data-raw/data.R
In elipousson/overedge: Assorted Functions For Easier Map Making
## code to prepare `DATASET` dataset goes here
library(httr)
library(dplyr)
library(stringr)
get_repo_svg <- function(repo, branch = "main") {
req <- GET(
paste0("https://api.github.com/repos/", repo, "/git/trees/", branch, "?recursive=1")
)
data.frame(
"path" = unlist(lapply(content(req)$tree, function(x) x$path))
) |>
filter(str_detect(path, "\\.svg$")) |>
transmute(
repo = repo,
name = str_extract(path, "(?<=/)[:graph:]+(?=.svg$)"),
url = paste0("https://raw.githubusercontent.com/", repo, "/", branch, "/", path)
)
}
maki <-
get_repo_svg(repo = "mapbox/maki") |>
mutate(
size = 15,
style = ""
)
temaki <-
get_repo_svg(repo = "ideditor/temaki") |>
mutate(
size = 40,
style = ""
)
wu_icons <-
get_repo_svg(repo = "manifestinteractive/weather-underground-icons", branch = "master") |>
mutate(
size = 64,
style = str_extract(name, "(?<=icons/).+(?=/svg)"),
name = str_extract(name, "(?<=svg/).+")
)
calcite <-
get_repo_svg(repo = "Esri/calcite-point-symbols", branch = "master") |>
mutate(
size = case_when(
str_detect(name, "13") ~ 13,
str_detect(name, "17") ~ 17,
str_detect(name, "21") ~ 21
),
style = "",
name = str_remove(name, "-[:digit:]+$")
) |>
arrange(name, desc(size))
lane_icons <-
get_repo_svg(repo = "openstreetmap/lane-icons", branch = "master") |>
mutate(
size = 40,
style = ""
)
osm_map_icons <-
get_repo_svg(repo = "openstreetmap/map-icons", branch = "master") |>
filter(str_detect(url, "/svg/")) |>
mutate(
size = 40,
style = ""
)
nps_icons <-
get_repo_svg(repo = "nationalparkservice/symbol-library", branch = "master") |>
mutate(
size = case_when(
str_detect(name, "14") ~ 14,
str_detect(name, "22") ~ 17,
str_detect(name, "30") ~ 30
),
style = case_when(
str_detect(url, "shielded.+") ~ "shielded-white",
str_detect(name, "standalone.+white") ~ "standalone-white",
str_detect(name, "standalone.+black") ~ "standalone-black"
),
name = str_remove(name, "-white-[:digit:]+$"),
name = str_remove(name, "-black-[:digit:]+$"),
name = str_remove(name, "^shielded/"),
name = str_remove(name, "^standalone/")
) |>
arrange(name, desc(size))
map_icons <-
bind_rows(
maki,
temaki,
wu_icons,
lane_icons,
osm_map_icons,
calcite,
nps_icons
) |>
relocate(
repo,
.after = everything()
)
usethis::use_data(map_icons, overwrite = TRUE)
# Derived from https://raw.githubusercontent.com/visioguy/PaperSizes/master/PaperSizes.json
paper_sizes <-
tibble::tribble(
~name, ~series, ~size, ~standard, ~units, ~width, ~height, ~orientation, ~type,
"9 in. x 12 in.", "Architecture", NA, "ANSI", "in", 9, 12, "portrait", "paper",
"18 in. x 24 in.", "Architecture", NA, "ANSI", "in", 18, 24, "portrait", "paper",
"Arch A", "Architecture", NA, "ANSI", "in", 9, 12, "portrait", "paper",
"Arch C", "Architecture", NA, "ANSI", "in", 18, 24, "portrait", "paper",
"Arch E", "Architecture", NA, "ANSI", "in", 36, 48, "portrait", "paper",
"30 in. x 42 in.", "Architecture", NA, "ANSI", "in", 30, 42, "portrait", "paper",
"Arch E1", "Architecture", NA, "ANSI", "in", 30, 42, "portrait", "paper",
"12 in. x 18 in.", "Architecture", NA, "ANSI", "in", 12, 18, "portrait", "paper",
"24 in. x 36 in.", "Architecture", NA, "ANSI", "in", 24, 36, "portrait", "paper",
"Arch B", "Architecture", NA, "ANSI", "in", 12, 18, "portrait", "paper",
"Arch D", "Architecture", NA, "ANSI", "in", 24, 36, "portrait", "paper",
"A", "Engineering", NA, "ANSI", "in", 8.5, 11, "portrait", "paper",
"C", "Engineering", NA, "ANSI", "in", 17, 22, "portrait", "paper",
"E", "Engineering", NA, "ANSI", "in", 34, 44, "portrait", "paper",
"B+", "Engineering", NA, "ANSI", "in", 13, 19, "portrait", "paper",
"Super B", "Engineering", NA, "ANSI", "in", 13, 19, "portrait", "paper",
"B", "Engineering", NA, "ANSI", "in", 11, 17, "portrait", "paper",
"D", "Engineering", NA, "ANSI", "in", 22, 34, "portrait", "paper",
"Letter", NA, NA, "ANSI", "in", 8.5, 11, "portrait", "paper",
"Folio", NA, NA, "ANSI", "in", 8.5, 13, "portrait", "paper",
"Ledger", NA, NA, "ANSI", "in", 11, 17, "portrait", "paper",
"Half Letter", NA, NA, "ANSI", "in", 5.5, 8.5, "portrait", "paper",
"Tabloid", NA, NA, "ANSI", "in", 11, 17, "portrait", "paper",
"Junior Legal", NA, NA, "ANSI", "in", 5, 8, "portrait", "paper",
"Legal", NA, NA, "ANSI", "in", 8.5, 14, "portrait", "paper",
"Large Post Quarto", NA, NA, "British Imperial", "in", 10, 10, "square", "paper",
"Foolscap Quarto", NA, NA, "British Imperial", "in", 6.5, 8, "portrait", "paper",
"Duke", NA, NA, "British Imperial", "in", 5.5, 7, "portrait", "paper",
"Small Post Quarto", NA, NA, "British Imperial", "in", 7, 9, "portrait", "paper",
"Duchess", NA, NA, "British Imperial", "in", 4.5, 6, "portrait", "paper",
"Albert", NA, NA, "British Imperial", "in", 4, 6, "portrait", "paper",
"Small Post Octavo", NA, NA, "British Imperial", "in", 4.5, 7, "portrait", "paper",
"Large Post Octavo", NA, NA, "British Imperial", "in", 5, 8, "portrait", "paper",
"Foolscap Folio", NA, NA, "British Imperial", "in", 8, 13, "portrait", "paper",
"Instagram post", NA, "post", "Instagram", "px", 1080, 1080, "square", "social",
"Instagram story", NA, "story", "Instagram", "px", 1080, 1920, "portrait", "social",
"Facebook post", NA, "post", "Facebook", "px", 1200, 630, "landscape", "social",
"Facebook story", NA, "story", "Facebook", "px", 1080, 1920, "portrait", "social",
"Facebook cover photo", NA, "cover", "Facebook", "px", 820, 312, "landscape", "social",
