使用 x,y x,y 坐标将数据框转换为 R 中的空间线数据框
Convert data frame to spatial lines data frame in R with x,y x,y coordintates
我在 R 中有一个数据框，其中一列包含沿线的点的坐标，形式为：
x,y x,y x,y x,y
所以整个数据框看起来像
id dist speed coord
1 45 6 1.294832,54.610240 -1.294883,54.610080 -1.294262,54.6482757
2 23 34 2.788732,34.787940 6.294883,24.567080 -5.564262,-45.7676757
我想将其转换为空间线数据框，并且我假设第一步是将坐标分成两列：
x, x, x, x
y, y, y, y
但我不确定如何进行。
编辑
对于那些要求输入实际文件的人
> finalsub <- final[final$rid <3,]
> dput(finalsub)
structure(list(rid = c(1, 2), start_id = c(1L, 1L), start_code = c("E02002536",
"E02002536"), end_id = c(106L, 106L), end_code = c("E02006909",
"E02006909"), strategy = c("fastest", "quietest"), distance = c(12655L,
12909L), time_seconds = c(2921L, 3422L), calories = c(211L, 201L
), document.id = c(1L, 1L), array.index = 1:2, start = c("Geranium Close",
"Geranium Close"), finish = c("Hylton Road", "Hylton Road"),
startBearing = c(0, 0), startSpeed = c(0, 0), start_longitude = c(-1.294832,
-1.294832), start_latitude = c(54.610241, 54.610241), finish_longitude = c(-1.249478,
-1.249478), finish_latitude = c(54.680691, 54.680691), crow_fly_distance = c(8362,
8362), event = c("depart", "depart"), whence = c(1473171787,
1473171787), speed = c(20, 20), itinerary = c(419956, 419957
), clientRouteId = c(0, 0), plan = c("fastest", "quietest"
), note = c("", ""), length = c(12655, 12909), time = c(2921,
3422), busynance = c(42172, 17242), quietness = c(30, 75),
signalledJunctions = c(3, 4), signalledCrossings = c(2, 0
), west = c(-1.300074, -1.294883), south = c(54.610006, 54.609851
), east = c(-1.232447, -1.232447), north = c(54.683814, 54.683814
), name = c("Geranium Close to Hylton Road", "Geranium Close to Hylton Road"
), walk = c(0, 0), leaving = c("2016-09-06 15:23:07", "2016-09-06 15:23:07"
), arriving = c("2016-09-06 16:11:48", "2016-09-06 16:20:09"
), coordinates = c("-1.294832,54.610240 -1.294883,54.610080 -1.294262,54.610016 -1.294141,54.610006 -1.293710,54.610038 -1.293726,54.610142 -1.293742,54.610247 -1.293510,54.610262 -1.293368,54.610258 -1.292816,54.610195 -1.292489,54.610152 -1.292298,54.610667 -1.292205,54.610951 -1.292182,54.611063 -1.292183,54.611153 -1.292239,54.611341 -1.292305,54.611447 -1.292375,54.611534 -1.292494,54.611639 -1.292739,54.611830 -1.292909,54.611980 -1.293010,54.612107 -1.293111,54.612262 -1.293192,54.612423 -1.293235,54.612546 -1.293267,54.612684 -1.293279,54.612818 -1.293510,54.612813 -1.293732,54.612790 -1.294324,54.612691 -1.295086,54.612568 -1.295313,54.612539 -1.295379,54.612543 -1.295889,54.612645 -1.295945,54.612648 -1.296006,54.612642 -1.297154,54.612414 -1.297502,54.612895 -1.297733,54.612847 -1.297990,54.612796 -1.298292,54.612747 -1.298515,54.612727 -1.299088,54.612681 -1.299564,54.612669 -1.299798,54.612663 -1.300006,54.612660 -1.300057,54.612809 -1.300056,54.613335 -1.300071,54.613693 -1.300074,54.614044 -1.300042,54.614482 -1.300015,54.614786 -1.299947,54.615220 -1.299907,54.615394 -1.299854,54.615644 -1.299730,54.616048 -1.299495,54.616700 -1.299196,54.617347 -1.298236,54.619313 -1.298010,54.619762 -1.297703,54.620418 -1.297520,54.620831 -1.297169,54.621690 -1.297061,54.621981 -1.296416,54.623873 -1.296310,54.624308 -1.296225,54.624888 -1.296215,54.625286 -1.296220,54.625546 -1.296241,54.625803 -1.296268,54.625913 -1.296323,54.626011 -1.296397,54.626096 -1.296540,54.626190 -1.296719,54.626323 -1.296893,54.626433 -1.297042,54.626589 -1.297111,54.626710 -1.297122,54.626825 -1.297110,54.626948 -1.297058,54.627052 -1.296961,54.627172 -1.296861,54.627258 -1.296760,54.627325 -1.296603,54.627397 -1.296491,54.627438 -1.296338,54.627472 -1.296154,54.627496 -1.295966,54.627513 -1.295746,54.627526 -1.295618,54.627522 -1.295421,54.627510 -1.295197,54.627466 -1.295102,54.627436 -1.294832,54.627376 -1.294665,54.627355 -1.294502,54.627350 -1.294331,54.627366 -1.294014,54.627415 -1.293557,54.627499 -1.293001,54.627611 -1.292613,54.627701 -1.291836,54.627902 -1.291248,54.628076 -1.290635,54.628269 -1.290142,54.628446 -1.289621,54.628649 -1.289031,54.628903 -1.288538,54.629137 -1.288132,54.629350 -1.287681,54.629604 -1.287262,54.629862 -1.286866,54.630124 -1.286103,54.630643 -1.285748,54.630868 -1.285396,54.631082 -1.284974,54.631322 -1.284541,54.631540 -1.284101,54.631752 -1.283390,54.632048 -1.282592,54.632385 -1.282161,54.632566 -1.281762,54.632734 -1.281111,54.633011 -1.280519,54.633274 -1.280009,54.633501 -1.279834,54.633579 -1.279392,54.633781 -1.278434,54.634210 -1.277896,54.634457 -1.276936,54.634898 -1.276210,54.635223 -1.275594,54.635485 -1.274596,54.635891 -1.273929,54.636180 -1.273392,54.636428 -1.272814,54.636726 -1.271627,54.637353 -1.271084,54.637605 -1.270550,54.637825 -1.269916,54.638054 -1.269389,54.638222 -1.268821,54.638383 -1.268165,54.638555 -1.265953,54.639131 -1.263766,54.639693 -1.262790,54.639953 -1.262005,54.640154 -1.261340,54.640329 -1.260658,54.640532 -1.260123,54.640712 -1.259489,54.640957 -1.258639,54.641312 -1.258010,54.641554 -1.257141,54.641861 -1.255996,54.642247 -1.254429,54.642787 -1.253524,54.643099 -1.252790,54.643343 -1.251636,54.643727 -1.250900,54.643982 -1.250258,54.644219 -1.249668,54.644419 -1.249123,54.644623 -1.248778,54.644762 -1.246709,54.645642 -1.244773,54.646492 -1.244140,54.646746 -1.243551,54.646973 -1.242738,54.647233 -1.242353,54.647360 -1.241810,54.647503 -1.241182,54.647642 -1.240373,54.647780 -1.239950,54.647854 -1.239961,54.647890 -1.239435,54.647967 -1.239583,54.649724 -1.239343,54.649878 -1.239011,54.650011 -1.237692,54.650177 -1.236610,54.650296 -1.236417,54.650323 -1.236257,54.650351 -1.236015,54.650414 -1.235833,54.650469 -1.235081,54.650723 -1.234846,54.650805 -1.234312,54.650977 -1.234094,54.651025 -1.233980,54.651044 -1.233308,54.651137 -1.233173,54.651160 -1.233063,54.651200 -1.232967,54.651252 -1.232849,54.651347 -1.232814,54.651423 -1.232810,54.651495 -1.232823,54.651569 -1.232840,54.651893 -1.232827,54.652010 -1.232504,54.653177 -1.232447,54.653500 -1.232451,54.653723 -1.232474,54.653943 -1.232535,54.654301 -1.232590,54.654635 -1.232903,54.654627 -1.232948,54.655599 -1.232982,54.656334 -1.232795,54.656365 -1.233131,54.658020 -1.233336,54.658428 -1.233507,54.658699 -1.233592,54.658803 -1.234197,54.659389 -1.234690,54.659825 -1.234979,54.660119 -1.235153,54.660314 -1.235343,54.660572 -1.235566,54.661037 -1.235656,54.661355 -1.235690,54.661638 -1.235677,54.661902 -1.235677,54.661984 -1.235683,54.663215 -1.235656,54.663632 -1.235639,54.664273 -1.235613,54.664639 -1.235593,54.664822 -1.235566,54.664957 -1.235508,54.665351 -1.235197,54.667327 -1.235120,54.668542 -1.235100,54.668897 -1.235199,54.669535 -1.235358,54.670231 -1.235437,54.670482 -1.235756,54.671241 -1.236144,54.672181 -1.236375,54.672971 -1.236309,54.673562 -1.236286,54.673704 -1.236127,54.674365 -1.235918,54.675272 -1.235827,54.675620 -1.235749,54.675960 -1.235735,54.676152 -1.235740,54.676328 -1.235754,54.676598 -1.235770,54.676987 -1.235771,54.677013 -1.235793,54.677480 -1.235758,54.677760 -1.235607,54.678134 -1.235470,54.678420 -1.235167,54.678875 -1.234263,54.679929 -1.234207,54.680065 -1.234175,54.680201 -1.234204,54.680465 -1.234300,54.681119 -1.234362,54.681549 -1.234427,54.681771 -1.234560,54.682172 -1.234782,54.682824 -1.236530,54.682837 -1.236725,54.682829 -1.237133,54.682813 -1.238813,54.683143 -1.241021,54.683814 -1.241819,54.683771 -1.242854,54.683717 -1.242946,54.683718 -1.243082,54.683716 -1.244694,54.683772 -1.244658,54.683077 -1.245038,54.682805 -1.245047,54.681990 -1.245011,54.681238 -1.245220,54.680975 -1.247056,54.680601 -1.248019,54.680404 -1.249478,54.680691",
"-1.294832,54.610240 -1.294883,54.610080 -1.294262,54.610016 -1.294141,54.610006 -1.293710,54.610038 -1.293726,54.610142 -1.293742,54.610247 -1.293510,54.610262 -1.293368,54.610258 -1.292816,54.610195 -1.292489,54.610152 -1.292298,54.610667 -1.292167,54.610651 -1.291371,54.610562 -1.291240,54.610556 -1.291107,54.610564 -1.290983,54.610581 -1.290467,54.610665 -1.290253,54.610690 -1.290017,54.610689 -1.289770,54.610665 -1.289500,54.610620 -1.289281,54.610570 -1.289124,54.610514 -1.288957,54.610440 -1.288611,54.610277 -1.288420,54.610222 -1.287445,54.610110 -1.287259,54.610664 -1.286758,54.610611 -1.285446,54.610462 -1.285308,54.610459 -1.283356,54.610475 -1.283159,54.610475 -1.283156,54.610324 -1.283153,54.610119 -1.282818,54.610118 -1.282560,54.610114 -1.282110,54.610131 -1.281962,54.610153 -1.281788,54.610200 -1.281639,54.610257 -1.281298,54.609964 -1.281196,54.609851 -1.280586,54.610008 -1.280272,54.610054 -1.279816,54.610091 -1.279480,54.610104 -1.279112,54.610121 -1.278953,54.610146 