├── .Rbuildignore
├── .gitignore
├── DESCRIPTION
├── LICENSE
├── NAMESPACE
├── R
├── boundaries.R
├── sysdata.rda
└── ussf.R
├── README.md
├── data-raw
├── 2018_cz_1990.R
├── 2018_state_county.R
└── sysdata.R
├── man
├── boundaries.Rd
└── ussf.Rd
└── tests
├── testthat.R
└── testthat
└── test-boundaries.R
/.Rbuildignore:
--------------------------------------------------------------------------------
1 | ^ussf\.Rproj$
2 | ^\.Rproj\.user$
3 | ^data-raw$
4 |
--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
1 | ussf.Rproj
2 | .Rproj.user
3 | .Rhistory
4 | .RData
5 |
--------------------------------------------------------------------------------
/DESCRIPTION:
--------------------------------------------------------------------------------
1 | Package: ussf
2 | Title: U.S. boundaries for states, counties, and commuting zones
3 | Description: U.S. boundaries for states, counties, and commuting zones as
4 | sf objects. Derived from U.S. Census Bureau 2018 cartographic boundaries.
5 | Version: 0.1.0.9000
6 | Authors@R:
7 | person(
8 | given = "Bill",
9 | family = "Behrman",
10 | email = "behrman@stanford.edu",
11 | role = c("aut", "cre")
12 | )
13 | URL: https://github.com/dcl-docs/ussf
14 | BugReports: https://github.com/dcl-docs/ussf/issues
15 | License: GPL-3 | file LICENSE
16 | Encoding: UTF-8
17 | LazyData: true
18 | Depends:
19 | R (>= 2.10)
20 | Imports:
21 | stringr
22 | Suggests:
23 | testthat (>= 2.1.0)
24 | RoxygenNote: 7.0.2
25 | Roxygen: list(markdown = TRUE)
26 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/NAMESPACE:
--------------------------------------------------------------------------------
1 | # Generated by roxygen2: do not edit by hand
2 |
3 | export(boundaries)
4 | importFrom(stringr,str_glue)
5 |
--------------------------------------------------------------------------------
/R/boundaries.R:
--------------------------------------------------------------------------------
1 | #' Boundaries for U.S. states, counties, or commuting zones
2 | #'
3 | #' Boundaries for U.S. states, counties, or commuting zones as sf object.
4 | #'
5 | #' @param geography One of:
6 | #' * `"state"` (Default)
7 | #' * `"county"`
8 | #' * `"cz"` Commuting zone.
9 | #' @param resolution One of:
10 | #' * `"20m"` 1:20,000,000 (Default)
11 | #' * `"5m"` 1:5,000,000
12 | #' * `"500k"` 1:500,000
13 | #' @param projection One of:
14 | #' * `"albers"` Albers equal area with Alaska and Hawaii scaled and below
15 | #' contiguous states. (Default)
16 | #' * `"longlat"` Longitude and latitude.
17 | #'
18 | #' @details The default 1:20,000,000 resolution is suitable for maps of the
19 | #' entire U.S. The other two higher resolutions are suitable for maps of smaller
20 | #' areas. The default Albers equal area projection is suitable for maps of the
21 | #' entire U.S. The longitude and latitude projection is suitable for maps of
22 | #' smaller areas.
23 | #'
24 | #' @return An sf object.
25 | #'
26 | #' @export
27 | #'
28 | #' @examples
29 | #' counties <- boundaries(geography = "county")
30 | #'
31 | #' @source U.S. Census Bureau. \href{https://www2.census.gov/geo/tiger/GENZ2018/shp/}{2018 Cartographic Boundary Shapefiles}.
32 | #' @source USDA Economic Research Service.
33 | #' \href{https://www.ers.usda.gov/data-products/commuting-zones-and-labor-market-areas/}{1990 Commuting Zones}.