"A0", "A", "0", "ISO", "mm", 841, 1189, "portrait", "paper",
"A1", "A", "1", "ISO", "mm", 594, 841, "portrait", "paper",
"A2", "A", "2", "ISO", "mm", 420, 594, "portrait", "paper",
"A3", "A", "3", "ISO", "mm", 297, 420, "portrait", "paper",
"A4", "A", "4", "ISO", "mm", 210, 297, "portrait", "paper",
"A5", "A", "5", "ISO", "mm", 148, 210, "portrait", "paper",
"A6", "A", "6", "ISO", "mm", 105, 148, "portrait", "paper",
"A7", "A", "7", "ISO", "mm", 74, 105, "portrait", "paper",
"A8", "A", "8", "ISO", "mm", 52, 74, "portrait", "paper",
"A9", "A", "9", "ISO", "mm", 37, 52, "portrait", "paper",
"A10", "A", "10", "ISO", "mm", 26, 37, "portrait", "paper",
"B0", "B", "0", "ISO", "mm", 1000, 1414, "portrait", "paper",
"B1", "B", "1", "ISO", "mm", 707, 1000, "portrait", "paper",
"B2", "B", "2", "ISO", "mm", 500, 707, "portrait", "paper",
"B3", "B", "3", "ISO", "mm", 353, 500, "portrait", "paper",
"B4", "B", "4", "ISO", "mm", 250, 353, "portrait", "paper",
"B5", "B", "5", "ISO", "mm", 176, 250, "portrait", "paper",
"B6", "B", "6", "ISO", "mm", 125, 176, "portrait", "paper",
"B7", "B", "7", "ISO", "mm", 88, 125, "portrait", "paper",
"B8", "B", "8", "ISO", "mm", 62, 88, "portrait", "paper",
"B9", "B", "9", "ISO", "mm", 44, 62, "portrait", "paper",
"B10", "B", "10", "ISO", "mm", 31, 44, "portrait", "paper",
"C0", "C", "0", "ISO", "mm", 917, 1297, "portrait", "paper",
"C1", "C", "1", "ISO", "mm", 648, 917, "portrait", "paper",
"C2", "C", "2", "ISO", "mm", 458, 648, "portrait", "paper",
"C3", "C", "3", "ISO", "mm", 324, 458, "portrait", "paper",
"C4", "C", "4", "ISO", "mm", 229, 324, "portrait", "paper",
"C5", "C", "5", "ISO", "mm", 162, 229, "portrait", "paper",
"C6", "C", "6", "ISO", "mm", 114, 162, "portrait", "paper",
"C7", "C", "7", "ISO", "mm", 81, 114, "portrait", "paper",
"C8", "C", "8", "ISO", "mm", 57, 81, "portrait", "paper",
"C9", "C", "9", "ISO", "mm", 40, 47, "portrait", "paper",
"C10", "C", "10", "ISO", "mm", 28, 28, "square", "paper",
"0", NA, "0", "JIS", "mm", 1030, 1456, "portrait", "paper",
"1", NA, "1", "JIS", "mm", 728, 1030, "portrait", "paper",
"2", NA, "2", "JIS", "mm", 515, 728, "portrait", "paper",
"3", NA, "3", "JIS", "mm", 364, 515, "portrait", "paper",
"4 Kiku", NA, "4", "JIS", "mm", 227, 306, "portrait", "paper",
"4", NA, "4", "JIS", "mm", 257, 364, "portrait", "paper",
"4 Shiroku ban", NA, "4", "JIS", "mm", 264, 379, "portrait", "paper",
"5 Shiroku ban", NA, "5", "JIS", "mm", 189, 262, "portrait", "paper",
"5", NA, "5", "JIS", "mm", 182, 257, "portrait", "paper",
"5 Kiku", NA, "5", "JIS", "mm", 151, 227, "portrait", "paper",
"6", NA, "6", "JIS", "mm", 128, 182, "portrait", "paper",
"6 Shiroku ban", NA, "6", "JIS", "mm", 127, 188, "portrait", "paper",
"7", NA, "7", "JIS", "mm", 91, 128, "portrait", "paper",
"8", NA, "8", "JIS", "mm", 64, 91, "portrait", "paper",
"9", NA, "9", "JIS", "mm", 45, 64, "portrait", "paper",
"10", NA, "10", "JIS", "mm", 32, 45, "portrait", "paper",
"11", NA, "11", "JIS", "mm", 22, 32, "portrait", "paper",
"12", NA, "12", "JIS", "mm", 16, 22, "portrait", "paper",
"Twitter single image post", NA, "post", "Twitter", "px", 1200, 675, "landscape", "social",
"Twitter image and link post", NA, "post", "Twitter", "px", 1200, 628, "landscape", "social",
"Twitter multiple image post", NA, "post", "Twitter", "px", 700, 800, "portrait", "social",
"Twitter cover photo", NA, "cover", "Twitter", "px", 1500, 500, "landscape", "social",
"8.5 in. x 7 in.", "EDDM", NA, "USPS", "in", 8.5, 7, "landscape", "postcard",
"8.5 in. x 11 in.", "EDDM", NA, "USPS", "in", 8.5, 11, "portrait", "postcard",
"6.25 in. x 9 in.", "EDDM", NA, "USPS", "in", 6.25, 9, "portrait", "postcard",
"6.25 in. x 11 in.", "EDDM", NA, "USPS", "in", 6.25, 11, "portrait", "postcard",
"6 in. x 12 in.", "EDDM", NA, "USPS", "in", 6, 12, "portrait", "postcard",
"4.25 in. x 11 in.", "EDDM", NA, "USPS", "in", 4.25, 11, "portrait", "postcard",
"USPS postcard (min)", NA, NA, "USPS", "in", 5, 3.5, "landscape", "postcard",
"USPS postcard (max)", NA, NA, "USPS", "in", 6, 4.25, "landscape", "postcard",
"6 in. x 11 in.", NA, NA, NA, "in", 11, 6, "landscape", "postcard",
"5.5 in. x 8.5 in.", NA, NA, NA, "in", 8.5, 5.5, "landscape", "postcard",
"4 in. x 6 in.", NA, NA, NA, "in", 6, 4, "landscape", "postcard",
"6 in. x 9 in.", NA, NA, NA, "in", 9, 6, "landscape", "postcard",
"6.5 in. x 9.5 in.", NA, NA, NA, "in", 9.5, 6.5, "landscape", "postcard",
"5 in. x 7 in.", NA, NA, NA, "in", 7, 5, "landscape", "postcard",
"3 in. x 4 in.", NA, NA, NA, "in", 4, 3, "landscape", "postcard",
"4.25 in. x 5.5 in.", NA, NA, NA, "in", 5.5, 4.25, "landscape", "postcard",
"8 x 10 inches", NA, NA, NA, "in", 8, 10, "portrait", "print",
"8.5 x 11 inches", NA, NA, NA, "in", 8.5, 11, "portrait", "print",
"18 x 24 inches", NA, NA, NA, "in", 18, 24, "portrait", "print",
"5 x 7 inches", NA, NA, NA, "in", 5, 7, "portrait", "print",
"4 x 6 inches", NA, NA, NA, "in", 4, 6, "portrait", "print",
"12 x 18 inches", NA, NA, NA, "in", 12, 18, "portrait", "print",
"24 x 36 inches", NA, NA, NA, "in", 24, 36, "portrait", "print"
)
usethis::use_data(paper_sizes, overwrite = TRUE)
osm_building_tags <-
osmdata::available_tags("building")
osm_building_tags <-
stringr::str_remove(osm_building_tags, "(?<=&)[:graph:]+") |>
stringr::str_remove("&")
usethis::use_data(
osm_building_tags,
overwrite = TRUE
)
select_unit_options <-
c(
"arc_degree", "arc_minute", "arc_second", "cm", "m", "metre", "meter", "meters", "km", "kilometer", "kilometers",
