data-raw/utils.R
In UrbanInstitute/uimapr: State and county shapefiles in sf and tibble format
### helper functions for parsing shapefile data --------------------------------
# load necessary libraries -----------------------------------------------------
library(dplyr)
library(readr)
library(rgeos)
library(maptools)
library(rgdal)
library(magrittr)
library(broom)
# transform AK/HI from shapefile -----------------------------------------------
# see https://github.com/wmurphyrd/fiftystater ---------------------------------
transform_state <- function(object, rot, scale, shift) {
object %>% elide(rotate = rot) %>%
elide(scale = max(apply(bbox(object), 1, diff)) / scale) %>%
elide(shift = shift)
}
# retrieve shapefile from census and apply AK/HI transformations ---------------
get_shapefile <- function(year, level, resolution) {
# set shapefile url
url_base <- 'http://www2.census.gov/geo/tiger/GENZ2016/shp/'
shape_base <- paste('cb', year, 'us', level, resolution, sep = '_')
url <- paste0(url_base, shape_base, '.zip')
# download data and unzip
temp <- tempfile(fileext = '.zip')
download.file(url, temp)
temp_dir <- tempdir()
unzip(temp, exdir = temp_dir)
# read shapefile
shapes <-
readOGR(dsn = temp_dir,
layer = shape_base,
verbose = F) %>%
spTransform(CRS("+init=epsg:2163"))
# transform AK
alaska <- shapes[shapes$STATEFP == "02", ] %>%
transform_state(-35, 2, c(-2600000, -2300000))
proj4string(alaska) <- proj4string(shapes)
# transform HI
hawaii <- shapes[shapes$STATEFP == "15", ] %>%
transform_state(-35, 0.8, c(-1170000, -2363000))
proj4string(hawaii) <- proj4string(shapes)
# transform Guam
guam <- shapes[shapes$STATEFP %in% c("66"), ] %>%
transform_state(rot = -35, scale = 0.2, shift = c(-180000, -2700000))
proj4string(guam) <- proj4string(shapes)
# transform Puerto Rico
puerto_rico <- shapes[shapes$STATEFP %in% c("72"), ] %>%
transform_state(rot = 20, scale = .5, shift = c(200000, -2500000))
proj4string(puerto_rico) <- proj4string(shapes)
# transform American Samoa
american_samoa <- shapes[shapes$STATEFP %in% c("60"), ] %>%
transform_state(rot = 0, scale = 0.1, shift = c(-1250000, -5050000))
proj4string(american_samoa) <- proj4string(shapes)
# treat islands differently
as1 <- american_samoa[!american_samoa$NAME %in% c("Rose Island", "Swains Island", "Manu'a"), ] %>%
transform_state(rot = 0, scale = 1.2, shift = c(950000, -2350000))
proj4string(as1) <- proj4string(shapes)
as2 <- american_samoa[american_samoa$NAME %in% c("Manu'a"), ] %>%
transform_state(rot = 0, scale = 1.2, shift = c(1450000, -2800000))
proj4string(as2) <- proj4string(shapes)
american_samoa <- rbind(as1, as2) %>%
# angle and size need to be slightly adjusted
transform_state(rot = 15, scale = 1, shift = c(1000000,-3000000))
proj4string(american_samoa) <- proj4string(shapes)
# transform US Virgin Islands
virgin_islands <- shapes[shapes$STATEFP %in% c("78"), ] %>%
transform_state(rot = 0, scale = 0.2, shift = c(1550000, -2700000))
proj4string(virgin_islands) <- proj4string(shapes)
# transform Mariana Islands
mariana_islands <- shapes[shapes$STATEFP %in% c("69"), ] %>%
