data-raw/create_data_sf.R
In arodionoff/geokz: Offers Various Kazakhstani Maps as Data Frames and 'sf' Objects
## code to prepare `kaz_amd0_sf`, `kaz_amd1_sf` & `kaz_amd2_sf` dataset goes here
##
library('sf') # Simple Features for R
library('dplyr') # A Grammar of Data Manipulation
library('magrittr') # A Forward-Pipe Operator for R
# MultiPolygon `sf` object for Kazakhstan country (Administrative unit level 0 - Country) in `UTF-8` encoding
agr = c(ADM0_EN = "constant", ADM0_KK = "constant", ADM0_RU = "constant", ADM0_PCODE = "constant")
kaz_adm0_sf <- sf::st_read('maps/kaz_admbnda_adm0_2019.shp', agr = agr) # Create 'sf' class from package 'sf'
kaz_adm0_sf <-
dplyr::transmute( kaz_adm0_sf,
KATO = "000000000",
ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE
)
base::Encoding(kaz_adm0_sf$ADM0_KK)
# Coordinate Reference System of object - sf::sf_extSoftVersion()
# EPSG:4326 or WGS84 for older version GDAL - see https://github.com/r-spatial/sf/issues/1419
sf::st_crs(x = kaz_adm0_sf) <- 4326L
sf::st_agr(kaz_adm0_sf) <-
factor( x = c(KATO = "identity", agr)
, levels = c("constant", "aggregate", "identity") )
usethis::use_data(kaz_adm0_sf, overwrite = TRUE, compress = 'xz', version = 3)
# MultiPolygon `sf` object for Kazakhstan's oblasts
# Administrative unit level 1 - the largest subnational unit of a country
agr = c(ADM0_EN = "constant", ADM0_KK = "constant", ADM0_RU = "constant", ADM0_PCODE = "constant",
ADM1_EN = "identity", ADM1_KK = "identity", ADM1_RU = "identity", ADM1_PCODE = "identity",
Year = "identity")
# Version 2018 Year
kaz_adm1_2018_sf <- sf::st_read('maps/kaz_admbnda_adm1_2019.shp', agr = agr) # Create 'sf' class from package 'sf'
kaz_adm1_2018_sf <-
dplyr::transmute( kaz_adm1_2018_sf,
KATO = paste0(substr(ADM1_PCODE, 3, 4), "0000000"),
ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE, ADM1_EN, ADM1_KK, ADM1_RU, ADM1_PCODE,
ISO_3166_2 = c("KZ-AKM", "KZ-AKT", "KZ-ALM", "KZ-ATY", "KZ-ZAP", "KZ-ZHA",
"KZ-KAR", "KZ-KUS", "KZ-KZY", "KZ-MAN", "KZ-PAV", "KZ-SEV",
"KZ-YUZ", "KZ-VOS", "KZ-AST", "KZ-ALA", "KZ-SHY"),
Year = 2018L
)
sf::st_agr(kaz_adm1_2018_sf) <-
factor( x = c(KATO = "identity", agr, ISO_3166_2 = "identity")
, levels = c("constant", "aggregate", "identity") )
usethis::use_data(kaz_adm1_2018_sf, overwrite = TRUE, compress = 'xz', version = 3)
# Version 2024 Year
kaz_adm1_sf <- sf::st_read('maps/kaz_admbnda_adm1_2024.shp', agr = agr) # Create 'sf' class from package 'sf'
kaz_adm1_sf <-
dplyr::transmute( kaz_adm1_sf,
KATO = paste0(substr(ADM1_PCODE, 3, 4), "0000000"),
ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE, ADM1_EN, ADM1_KK, ADM1_RU, ADM1_PCODE,
ISO_3166_2 = c("KZ-ABY",
"KZ-AKM", "KZ-AKT", "KZ-ALM", "KZ-ATY", "KZ-ZAP", "KZ-ZHA",
"KZ-ZHT",
"KZ-KAR", "KZ-KUS", "KZ-KZY", "KZ-MAN", "KZ-PAV", "KZ-SEV",
"KZ-YUZ",
"KZ-ULT",
"KZ-VOS", "KZ-AST", "KZ-ALA", "KZ-SHY"),
Year = 2024L
)
sf::st_agr(kaz_adm1_sf) <-
factor( x = c(KATO = "identity", agr, ISO_3166_2 = "identity")