"inch", "in", "ft", "foot", "feet", "yard", "yards", "mi", "mile", "miles", "nautical_mile", "radian",
"mm", "millimeter", "millimeters", "millimetre", "millimetres"
)
add_metric_units <-
tibble::tribble(
~symbol, ~symbol_aliases, ~name_singular, ~name_singular_aliases, ~name_plural, ~name_plural_aliases, ~def, ~definition, ~comment, ~dimensionless, ~source_xml,
"km", "", "kilometer", "kilometre", "kilometers", "kilometres", "m * 1000", "length equivalent to 1000 meters", NA, FALSE, NA,
"cm", "", "centimeter", "centimetre", "centimeters", "centimetres", "m/100", "length equivalent to 0.01 meter", NA, FALSE, NA
)
valid_units <-
units::valid_udunits()
valid_units <-
valid_units |>
dplyr::bind_rows(add_metric_units)
units_filter_cols <- c("symbol", "symbol_aliases", "name_singular", "name_singular_aliases", "name_plural", "name_plural_aliases")
dist_units <-
valid_units |>
filter(
if_any(
any_of(units_filter_cols),
~ (.x %in% select_unit_options)
)
)
dist_units <-
naniar::replace_with_na_if(dist_units, is.character, ~ .x == "")
usethis::use_data(
dist_units,
overwrite = TRUE
)
dist_unit_options <-
unique(c(
dist_units$name_singular,
dist_units$name_plural,
stringr::str_split(dist_units$name_singular_aliases, ", ", simplify = TRUE),
stringr::str_split(dist_units$name_plural_aliases, ", ", simplify = TRUE),
dist_units$symbol,
dist_units$symbol_aliases,
select_unit_options
))
dist_unit_options <- dist_unit_options[!is.na(dist_unit_options)]
dist_unit_options <- dist_unit_options[dist_unit_options != ""]
usethis::use_data(
dist_unit_options,
overwrite = TRUE,
internal = FALSE
)
area_units <-
valid_units |>
filter(
if_any(
any_of(units_filter_cols),
~ (.x %in% c("hectare", "acre", "acre_foot"))
)
)
area_units <-
naniar::replace_with_na_if(area_units, is.character, ~ .x == "")
area_unit_options <-
c(paste0(c(
"meter", "meters", "nautical_mile", "international_inch", "international_foot",
"international_yard", "international_mile", "kilometer", "centimeter", "m", "in",
"ft", "yd", "mi", "km", "cm", "metre", "inch", "foot", "yard", "mile", "kilometre",
"centimetre", "international_feet", "kilometers", "centimeters", "feet", "kilometres",
"centimetres"
), "^2"), "hectare", "hectares", "acre", "acres", "acre_foot", "acre_feet")
usethis::use_data(
area_unit_options,
overwrite = TRUE
)
standard_scales <-
tibble::tribble(
~scale, ~standard, ~series, ~actual_ft, ~actual_ft_unit, ~scale_in, ~scale_in_unit, ~scale_in_accuracy, ~scale_cm, ~scale_cm_unit, ~scale_cm_accuracy, ~size_latlon, ~size_latlon_unit, ~area_approx, ~area_approx_unit, ~series_status,
"1:20,000", "USGS", "Puerto Rico 7.5 minute", 6e-04, "in", 1670, "ft", "approximate", 200, "m", "exact", "7.5 by 7.5 minute", "minute", "7.10E+01", "sq mi", NA,
"1:24,000", "USGS", "7.5 minute", 5e-04, "in", 2000, "ft", "exact", 240, "m", "exact", "7.5 by 7.5 minute", "minute", "49 to 70", "sq mi", NA,
"1:25,000", "USGS", "7.5 minute", 0.00048, "in", 2080, "ft", "approximate", 250, "m", "exact", "7.5 by 7.5 minute", "minute", "49 to 70", "sq mi", NA,
"1:25,000", "USGS", "7.5 by 15 minute", 0.00048, "in", 2080, "ft", "approximate", 250, "m", "exact", "7.5 by 15 minute", "minute", "98 to 140", "sq mi", NA,
"1:50,000", "USGS", "USGS-DMA 15 minute", 0.00024, "in", 4170, "ft", "approximate", 500, "m", "exact", "15 by 15 minute", "minute", "197 to 282", "sq mi", NA,
"1:62,500", "USGS", "15 minute*", NA, NA, 1, "mi", "approximate", 625, "m", "exact", "15 by 15 minute", "minute", "197 to 282", "sq mi", "abandoned",
"1:63,360", "USGS", "Alaska Maps", NA, NA, 1, "mi", "exact", 634, "m", "exact", "15 by 20 to 36 minute", "minute", "207 to 281", "sq mi", NA,
"1:50,000", "USGS", "County Maps", 0.00024, "in", 4170, "ft", "approximate", 500, "m", "exact", "County area", NA, "Varies", NA, NA,
"1:100,000", "USGS", "County Maps", NA, NA, 1.6, "mi", "approximate", 1, "km", "exact", "County area", NA, "Varies", NA, NA,
"1:100,000", "USGS", "30 by 60 minute", NA, NA, 1.6, "mi", "approximate", 1, "km", "exact", "30 by 60 minute", "minute", "1,568 to 2,240", "sq mi", NA,
"1:125,000", "USGS", "30 minute*", NA, NA, 2, "mi", "approximate", 1.25, "km", "exact", "30 by 30 minute", "minute", "786 to 1,124", "sq mi", "abandoned",
"1:250,000", "USGS", "1 degree by 2 degrees or 3 degrees", NA, NA, 4, "mi", "approximate", 2.5, "km", "exact", "1° by 2° or 3°", "degree", "4,580 to 8,669", "sq mi", NA,
"1:500,000", "USGS", "State Maps", NA, NA, 8, "mi", "approximate", 5, "km", "exact", "State area", NA, "Varies", NA, NA,
"1:1,000,000", "USGS", "State Maps", NA, NA, 16, "mi", "approximate", 10, "km", "exact", "State area", NA, "Varies", NA, NA,
"1:2,000,000", "USGS", "U.S. Sectional Maps", NA, NA, 32, "mi", "approximate", 20, "km", "exact", "State groups", NA, "Varies", NA, NA,
"1:250,000", "USGS", "Antarctica Maps", NA, NA, 4, "mi", "approximate", 2.5, "km", "exact", "1° by 3° to 15°", "degree", "4,089 to 8,336", "sq mi", NA,
"1:500,000", "USGS", "Antarctica Maps", NA, NA, 8, "mi", "approximate", 5, "km", "exact", "2° by 7.5°", "degree", "28,174 to 30,462", "sq mi", NA,
"1 in = 10 feet", "Engineering", NA, 0.1, "in", 10, "feet", "exact", 3.05, "m", "approximate", NA, NA, NA, NA, "common",
"1 in = 20 feet", "Engineering", NA, 0.05, "in", 20, "feet", "exact", 6.1, "m", "approximate", NA, NA, NA, NA, "common",
"1 in = 30 feet", "Engineering", NA, 0.0333, "in", 30, "feet", "exact", 9.14, "m", "approximate", NA, NA, NA, NA, "common",