transform_state(rot = -35, scale = 0.3, shift = c(2150000, -2450000))
proj4string(mariana_islands) <- proj4string(shapes)
# recombine shapefile
exclude <- c("02", "15", "60", "66", "69", "72", "78")
mapdata <-
shapes[!shapes$STATEFP %in% exclude, ] %>%
rbind(alaska, hawaii, guam, puerto_rico, virgin_islands,
mariana_islands, american_samoa) %>%
# convert from EPSG2163 to (US National Atlas Equal Area) WGS84
spTransform(CRS("+init=epsg:4326"))
mapdata
}
# retrieve shapefile from census and apply AK/HI transformations ---------------
get_ccdf_shapefile <- function(year, level, resolution) {
# set shapefile url
url_base <- 'http://www2.census.gov/geo/tiger/GENZ2016/shp/'
shape_base <- paste('cb', year, 'us', level, resolution, sep = '_')
url <- paste0(url_base, shape_base, '.zip')
# download data and unzip
temp <- tempfile(fileext = '.zip')
download.file(url, temp)
temp_dir <- tempdir()
unzip(temp, exdir = temp_dir)
# read shapefile
shapes <-
readOGR(dsn = temp_dir,
layer = shape_base,
verbose = F) %>%
spTransform(CRS("+init=epsg:2163"))
# transform AK
alaska <- shapes[shapes$STATEFP == "02", ] %>%
transform_state(-5, 2, c(-3500000, -200000))
proj4string(alaska) <- proj4string(shapes)
# transform HI
hawaii <- shapes[shapes$STATEFP == "15", ] %>%
transform_state(0, 0.8, c(-2500000, -1000000))
proj4string(hawaii) <- proj4string(shapes)
# transform Guam
guam <- shapes[shapes$STATEFP %in% c("66"), ] %>%
transform_state(rot = -35, scale = 0.3, shift = c(-2600000, -700000))
proj4string(guam) <- proj4string(shapes)
# transform Puerto Rico
puerto_rico <- shapes[shapes$STATEFP %in% c("72"), ] %>%
transform_state(rot = 0, scale = 1, shift = c(-1350000, 1000000))
proj4string(puerto_rico) <- proj4string(shapes)
# transform American Samoa
american_samoa <- shapes[shapes$STATEFP %in% c("60"), ] %>%
transform_state(rot = 0, scale = 0.1, shift = c(-1250000, -5050000))
proj4string(american_samoa) <- proj4string(shapes)
# treat islands differently
as1 <- american_samoa[!american_samoa$NAME %in% c("Rose Island", "Swains Island", "Manu'a"), ] %>%
transform_state(rot = 0, scale = 1.2, shift = c(200000, -2800000))
proj4string(as1) <- proj4string(shapes)
as2 <- american_samoa[american_samoa$NAME %in% c("Manu'a"), ] %>%
transform_state(rot = 0, scale = 1.2, shift = c(400000, -2800000))
proj4string(as2) <- proj4string(shapes)
american_samoa <- rbind(as1, as2) %>%
# angle and size need to be slightly adjusted
transform_state(rot = 15, scale = 1, shift = c(-2800000,-1300000))
proj4string(american_samoa) <- proj4string(shapes)
# transform US Virgin Islands
virgin_islands <- shapes[shapes$STATEFP %in% c("78"), ] %>%
transform_state(rot = 0, scale = 0.2, shift = c(2200000, -1850000))
proj4string(virgin_islands) <- proj4string(shapes)
# transform Mariana Islands
mariana_islands <- shapes[shapes$STATEFP %in% c("69"), ] %>%
transform_state(rot = -35, scale = 0.1, shift = c(-3600000, -1250000))
proj4string(mariana_islands) <- proj4string(shapes)
mariana_islands <- mariana_islands %>%
# transforms into longitude/latitude
spTransform(CRS("+init=epsg:4326")) %>% # prime meridian/equator