34 | boundaries <- function(
35 | geography = c("state", "county", "cz"),
36 | resolution = c("20m", "5m", "500k"),
37 | projection = c("albers", "longlat")
38 | ) {
39 | geography <- match.arg(geography)
40 | resolution <- match.arg(resolution)
41 | projection <- match.arg(projection)
42 |
43 | get(str_glue("{geography}_{resolution}_{projection}"))
44 | }
45 |
--------------------------------------------------------------------------------
/R/sysdata.rda:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/dcl-docs/ussf/9cf770595a292f8bcac95ae7d2a4a0a059472b52/R/sysdata.rda
--------------------------------------------------------------------------------
/R/ussf.R:
--------------------------------------------------------------------------------
1 | #' ussf
2 | #'
3 | #' U.S. boundaries for states, counties, and commuting zones.
4 | #'
5 | #' @docType package
6 | #' @name ussf
7 | #' @keywords internal
8 | #' @importFrom stringr str_glue
9 | NULL
10 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # U.S. Boundaries
2 |
3 | U.S. boundaries for states, counties, and commuting zones as sf objects. Derived from U.S. Census Bureau 2018 cartographic boundaries.
4 |
5 | You can install this R package with:
6 |
7 | ``` r
8 | # install.packages("remotes")
9 | remotes::install_github("dcl-docs/ussf")
10 | ```
11 |
12 | Boundaries are available for each combination of the following:
13 |
14 | * Geometry:
15 | * State
16 | * County
17 | * Commuting zone
18 | * Resolution:
19 | * 1:20,000,000
20 | * 1:5,000,000
21 | * 1:500,000
22 | * Projection:
23 | * Albers equal area with Alaska and Hawaii scaled and below contiguous states
24 | * Longitude and latitude
25 |
--------------------------------------------------------------------------------
/data-raw/2018_cz_1990.R:
--------------------------------------------------------------------------------
1 | # Create boundaries of US 1990 commuting zones in R sf format using US Census
2 | # Bureau shapefiles
3 |
4 | # Author: Bill Behrman
5 | # Version: 2019-12-27
6 |
7 | # Libraries
8 | library(tidyverse)
9 | library(readxl)
10 | library(sf)
11 |
12 | # Parameters
13 | # Boundary year
14 | year <- "2018"
15 | # URL for US Census Bureau shapefiles
16 | url_cb <- str_glue("http://www2.census.gov/geo/tiger/GENZ{year}/shp")
17 | # URL for commuting zone county partition using 1990 counties
18 | url_cz <- "https://www.ers.usda.gov/webdocs/DataFiles/48457/czlma903.xls?v=40961"
19 | # Boundary resolutions
20 | resolutions <- c("20m", "5m", "500k")
21 | # FIPS codes for US states and District of Columbia
22 | fips_states <-
23 | c(
24 | "01", "02", "04", "05", "06", "08", "09", "10", "11", "12", "13", "15",
25 | "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27",
26 | "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39",
27 | "40", "41", "42", "44", "45", "46", "47", "48", "49", "50", "51", "53",
28 | "54", "55", "56"
29 | )
30 | # Tranformation parameters for Alaska and Hawaii
31 | alaska_crop <- c(xmin = -172, xmax = -125, ymin = 50, ymax = 75)
32 | alaska_scale <- 0.35
33 | alaska_x_offset <- 0.095
34 | alaska_y_offset <- 0.150
35 | hawaii_crop <- c(xmin = -165, xmax = -150, ymin = 15, ymax = 25)
36 | hawaii_scale <- 1
37 | hawaii_x_offset <- 0.305
38 | hawaii_y_offset <- 0.065
39 | # Coordinate reference systems
40 | longlat <- "+proj=longlat +datum=WGS84 +no_defs"
41 | albers_contig <-