"1 in = 40 feet", "Engineering", NA, 0.025, "in", 40, "feet", "exact", 12.2, "m", "approximate", NA, NA, NA, NA, "common",
"1 in = 50 feet", "Engineering", NA, 0.02, "in", 50, "feet", "exact", 15.2, "m", "approximate", NA, NA, NA, NA, "common",
"1 in = 60 feet", "Engineering", NA, 0.0167, "in", 60, "feet", "exact", 18.3, "m", "approximate", NA, NA, NA, NA, "common",
"3/32 in = 1 foot", "Architectural", NA, 0.0938, "in", 10.7, "feet", "approximate", 3.25, "m", "approximate", NA, NA, NA, NA, "common",
"3/16 in = 1 foot", "Architectural", NA, 0.188, "in", 5.33, "feet", "approximate", 1.63, "m", "approximate", NA, NA, NA, NA, "common",
"1/8 in = 1 foot", "Architectural", NA, 0.125, "in", 8, "feet", "exact", 2.44, "m", "approximate", NA, NA, NA, NA, "common",
"1/4 in = 1 foot", "Architectural", NA, 0.25, "in", 4, "feet", "exact", 1.22, "m", "approximate", NA, NA, NA, NA, "common",
"3/8 in = 1 foot", "Architectural", NA, 0.375, "in", 2.67, "feet", "approximate", 0.813, "m", "approximate", NA, NA, NA, NA, "common",
"1/2 in = 1 foot", "Architectural", NA, 0.5, "in", 2, "feet", "exact", 0.61, "m", "approximate", NA, NA, NA, NA, "common",
"3/4 in = 1 foot", "Architectural", NA, 0.75, "in", 1.33, "feet", "approximate", 0.406, "m", "approximate", NA, NA, NA, NA, "common",
"1 in = 1 foot", "Architectural", NA, 1, "in", 1, "feet", "exact", 0.305, "m", "approximate", NA, NA, NA, NA, "common",
"1½ in = 1 foot", "Architectural", NA, 1.5, "in", 0.667, "feet", "approximate", 0.203, "m", "approximate", NA, NA, NA, NA, "common"
)
usethis::use_data(
standard_scales,
overwrite = TRUE
)
us_states <-
tigris::states(
cb = TRUE,
resolution = "5m"
)
us_states <-
us_states %>%
janitor::clean_names("snake") %>%
sf::st_transform(3857)
nest_states <-
us_states %>%
dplyr::group_by(geoid) %>%
dplyr::group_nest(keep = TRUE)
us_states <-
purrr::map_dfr(
.x = nest_states$data,
~ tibble::tibble(
name = unique(.x$name),
geoid = unique(.x$geoid),
statefp = unique(.x$statefp),
abb = unique(.x$stusps),
bbox = list(sf::st_bbox(.x)),
wkt = sf::st_as_text(sf::st_as_sfc(.x))
)
)
get_state_pop <-
function(x) {
tidycensus::get_acs(
geography = "state",
variables = "B01001_001",
year = 2019,
state = x,
cache_table = TRUE
) %>%
janitor::clean_names("snake")
}
possibly_get_state_pop <-
purrr::possibly(
~ get_state_pop(.x),
NULL,
quiet = FALSE
)
us_states_census <-
purrr::map_dfr(
us_states$statefp,
~ possibly_get_state_pop(.x)
)
us_states_census <-
us_states_census %>%
dplyr::select(-c(name, variable, moe)) %>%
dplyr::rename(est_pop = estimate)
us_states <- us_states %>%
dplyr::left_join(
us_states_census,
by = "geoid"
) %>%
dplyr::relocate(
abb,
est_pop,
.after = geoid
)
names(us_states$geoid) <- tolower(us_states$abb)
names(us_states$wkt) <- tolower(us_states$abb)
textclean::replace_non_ascii(us_states)
usethis::use_data(
us_states,
overwrite = TRUE,
internal = FALSE
)
us_counties <-
purrr::map_dfr(
us_states$statefp,
~ tigris::counties(
state = .x,
cb = TRUE,
resolution = "5m"
)
)
us_counties <-
us_counties %>%
janitor::clean_names("snake") %>%
sf::st_transform(3857)
us_counties <-
us_counties %>%
dplyr::left_join(
dplyr::select(us_states, state_abb = abb, statefp),
by = "statefp"
)
nest_counties <- us_counties %>%
dplyr::group_by(geoid) %>%
dplyr::group_nest(keep = TRUE)
us_counties <-
purrr::map_dfr(
.x = nest_counties$data,
~ tibble::tibble(
name_short = unique(.x$name),
geoid = unique(.x$geoid),
countyfp = unique(.x$countyfp),
statefp = unique(.x$statefp),
abb_state = unique(.x$state_abb),
bbox = list(sf::st_bbox(.x)),
wkt = sf::st_as_text(sf::st_as_sfc(.x))
)
)
get_county_pop <-
function(x, y) {
tidycensus::get_acs(
geography = "county",
variables = "B01001_001",
year = 2019,
state = x,
county = y,
cache_table = TRUE
)
}
possibly_get_county_pop <-
purrr::possibly(
~ get_county_pop(.x, .y),
NULL,
quiet = FALSE
)
us_counties_census <-
purrr::map2_dfr(
us_counties$statefp,
us_counties$countyfp,
~ possibly_get_county_pop(
.x,
.y
)
) %>%
janitor::clean_names("snake")
us_counties <-
us_counties %>%
dplyr::left_join(us_counties_census, by = "geoid") %>%
dplyr::select(-c(moe, variable)) %>%
dplyr::rename(
est_pop = estimate
) %>%
dplyr::relocate(
abb_state, est_pop,
.after = name_short
) %>%
dplyr::relocate(
name,
.before = dplyr::everything()
) %>%
dplyr::mutate(
name = dplyr::case_when(
(is.na(name) && state_abb == "VI") ~ paste0(name_short, ", ", "U.S. Virgin Islands"),
(is.na(name) && state_abb == "GU") ~ paste0(name_short, ", ", "Guam"),
(is.na(name) && state_abb == "MP") ~ paste0(name_short, ", ", "Northern Mariana Islands"),
(is.na(name) && state_abb == "AS") ~ paste0(name_short, ", ", "American Samoa"),
TRUE ~ name
)
) %>%
dplyr::mutate(
label = stringr::str_extract(name, ".+(?=,)"),
label = janitor::make_clean_names(paste0(label, ", ", abb_state))
) %>%
dplyr::mutate(
label = stringr::str_replace(
label, "_borough_", "_bor_"
),
label = stringr::str_replace(
label, "_county_", "_co_"
),
label = stringr::str_replace(
label, "_parish_", "_par_"
),
label = stringr::str_replace(
label, "_township_", "_twp_"
),
label = stringr::str_replace(
label, "_census_area_", "_"
),
label = stringr::str_replace(
label, "_municipio_", "_muni_"
)
)
names(us_counties$wkt) <- us_counties$label
names(us_counties$geoid) <- us_counties$label
us_counties <- dplyr::select(
us_counties,
-label
)
usethis::use_data(
us_counties,
overwrite = TRUE,
internal = FALSE
)