tidy(region = "STATEFP") %>%
as_tibble() %>%
filter(piece %in% 1:2)
# combine, transform, and tidy
ellided_areas <- c("02", "14", "15", "43", "52", "60", "66", "69", "72", "78")
fifty_states <-
shapes[!shapes$STATEFP %in% ellided_areas, ] %>%
rbind(alaska) %>%
rbind(hawaii) %>%
rbind(puerto_rico) %>%
rbind(virgin_islands) %>%
rbind(guam) %>%
rbind(american_samoa) %>%
# convert from EPSG2163 to (US National Atlas Equal Area) WGS84
spTransform(CRS("+init=epsg:4326")) %>%
tidy(region = "STATEFP") %>%
mutate(id = id) %>%
as_tibble() %>%
rbind(mariana_islands)
# star for Washington, D.C. -----------------------------------------------
star <- tribble(
~x, ~y, ~order, ~hole, ~piece, ~group, ~id,
1, 0, 1, FALSE, 1, "11", "11",
0.3, -0.2, 2, FALSE, 1, "11", "11",
0.3, -0.95, 3, FALSE, 1, "11", "11",
-0.1, -0.375, 4, FALSE, 1, "11", "11",
-0.8, -0.6, 5, FALSE, 1, "11", "11",
-0.39, 0, 6, FALSE, 1, "11", "11",
-0.8, 0.6, 7, FALSE, 1, "11", "11",
-0.1, 0.375, 8, FALSE, 1, "11", "11",
0.3, 0.95, 9, FALSE, 1, "11", "11",
0.3, 0.2, 10, FALSE, 1, "11", "11",
1, 0, 11, FALSE, 1, "11", "11"
) %>%
# roate star by mutliplying points by rotation matrix
mutate(long = x * cos(pi / 2) + y * -sin(pi / 2),
lat = x * sin(pi / 2) + y * cos(pi / 2)) %>%
# scale the plots big time
mutate(long = long,
lat = lat) %>%
# transpose the plot to near DC
mutate(long = long - 75,
lat = lat + 37) %>%
select(-x, -y)
# combine states and territories ------------------------------------------
ccdf <- rbind(fifty_states, star) %>%
filter(id %in% c("11", ellided_areas)) %>%
rename(state_fips = id)
ids <- tribble(~state_name, ~state_abbv, ~state_fips,
"Alaska", "AK", "02",
"Hawaii", "HI", "15",
"American Samoa", "AS", "60",
"Commonwealth of the Northern Mariana Islands", "MP", "69",
"Guam", "GU", "66",
"Puerto Rico", "PR", "72",
"United States Virgin Islands", "VI", "52",
"United States Virgin Islands", "VI", "78",
"Washington, D.C.", "DC", "11")
ccdf <- left_join(ccdf, ids, by = "state_fips")
ccdf
}
# download and clean state fips data from census
get_state_fips <- function() {
state_fips <-
tribble(~state_fips, ~state_abbv, ~state_name,
"01", "AL", "Alabama",
"02", "AK", "Alaska",
"04", "AZ", "Arizona",
"05", "AR", "Arkansas",
"06", "CA", "California",
"08", "CO", "Colorado",
"09", "CT", "Connecticut",
"10", "DE", "Delaware",
"11", "DC", "District of Columbia",
"12", "FL", "Florida",
"13", "GA", "Georgia",
"15", "HI", "Hawaii",
"16", "ID", "Idaho",
"17", "IL", "Illinois",
"18", "IN", "Indiana",
"19", "IA", "Iowa",
"20", "KS", "Kansas",
"21", "KY", "Kentucky",
"22", "LA", "Louisiana",
"23", "ME", "Maine",
"24", "MD", "Maryland",
"25", "MA", "Massachusetts",
"26", "MI", "Michigan",
"27", "MN", "Minnesota",
"28", "MS", "Mississippi",
"29", "MO", "Missouri",
"30", "MT", "Montana",
"31", "NE", "Nebraska",
"32", "NV", "Nevada",
"33", "NH", "New Hampshire",
"34", "NJ", "New Jersey",
"35", "NM", "New Mexico",
"36", "NY", "New York",
"37", "NC", "North Carolina",
"38", "ND", "North Dakota",
"39", "OH", "Ohio",
"40", "OK", "Oklahoma",
"41", "OR", "Oregon",
"42", "PA", "Pennsylvania",
