: Grid for counties of Sweden. Image reference here. Thanks to richardohrvall.

uk_regions1: Grid for regions of the UK (aka EU standard NUTS 1 areas). Image reference here. Thanks to paulb20.

us_state_contiguous_grid1: Grid layout for the contiguous US states (including DC). Image reference here. Thanks to andrewsr.

sk_province_grid1: Grid layout for South Korean sis and dos (metropolitan/special/autonomous cities and provinces). Image reference here. Thanks to heon131.

ch_aargau_districts_grid1: Grid layout for Districts of the Canton of Aargau, Switzerland. Image reference here. Thanks to zumbov2.

jo_gov_grid1: Grid layout for Governorates of Jordan. Image reference here. Thanks to aledemogr.

spain_ccaa_grid1: Grid layout for Spanish 'Comunidades Autónomas'. Image reference here. Thanks to JoseAntonioOrtega.

spain_prov_grid2: Grid layout for Provinces of Spain. Image reference here. Thanks to JoseAntonioOrtega.

world_countries_grid1: Grid layout for countries of the world, with a few exclusions. See . Image reference here. Thanks to JoseAntonioOrtega.

br_states_grid2: Grid for the 27 states of Brazil. Image reference here. Thanks to hafen.

china_city_grid1: Grid layout of cities in China. Image reference here. Thanks to CharleneDeng1.

kr_seoul_district_grid1: Grid layout of Seoul's 25 districts. Image reference here. Thanks to yonghah.

nz_regions_grid1: Grid layout for regions of New Zealand. Image reference here. Thanks to pierreroudier.

sl_regions_grid1: Grid layout of Slovenian regions. Image reference here. Thanks to SR1986.

us_census_div_grid1: Grid layout of US Census divisions. Image reference here. Thanks to mkiang.

ar_tucuman_province_grid1: Grid layout for Argentina Tucumán Province political divisions (departments) Image reference here. Thanks to TuQmano.

us_nh_counties_grid1: Grid layout for the 10 counties in New Hampshire. Image reference here. Thanks to ghost.

china_prov_grid2: Grid layout for Provinces of China. Image reference here. Thanks to jw2531.

pl_voivodeships_grid1: Grid layout for Polish voivodeships (provinces) Image reference here. Thanks to erzk.

us_ia_counties_grid1: Grid layout for counties in Iowa Image reference here. Thanks to jrennyb.

us_id_counties_grid1: Grid layout for counties in Idaho Image reference here. Thanks to hathawayj.

ar_cordoba_dep_grid1: Grid layout for departments of Cordoba province in Argentina. Image reference here. Thanks to TuQmano.

us_fl_counties_grid1: Grid for Florida counties. Image reference here. Thanks to ejr248.

ar_buenosaires_communes_grid1: Grid for communes of Buenos Aires, Argentina. Image reference here. Thanks to TuQmano.

nz_regions_grid2: Grid layout for regions of New Zealand. Image reference here. Thanks to pierreroudier.

oecd_grid1: Grid layout for OECD member countries. Image reference here. Thanks to arcruz0.

ec_prov_grid1: Grid layout for provinces of Ecuador Image reference here. Thanks to Ricardo95RM.

nl_prov_grid1: Grid layout for provinces of Netherlands Image reference here. Thanks to ruditurksema.

ca_prov_grid1: Grid layout for provinces of Canada Image reference here. Thanks to michael-chong.

us_nc_counties_grid1: Grid layout for Counties of North Carolina, United States Image reference here. Thanks to mtdukes.

mx_ciudad_prov_grid1: Grid layout for Districts of Mexico City, Mexico Image reference here. Thanks to Ivangea.

bg_prov_grid1: Grid layout for provinces of Bulgaria Image reference here. Thanks to savinastoitsova.

us_hhs_regions_grid1: This grid approximates the U.S. Health and Human Services Region map Image reference here. Thanks to akitepowell.

tw_counties_grid1: Grid layout for Counties of Taiwan Image reference here. Thanks to csh484912274.

