我有一個要轉換為 UTM 的經緯度資料框。緯度和經度具有不同的 UTM 區域。我有一個腳本用于將單個區域轉換為適當的 UTM，但我將如何創建一個回圈以根據其區域將各種緯度和經度轉換為 UTM。在資料中，我有一列用于正確區域。
資料
data <- structure(list(site_code = c("pa1", "pa2", "pa5", "pa7", "pa4",
"pa9", "pa11", "pa6", "pa12", "pa10", "pa3", "pa8", "tn1", "tn2",
"tn5", "tn4", "tn3", "vt1", "vt2", "vt8", "vt7", "vt4", "vt3",
"vt5", "vt6", "la2", "la1", "la4", "la3", "la5", "la6", "la7",
"nm19", "nm10", "nm8", "nm4", "nm11", "nm1", "nm2", "nm15", "nm13",
"nm14", "nm12", "nm6", "nm7", "nm3", "nm20", "nm5", "nm16", "nm18",
"nm17", "nm36", "nm35", "nm9", "la8", "nm21", "nm22", "nm23",
"nm24", "nm25", "nm26", "nm27", "nm28", "nm29", "nm30", "nm31",
"nm32", "nm33", "nm34"), region = c("pennsylvania", "pennsylvania",
"pennsylvania", "pennsylvania", "pennsylvania", "pennsylvania",
"pennsylvania", "pennsylvania", "pennsylvania", "pennsylvania",
"pennsylvania", "pennsylvania", "tennessee", "tennessee", "tennessee",
"tennessee", "tennessee", "vermont", "vermont", "vermont", "vermont",
"vermont", "vermont", "vermont", "vermont", "louisiana", "louisiana",
"louisiana", "louisiana", "louisiana", "louisiana", "louisiana",
"new mexico", "new mexico", "new mexico", "new mexico", "new mexico",
"new mexico", "new mexico", "new mexico", "new mexico", "new mexico",
"new mexico", "new mexico", "new mexico", "new mexico", "new mexico",
"new mexico", "new mexico", "new mexico", "new mexico", "new mexico",
"new mexico", "new mexico", "louisiana", "new mexico", "new mexico",
"new mexico", "new mexico", "new mexico", "new mexico", "new mexico",
"new mexico", "new mexico", "new mexico", "new mexico", "new mexico",
"new mexico", "new mexico"), site = c("wood lab pond", "rv pond",
"tryon-weber", "beaver", "cow pit", "david's pond", "church",
"tuttle pond", "black jack", "phelps pond", "hatchery", "vorisek pond",
"newt", "pine", "sink", "deer", "west", "camp johnson pond 1",
"camp johnson pond 2", "natural area close to vt01 and vt02",
"adjacent to sand bar state park", "camp ethan allen pond", "north beach pond",
"shelbourne bay", "shelbourne pond", "solomon lane", "kurthwood",
"horse head permanent", "kisatchie bayou", "horse head ephemeral",
"la6", "la7", "leasburg dam state park, bridge", "garcia well",
"johnson tank", "davie's playa- doug burkett", "cuchillo", "red tank",
"red tank 2", "bupu0039", "roadside ditch bude2026", "speamulti2237",
"2330scacou", "rouse tank", "avilas", "rhodes spring", "davie's playa",
"circle 7", "range road12", "doug's office", "culvert 0", "artesia",
"paseo del rio", "leasburg dam", "la8", "n. caballo lake", "n. caballo lake 2",
"fish well", "lm bar, 1443 tank - poss south seco tank", "1342 tank - possibly south seco tank",
"pague well", "n. seco well", "sissel tank", "polo tank", "n. caballo lake 3",
"percha dam state park", "southwell tank", "unid jornada tank",
"frog tank"), lat = c(41.569722, 41.691705, 41.593133, 41.665167,
41.672974, 41.621208, 41.650494, 41.638883, 41.665733, 41.691276,
41.643053, 41.712117, 35.35072, 35.343463, 35.40982, 35.42517,
35.41078, 44.507673, 44.515034, 44.498724, 44.627464, 44.496218,
44.493559, 44.400566, 44.376923, 31.700297, 31.484564, 31.46769,
31.444947, 31.320851, 31.255013, NA, NA, 32.739025, 33.090787,
32.355656, NA, 33.499471, 33.495826, 33.493784, 33.133323, 33.337197,