, levels = c("constant", "aggregate", "identity") )
usethis::use_data(kaz_adm1_sf, overwrite = TRUE, compress = 'xz', version = 3)
# MultiPolygon `sf` object for Kazakhstan's rayons
# Administrative unit level 2 - the secondary subnational unit of a country
agr = c(ADM0_EN = "constant", ADM0_KK = "constant", ADM0_RU = "constant", ADM0_PCODE = "constant",
ADM1_EN = "constant", ADM1_KK = "constant", ADM1_RU = "constant", ADM1_PCODE = "constant",
ADM2_EN = "identity", ADM2_KK = "identity", ADM2_RU = "identity", ADM2_PCODE = "identity",
Year = "identity")
# Version 2018 Year
kaz_adm2_2018_sf <- sf::st_read('maps/kaz_admbnda_adm2_2021.shp', agr = agr) # Create 'sf' class from package 'sf'
# kaz_adm2_sf <- # 26 times more slower
# sf::st_join( x = kaz_adm2_sf,
# y = dplyr::select(kaz_adm1_sf, ISO_3166_2),
# left = TRUE )
kaz_adm2_2018_sf <-
dplyr::inner_join( x = kaz_adm2_2018_sf,
y = dplyr::select(kaz_adm1_2018_sf, ADM1_PCODE, ISO_3166_2, Year) |>
dplyr::filter( Year == 2018L ) |>
sf::st_drop_geometry(),
by = c('ADM1_PCODE' = 'ADM1_PCODE')
) |>
dplyr::transmute(
KATO = paste0(substr(ADM2_PCODE, 3, 8), "000"),
ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE,
ADM1_EN, ADM1_KK, ADM1_RU, ADM1_PCODE,
ADM2_EN, ADM2_KK, ADM2_RU, ADM2_PCODE,
ISO_3166_2, Year
) |>
dplyr::arrange(KATO) # Change order of Turkistani Cities
sf::st_agr(kaz_adm2_2018_sf) <-
factor( x = c(KATO = "identity", agr, ISO_3166_2 = "constant")
, levels = c("constant", "aggregate", "identity") )
usethis::use_data(kaz_adm2_2018_sf, overwrite = TRUE, compress = 'xz', version = 3)
# Version 2024 Year
kaz_adm2_sf <- sf::st_read('maps/kaz_admbnda_adm2_2024.shp', agr = agr) # Create 'sf' class from package 'sf'
kaz_adm2_sf <-
dplyr::inner_join( x = kaz_adm2_sf,
y = dplyr::select(kaz_adm1_sf, ADM1_PCODE, ISO_3166_2, Year) |>
dplyr::filter( Year == 2024L ) |>
sf::st_drop_geometry(),
by = c('ADM1_PCODE' = 'ADM1_PCODE')
) |>
dplyr::transmute(
KATO = paste0(substr(ADM2_PCODE, 3, 8), "000"),
ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE,
ADM1_EN, ADM1_KK, ADM1_RU, ADM1_PCODE,
ADM2_EN, ADM2_KK, ADM2_RU, ADM2_PCODE,
ISO_3166_2, Year
) |>
dplyr::arrange(KATO) # Change order of Turkistani Cities
sf::st_agr(kaz_adm2_sf) <-
factor( x = c(KATO = "identity", agr, ISO_3166_2 = "constant")
, levels = c("constant", "aggregate", "identity") )
# Correct Geometry field
kaz_adm2_sf <-
kaz_adm2_sf |>
sf::st_make_valid() # see https://github.com/r-spatial/sf/issues/1902
usethis::use_data(kaz_adm2_sf, overwrite = TRUE, compress = 'xz', version = 3)
# Point `sf` object for Kazakhstan's oblasts (City - Administrative Center of unit level 1)
agr = c(KATO = "identity", NAME_KK = "identity", NAME_RU = "identity", NAME_EN = "identity")
kaz_cnt1_sf <- sf::st_read('maps/kaz_admcntr_adm1_2022.shp', agr = agr) # Create 'sf' class from package 'sf'
kaz_cnt1_sf <-
dplyr::mutate( kaz_cnt1_sf,
TYPE_CNT1 = dplyr::case_when( # c(rep(3L, times = 14L), 1L, 2L, 2L, 4L),