tw_counties_grid2: Grid layout for Counties of Taiwan including Lienchiang County Image reference here. Thanks to csh484912274.

af_prov_grid1: Grid layout for Provinces of Afghanistan Image reference here. Thanks to jrennyb.

us_mi_counties_grid1: Grid layout for Counties of Michigan, United States Image reference here. Thanks to jrennyb.

pe_prov_grid1: Grid layout for Provinces of Peru Image reference here. Thanks to jmcastagnetto.

sa_prov_grid2: Grid layout for Provinces of South Africa Image reference here. Thanks to kamermanpr.

mx_state_grid3: Grid layout for States of Mexico Image reference here. Thanks to ikashnitsky.

cn_bj_districts_grid1: Grids for Administrative Districts of Beijing, China Image reference here. Thanks to shiedelweiss.

us_va_counties_grid1: Grids for Counties of Virginia, United States Image reference here. Thanks to joshyazman.

us_mo_counties_grid1: Grids for Counties of Missouri, United States Image reference here. Thanks to Yanqi-Xu.

cl_santiago_prov_grid1: Communes of Santiago Province, Chile Image reference here. Thanks to robsalasco.

us_tx_capcog_counties_grid1: This is a grid of a 10 county planning region around Austin, Texas, United States Image reference here. Thanks to mth444.

sg_planning_area_grid1: Grids for Planning Areas of Singapore Image reference here. Thanks to Elenafuyi.

in_state_ut_grid1: Grid of Indian States and Union Territories Image reference here. Thanks to seanangio.

cn_fujian_prov_grid1: Grid of counties of Fujian Province, China Image reference here. Thanks to nannanchen333.

ca_quebec_electoral_districts_grid1: Grid of Electoral Districts of Québec, Canada Image reference here. Thanks to jhroy.

nl_prov_grid2: Grid with the provinces of The Netherlands with codes that are used by the statistical institute of NL Image reference here. Thanks to edwindj.

cn_bj_districts_grid2: Grid with districts of Beijing, China Image reference here. Thanks to zouhx11.

ar_santiago_del_estero_prov_grid1: Grid with districts of Santiago del Estero Province, Argentina Image reference here. Thanks to TuQmano.

ar_formosa_prov_grid1: Grid with districts of Formosa Province, Argentina Image reference here. Thanks to TuQmano.

ar_chaco_prov_grid1: Grid with districts of Chaco Province, Argentina Image reference here. Thanks to TuQmano.

ar_catamarca_prov_grid1: Grid with districts of Catamarca Province, Argentina Image reference here. Thanks to TuQmano.

ar_jujuy_prov_grid1: Grid with districts of Jujuy Province, Argentina Image reference here. Thanks to TuQmano.

ar_neuquen_prov_grid1: Grid with districts of Neuquen Province, Argentina Image reference here. Thanks to TuQmano.

ar_san_luis_prov_grid1: Grid with districts of San Luis Province, Argentina Image reference here. Thanks to TuQmano.

ar_san_juan_prov_grid1: Grid with districts of San Juan Province, Argentina Image reference here. Thanks to TuQmano.

ar_santa_fe_prov_grid1: Grid with districts of Santa Fe Province, Argentina Image reference here. Thanks to TuQmano.

ar_la_rioja_prov_grid1: Grid with districts of La Rioja Province, Argentina Image reference here. Thanks to TuQmano.

ar_mendoza_prov_grid1: Grid with districts of Mendoza Province, Argentina Image reference here. Thanks to TuQmano.

ar_salta_prov_grid1: Grid with districts of Salta Province, Argentina Image reference here. Thanks to TuQmano.

ar_rio_negro_prov_grid1: Grid with districts of Rio Negro Province, Argentina Image reference here. Thanks to TuQmano.

uy_departamentos_grid1: Grid with Departamentos of Uruguay Image reference here. Thanks to TuQmano.

ar_buenos_aires_prov_electoral_dist_grid1: Grid with Electoral Districts of Buenos Aires Province, Argentina Image reference here. Thanks to TuQmano.