42 | "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +datum=WGS84 +no_defs"
43 | albers_alaska <-
44 | "+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +datum=WGS84 +no_defs"
45 | albers_hawaii <-
46 | "+proj=aea +lat_1=8 +lat_2=18 +lat_0=13 +lon_0=-157 +x_0=0 +y_0=0 +datum=WGS84 +no_defs"
47 | # FIPS codes for counties deleted since 1990 census
48 | counties_deleted <-
49 | c(
50 | "02201", "02231", "02270", "02280", "12025", "30113", "46113", "51515",
51 | "51560", "51780"
52 | )
53 | # FIPS codes for counties added since 1990 census with 1990 commuting zone
54 | counties_added <-
55 | tribble(
56 | ~fips_county, ~cz_1990,
57 | "02068", "34115",
58 | "02105", "34109",
59 | "02158", "34112",
60 | "02195", "34110",
61 | "02198", "34111",
62 | "02230", "34109",
63 | "02275", "34110",
64 | "02282", "34109",
65 | "08014", "28900",
66 | "12086", "07000",
67 | "46102", "27704"
68 | )
69 | # Rename commuting zones
70 | rename_cz <-
71 | c(
72 | "34103" = "Valdez-Cordova"
73 | )
74 | # Temporary directory
75 | tmp <- str_glue("{tempdir()}/2019_cz_1990")
76 |
77 | #===============================================================================
78 |
79 | # Create temporary directory
80 | if (!file.exists(tmp)) {
81 | dir.create(tmp, recursive = TRUE)
82 | }
83 |
84 | # Download commuting zones county partition
85 | dest <- str_glue("{tmp}/czlma903.xls")
86 | if (download.file(url = url_cz, destfile = dest, quiet = TRUE)) {
87 | stop("Error: Commuting zones download failed")
88 | }
89 |
90 | # Read commuting zone county partition, add place and state variables
91 | cz <-
92 | read_excel(dest, sheet = "CZLMA903", na = ".") %>%
93 | select(
94 | fips_county = "County FIPS Code",
95 | cz_1990 = CZ90,
96 | place_state = "Name of largest place in Commuting Zone"
97 | ) %>%
98 | mutate(
99 | place =
100 | place_state %>%
101 | str_remove(" borough.*| CDP.*| city.*| town.*| \\(rem.*|,.*"),
102 | state = place_state %>% str_sub(start = -2L)
103 | ) %>%
104 | select(-place_state)
105 |
106 | # Adjust county partition for counties added and deleted since 1990
107 | cz <-
108 | counties_added %>%
109 | left_join(cz %>% select(-fips_county) %>% distinct(), by = "cz_1990") %>%
110 | bind_rows(cz, .) %>%
111 | filter(!fips_county %in% counties_deleted)
112 |
113 | # Rename commuting zones
114 | for (zone in names(rename_cz)) {
115 | cz$place[cz$cz_1990 == zone] <- rename_cz[zone]
116 | }
117 |
118 | # For resolution
119 | for (resolution in resolutions) {
120 |
121 | # Boundary specification
122 | boundary <- str_glue("cb_{year}_us_county_{resolution}")
123 |
124 | # Download and unzip US Census Bureau shapefile
125 | url <- str_glue("{url_cb}/{boundary}.zip")
126 | dest <- str_glue("{tmp}/{boundary}.zip")
127 | if (download.file(url = url, destfile = dest)) {
128 | print(str_glue("Error: Download for {boundary} failed"))
129 | next
130 | }
131 | unzip(zipfile = dest, exdir = str_glue("{tmp}/{boundary}"))
132 |
133 | # sf object for commuting zones for states and District of Columbia
134 | v <-
135 | read_sf(dsn = str_glue("{tmp}/{boundary}/{boundary}.shp")) %>%
136 | filter(STATEFP %in% fips_states)
137 | stopifnot(all(v$GEOID %in% cz$fips_county))
138 |
139 | us <-
140 | v %>%
141 | left_join(cz, by = c("GEOID" = "fips_county")) %>%
142 | group_by(cz_1990, place, state) %>%
143 | summarize() %>%
144 | ungroup() %>%
145 | st_cast("MULTIPOLYGON")
146 |