"44", "RI", "Rhode Island",
"45", "SC", "South Carolina",
"46", "SD", "South Dakota",
"47", "TN", "Tennessee",
"48", "TX", "Texas",
"49", "UT", "Utah",
"50", "VT", "Vermont",
"51", "VA", "Virginia",
"53", "WA", "Washington",
"54", "WV", "West Virginia",
"55", "WI", "Wisconsin",
"56", "WY", "Wyoming",
"60", "AS", "American Samoa",
"66", "GU", "Guam",
"69", "MP", "Mariana Islands",
"72", "PR", "Puerto Rico",
"78", "VI", "Virgin Islands")
state_fips
}
# download and clean county fips data from census
get_county_fips <- function() {
url <- 'https://www2.census.gov/geo/docs/reference/codes/national_county.txt'
county_fips <-
read_csv(url,
col_names = c('state_abbv', 'state_fips', 'county_fips', 'county_name', 'fips_class'),
col_types = 'ccccc') %>%
mutate(county_fips = paste0(state_fips, county_fips)) %>%
# recode county changes - see https://www.census.gov/geo/reference/county-changes.html
# Shannon County, SD changed name to Oglala Lakota County effective May 1, 2015
mutate(county_name = if_else(county_fips == '46113', 'Oglala Lakota County', county_name)) %>%
mutate(county_fips = if_else(county_fips == '46113', '46102', county_fips)) %>%
# Wade Hampton Census Area, AK changed name and code to Kusilvak Census Area effective July 1, 2015
mutate(county_name = if_else(county_fips == '02270', 'Kusilvak Census Area', county_name)) %>%
mutate(county_fips = if_else(county_fips == '02270', '02158', county_fips)) %>%
# add in state name from state fips file
left_join(get_state_fips())
county_fips
}
UrbanInstitute/uimapr documentation built on Sept. 7, 2019, 10:21 a.m.
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### helper functions for parsing shapefile data --------------------------------
# load necessary libraries -----------------------------------------------------
library(dplyr)
library(readr)
library(rgeos)
library(maptools)
library(rgdal)
library(magrittr)
library(broom)
# transform AK/HI from shapefile -----------------------------------------------
# see https://github.com/wmurphyrd/fiftystater ---------------------------------
transform_state <- function(object, rot, scale, shift) {
object %>% elide(rotate = rot) %>%
elide(scale = max(apply(bbox(object), 1, diff)) / scale) %>%
elide(shift = shift)
}
# retrieve shapefile from census and apply AK/HI transformations ---------------
get_shapefile <- function(year, level, resolution) {
# set shapefile url
url_base <- 'http://www2.census.gov/geo/tiger/GENZ2016/shp/'
shape_base <- paste('cb', year, 'us', level, resolution, sep = '_')
url <- paste0(url_base, shape_base, '.zip')
# download data and unzip
temp <- tempfile(fileext = '.zip')
download.file(url, temp)
temp_dir <- tempdir()
unzip(temp, exdir = temp_dir)
# read shapefile
shapes <-
readOGR(dsn = temp_dir,
layer = shape_base,
verbose = F) %>%
spTransform(CRS("+init=epsg:2163"))
# transform AK
alaska <- shapes[shapes$STATEFP == "02", ] %>%
transform_state(-35, 2, c(-2600000, -2300000))
proj4string(alaska) <- proj4string(shapes)
# transform HI
hawaii <- shapes[shapes$STATEFP == "15", ] %>%
transform_state(-35, 0.8, c(-1170000, -2363000))