KATO == "710000000" ~ 1L, # Nur-Sultan - The Capital
# base::substr(KATO, 3, 9) == "0000000" ~ 2L # Cities of Republican Significance
KATO == "431900000" ~ 4L, # Baykonyr - the World's First Spaceport
TRUE ~ 3L # Center of Administrative unit level 1
),
ISO_3166_2 = c("KZ-ABY",
"KZ-AKM", "KZ-AKT", "KZ-ALM", "KZ-ATY", "KZ-ZAP", "KZ-ZHA",
"KZ-ZHT",
"KZ-KAR", "KZ-KUS", "KZ-KZY", "KZ-BAY", "KZ-MAN", "KZ-PAV", "KZ-SEV",
"KZ-YUZ",
"KZ-ULT",
"KZ-VOS", "KZ-AST", "KZ-ALA", "KZ-SHY") ) |>
sf::st_join(
y = dplyr::select(kaz_adm1_sf, ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE,
ADM1_EN, ADM1_KK, ADM1_RU, ADM1_PCODE, Year) |>
dplyr::filter( Year == 2024L ),
left = TRUE
) |>
dplyr::transmute(
KATO,
ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE,
ADM1_EN, ADM1_KK, ADM1_RU, ADM1_PCODE,
TYPE_CNT1, ISO_3166_2, NAME_EN, NAME_KK, NAME_RU
)
kaz_cnt1_sf$TYPE_CNT1 <-
ifelse( kaz_cnt1_sf$KATO != "710000000" & # Astana - The Capital
substr(kaz_cnt1_sf$KATO, 3, 9) == "0000000", # Cities of Republican Significance
2L, kaz_cnt1_sf$TYPE_CNT1 )
sf::st_agr(kaz_cnt1_sf) <-
factor( x = c(KATO = "identity", ADM0_EN = "constant", ADM0_KK = "constant", ADM0_RU = "constant", ADM0_PCODE = "constant",
ADM1_EN = "identity", ADM1_KK = "identity", ADM1_RU = "identity", ADM1_PCODE = "identity",
TYPE_CNT1 = "identity", ISO_3166_2 = "identity",
NAME_EN = "identity", NAME_KK = "identity", NAME_RU = "identity")
, levels = c("constant", "aggregate", "identity") )
usethis::use_data(kaz_cnt1_sf, overwrite = TRUE, compress = 'xz', version = 3)
# Zones Of Kazakhstan according to the natural conditions
natural_zones <-
dplyr::inner_join( x = utils::read.csv( file = "data-raw/kaz_zones.csv", encoding = "UTF-8" ),
y = data.frame(ZONE_RU = c("Степная", "Сухостепная", "Полупустынная",
"Предгорно-пустынно-степная", "Пустынная",
"Южно-Сибирская горная и предгорная"),
ZONE_KK = c("Дала", "Құрғақ дала", "Жартылай шөл",
"Тау етегі-шөл-дала", "Шөл",
"Оңтүстік Сібір тауы мен тау бөктері"),
ZONE_EN = c("Steppe", "Dry-steppe", "Semi-desert",
"Foothill-desert-steppe", "Desert",
"South Siberian mountain and foothill")),
by = c("ZONE_RU" = "ZONE_RU") ) |>
dplyr::transmute(KATO = as.character(KATO),
ZONE_EN = factor(ZONE_EN,
levels = c("Steppe", "Dry-steppe", "Foothill-desert-steppe",
"Desert", "South Siberian mountain and foothill", "Semi-desert")),
ZONE_KK = factor(ZONE_KK,
levels = c("Дала", "Құрғақ далаe", "Тау етегі-шөл-дала",
"Шөл", "Оңтүстік Сібір тауы мен тау бөктері", "Жартылай шөл")),
ZONE_RU = factor(ZONE_RU,
levels = c("Степная", "Сухостепная", "Предгорно-пустынно-степная",
"Пустынная", "Южно-Сибирская горная и предгорная", "Полупустынная")),
ADM1_RU, ADM2_RU, ADM2_PCODE)
utils::write.csv(natural_zones, file = paste0("inst/extdata/", "kaz_zones.csv"),
row.names = FALSE, fileEncoding = "UTF-8")
usethis::use_data(natural_zones, overwrite = TRUE, compress = 'xz', version = 3)
arodionoff/geokz documentation built on July 1, 2024, 9:02 p.m.