elipousson/overedge documentation built on Aug. 13, 2022, 7:41 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
## code to prepare `DATASET` dataset goes here
library(httr)
library(dplyr)
library(stringr)
get_repo_svg <- function(repo, branch = "main") {
req <- GET(
paste0("https://api.github.com/repos/", repo, "/git/trees/", branch, "?recursive=1")
)
data.frame(
"path" = unlist(lapply(content(req)$tree, function(x) x$path))
) |>
filter(str_detect(path, "\\.svg$")) |>
transmute(
repo = repo,
name = str_extract(path, "(?<=/)[:graph:]+(?=.svg$)"),
url = paste0("https://raw.githubusercontent.com/", repo, "/", branch, "/", path)
)
}
maki <-
get_repo_svg(repo = "mapbox/maki") |>
mutate(
size = 15,
style = ""
)
temaki <-
get_repo_svg(repo = "ideditor/temaki") |>
mutate(
size = 40,
style = ""
)
wu_icons <-
get_repo_svg(repo = "manifestinteractive/weather-underground-icons", branch = "master") |>
mutate(
size = 64,
style = str_extract(name, "(?<=icons/).+(?=/svg)"),
name = str_extract(name, "(?<=svg/).+")
)
calcite <-
get_repo_svg(repo = "Esri/calcite-point-symbols", branch = "master") |>
mutate(
size = case_when(
str_detect(name, "13") ~ 13,
str_detect(name, "17") ~ 17,
str_detect(name, "21") ~ 21
),
style = "",
name = str_remove(name, "-[:digit:]+$")
) |>
arrange(name, desc(size))
lane_icons <-
get_repo_svg(repo = "openstreetmap/lane-icons", branch = "master") |>
mutate(
size = 40,
style = ""
)
osm_map_icons <-
get_repo_svg(repo = "openstreetmap/map-icons", branch = "master") |>
filter(str_detect(url, "/svg/")) |>
mutate(
size = 40,
style = ""
)
nps_icons <-
get_repo_svg(repo = "nationalparkservice/symbol-library", branch = "master") |>
mutate(
size = case_when(
str_detect(name, "14") ~ 14,
str_detect(name, "22") ~ 17,
str_detect(name, "30") ~ 30
),
style = case_when(
str_detect(url, "shielded.+") ~ "shielded-white",
str_detect(name, "standalone.+white") ~ "standalone-white",
str_detect(name, "standalone.+black") ~ "standalone-black"
),
name = str_remove(name, "-white-[:digit:]+$"),
name = str_remove(name, "-black-[:digit:]+$"),
name = str_remove(name, "^shielded/"),
name = str_remove(name, "^standalone/")
) |>
arrange(name, desc(size))
map_icons <-
bind_rows(
maki,
temaki,
wu_icons,
lane_icons,
osm_map_icons,
calcite,
nps_icons
) |>
relocate(
repo,
.after = everything()
)
usethis::use_data(map_icons, overwrite = TRUE)
# Derived from https://raw.githubusercontent.com/visioguy/PaperSizes/master/PaperSizes.json
paper_sizes <-
tibble::tribble(
~name, ~series, ~size, ~standard, ~units, ~width, ~height, ~orientation, ~type,
"9 in. x 12 in.", "Architecture", NA, "ANSI", "in", 9, 12, "portrait", "paper",
"18 in. x 24 in.", "Architecture", NA, "ANSI", "in", 18, 24, "portrait", "paper",
"Arch A", "Architecture", NA, "ANSI", "in", 9, 12, "portrait", "paper",
"Arch C", "Architecture", NA, "ANSI", "in", 18, 24, "portrait", "paper",
"Arch E", "Architecture", NA, "ANSI", "in", 36, 48, "portrait", "paper",
"30 in. x 42 in.", "Architecture", NA, "ANSI", "in", 30, 42, "portrait", "paper",
"Arch E1", "Architecture", NA, "ANSI", "in", 30, 42, "portrait", "paper",
"12 in. x 18 in.", "Architecture", NA, "ANSI", "in", 12, 18, "portrait", "paper",
"24 in. x 36 in.", "Architecture", NA, "ANSI", "in", 24, 36, "portrait", "paper",
"Arch B", "Architecture", NA, "ANSI", "in", 12, 18, "portrait", "paper",
"Arch D", "Architecture", NA, "ANSI", "in", 24, 36, "portrait", "paper",
"A", "Engineering", NA, "ANSI", "in", 8.5, 11, "portrait", "paper",
"C", "Engineering", NA, "ANSI", "in", 17, 22, "portrait", "paper",
"E", "Engineering", NA, "ANSI", "in", 34, 44, "portrait", "paper",
"B+", "Engineering", NA, "ANSI", "in", 13, 19, "portrait", "paper",
"Super B", "Engineering", NA, "ANSI", "in", 13, 19, "portrait", "paper",
"B", "Engineering", NA, "ANSI", "in", 11, 17, "portrait", "paper",
"D", "Engineering", NA, "ANSI", "in", 22, 34, "portrait", "paper",
"Letter", NA, NA, "ANSI", "in", 8.5, 11, "portrait", "paper",
"Folio", NA, NA, "ANSI", "in", 8.5, 13, "portrait", "paper",
"Ledger", NA, NA, "ANSI", "in", 11, 17, "portrait", "paper",
"Half Letter", NA, NA, "ANSI", "in", 5.5, 8.5, "portrait", "paper",
"Tabloid", NA, NA, "ANSI", "in", 11, 17, "portrait", "paper",
"Junior Legal", NA, NA, "ANSI", "in", 5, 8, "portrait", "paper",
"Legal", NA, NA, "ANSI", "in", 8.5, 14, "portrait", "paper",
"Large Post Quarto", NA, NA, "British Imperial", "in", 10, 10, "square", "paper",
"Foolscap Quarto", NA, NA, "British Imperial", "in", 6.5, 8, "portrait", "paper",
"Duke", NA, NA, "British Imperial", "in", 5.5, 7, "portrait", "paper",
"Small Post Quarto", NA, NA, "British Imperial", "in", 7, 9, "portrait", "paper",
"Duchess", NA, NA, "British Imperial", "in", 4.5, 6, "portrait", "paper",
"Albert", NA, NA, "British Imperial", "in", 4, 6, "portrait", "paper",
"Small Post Octavo", NA, NA, "British Imperial", "in", 4.5, 7, "portrait", "paper",
"Large Post Octavo", NA, NA, "British Imperial", "in", 5, 8, "portrait", "paper",
"Foolscap Folio", NA, NA, "British Imperial", "in", 8, 13, "portrait", "paper",
"Instagram post", NA, "post", "Instagram", "px", 1080, 1080, "square", "social",
"Instagram story", NA, "story", "Instagram", "px", 1080, 1920, "portrait", "social",
"Facebook post", NA, "post", "Facebook", "px", 1200, 630, "landscape", "social",
"Facebook story", NA, "story", "Facebook", "px", 1080, 1920, "portrait", "social",
"Facebook cover photo", NA, "cover", "Facebook", "px", 820, 312, "landscape", "social",