proj4string(hawaii) <- proj4string(shapes)
# transform Guam
guam <- shapes[shapes$STATEFP %in% c("66"), ] %>%
transform_state(rot = -35, scale = 0.2, shift = c(-180000, -2700000))
proj4string(guam) <- proj4string(shapes)
# transform Puerto Rico
puerto_rico <- shapes[shapes$STATEFP %in% c("72"), ] %>%
transform_state(rot = 20, scale = .5, shift = c(200000, -2500000))
proj4string(puerto_rico) <- proj4string(shapes)
# transform American Samoa
american_samoa <- shapes[shapes$STATEFP %in% c("60"), ] %>%
transform_state(rot = 0, scale = 0.1, shift = c(-1250000, -5050000))
proj4string(american_samoa) <- proj4string(shapes)
# treat islands differently
as1 <- american_samoa[!american_samoa$NAME %in% c("Rose Island", "Swains Island", "Manu'a"), ] %>%
transform_state(rot = 0, scale = 1.2, shift = c(950000, -2350000))
proj4string(as1) <- proj4string(shapes)
as2 <- american_samoa[american_samoa$NAME %in% c("Manu'a"), ] %>%
transform_state(rot = 0, scale = 1.2, shift = c(1450000, -2800000))
proj4string(as2) <- proj4string(shapes)
american_samoa <- rbind(as1, as2) %>%
# angle and size need to be slightly adjusted
transform_state(rot = 15, scale = 1, shift = c(1000000,-3000000))
proj4string(american_samoa) <- proj4string(shapes)
# transform US Virgin Islands
virgin_islands <- shapes[shapes$STATEFP %in% c("78"), ] %>%
transform_state(rot = 0, scale = 0.2, shift = c(1550000, -2700000))
proj4string(virgin_islands) <- proj4string(shapes)
# transform Mariana Islands
mariana_islands <- shapes[shapes$STATEFP %in% c("69"), ] %>%
transform_state(rot = -35, scale = 0.3, shift = c(2150000, -2450000))
proj4string(mariana_islands) <- proj4string(shapes)
# recombine shapefile
exclude <- c("02", "15", "60", "66", "69", "72", "78")
mapdata <-
shapes[!shapes$STATEFP %in% exclude, ] %>%
rbind(alaska, hawaii, guam, puerto_rico, virgin_islands,
mariana_islands, american_samoa) %>%
# convert from EPSG2163 to (US National Atlas Equal Area) WGS84
spTransform(CRS("+init=epsg:4326"))
mapdata
}
# retrieve shapefile from census and apply AK/HI transformations ---------------
get_ccdf_shapefile <- function(year, level, resolution) {
# set shapefile url
url_base <- 'http://www2.census.gov/geo/tiger/GENZ2016/shp/'
shape_base <- paste('cb', year, 'us', level, resolution, sep = '_')
url <- paste0(url_base, shape_base, '.zip')
# download data and unzip
temp <- tempfile(fileext = '.zip')
download.file(url, temp)
temp_dir <- tempdir()
unzip(temp, exdir = temp_dir)
# read shapefile
shapes <-
readOGR(dsn = temp_dir,
layer = shape_base,
verbose = F) %>%
spTransform(CRS("+init=epsg:2163"))
# transform AK
alaska <- shapes[shapes$STATEFP == "02", ] %>%
transform_state(-5, 2, c(-3500000, -200000))
proj4string(alaska) <- proj4string(shapes)
# transform HI
hawaii <- shapes[shapes$STATEFP == "15", ] %>%
transform_state(0, 0.8, c(-2500000, -1000000))
proj4string(hawaii) <- proj4string(shapes)
# transform Guam
guam <- shapes[shapes$STATEFP %in% c("66"), ] %>%
transform_state(rot = -35, scale = 0.3, shift = c(-2600000, -700000))
proj4string(guam) <- proj4string(shapes)