europe_countries_grid1: Grid layout for all European countries except Vatican City, Monaco, San Marino and Liechtenstein Image reference here. Thanks to THargreaves.

argentina_grid2: Grid layout for Argentina without Islas Malvinas and Antártida Argentina Image reference here. Thanks to TuQmano.

us_state_without_DC_grid2: Grid layout for United States with AK and HI flush with CA Image reference here. Thanks to christophercannon.

jp_prefs_grid2: Grid layout for Prefectures of Japan Image reference here. Thanks to Ryo-N7.

na_regions_grid1: Regions of Namibia Image reference here. Thanks to stedy.

mm_state_grid1: States of Myanmar Image reference here. Thanks to htinkyawaye.

us_state_with_DC_PR_grid1: United States of America including Washington, D.C. and Puerto Rico Image reference here. Thanks to krothkin.

fr_departements_grid1: Grid for France's departements, the second levels of administrative boundaries after the regions Image reference here. Thanks to tvroylandt.

ar_salta_prov_grid2: Grids for Salta Province Argentina Image reference here. Thanks to tartagalensis.

ie_counties_grid1: Ireland counties. Code is the car number plate abbreviation for Republic counties, similar for the six counties of Northern Ireland. Tipperary is split North / South for historical reasons Image reference here. Thanks to eugene100hickey.

sg_regions_grid1: Urban planning regions of Singapore Image reference here. Thanks to erhuttk.

us_ny_counties_grid1: Counties of New York State, United States Image reference here. Thanks to jjdfsny.

ru_federal_subjects_grid1: Federal Subjects of Russia Image reference here. Thanks to ParanoidAndroid18.

us_ca_counties_grid1: Counties of the State of California, United States Image reference here. Thanks to MartinLe5.

lk_districts_grid1: Second level administrative divisions of Sri Lanka Image reference here. Thanks to thiyangt.

us_state_without_DC_grid3: United States grid without Washington, D.C Image reference here. Thanks to ghost.

co_cali_subdivisions_grid1: Corregimientos of Cali, Columbia Image reference here. Thanks to Carolina101.

us_in_northern_counties_grid1: Northern Counties of Indiana, United States Image reference here. Thanks to robertoge.

italy_grid3: Autonomous Provinces of Italy Image reference here. Thanks to danilolofaro.

us_state_with_DC_PR_grid2: Grid of 50 states, DC, and Puerto Rico Image reference here. Thanks to krmaas.

us_state_grid7: United States grid with Washington, D.C Image reference here. Thanks to yichenqin.

sg_planning_area_grid2: Singapore Planning Areas Image reference here. Thanks to ZhimaoElliott.

ch_cantons_fl_grid1: Grid layout for Cantons of Switzerland and the neighbouring Prinicipality of Liechtenstein Image reference here. Thanks to rastrau.

europe_countries_grid2: Grid layout for European countries (minus micro nations) Image reference here. Thanks to rastrau.

us_states_territories_grid1: Grid layout for U.S. states and territories Image reference here. Thanks to rastrau.

us_tn_counties_grid1: Grid layout for counties of Tennesee, United States Image reference here. Thanks to binkleym.

us_il_chicago_community_areas_grid1: Grid layout for the Community Areas of Chicago Image reference here. Thanks to leungkp.

us_state_with_DC_PR_grid3: United States grid with Washington, D.C. and Puerto Rico Image reference here. Thanks to klittle314.

in_state_ut_grid2: Grid of Indian States and Union Territories Image reference here. Thanks to dnyansagar.

at_states_grid1: Grid layout for States of Austria Image reference here. Thanks to werkstattcodes.

us_pa_counties_grid1: Grid layout of Counties of Pennsylvania, United States Image reference here. Thanks to urbanSpatial.

us_oh_counties_grid1: Grid layout of Counties of Ohio, United States Image reference here. Thanks to taylorokonek.

fr_departements_grid2: Grid layout of Departements of France Image reference here. Thanks to jerbou.

us_wi_counties_grid1: Grid layout for counties of Wisconsin, United States Image reference here. Thanks to aravamu2.

africa_countries_grid1: Grid for all countries in Africa. Namibia added as 'NAM' to avoid NA collisions Image reference here. Thanks to ntncmch.

no_counties_grid1: Grid of counties of Norway Image reference here. Thanks to NanAmalie1.

tr_provinces_grid1: Grid of Provinces of Turkey Image reference here. Thanks to sadettindemirel.