"A0", "A", "0", "ISO", "mm", 841, 1189, "portrait", "paper",
"A1", "A", "1", "ISO", "mm", 594, 841, "portrait", "paper",
"A2", "A", "2", "ISO", "mm", 420, 594, "portrait", "paper",
"A3", "A", "3", "ISO", "mm", 297, 420, "portrait", "paper",
"A4", "A", "4", "ISO", "mm", 210, 297, "portrait", "paper",
"A5", "A", "5", "ISO", "mm", 148, 210, "portrait", "paper",
"A6", "A", "6", "ISO", "mm", 105, 148, "portrait", "paper",
"A7", "A", "7", "ISO", "mm", 74, 105, "portrait", "paper",
"A8", "A", "8", "ISO", "mm", 52, 74, "portrait", "paper",
"A9", "A", "9", "ISO", "mm", 37, 52, "portrait", "paper",
"A10", "A", "10", "ISO", "mm", 26, 37, "portrait", "paper",
"B0", "B", "0", "ISO", "mm", 1000, 1414, "portrait", "paper",
"B1", "B", "1", "ISO", "mm", 707, 1000, "portrait", "paper",
"B2", "B", "2", "ISO", "mm", 500, 707, "portrait", "paper",
"B3", "B", "3", "ISO", "mm", 353, 500, "portrait", "paper",
"B4", "B", "4", "ISO", "mm", 250, 353, "portrait", "paper",
"B5", "B", "5", "ISO", "mm", 176, 250, "portrait", "paper",
"B6", "B", "6", "ISO", "mm", 125, 176, "portrait", "paper",
"B7", "B", "7", "ISO", "mm", 88, 125, "portrait", "paper",
"B8", "B", "8", "ISO", "mm", 62, 88, "portrait", "paper",
"B9", "B", "9", "ISO", "mm", 44, 62, "portrait", "paper",
"B10", "B", "10", "ISO", "mm", 31, 44, "portrait", "paper",
"C0", "C", "0", "ISO", "mm", 917, 1297, "portrait", "paper",
"C1", "C", "1", "ISO", "mm", 648, 917, "portrait", "paper",
"C2", "C", "2", "ISO", "mm", 458, 648, "portrait", "paper",
"C3", "C", "3", "ISO", "mm", 324, 458, "portrait", "paper",
"C4", "C", "4", "ISO", "mm", 229, 324, "portrait", "paper",
"C5", "C", "5", "ISO", "mm", 162, 229, "portrait", "paper",
"C6", "C", "6", "ISO", "mm", 114, 162, "portrait", "paper",
"C7", "C", "7", "ISO", "mm", 81, 114, "portrait", "paper",
"C8", "C", "8", "ISO", "mm", 57, 81, "portrait", "paper",
"C9", "C", "9", "ISO", "mm", 40, 47, "portrait", "paper",
"C10", "C", "10", "ISO", "mm", 28, 28, "square", "paper",
"0", NA, "0", "JIS", "mm", 1030, 1456, "portrait", "paper",
"1", NA, "1", "JIS", "mm", 728, 1030, "portrait", "paper",
"2", NA, "2", "JIS", "mm", 515, 728, "portrait", "paper",
"3", NA, "3", "JIS", "mm", 364, 515, "portrait", "paper",
"4 Kiku", NA, "4", "JIS", "mm", 227, 306, "portrait", "paper",
"4", NA, "4", "JIS", "mm", 257, 364, "portrait", "paper",
"4 Shiroku ban", NA, "4", "JIS", "mm", 264, 379, "portrait", "paper",
"5 Shiroku ban", NA, "5", "JIS", "mm", 189, 262, "portrait", "paper",
"5", NA, "5", "JIS", "mm", 182, 257, "portrait", "paper",
"5 Kiku", NA, "5", "JIS", "mm", 151, 227, "portrait", "paper",
"6", NA, "6", "JIS", "mm", 128, 182, "portrait", "paper",
"6 Shiroku ban", NA, "6", "JIS", "mm", 127, 188, "portrait", "paper",
"7", NA, "7", "JIS", "mm", 91, 128, "portrait", "paper",
"8", NA, "8", "JIS", "mm", 64, 91, "portrait", "paper",
"9", NA, "9", "JIS", "mm", 45, 64, "portrait", "paper",
"10", NA, "10", "JIS", "mm", 32, 45, "portrait", "paper",
"11", NA, "11", "JIS", "mm", 22, 32, "portrait", "paper",
"12", NA, "12", "JIS", "mm", 16, 22, "portrait", "paper",
"Twitter single image post", NA, "post", "Twitter", "px", 1200, 675, "landscape", "social",
"Twitter image and link post", NA, "post", "Twitter", "px", 1200, 628, "landscape", "social",
"Twitter multiple image post", NA, "post", "Twitter", "px", 700, 800, "portrait", "social",
"Twitter cover photo", NA, "cover", "Twitter", "px", 1500, 500, "landscape", "social",
"8.5 in. x 7 in.", "EDDM", NA, "USPS", "in", 8.5, 7, "landscape", "postcard",
"8.5 in. x 11 in.", "EDDM", NA, "USPS", "in", 8.5, 11, "portrait", "postcard",
"6.25 in. x 9 in.", "EDDM", NA, "USPS", "in", 6.25, 9, "portrait", "postcard",
"6.25 in. x 11 in.", "EDDM", NA, "USPS", "in", 6.25, 11, "portrait", "postcard",
"6 in. x 12 in.", "EDDM", NA, "USPS", "in", 6, 12, "portrait", "postcard",
"4.25 in. x 11 in.", "EDDM", NA, "USPS", "in", 4.25, 11, "portrait", "postcard",
"USPS postcard (min)", NA, NA, "USPS", "in", 5, 3.5, "landscape", "postcard",
"USPS postcard (max)", NA, NA, "USPS", "in", 6, 4.25, "landscape", "postcard",
"6 in. x 11 in.", NA, NA, NA, "in", 11, 6, "landscape", "postcard",
"5.5 in. x 8.5 in.", NA, NA, NA, "in", 8.5, 5.5, "landscape", "postcard",
"4 in. x 6 in.", NA, NA, NA, "in", 6, 4, "landscape", "postcard",
"6 in. x 9 in.", NA, NA, NA, "in", 9, 6, "landscape", "postcard",
"6.5 in. x 9.5 in.", NA, NA, NA, "in", 9.5, 6.5, "landscape", "postcard",
"5 in. x 7 in.", NA, NA, NA, "in", 7, 5, "landscape", "postcard",
"3 in. x 4 in.", NA, NA, NA, "in", 4, 3, "landscape", "postcard",
"4.25 in. x 5.5 in.", NA, NA, NA, "in", 5.5, 4.25, "landscape", "postcard",
"8 x 10 inches", NA, NA, NA, "in", 8, 10, "portrait", "print",
"8.5 x 11 inches", NA, NA, NA, "in", 8.5, 11, "portrait", "print",
"18 x 24 inches", NA, NA, NA, "in", 18, 24, "portrait", "print",
"5 x 7 inches", NA, NA, NA, "in", 5, 7, "portrait", "print",
"4 x 6 inches", NA, NA, NA, "in", 4, 6, "portrait", "print",
"12 x 18 inches", NA, NA, NA, "in", 12, 18, "portrait", "print",
"24 x 36 inches", NA, NA, NA, "in", 24, 36, "portrait", "print"
)
usethis::use_data(paper_sizes, overwrite = TRUE)
osm_building_tags <-
osmdata::available_tags("building")
osm_building_tags <-
stringr::str_remove(osm_building_tags, "(?<=&)[:graph:]+") |>
stringr::str_remove("&")
usethis::use_data(
osm_building_tags,
overwrite = TRUE
)
select_unit_options <-
c(
"arc_degree", "arc_minute", "arc_second", "cm", "m", "metre", "meter", "meters", "km", "kilometer", "kilometers",