# transform Puerto Rico
puerto_rico <- shapes[shapes$STATEFP %in% c("72"), ] %>%
transform_state(rot = 0, scale = 1, shift = c(-1350000, 1000000))
proj4string(puerto_rico) <- proj4string(shapes)
# transform American Samoa
american_samoa <- shapes[shapes$STATEFP %in% c("60"), ] %>%
transform_state(rot = 0, scale = 0.1, shift = c(-1250000, -5050000))
proj4string(american_samoa) <- proj4string(shapes)
# treat islands differently
as1 <- american_samoa[!american_samoa$NAME %in% c("Rose Island", "Swains Island", "Manu'a"), ] %>%
transform_state(rot = 0, scale = 1.2, shift = c(200000, -2800000))
proj4string(as1) <- proj4string(shapes)
as2 <- american_samoa[american_samoa$NAME %in% c("Manu'a"), ] %>%
transform_state(rot = 0, scale = 1.2, shift = c(400000, -2800000))
proj4string(as2) <- proj4string(shapes)
american_samoa <- rbind(as1, as2) %>%
# angle and size need to be slightly adjusted
transform_state(rot = 15, scale = 1, shift = c(-2800000,-1300000))
proj4string(american_samoa) <- proj4string(shapes)
# transform US Virgin Islands
virgin_islands <- shapes[shapes$STATEFP %in% c("78"), ] %>%
transform_state(rot = 0, scale = 0.2, shift = c(2200000, -1850000))
proj4string(virgin_islands) <- proj4string(shapes)
# transform Mariana Islands
mariana_islands <- shapes[shapes$STATEFP %in% c("69"), ] %>%
transform_state(rot = -35, scale = 0.1, shift = c(-3600000, -1250000))
proj4string(mariana_islands) <- proj4string(shapes)
mariana_islands <- mariana_islands %>%
# transforms into longitude/latitude
spTransform(CRS("+init=epsg:4326")) %>% # prime meridian/equator
tidy(region = "STATEFP") %>%
as_tibble() %>%
filter(piece %in% 1:2)
# combine, transform, and tidy
ellided_areas <- c("02", "14", "15", "43", "52", "60", "66", "69", "72", "78")
fifty_states <-
shapes[!shapes$STATEFP %in% ellided_areas, ] %>%
rbind(alaska) %>%
rbind(hawaii) %>%
rbind(puerto_rico) %>%
rbind(virgin_islands) %>%
rbind(guam) %>%
rbind(american_samoa) %>%
# convert from EPSG2163 to (US National Atlas Equal Area) WGS84
spTransform(CRS("+init=epsg:4326")) %>%
tidy(region = "STATEFP") %>%
mutate(id = id) %>%
as_tibble() %>%
rbind(mariana_islands)
# star for Washington, D.C. -----------------------------------------------
star <- tribble(
~x, ~y, ~order, ~hole, ~piece, ~group, ~id,
1, 0, 1, FALSE, 1, "11", "11",
0.3, -0.2, 2, FALSE, 1, "11", "11",
0.3, -0.95, 3, FALSE, 1, "11", "11",
-0.1, -0.375, 4, FALSE, 1, "11", "11",
-0.8, -0.6, 5, FALSE, 1, "11", "11",
-0.39, 0, 6, FALSE, 1, "11", "11",
-0.8, 0.6, 7, FALSE, 1, "11", "11",
-0.1, 0.375, 8, FALSE, 1, "11", "11",
0.3, 0.95, 9, FALSE, 1, "11", "11",
0.3, 0.2, 10, FALSE, 1, "11", "11",
1, 0, 11, FALSE, 1, "11", "11"
) %>%
# roate star by mutliplying points by rotation matrix
mutate(long = x * cos(pi / 2) + y * -sin(pi / 2),
lat = x * sin(pi / 2) + y * cos(pi / 2)) %>%
# scale the plots big time
mutate(long = long,
lat = lat) %>%
# transpose the plot to near DC
mutate(long = long - 75,
lat = lat + 37) %>%
select(-x, -y)
# combine states and territories ------------------------------------------
ccdf <- rbind(fifty_states, star) %>%
filter(id %in% c("11", ellided_areas)) %>%