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## code to prepare `kaz_amd0_sf`, `kaz_amd1_sf` & `kaz_amd2_sf` dataset goes here
##
library('sf') # Simple Features for R
library('dplyr') # A Grammar of Data Manipulation
library('magrittr') # A Forward-Pipe Operator for R
# MultiPolygon `sf` object for Kazakhstan country (Administrative unit level 0 - Country) in `UTF-8` encoding
agr = c(ADM0_EN = "constant", ADM0_KK = "constant", ADM0_RU = "constant", ADM0_PCODE = "constant")
kaz_adm0_sf <- sf::st_read('maps/kaz_admbnda_adm0_2019.shp', agr = agr) # Create 'sf' class from package 'sf'
kaz_adm0_sf <-
dplyr::transmute( kaz_adm0_sf,
KATO = "000000000",
ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE
)
base::Encoding(kaz_adm0_sf$ADM0_KK)
# Coordinate Reference System of object - sf::sf_extSoftVersion()
# EPSG:4326 or WGS84 for older version GDAL - see https://github.com/r-spatial/sf/issues/1419
sf::st_crs(x = kaz_adm0_sf) <- 4326L
sf::st_agr(kaz_adm0_sf) <-
factor( x = c(KATO = "identity", agr)
, levels = c("constant", "aggregate", "identity") )
usethis::use_data(kaz_adm0_sf, overwrite = TRUE, compress = 'xz', version = 3)
# MultiPolygon `sf` object for Kazakhstan's oblasts
# Administrative unit level 1 - the largest subnational unit of a country
agr = c(ADM0_EN = "constant", ADM0_KK = "constant", ADM0_RU = "constant", ADM0_PCODE = "constant",
ADM1_EN = "identity", ADM1_KK = "identity", ADM1_RU = "identity", ADM1_PCODE = "identity",
Year = "identity")
# Version 2018 Year
kaz_adm1_2018_sf <- sf::st_read('maps/kaz_admbnda_adm1_2019.shp', agr = agr) # Create 'sf' class from package 'sf'
kaz_adm1_2018_sf <-
dplyr::transmute( kaz_adm1_2018_sf,
KATO = paste0(substr(ADM1_PCODE, 3, 4), "0000000"),
ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE, ADM1_EN, ADM1_KK, ADM1_RU, ADM1_PCODE,
ISO_3166_2 = c("KZ-AKM", "KZ-AKT", "KZ-ALM", "KZ-ATY", "KZ-ZAP", "KZ-ZHA",
"KZ-KAR", "KZ-KUS", "KZ-KZY", "KZ-MAN", "KZ-PAV", "KZ-SEV",
"KZ-YUZ", "KZ-VOS", "KZ-AST", "KZ-ALA", "KZ-SHY"),
Year = 2018L
)
sf::st_agr(kaz_adm1_2018_sf) <-
factor( x = c(KATO = "identity", agr, ISO_3166_2 = "identity")
, levels = c("constant", "aggregate", "identity") )
usethis::use_data(kaz_adm1_2018_sf, overwrite = TRUE, compress = 'xz', version = 3)
# Version 2024 Year
kaz_adm1_sf <- sf::st_read('maps/kaz_admbnda_adm1_2024.shp', agr = agr) # Create 'sf' class from package 'sf'
kaz_adm1_sf <-
dplyr::transmute( kaz_adm1_sf,
KATO = paste0(substr(ADM1_PCODE, 3, 4), "0000000"),
ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE, ADM1_EN, ADM1_KK, ADM1_RU, ADM1_PCODE,
ISO_3166_2 = c("KZ-ABY",
"KZ-AKM", "KZ-AKT", "KZ-ALM", "KZ-ATY", "KZ-ZAP", "KZ-ZHA",
"KZ-ZHT",
"KZ-KAR", "KZ-KUS", "KZ-KZY", "KZ-MAN", "KZ-PAV", "KZ-SEV",
"KZ-YUZ",
"KZ-ULT",
"KZ-VOS", "KZ-AST", "KZ-ALA", "KZ-SHY"),
Year = 2024L
)
sf::st_agr(kaz_adm1_sf) <-
factor( x = c(KATO = "identity", agr, ISO_3166_2 = "identity")