Usage

us_state_grid1

us_state_grid2

eu_grid1

aus_grid1

sa_prov_grid1

gb_london_boroughs_grid

nhs_scot_grid

india_grid1

india_grid2

argentina_grid1

br_states_grid1

sea_grid1

mys_grid1

fr_regions_grid1

de_states_grid1

us_or_counties_grid1

us_wa_counties_grid1

us_in_counties_grid1

us_in_central_counties_grid1

se_counties_grid1

sf_bay_area_counties_grid1

ua_region_grid1

mx_state_grid1

mx_state_grid2

scotland_local_authority_grid1

us_state_without_DC_grid1

italy_grid1

italy_grid2

be_province_grid1

us_state_grid3

jp_prefs_grid1

ng_state_grid1

bd_upazila_grid1

spain_prov_grid1

ch_cantons_grid1

ch_cantons_grid2

china_prov_grid1

world_86countries_grid

se_counties_grid2

uk_regions1

us_state_contiguous_grid1

sk_province_grid1

ch_aargau_districts_grid1

jo_gov_grid1

spain_ccaa_grid1

spain_prov_grid2

world_countries_grid1

br_states_grid2

china_city_grid1

kr_seoul_district_grid1

nz_regions_grid1

sl_regions_grid1

us_census_div_grid1

ar_tucuman_province_grid1

us_nh_counties_grid1

china_prov_grid2

pl_voivodeships_grid1

us_ia_counties_grid1

us_id_counties_grid1

ar_cordoba_dep_grid1

us_fl_counties_grid1

ar_buenosaires_communes_grid1

nz_regions_grid2

oecd_grid1

ec_prov_grid1

nl_prov_grid1

ca_prov_grid1

us_nc_counties_grid1

mx_ciudad_prov_grid1

bg_prov_grid1

us_hhs_regions_grid1

tw_counties_grid1

tw_counties_grid2

af_prov_grid1

us_mi_counties_grid1

pe_prov_grid1

sa_prov_grid2

mx_state_grid3

cn_bj_districts_grid1

us_va_counties_grid1

us_mo_counties_grid1

cl_santiago_prov_grid1

us_tx_capcog_counties_grid1

sg_planning_area_grid1

in_state_ut_grid1

cn_fujian_prov_grid1

ca_quebec_electoral_districts_grid1

nl_prov_grid2

cn_bj_districts_grid2

ar_santiago_del_estero_prov_grid1

ar_formosa_prov_grid1

ar_chaco_prov_grid1

ar_catamarca_prov_grid1

ar_jujuy_prov_grid1

ar_neuquen_prov_grid1

ar_san_luis_prov_grid1

ar_san_juan_prov_grid1

ar_santa_fe_prov_grid1

ar_la_rioja_prov_grid1

ar_mendoza_prov_grid1

ar_salta_prov_grid1

ar_rio_negro_prov_grid1

uy_departamentos_grid1

ar_buenos_aires_prov_electoral_dist_grid1

europe_countries_grid1

argentina_grid2

us_state_without_DC_grid2

jp_prefs_grid2

na_regions_grid1

mm_state_grid1

us_state_with_DC_PR_grid1

fr_departements_grid1

ar_salta_prov_grid2

ie_counties_grid1

sg_regions_grid1

us_ny_counties_grid1

ru_federal_subjects_grid1

us_ca_counties_grid1

lk_districts_grid1

us_state_without_DC_grid3

co_cali_subdivisions_grid1

us_in_northern_counties_grid1

italy_grid3

us_state_with_DC_PR_grid2

us_state_grid7

sg_planning_area_grid2

ch_cantons_fl_grid1

europe_countries_grid2

us_states_territories_grid1

us_tn_counties_grid1

us_il_chicago_community_areas_grid1

us_state_with_DC_PR_grid3

in_state_ut_grid2

at_states_grid1

us_pa_counties_grid1

us_oh_counties_grid1

fr_departements_grid2

us_wi_counties_grid1

africa_countries_grid1

no_counties_grid1

tr_provinces_grid1
us_state_grid1

us_state_grid2

eu_grid1

aus_grid1

sa_prov_grid1

gb_london_boroughs_grid