33.495381, 33.331613, 33.164875, 33.194709, NA, 33.169416, 33.507536,
NA, 32.991219, 33.050854, 33.151993, 32.487049, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), long = c(-80.4525,
-80.50082, -80.356283, -80.51465, -80.513485, -80.469131, -80.423694,
-80.495117, -80.511781, -80.512482, -80.430097, -80.389028, -86.13993,
-86.129624, -86.06988, -86.07452, -86.07966, -73.162141, -73.162287,
-73.171326, -73.237832, -72.890135, -73.242776, -73.23624, -73.161736,
-92.970663, -93.174226, -93.147099, -93.093505, -93.134511, -93.24072,
NA, NA, -106.71321, -107.566088, -106.404037, NA, -106.359682,
-106.293403, -106.327882, -106.505509, -106.313692, -106.292815,
-107.64518, -107.61595, -106.696956, NA, -107.684043, -106.205415,
NA, -106.519744, -107.529161, -107.203096, -106.924443, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), site_comments = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Radium Springs Exit",
"Jornada", "Ladder Ranch", "White Sand Missile Range", "Ladder Ranch",
"White Sand Missile Range", "White Sand Missile Range", "White Sand Missile Range",
"White Sand Missile Range", "White Sand Missile Range", "White Sand Missile Range",
"Ladder Ranch", "Ladder Ranch", "White Sand Missile Range", "White Sand Missile Range",
"Ladder Ranch", "White Sand Missile Range", "Leesburg Dam", "White Sand Missile Range",
"Ladder Ranch", "Elephant Butte", "Leasburg Dam", NA, "Williamsburg",
"Williamsburg", "Ladder Ranch", "Ladder Ranch", "Ladder Ranch",
"Ladder Ranch", "Ladder Ranch", "Ladder Ranch", "Ladder Ranch",
"Williamsburg", "Caballo Lake", "Jornada", "Jornada", "Jornada"
), location = c("usa", "usa", "usa", "usa", "usa", "usa", "usa",
"usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa",
"usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa",
"usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa",
"usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa",
"usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa",
"usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa",
"usa", "usa", "usa", "usa", "usa", "usa", "usa", "usa"), utm_zone = c(17,
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 16, 16, 16, 16, 16,
18, 18, 18, 18, 18, 18, 18, 18, 15, 15, 15, 15, 15, 15, NA, NA,
13, 13, 13, NA, 13, 13, 13, 13, 13, 13, 13, 13, 13, NA, 13, 13,
NA, 13, 13, 13, 13, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA)), row.names = c(NA, -69L), class = c("tbl_df",
"tbl", "data.frame"))
轉換一個區域的腳本
v <- vect(data, c("long", "lat"), crs=" proj=longlat")
u <- terra::project(v, " proj=utm zone=23")
utm <- crds(u)
i <- which(!is.na(data$long))
data[i, c("utme", "utmn")] <- utm
data <- data %>%
select(!c(long, lat))
uj5u.com熱心網友回復：
我想這就是你要找的。使用 utm_zone 列來確定要轉換的區域。
這是一個直截了當、拆分、應用和合并的策略。有關更多詳細資訊，請參閱評論。
#split data by utm_zone
utmsplit <- split(data, data$utm_zone)
#apply function to each groupings
dfs <- lapply(utmsplit, function(dx){
utm_zone <- dx$utm_zone[1] #get utm zone for the group
project_string <- paste0(" proj=utm zone=", utm_zone)
v <- vect(dx, c("long", "lat"), crs=" proj=longlat")
u <- terra::project(v, project_string)
utm <- crds(u)
#add new columns to data frame
cbind(dx, utm)
})
#combine individuals data frames back into one.
answer <- dplyr:bind_rows(dfs)
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