147 | # Version with longitude and latitude projection
148 | var <- str_glue("cz_{resolution}_longlat")
149 | us %>%
150 | st_transform(crs = longlat) %>%
151 | assign(x = var, value = ., envir = .GlobalEnv)
152 |
153 | # Version with Albers projection and Alaska and Hawaii below other states
154 | contig <-
155 | us %>%
156 | filter(!state %in% c("AK", "HI")) %>%
157 | st_transform(crs = albers_contig)
158 |
159 | bbox_contig <- st_bbox(contig)
160 |
161 | position <- function(bbox, x_offset, y_offset) {
162 | st_sfc(st_point(c(
163 | bbox$xmin + x_offset * (bbox$xmax - bbox$xmin),
164 | bbox$ymin + y_offset * (bbox$ymax - bbox$ymin)
165 | )))
166 | }
167 |
168 | alaska <-
169 | us %>%
170 | filter(state == "AK") %>%
171 | st_crop(alaska_crop) %>%
172 | st_transform(crs = albers_alaska) %>%
173 | st_set_geometry(
174 | (st_geometry(.) - st_centroid(st_union(st_geometry(.)))) *
175 | alaska_scale +
176 | position(
177 | bbox = bbox_contig,
178 | x_offset = alaska_x_offset,
179 | y_offset = alaska_y_offset
180 | )
181 | ) %>%
182 | st_set_crs(value = albers_contig)
183 |
184 | hawaii <-
185 | us %>%
186 | filter(state == "HI") %>%
187 | st_crop(hawaii_crop) %>%
188 | st_transform(crs = albers_hawaii) %>%
189 | st_set_geometry(
190 | (st_geometry(.) - st_centroid(st_union(st_geometry(.)))) *
191 | hawaii_scale +
192 | position(
193 | bbox = bbox_contig,
194 | x_offset = hawaii_x_offset,
195 | y_offset = hawaii_y_offset
196 | )
197 | ) %>%
198 | st_set_crs(value = albers_contig)
199 |
200 | var <- str_glue("cz_{resolution}_albers")
201 | rbind(contig, alaska, hawaii) %>%
202 | assign(x = var, value = ., envir = .GlobalEnv)
203 | }
204 |
205 | # Remove temporary directory
206 | if (unlink(tmp, recursive = TRUE, force = TRUE)) {
207 | print("Error: Remove temporary directory failed")
208 | }
209 |
--------------------------------------------------------------------------------
/data-raw/2018_state_county.R:
--------------------------------------------------------------------------------
1 | # Create boundaries of US states and counties in R sf format using US Census
2 | # Bureau shapefiles
3 |
4 | # Author: Bill Behrman
5 | # Version: 2019-12-27
6 |
7 | # Libraries
8 | library(tidyverse)
9 | library(sf)
10 |
11 | # Parameters
12 | # Boundary year
13 | year <- "2018"
14 | # URL for US Census Bureau shapefiles
15 | url_cb <- str_glue("http://www2.census.gov/geo/tiger/GENZ{year}/shp")
16 | # Boundary geographies
17 | geographies <- c("state", "county")
18 | # Boundary resolutions
19 | resolutions <- c("20m", "5m", "500k")
20 | # FIPS codes for US states and District of Columbia
21 | fips_states <-
22 | c(
23 | "01", "02", "04", "05", "06", "08", "09", "10", "11", "12", "13", "15",
24 | "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27",
25 | "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39",
26 | "40", "41", "42", "44", "45", "46", "47", "48", "49", "50", "51", "53",
27 | "54", "55", "56"
28 | )
29 | # Tranformation parameters for Alaska and Hawaii
30 | alaska_crop <- c(xmin = -172, xmax = -125, ymin = 50, ymax = 75)
31 | alaska_scale <- 0.35
32 | alaska_x_offset <- 0.095
33 | alaska_y_offset <- 0.150
34 | hawaii_crop <- c(xmin = -165, xmax = -150, ymin = 15, ymax = 25)
35 | hawaii_scale <- 1
36 | hawaii_x_offset <- 0.305
37 | hawaii_y_offset <- 0.065
38 | # Coordinate reference systems
39 | longlat <- "+proj=longlat +datum=WGS84 +no_defs"
40 | albers_contig <-