nhs_scot_grid

india_grid1

india_grid2

argentina_grid1

br_states_grid1

sea_grid1

mys_grid1

fr_regions_grid1

de_states_grid1

us_or_counties_grid1

us_wa_counties_grid1

us_in_counties_grid1

us_in_central_counties_grid1

se_counties_grid1

sf_bay_area_counties_grid1

ua_region_grid1

mx_state_grid1

mx_state_grid2

scotland_local_authority_grid1

us_state_without_DC_grid1

italy_grid1

italy_grid2

be_province_grid1

us_state_grid3

jp_prefs_grid1

ng_state_grid1

bd_upazila_grid1

spain_prov_grid1

ch_cantons_grid1

ch_cantons_grid2

china_prov_grid1

world_86countries_grid

se_counties_grid2

uk_regions1

us_state_contiguous_grid1

sk_province_grid1

ch_aargau_districts_grid1

jo_gov_grid1

spain_ccaa_grid1

spain_prov_grid2

world_countries_grid1

br_states_grid2

china_city_grid1

kr_seoul_district_grid1

nz_regions_grid1

sl_regions_grid1

us_census_div_grid1

ar_tucuman_province_grid1

us_nh_counties_grid1

china_prov_grid2

pl_voivodeships_grid1

us_ia_counties_grid1

us_id_counties_grid1

ar_cordoba_dep_grid1

us_fl_counties_grid1

ar_buenosaires_communes_grid1

nz_regions_grid2

oecd_grid1

ec_prov_grid1

nl_prov_grid1

ca_prov_grid1

us_nc_counties_grid1

mx_ciudad_prov_grid1

bg_prov_grid1

us_hhs_regions_grid1

tw_counties_grid1

tw_counties_grid2

af_prov_grid1

us_mi_counties_grid1

pe_prov_grid1

sa_prov_grid2

mx_state_grid3

cn_bj_districts_grid1

us_va_counties_grid1

us_mo_counties_grid1

cl_santiago_prov_grid1

us_tx_capcog_counties_grid1

sg_planning_area_grid1

in_state_ut_grid1

cn_fujian_prov_grid1

ca_quebec_electoral_districts_grid1

nl_prov_grid2

cn_bj_districts_grid2

ar_santiago_del_estero_prov_grid1

ar_formosa_prov_grid1

ar_chaco_prov_grid1

ar_catamarca_prov_grid1

ar_jujuy_prov_grid1

ar_neuquen_prov_grid1

ar_san_luis_prov_grid1

ar_san_juan_prov_grid1

ar_santa_fe_prov_grid1

ar_la_rioja_prov_grid1

ar_mendoza_prov_grid1

ar_salta_prov_grid1

ar_rio_negro_prov_grid1

uy_departamentos_grid1

ar_buenos_aires_prov_electoral_dist_grid1

europe_countries_grid1

argentina_grid2

us_state_without_DC_grid2

jp_prefs_grid2

na_regions_grid1

mm_state_grid1

us_state_with_DC_PR_grid1

fr_departements_grid1

ar_salta_prov_grid2

ie_counties_grid1

sg_regions_grid1

us_ny_counties_grid1

ru_federal_subjects_grid1

us_ca_counties_grid1

lk_districts_grid1

us_state_without_DC_grid3

co_cali_subdivisions_grid1

us_in_northern_counties_grid1

italy_grid3

us_state_with_DC_PR_grid2

us_state_grid7

sg_planning_area_grid2

ch_cantons_fl_grid1

europe_countries_grid2

us_states_territories_grid1

us_tn_counties_grid1

us_il_chicago_community_areas_grid1

us_state_with_DC_PR_grid3

in_state_ut_grid2

at_states_grid1

us_pa_counties_grid1

us_oh_counties_grid1

fr_departements_grid2

us_wi_counties_grid1

africa_countries_grid1

no_counties_grid1

tr_provinces_grid1

india_pop

Description

2011 population data for India, broken down by urban and rural. Source: https://en.wikipedia.org/wiki/List_of_states_and_union_territories_of_India_by_population.