rename(state_fips = id)
ids <- tribble(~state_name, ~state_abbv, ~state_fips,
"Alaska", "AK", "02",
"Hawaii", "HI", "15",
"American Samoa", "AS", "60",
"Commonwealth of the Northern Mariana Islands", "MP", "69",
"Guam", "GU", "66",
"Puerto Rico", "PR", "72",
"United States Virgin Islands", "VI", "52",
"United States Virgin Islands", "VI", "78",
"Washington, D.C.", "DC", "11")
ccdf <- left_join(ccdf, ids, by = "state_fips")
ccdf
}
# download and clean state fips data from census
get_state_fips <- function() {
state_fips <-
tribble(~state_fips, ~state_abbv, ~state_name,
"01", "AL", "Alabama",
"02", "AK", "Alaska",
"04", "AZ", "Arizona",
"05", "AR", "Arkansas",
"06", "CA", "California",
"08", "CO", "Colorado",
"09", "CT", "Connecticut",
"10", "DE", "Delaware",
"11", "DC", "District of Columbia",
"12", "FL", "Florida",
"13", "GA", "Georgia",
"15", "HI", "Hawaii",
"16", "ID", "Idaho",
"17", "IL", "Illinois",
"18", "IN", "Indiana",
"19", "IA", "Iowa",
"20", "KS", "Kansas",
"21", "KY", "Kentucky",
"22", "LA", "Louisiana",
"23", "ME", "Maine",
"24", "MD", "Maryland",
"25", "MA", "Massachusetts",
"26", "MI", "Michigan",
"27", "MN", "Minnesota",
"28", "MS", "Mississippi",
"29", "MO", "Missouri",
"30", "MT", "Montana",
"31", "NE", "Nebraska",
"32", "NV", "Nevada",
"33", "NH", "New Hampshire",
"34", "NJ", "New Jersey",
"35", "NM", "New Mexico",
"36", "NY", "New York",
"37", "NC", "North Carolina",
"38", "ND", "North Dakota",
"39", "OH", "Ohio",
"40", "OK", "Oklahoma",
"41", "OR", "Oregon",
"42", "PA", "Pennsylvania",
"44", "RI", "Rhode Island",
"45", "SC", "South Carolina",
"46", "SD", "South Dakota",
"47", "TN", "Tennessee",
"48", "TX", "Texas",
"49", "UT", "Utah",
"50", "VT", "Vermont",
"51", "VA", "Virginia",
"53", "WA", "Washington",
"54", "WV", "West Virginia",
"55", "WI", "Wisconsin",
"56", "WY", "Wyoming",
"60", "AS", "American Samoa",
"66", "GU", "Guam",
"69", "MP", "Mariana Islands",
"72", "PR", "Puerto Rico",
"78", "VI", "Virgin Islands")
state_fips
}
# download and clean county fips data from census
get_county_fips <- function() {
url <- 'https://www2.census.gov/geo/docs/reference/codes/national_county.txt'
county_fips <-
read_csv(url,
col_names = c('state_abbv', 'state_fips', 'county_fips', 'county_name', 'fips_class'),
col_types = 'ccccc') %>%
mutate(county_fips = paste0(state_fips, county_fips)) %>%
# recode county changes - see https://www.census.gov/geo/reference/county-changes.html
# Shannon County, SD changed name to Oglala Lakota County effective May 1, 2015
mutate(county_name = if_else(county_fips == '46113', 'Oglala Lakota County', county_name)) %>%
mutate(county_fips = if_else(county_fips == '46113', '46102', county_fips)) %>%
# Wade Hampton Census Area, AK changed name and code to Kusilvak Census Area effective July 1, 2015
mutate(county_name = if_else(county_fips == '02270', 'Kusilvak Census Area', county_name)) %>%
mutate(county_fips = if_else(county_fips == '02270', '02158', county_fips)) %>%
# add in state name from state fips file
left_join(get_state_fips())
county_fips
}
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