"inch", "in", "ft", "foot", "feet", "yard", "yards", "mi", "mile", "miles", "nautical_mile", "radian",
"mm", "millimeter", "millimeters", "millimetre", "millimetres"
)
add_metric_units <-
tibble::tribble(
~symbol, ~symbol_aliases, ~name_singular, ~name_singular_aliases, ~name_plural, ~name_plural_aliases, ~def, ~definition, ~comment, ~dimensionless, ~source_xml,
"km", "", "kilometer", "kilometre", "kilometers", "kilometres", "m * 1000", "length equivalent to 1000 meters", NA, FALSE, NA,
"cm", "", "centimeter", "centimetre", "centimeters", "centimetres", "m/100", "length equivalent to 0.01 meter", NA, FALSE, NA
)
valid_units <-
units::valid_udunits()
valid_units <-
valid_units |>
dplyr::bind_rows(add_metric_units)
units_filter_cols <- c("symbol", "symbol_aliases", "name_singular", "name_singular_aliases", "name_plural", "name_plural_aliases")
dist_units <-
valid_units |>
filter(
if_any(
any_of(units_filter_cols),
~ (.x %in% select_unit_options)
)
)
dist_units <-
naniar::replace_with_na_if(dist_units, is.character, ~ .x == "")
usethis::use_data(
dist_units,
overwrite = TRUE
)
dist_unit_options <-
unique(c(
dist_units$name_singular,
dist_units$name_plural,
stringr::str_split(dist_units$name_singular_aliases, ", ", simplify = TRUE),
stringr::str_split(dist_units$name_plural_aliases, ", ", simplify = TRUE),
dist_units$symbol,
dist_units$symbol_aliases,
select_unit_options
))
dist_unit_options <- dist_unit_options[!is.na(dist_unit_options)]
dist_unit_options <- dist_unit_options[dist_unit_options != ""]
usethis::use_data(
dist_unit_options,
overwrite = TRUE,
internal = FALSE
)
area_units <-
valid_units |>
filter(
if_any(
any_of(units_filter_cols),
~ (.x %in% c("hectare", "acre", "acre_foot"))
)
)
area_units <-
naniar::replace_with_na_if(area_units, is.character, ~ .x == "")
area_unit_options <-
c(paste0(c(
"meter", "meters", "nautical_mile", "international_inch", "international_foot",
"international_yard", "international_mile", "kilometer", "centimeter", "m", "in",
"ft", "yd", "mi", "km", "cm", "metre", "inch", "foot", "yard", "mile", "kilometre",
"centimetre", "international_feet", "kilometers", "centimeters", "feet", "kilometres",
"centimetres"
), "^2"), "hectare", "hectares", "acre", "acres", "acre_foot", "acre_feet")
usethis::use_data(
area_unit_options,
overwrite = TRUE
)
standard_scales <-
tibble::tribble(
~scale, ~standard, ~series, ~actual_ft, ~actual_ft_unit, ~scale_in, ~scale_in_unit, ~scale_in_accuracy, ~scale_cm, ~scale_cm_unit, ~scale_cm_accuracy, ~size_latlon, ~size_latlon_unit, ~area_approx, ~area_approx_unit, ~series_status,
"1:20,000", "USGS", "Puerto Rico 7.5 minute", 6e-04, "in", 1670, "ft", "approximate", 200, "m", "exact", "7.5 by 7.5 minute", "minute", "7.10E+01", "sq mi", NA,
"1:24,000", "USGS", "7.5 minute", 5e-04, "in", 2000, "ft", "exact", 240, "m", "exact", "7.5 by 7.5 minute", "minute", "49 to 70", "sq mi", NA,
"1:25,000", "USGS", "7.5 minute", 0.00048, "in", 2080, "ft", "approximate", 250, "m", "exact", "7.5 by 7.5 minute", "minute", "49 to 70", "sq mi", NA,
"1:25,000", "USGS", "7.5 by 15 minute", 0.00048, "in", 2080, "ft", "approximate", 250, "m", "exact", "7.5 by 15 minute", "minute", "98 to 140", "sq mi", NA,
"1:50,000", "USGS", "USGS-DMA 15 minute", 0.00024, "in", 4170, "ft", "approximate", 500, "m", "exact", "15 by 15 minute", "minute", "197 to 282", "sq mi", NA,
"1:62,500", "USGS", "15 minute*", NA, NA, 1, "mi", "approximate", 625, "m", "exact", "15 by 15 minute", "minute", "197 to 282", "sq mi", "abandoned",
"1:63,360", "USGS", "Alaska Maps", NA, NA, 1, "mi", "exact", 634, "m", "exact", "15 by 20 to 36 minute", "minute", "207 to 281", "sq mi", NA,
"1:50,000", "USGS", "County Maps", 0.00024, "in", 4170, "ft", "approximate", 500, "m", "exact", "County area", NA, "Varies", NA, NA,
"1:100,000", "USGS", "County Maps", NA, NA, 1.6, "mi", "approximate", 1, "km", "exact", "County area", NA, "Varies", NA, NA,
"1:100,000", "USGS", "30 by 60 minute", NA, NA, 1.6, "mi", "approximate", 1, "km", "exact", "30 by 60 minute", "minute", "1,568 to 2,240", "sq mi", NA,
"1:125,000", "USGS", "30 minute*", NA, NA, 2, "mi", "approximate", 1.25, "km", "exact", "30 by 30 minute", "minute", "786 to 1,124", "sq mi", "abandoned",
"1:250,000", "USGS", "1 degree by 2 degrees or 3 degrees", NA, NA, 4, "mi", "approximate", 2.5, "km", "exact", "1° by 2° or 3°", "degree", "4,580 to 8,669", "sq mi", NA,
"1:500,000", "USGS", "State Maps", NA, NA, 8, "mi", "approximate", 5, "km", "exact", "State area", NA, "Varies", NA, NA,
"1:1,000,000", "USGS", "State Maps", NA, NA, 16, "mi", "approximate", 10, "km", "exact", "State area", NA, "Varies", NA, NA,
"1:2,000,000", "USGS", "U.S. Sectional Maps", NA, NA, 32, "mi", "approximate", 20, "km", "exact", "State groups", NA, "Varies", NA, NA,
"1:250,000", "USGS", "Antarctica Maps", NA, NA, 4, "mi", "approximate", 2.5, "km", "exact", "1° by 3° to 15°", "degree", "4,089 to 8,336", "sq mi", NA,
"1:500,000", "USGS", "Antarctica Maps", NA, NA, 8, "mi", "approximate", 5, "km", "exact", "2° by 7.5°", "degree", "28,174 to 30,462", "sq mi", NA,
"1 in = 10 feet", "Engineering", NA, 0.1, "in", 10, "feet", "exact", 3.05, "m", "approximate", NA, NA, NA, NA, "common",
"1 in = 20 feet", "Engineering", NA, 0.05, "in", 20, "feet", "exact", 6.1, "m", "approximate", NA, NA, NA, NA, "common",
"1 in = 30 feet", "Engineering", NA, 0.0333, "in", 30, "feet", "exact", 9.14, "m", "approximate", NA, NA, NA, NA, "common",