, levels = c("constant", "aggregate", "identity") )
usethis::use_data(kaz_adm1_sf, overwrite = TRUE, compress = 'xz', version = 3)
# MultiPolygon `sf` object for Kazakhstan's rayons
# Administrative unit level 2 - the secondary subnational unit of a country
agr = c(ADM0_EN = "constant", ADM0_KK = "constant", ADM0_RU = "constant", ADM0_PCODE = "constant",
ADM1_EN = "constant", ADM1_KK = "constant", ADM1_RU = "constant", ADM1_PCODE = "constant",
ADM2_EN = "identity", ADM2_KK = "identity", ADM2_RU = "identity", ADM2_PCODE = "identity",
Year = "identity")
# Version 2018 Year
kaz_adm2_2018_sf <- sf::st_read('maps/kaz_admbnda_adm2_2021.shp', agr = agr) # Create 'sf' class from package 'sf'
# kaz_adm2_sf <- # 26 times more slower
# sf::st_join( x = kaz_adm2_sf,
# y = dplyr::select(kaz_adm1_sf, ISO_3166_2),
# left = TRUE )
kaz_adm2_2018_sf <-
dplyr::inner_join( x = kaz_adm2_2018_sf,
y = dplyr::select(kaz_adm1_2018_sf, ADM1_PCODE, ISO_3166_2, Year) |>
dplyr::filter( Year == 2018L ) |>
sf::st_drop_geometry(),
by = c('ADM1_PCODE' = 'ADM1_PCODE')
) |>
dplyr::transmute(
KATO = paste0(substr(ADM2_PCODE, 3, 8), "000"),
ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE,
ADM1_EN, ADM1_KK, ADM1_RU, ADM1_PCODE,
ADM2_EN, ADM2_KK, ADM2_RU, ADM2_PCODE,
ISO_3166_2, Year
) |>
dplyr::arrange(KATO) # Change order of Turkistani Cities
sf::st_agr(kaz_adm2_2018_sf) <-
factor( x = c(KATO = "identity", agr, ISO_3166_2 = "constant")
, levels = c("constant", "aggregate", "identity") )
usethis::use_data(kaz_adm2_2018_sf, overwrite = TRUE, compress = 'xz', version = 3)
# Version 2024 Year
kaz_adm2_sf <- sf::st_read('maps/kaz_admbnda_adm2_2024.shp', agr = agr) # Create 'sf' class from package 'sf'
kaz_adm2_sf <-
dplyr::inner_join( x = kaz_adm2_sf,
y = dplyr::select(kaz_adm1_sf, ADM1_PCODE, ISO_3166_2, Year) |>
dplyr::filter( Year == 2024L ) |>
sf::st_drop_geometry(),
by = c('ADM1_PCODE' = 'ADM1_PCODE')
) |>
dplyr::transmute(
KATO = paste0(substr(ADM2_PCODE, 3, 8), "000"),
ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE,
ADM1_EN, ADM1_KK, ADM1_RU, ADM1_PCODE,
ADM2_EN, ADM2_KK, ADM2_RU, ADM2_PCODE,
ISO_3166_2, Year
) |>
dplyr::arrange(KATO) # Change order of Turkistani Cities
sf::st_agr(kaz_adm2_sf) <-
factor( x = c(KATO = "identity", agr, ISO_3166_2 = "constant")
, levels = c("constant", "aggregate", "identity") )
# Correct Geometry field
kaz_adm2_sf <-
kaz_adm2_sf |>
sf::st_make_valid() # see https://github.com/r-spatial/sf/issues/1902
usethis::use_data(kaz_adm2_sf, overwrite = TRUE, compress = 'xz', version = 3)
# Point `sf` object for Kazakhstan's oblasts (City - Administrative Center of unit level 1)
agr = c(KATO = "identity", NAME_KK = "identity", NAME_RU = "identity", NAME_EN = "identity")
kaz_cnt1_sf <- sf::st_read('maps/kaz_admcntr_adm1_2022.shp', agr = agr) # Create 'sf' class from package 'sf'
kaz_cnt1_sf <-
dplyr::mutate( kaz_cnt1_sf,
TYPE_CNT1 = dplyr::case_when( # c(rep(3L, times = 14L), 1L, 2L, 2L, 4L),