41 | "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +datum=WGS84 +no_defs"
42 | albers_alaska <-
43 | "+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +datum=WGS84 +no_defs"
44 | albers_hawaii <-
45 | "+proj=aea +lat_1=8 +lat_2=18 +lat_0=13 +lon_0=-157 +x_0=0 +y_0=0 +datum=WGS84 +no_defs"
46 | # Temporary directory
47 | tmp <- str_glue("{tempdir()}/2019_county_state")
48 |
49 | #===============================================================================
50 |
51 | # Create temporary directory for US Census Bureau shapefiles
52 | if (!file.exists(tmp)) {
53 | dir.create(tmp, recursive = TRUE)
54 | }
55 |
56 | for (geography in geographies) {
57 | for (resolution in resolutions) {
58 |
59 | boundary <- str_glue("cb_{year}_us_{geography}_{resolution}")
60 |
61 | # Download and unzip US Census Bureau shapefile
62 | url <- str_glue("{url_cb}/{boundary}.zip")
63 | dest <- str_glue("{tmp}/{boundary}.zip")
64 | if (download.file(url = url, destfile = dest, quiet = TRUE)) {
65 | print(str_glue("Error: Download for {boundary} failed"))
66 | next
67 | }
68 | unzip(zipfile = dest, exdir = str_glue("{tmp}/{boundary}"))
69 |
70 | # sf object for states and District of Columbia
71 | us <-
72 | read_sf(dsn = str_glue("{tmp}/{boundary}/{boundary}.shp")) %>%
73 | filter(STATEFP %in% fips_states)
74 |
75 | # Version with longitude and latitude projection
76 | var <- str_glue("{geography}_{resolution}_longlat")
77 | us %>%
78 | st_transform(crs = longlat) %>%
79 | assign(x = var, value = ., envir = .GlobalEnv)
80 |
81 | # Version with Albers projection and Alaska and Hawaii below other states
82 | contig <-
83 | us %>%
84 | filter(!STATEFP %in% c("02", "15")) %>%
85 | st_transform(crs = albers_contig)
86 |
87 | bbox_contig <- st_bbox(contig)
88 |
89 | position <- function(bbox, x_offset, y_offset) {
90 | st_sfc(st_point(c(
91 | bbox$xmin + x_offset * (bbox$xmax - bbox$xmin),
92 | bbox$ymin + y_offset * (bbox$ymax - bbox$ymin)
93 | )))
94 | }
95 |
96 | alaska <-
97 | us %>%
98 | filter(STATEFP == "02") %>%
99 | st_crop(alaska_crop) %>%
100 | st_transform(crs = albers_alaska) %>%
101 | st_set_geometry(
102 | (st_geometry(.) - st_centroid(st_union(st_geometry(.)))) *
103 | alaska_scale +
104 | position(
105 | bbox = bbox_contig,
106 | x_offset = alaska_x_offset,
107 | y_offset = alaska_y_offset
108 | )
109 | ) %>%
110 | st_set_crs(value = albers_contig)
111 |
112 | hawaii <-
113 | us %>%
114 | filter(STATEFP == "15") %>%
115 | st_crop(hawaii_crop) %>%
116 | st_transform(crs = albers_hawaii) %>%
117 | st_set_geometry(
118 | (st_geometry(.) - st_centroid(st_union(st_geometry(.)))) *
119 | hawaii_scale +
120 | position(
121 | bbox = bbox_contig,
122 | x_offset = hawaii_x_offset,
123 | y_offset = hawaii_y_offset
124 | )
125 | ) %>%
126 | st_set_crs(value = albers_contig)
127 |
128 | var <- str_glue("{geography}_{resolution}_albers")
129 | rbind(contig, alaska, hawaii) %>%
130 | assign(x = var, value = ., envir = .GlobalEnv)
131 | }
132 | }
133 |
134 | # Remove temporary directory
135 | if (unlink(tmp, recursive = TRUE, force = TRUE)) {
136 | print("Error: Remove temporary directory failed")
137 | }
138 |
--------------------------------------------------------------------------------
/data-raw/sysdata.R:
--------------------------------------------------------------------------------
1 | # Save boundaries for states, counties, and commuting zones.