"1 in = 40 feet", "Engineering", NA, 0.025, "in", 40, "feet", "exact", 12.2, "m", "approximate", NA, NA, NA, NA, "common",
"1 in = 50 feet", "Engineering", NA, 0.02, "in", 50, "feet", "exact", 15.2, "m", "approximate", NA, NA, NA, NA, "common",
"1 in = 60 feet", "Engineering", NA, 0.0167, "in", 60, "feet", "exact", 18.3, "m", "approximate", NA, NA, NA, NA, "common",
"3/32 in = 1 foot", "Architectural", NA, 0.0938, "in", 10.7, "feet", "approximate", 3.25, "m", "approximate", NA, NA, NA, NA, "common",
"3/16 in = 1 foot", "Architectural", NA, 0.188, "in", 5.33, "feet", "approximate", 1.63, "m", "approximate", NA, NA, NA, NA, "common",
"1/8 in = 1 foot", "Architectural", NA, 0.125, "in", 8, "feet", "exact", 2.44, "m", "approximate", NA, NA, NA, NA, "common",
"1/4 in = 1 foot", "Architectural", NA, 0.25, "in", 4, "feet", "exact", 1.22, "m", "approximate", NA, NA, NA, NA, "common",
"3/8 in = 1 foot", "Architectural", NA, 0.375, "in", 2.67, "feet", "approximate", 0.813, "m", "approximate", NA, NA, NA, NA, "common",
"1/2 in = 1 foot", "Architectural", NA, 0.5, "in", 2, "feet", "exact", 0.61, "m", "approximate", NA, NA, NA, NA, "common",
"3/4 in = 1 foot", "Architectural", NA, 0.75, "in", 1.33, "feet", "approximate", 0.406, "m", "approximate", NA, NA, NA, NA, "common",
"1 in = 1 foot", "Architectural", NA, 1, "in", 1, "feet", "exact", 0.305, "m", "approximate", NA, NA, NA, NA, "common",
"1½ in = 1 foot", "Architectural", NA, 1.5, "in", 0.667, "feet", "approximate", 0.203, "m", "approximate", NA, NA, NA, NA, "common"
)
usethis::use_data(
standard_scales,
overwrite = TRUE
)
us_states <-
tigris::states(
cb = TRUE,
resolution = "5m"
)
us_states <-
us_states %>%
janitor::clean_names("snake") %>%
sf::st_transform(3857)
nest_states <-
us_states %>%
dplyr::group_by(geoid) %>%
dplyr::group_nest(keep = TRUE)
us_states <-
purrr::map_dfr(
.x = nest_states$data,
~ tibble::tibble(
name = unique(.x$name),
geoid = unique(.x$geoid),
statefp = unique(.x$statefp),
abb = unique(.x$stusps),
bbox = list(sf::st_bbox(.x)),
wkt = sf::st_as_text(sf::st_as_sfc(.x))
)
)
get_state_pop <-
function(x) {
tidycensus::get_acs(
geography = "state",
variables = "B01001_001",
year = 2019,
state = x,
cache_table = TRUE
) %>%
janitor::clean_names("snake")
}
possibly_get_state_pop <-
purrr::possibly(
~ get_state_pop(.x),
NULL,
quiet = FALSE
)
us_states_census <-
purrr::map_dfr(
us_states$statefp,
~ possibly_get_state_pop(.x)
)
us_states_census <-
us_states_census %>%
dplyr::select(-c(name, variable, moe)) %>%
dplyr::rename(est_pop = estimate)
us_states <- us_states %>%
dplyr::left_join(
us_states_census,
by = "geoid"
) %>%
dplyr::relocate(
abb,
est_pop,
.after = geoid
)
names(us_states$geoid) <- tolower(us_states$abb)
names(us_states$wkt) <- tolower(us_states$abb)
textclean::replace_non_ascii(us_states)
usethis::use_data(
us_states,
overwrite = TRUE,
internal = FALSE
)
us_counties <-
purrr::map_dfr(
us_states$statefp,
~ tigris::counties(
state = .x,
cb = TRUE,
resolution = "5m"
)
)
us_counties <-
us_counties %>%
janitor::clean_names("snake") %>%
sf::st_transform(3857)
us_counties <-
us_counties %>%
dplyr::left_join(
dplyr::select(us_states, state_abb = abb, statefp),
by = "statefp"
)
nest_counties <- us_counties %>%
dplyr::group_by(geoid) %>%
dplyr::group_nest(keep = TRUE)
us_counties <-
purrr::map_dfr(
.x = nest_counties$data,
~ tibble::tibble(
name_short = unique(.x$name),
geoid = unique(.x$geoid),
countyfp = unique(.x$countyfp),
statefp = unique(.x$statefp),
abb_state = unique(.x$state_abb),
bbox = list(sf::st_bbox(.x)),
wkt = sf::st_as_text(sf::st_as_sfc(.x))
)
)
get_county_pop <-
function(x, y) {
tidycensus::get_acs(
geography = "county",
variables = "B01001_001",
year = 2019,
state = x,
county = y,
cache_table = TRUE
)
}
possibly_get_county_pop <-
purrr::possibly(
~ get_county_pop(.x, .y),
NULL,
quiet = FALSE
)
us_counties_census <-
purrr::map2_dfr(
us_counties$statefp,
us_counties$countyfp,
~ possibly_get_county_pop(
.x,
.y
)
) %>%
janitor::clean_names("snake")
us_counties <-
us_counties %>%
dplyr::left_join(us_counties_census, by = "geoid") %>%
dplyr::select(-c(moe, variable)) %>%
dplyr::rename(
est_pop = estimate
) %>%
dplyr::relocate(
abb_state, est_pop,
.after = name_short
) %>%
dplyr::relocate(
name,
.before = dplyr::everything()
) %>%
dplyr::mutate(
name = dplyr::case_when(
(is.na(name) && state_abb == "VI") ~ paste0(name_short, ", ", "U.S. Virgin Islands"),
(is.na(name) && state_abb == "GU") ~ paste0(name_short, ", ", "Guam"),
(is.na(name) && state_abb == "MP") ~ paste0(name_short, ", ", "Northern Mariana Islands"),
(is.na(name) && state_abb == "AS") ~ paste0(name_short, ", ", "American Samoa"),
TRUE ~ name
)
) %>%
dplyr::mutate(
label = stringr::str_extract(name, ".+(?=,)"),
label = janitor::make_clean_names(paste0(label, ", ", abb_state))
) %>%
dplyr::mutate(
label = stringr::str_replace(
label, "_borough_", "_bor_"
),
label = stringr::str_replace(
label, "_county_", "_co_"
),
label = stringr::str_replace(
label, "_parish_", "_par_"
),
label = stringr::str_replace(
label, "_township_", "_twp_"
),
label = stringr::str_replace(
label, "_census_area_", "_"
),
label = stringr::str_replace(
label, "_municipio_", "_muni_"
)
)
names(us_counties$wkt) <- us_counties$label
names(us_counties$geoid) <- us_counties$label
us_counties <- dplyr::select(
us_counties,
-label
)
usethis::use_data(
us_counties,
overwrite = TRUE,
internal = FALSE
)
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