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-1.233336,54.658428 -1.233507,54.658699 -1.233592,54.658803 -1.234197,54.659389 -1.234690,54.659825 -1.234979,54.660119 -1.235153,54.660314 -1.235343,54.660572 -1.235566,54.661037 -1.235656,54.661355 -1.235690,54.661638 -1.235677,54.661902 -1.235677,54.661984 -1.235683,54.663215 -1.235656,54.663632 -1.235639,54.664273 -1.235613,54.664639 -1.235593,54.664822 -1.235566,54.664957 -1.235508,54.665351 -1.235197,54.667327 -1.235120,54.668542 -1.235100,54.668897 -1.235199,54.669535 -1.235358,54.670231 -1.235437,54.670482 -1.235756,54.671241 -1.236144,54.672181 -1.236375,54.672971 -1.236309,54.673562 -1.236286,54.673704 -1.236127,54.674365 -1.235918,54.675272 -1.235827,54.675620 -1.235749,54.675960 -1.235735,54.676152 -1.235740,54.676328 -1.235754,54.676598 -1.235770,54.676987 -1.235771,54.677013 -1.235793,54.677480 -1.235758,54.677760 -1.235607,54.678134 -1.235470,54.678420 -1.235167,54.678875 -1.234263,54.679929 -1.234207,54.680065 -1.234175,54.680201 -1.234204,54.680465 -1.234300,54.681119 -1.234362,54.681549 -1.234427,54.681771 -1.234560,54.682172 -1.234782,54.682824 -1.236530,54.682837 -1.236725,54.682829 -1.237133,54.682813 -1.238813,54.683143 -1.241021,54.683814 -1.241819,54.683771 -1.242854,54.683717 -1.242946,54.683718 -1.243082,54.683716 -1.244694,54.683772 -1.244658,54.683077 -1.245038,54.682805 -1.245047,54.681990 -1.245011,54.681238 -1.245220,54.680975 -1.247056,54.680601 -1.248019,54.680404 -1.249478,54.680691"
), grammesCO2saved = c(2359, 2406), calories = c(211, 201
), type = c("route", "route")), .Names = c("rid", "start_id",
"start_code", "end_id", "end_code", "strategy", "distance", "time_seconds",
"calories", "document.id", "array.index", "start", "finish",
"startBearing", "startSpeed", "start_longitude", "start_latitude",
"finish_longitude", "finish_latitude", "crow_fly_distance", "event",
"whence", "speed", "itinerary", "clientRouteId", "plan", "note",
"length", "time", "busynance", "quietness", "signalledJunctions",
"signalledCrossings", "west", "south", "east", "north", "name",
"walk", "leaving", "arriving", "coordinates", "grammesCO2saved",
"calories", "type"), row.names = 1:2, class = "data.frame")
>
df1 <- data.frame(id= c(1,2), dist =c(45,23), speed = c(6,24) ,do.call(rbind,strsplit(df$cord,split = " ")))
library(reshape2)
df1 <- melt(df1,id=c("id","dist","speed"))
df2<- data.frame(do.call(rbind,strsplit(df1$value, split=",")))
df1$value <- NULL
df1 <- cbind(df1,df2)
names(df1)[5:6] <- c("x","y")
id dist speed variable x y
1 1 45 6 X1 1.294832 54.610240
2 2 23 24 X1 2.788732 34.787940
3 1 45 6 X2 -1.294883 54.610080
4 2 23 24 X2 6.294883 24.567080
5 1 45 6 X3 -1.294262 54.6482757
6 2 23 24 X3 -5.564262 -45.7676757
我相信您最终想要的是数据框中的一列，每一行都是一个包含 x.coord 和 y.coord 列的列表（或数据框）。为此，我们可以使用 tidyr 中的 unnest 和 nest 以及 dplyr:
library(dplyr)
library(tidyr)