2 |
3 | # Author: Bill Behrman
4 | # Version: 2019-12-27
5 |
6 | # Parameters
7 | # Script to create boundaries for states and counties
8 | script_state_country <- here::here("data-raw/2018_state_county.R")
9 | # Script to create boundaries for commuting zones
10 | script_cz <- here::here("data-raw/2018_cz_1990.R")
11 | # Variable pattern
12 | var_pattern <- "^(state|county|cz)_(20m|5m|500k)_(albers|longlat)$"
13 | # Output file
14 | file_out <- here::here("R/sysdata.rda")
15 |
16 | #===============================================================================
17 |
18 | source(script_state_country)
19 | source(script_cz)
20 |
21 | stopifnot(length(ls(pattern = var_pattern)) == 18)
22 |
23 | save(
24 | list = ls(pattern = var_pattern),
25 | file = file_out,
26 | version = 2,
27 | compress = "xz"
28 | )
29 |
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/man/boundaries.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/boundaries.R
3 | \name{boundaries}
4 | \alias{boundaries}
5 | \title{Boundaries for U.S. states, counties, or commuting zones}
6 | \source{
7 | U.S. Census Bureau. \href{https://www2.census.gov/geo/tiger/GENZ2018/shp/}{2018 Cartographic Boundary Shapefiles}.
8 |
9 | USDA Economic Research Service.
10 | \href{https://www.ers.usda.gov/data-products/commuting-zones-and-labor-market-areas/}{1990 Commuting Zones}.
11 | }
12 | \usage{
13 | boundaries(
14 | geography = c("state", "county", "cz"),
15 | resolution = c("20m", "5m", "500k"),
16 | projection = c("albers", "longlat")
17 | )
18 | }
19 | \arguments{
20 | \item{geography}{One of:
21 | \itemize{
22 | \item \code{"state"} (Default)
23 | \item \code{"county"}
24 | \item \code{"cz"} Commuting zone.
25 | }}
26 |
27 | \item{resolution}{One of:
28 | \itemize{
29 | \item \code{"20m"} 1:20,000,000 (Default)
30 | \item \code{"5m"} 1:5,000,000
31 | \item \code{"500k"} 1:500,000
32 | }}
33 |
34 | \item{projection}{One of:
35 | \itemize{
36 | \item \code{"albers"} Albers equal area with Alaska and Hawaii scaled and below
37 | contiguous states. (Default)
38 | \item \code{"longlat"} Longitude and latitude.
39 | }}
40 | }
41 | \value{
42 | An sf object.
43 | }
44 | \description{
45 | Boundaries for U.S. states, counties, or commuting zones as sf object.
46 | }
47 | \details{
48 | The default 1:20,000,000 resolution is suitable for maps of the
49 | entire U.S. The other two higher resolutions are suitable for maps of smaller
50 | areas. The default Albers equal area projection is suitable for maps of the
51 | entire U.S. The longitude and latitude projection is suitable for maps of
52 | smaller areas.
53 | }
54 | \examples{
55 | counties <- boundaries(geography = "county")
56 |
57 | }
58 |
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/man/ussf.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/ussf.R
3 | \docType{package}
4 | \name{ussf}
5 | \alias{ussf}
6 | \title{ussf}
7 | \description{
8 | U.S. boundaries for states, counties, and commuting zones.