KATO == "710000000" ~ 1L, # Nur-Sultan - The Capital
# base::substr(KATO, 3, 9) == "0000000" ~ 2L # Cities of Republican Significance
KATO == "431900000" ~ 4L, # Baykonyr - the World's First Spaceport
TRUE ~ 3L # Center of Administrative unit level 1
),
ISO_3166_2 = c("KZ-ABY",
"KZ-AKM", "KZ-AKT", "KZ-ALM", "KZ-ATY", "KZ-ZAP", "KZ-ZHA",
"KZ-ZHT",
"KZ-KAR", "KZ-KUS", "KZ-KZY", "KZ-BAY", "KZ-MAN", "KZ-PAV", "KZ-SEV",
"KZ-YUZ",
"KZ-ULT",
"KZ-VOS", "KZ-AST", "KZ-ALA", "KZ-SHY") ) |>
sf::st_join(
y = dplyr::select(kaz_adm1_sf, ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE,
ADM1_EN, ADM1_KK, ADM1_RU, ADM1_PCODE, Year) |>
dplyr::filter( Year == 2024L ),
left = TRUE
) |>
dplyr::transmute(
KATO,
ADM0_EN, ADM0_KK, ADM0_RU, ADM0_PCODE,
ADM1_EN, ADM1_KK, ADM1_RU, ADM1_PCODE,
TYPE_CNT1, ISO_3166_2, NAME_EN, NAME_KK, NAME_RU
)
kaz_cnt1_sf$TYPE_CNT1 <-
ifelse( kaz_cnt1_sf$KATO != "710000000" & # Astana - The Capital
substr(kaz_cnt1_sf$KATO, 3, 9) == "0000000", # Cities of Republican Significance
2L, kaz_cnt1_sf$TYPE_CNT1 )
sf::st_agr(kaz_cnt1_sf) <-
factor( x = c(KATO = "identity", ADM0_EN = "constant", ADM0_KK = "constant", ADM0_RU = "constant", ADM0_PCODE = "constant",
ADM1_EN = "identity", ADM1_KK = "identity", ADM1_RU = "identity", ADM1_PCODE = "identity",
TYPE_CNT1 = "identity", ISO_3166_2 = "identity",
NAME_EN = "identity", NAME_KK = "identity", NAME_RU = "identity")
, levels = c("constant", "aggregate", "identity") )
usethis::use_data(kaz_cnt1_sf, overwrite = TRUE, compress = 'xz', version = 3)
# Zones Of Kazakhstan according to the natural conditions
natural_zones <-
dplyr::inner_join( x = utils::read.csv( file = "data-raw/kaz_zones.csv", encoding = "UTF-8" ),
y = data.frame(ZONE_RU = c("Степная", "Сухостепная", "Полупустынная",
"Предгорно-пустынно-степная", "Пустынная",
"Южно-Сибирская горная и предгорная"),
ZONE_KK = c("Дала", "Құрғақ дала", "Жартылай шөл",
"Тау етегі-шөл-дала", "Шөл",
"Оңтүстік Сібір тауы мен тау бөктері"),
ZONE_EN = c("Steppe", "Dry-steppe", "Semi-desert",
"Foothill-desert-steppe", "Desert",
"South Siberian mountain and foothill")),
by = c("ZONE_RU" = "ZONE_RU") ) |>
dplyr::transmute(KATO = as.character(KATO),
ZONE_EN = factor(ZONE_EN,
levels = c("Steppe", "Dry-steppe", "Foothill-desert-steppe",
"Desert", "South Siberian mountain and foothill", "Semi-desert")),
ZONE_KK = factor(ZONE_KK,
levels = c("Дала", "Құрғақ далаe", "Тау етегі-шөл-дала",
"Шөл", "Оңтүстік Сібір тауы мен тау бөктері", "Жартылай шөл")),
ZONE_RU = factor(ZONE_RU,
levels = c("Степная", "Сухостепная", "Предгорно-пустынно-степная",
"Пустынная", "Южно-Сибирская горная и предгорная", "Полупустынная")),
ADM1_RU, ADM2_RU, ADM2_PCODE)
utils::write.csv(natural_zones, file = paste0("inst/extdata/", "kaz_zones.csv"),
row.names = FALSE, fileEncoding = "UTF-8")
usethis::use_data(natural_zones, overwrite = TRUE, compress = 'xz', version = 3)
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