Usage

india_pop
india_pop

london_afford

Description

Total affordable housing completions by financial year in each London borough since 2015/16. Source: https://data.london.gov.uk/dataset/dclg-affordable-housing-supply-borough

Usage

london_afford
london_afford

nhs_scot_dental

Description

Child dental health data in Scotland. Source: http://statistics.gov.scot/data/child-dental-health

Usage

nhs_scot_dental
nhs_scot_dental

Plot geofaceted ggplot2 object

Description

Plot geofaceted ggplot2 object

Usage

## S3 method for class 'facet_geo'
plot(x, ...)
## S3 method for class 'facet_geo'
plot(x, ...)

Arguments

`x`	plot object
`...`	ignored

Print geofaceted ggplot2 object

Description

Print geofaceted ggplot2 object

Usage

## S3 method for class 'facet_geo'
print(x, newpage = is.null(vp), vp = NULL, ...)
## S3 method for class 'facet_geo'
print(x, newpage = is.null(vp), vp = NULL, ...)

Arguments

`x`	plot object
`newpage`	draw new (empty) page first?
`vp`	viewport to draw plot in
`...`	other arguments not used by this method

sa_pop_dens

Description

Population density for each province in South Africa for 1996, 2001, and 2011. Source: https://en.wikipedia.org/wiki/List_of_South_African_provinces_by_population_density

Usage

sa_pop_dens
sa_pop_dens

state_ranks

Description

State rankings in the following categories with the variable upon which ranking is based in parentheses: education (adults over 25 with a bachelor's degree in 2015), employment (March 2017 unemployment rate - Bureau of Labor Statistics), health (obesity rate from 2015 - Centers for Disease Control), insured (uninsured rate in 2015 - US Census), sleep (share of adults that report at least 7 hours of sleep each night from 2016 - Disease Control), wealth (poverty rate 2014/15 - US Census). In each category, the lower the ranking, the more favorable. This data is based on data presented here: https://www.axios.com/2017/12/15/the-emoji-states-of-america-1513302318

Usage

state_ranks
state_ranks

state_unemp

Description

Seasonally-adjusted December unemployment rate for each state (including DC) from 2000 to 2017. Obtained from bls.gov.

Usage

state_unemp
state_unemp

Package 'geofacet'

Help Index

Attach a SpatialPolygonsDataFrame object to a grid

Description

Usage

Arguments

aus_pop

Description

Usage

auto_states

Description

election

Description

Usage

eu_gdp

Description

Usage

eu_imm

Description

Usage

Arrange a sequence of geographical panels into a grid that preserves some geographical orientation

Description

Usage

Arguments

Examples

geofacet

Description

Author(s)

See Also

Perform post-processing on a facet_geo ggplot object

Description

Usage

Arguments

Get a list of valid grid names

Description

Usage

Get rnaturalearth data

Description

Usage

Arguments

Examples

Generate a grid automatically from a country/continent name or a SpatialPolygonsDataFrame or 'sf' polygons

Description

Usage

Arguments

Details

Examples

Interactively design a grid

Description

Usage

Arguments

Examples

Plot a preview of a grid

Description

Usage

Arguments

Examples

Submit a grid to be included in the package

Description

Usage

Arguments

Details

Examples

Geo Grids

Description

Usage

india_pop

Description

Usage

london_afford

Description

Usage

nhs_scot_dental

Description

Usage

Plot geofaceted ggplot2 object

Description

Usage

Arguments

Print geofaceted ggplot2 object