9 | }
10 | \keyword{internal}
11 |
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/tests/testthat.R:
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1 | library(testthat)
2 | library(ussf)
3 |
4 | test_check("ussf")
5 |
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/tests/testthat/test-boundaries.R:
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1 | test_that("state 20m albers", {
2 | expect_identical(
3 | boundaries(
4 | geography = "state",
5 | resolution = "20m",
6 | projection = "albers"
7 | ),
8 | state_20m_albers
9 | )
10 | })
11 |
12 | test_that("state 20m longlat", {
13 | expect_identical(
14 | boundaries(
15 | geography = "state",
16 | resolution = "20m",
17 | projection = "longlat"
18 | ),
19 | state_20m_longlat
20 | )
21 | })
22 |
23 | test_that("state 5m albers", {
24 | expect_identical(
25 | boundaries(
26 | geography = "state",
27 | resolution = "5m",
28 | projection = "albers"
29 | ),
30 | state_5m_albers
31 | )
32 | })
33 |
34 | test_that("state 5m longlat", {
35 | expect_identical(
36 | boundaries(
37 | geography = "state",
38 | resolution = "5m",
39 | projection = "longlat"
40 | ),
41 | state_5m_longlat
42 | )
43 | })
44 |
45 | test_that("state 500k albers", {
46 | expect_identical(
47 | boundaries(
48 | geography = "state",
49 | resolution = "500k",
50 | projection = "albers"
51 | ),
52 | state_500k_albers
53 | )
54 | })
55 |
56 | test_that("state 500k longlat", {
57 | expect_identical(
58 | boundaries(
59 | geography = "state",
60 | resolution = "500k",
61 | projection = "longlat"
62 | ),
63 | state_500k_longlat
64 | )
65 | })
66 |
67 | test_that("county 20m albers", {
68 | expect_identical(
69 | boundaries(
70 | geography = "county",
71 | resolution = "20m",
72 | projection = "albers"
73 | ),
74 | county_20m_albers
75 | )
76 | })
77 |
78 | test_that("county 20m longlat", {
79 | expect_identical(
80 | boundaries(
81 | geography = "county",
82 | resolution = "20m",
83 | projection = "longlat"
84 | ),
85 | county_20m_longlat
86 | )
87 | })
88 |
89 | test_that("county 5m albers", {
90 | expect_identical(
91 | boundaries(
92 | geography = "county",
93 | resolution = "5m",
94 | projection = "albers"
95 | ),
96 | county_5m_albers
97 | )
98 | })
99 |
100 | test_that("county 5m longlat", {
101 | expect_identical(
102 | boundaries(
103 | geography = "county",
104 | resolution = "5m",
105 | projection = "longlat"
106 | ),
107 | county_5m_longlat
108 | )
109 | })
110 |
111 | test_that("county 500k albers", {
112 | expect_identical(
113 | boundaries(
114 | geography = "county",
115 | resolution = "500k",
116 | projection = "albers"
117 | ),
118 | county_500k_albers
119 | )
120 | })
121 |
122 | test_that("county 500k longlat", {
123 | expect_identical(
124 | boundaries(
125 | geography = "county",
126 | resolution = "500k",
127 | projection = "longlat"
128 | ),
129 | county_500k_longlat
130 | )
131 | })
132 |
133 | test_that("cz 20m albers", {
134 | expect_identical(
135 | boundaries(
136 | geography = "cz",
137 | resolution = "20m",
138 | projection = "albers"
139 | ),
140 | cz_20m_albers
141 | )
142 | })
143 |
144 | test_that("cz 20m longlat", {
145 | expect_identical(
146 | boundaries(
147 | geography = "cz",
148 | resolution = "20m",
149 | projection = "longlat"
150 | ),
151 | cz_20m_longlat
152 | )
153 | })
154 |
155 | test_that("cz 5m albers", {
156 | expect_identical(
157 | boundaries(
158 | geography = "cz",
159 | resolution = "5m",
160 | projection = "albers"
161 | ),
162 | cz_5m_albers
163 | )
164 | })
165 |
166 | test_that("cz 5m longlat", {
167 | expect_identical(
168 | boundaries(
169 | geography = "cz",
170 | resolution = "5m",
171 | projection = "longlat"
172 | ),
173 | cz_5m_longlat
174 | )
175 | })
176 |
177 | test_that("cz 500k albers", {
178 | expect_identical(
179 | boundaries(
180 | geography = "cz",
181 | resolution = "500k",
182 | projection = "albers"
183 | ),
184 | cz_500k_albers
185 | )
186 | })
187 |
188 | test_that("cz 500k longlat", {
189 | expect_identical(
190 | boundaries(
191 | geography = "cz",
192 | resolution = "500k",
193 | projection = "longlat"
194 | ),
195 | cz_500k_longlat
196 | )
197 | })
198 |
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