Loop to convert lat and long to various UTM zones-CodePudding

I have a data frame with lat and long that I want to convert to UTM. The lat and longs have various UTM zones. I have a script used to convert a single zone to the appropriate UTM but how would I create a loop to convert the various lat and longs to UTM based on their zones. With in the data I have a column for the correct zone.

Data

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"))

Script to Convert one zone

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))

CodePudding user response：

I think this what you are looking for. Use the utm_zone column to determine which zone to convert too.
This is a straight forward, split, apply and combine strategy. See comments for more detail.

#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)