result <- finalsub %>% mutate(coordinates = strsplit(coordinates,split=" ",fixed=TRUE)) %>%
unnest(coordinates) %>%
mutate(coordinates = strsplit(coordinates,split=",",fixed=TRUE),
x.coord = as.numeric(unlist(coordinates)[c(TRUE,FALSE)]),
y.coord = as.numeric(unlist(coordinates)[c(FALSE,TRUE)])) %>%
select(-coordinates) %>%
nest(x.coord,y.coord,.key=coordinates)
备注：
- 第一个
mutate 将 coordinates 列中的字符向量拆分为 " " 以分隔每个坐标 x,y，从而得到其中的 list。
unnest 将此列表分成几行。- 在第二个
mutate中，我们首先拆分每个坐标x,y，这次通过","将每个坐标拆分为x和y。然后我们创建单独的 x.coord 和 y.coord 列来保存这些。注意此处转换为数字。
- 最后，我们使用
nest 将 x.coord 和 y.coord 列收集为名为 coordinates 的列下的列表。请注意，我们首先必须删除原始的 coordinates 列。
结果使用您的 dput 数据，仅打印 coordinates 列：
print(result$coordinates)
##[[1]]
### A tibble: 284 x 2
## x.coord y.coord
## <dbl> <dbl>
##1 -1.294832 54.61024
##2 -1.294883 54.61008
##3 -1.294262 54.61002
##4 -1.294141 54.61001
##5 -1.293710 54.61004
##6 -1.293726 54.61014
##7 -1.293742 54.61025
##8 -1.293510 54.61026
##9 -1.293368 54.61026
##10 -1.292816 54.61019
### ... with 274 more rows
##
##[[2]]
### A tibble: 322 x 2
## x.coord y.coord
## <dbl> <dbl>
##1 -1.294832 54.61024
##2 -1.294883 54.61008
##3 -1.294262 54.61002
##4 -1.294141 54.61001
##5 -1.293710 54.61004
##6 -1.293726 54.61014
##7 -1.293742 54.61025
##8 -1.293510 54.61026
##9 -1.293368 54.61026
##10 -1.292816 54.61019
### ... with 312 more rows
我在 R 中有一个数据框，其中一列包含沿线的点的坐标，形式为：
x,y x,y x,y x,y
所以整个数据框看起来像
id dist speed coord
1 45 6 1.294832,54.610240 -1.294883,54.610080 -1.294262,54.6482757
2 23 34 2.788732,34.787940 6.294883,24.567080 -5.564262,-45.7676757
我想将其转换为空间线数据框，并且我假设第一步是将坐标分成两列：
x, x, x, x
y, y, y, y
但我不确定如何进行。
编辑
对于那些要求输入实际文件的人
> finalsub <- final[final$rid <3,]
> dput(finalsub)
structure(list(rid = c(1, 2), start_id = c(1L, 1L), start_code = c("E02002536",
"E02002536"), end_id = c(106L, 106L), end_code = c("E02006909",
"E02006909"), strategy = c("fastest", "quietest"), distance = c(12655L,
12909L), time_seconds = c(2921L, 3422L), calories = c(211L, 201L
), document.id = c(1L, 1L), array.index = 1:2, start = c("Geranium Close",
"Geranium Close"), finish = c("Hylton Road", "Hylton Road"),
startBearing = c(0, 0), startSpeed = c(0, 0), start_longitude = c(-1.294832,
-1.294832), start_latitude = c(54.610241, 54.610241), finish_longitude = c(-1.249478,
-1.249478), finish_latitude = c(54.680691, 54.680691), crow_fly_distance = c(8362,
8362), event = c("depart", "depart"), whence = c(1473171787,
1473171787), speed = c(20, 20), itinerary = c(419956, 419957
), clientRouteId = c(0, 0), plan = c("fastest", "quietest"
), note = c("", ""), length = c(12655, 12909), time = c(2921,
3422), busynance = c(42172, 17242), quietness = c(30, 75),
signalledJunctions = c(3, 4), signalledCrossings = c(2, 0
), west = c(-1.300074, -1.294883), south = c(54.610006, 54.609851
), east = c(-1.232447, -1.232447), north = c(54.683814, 54.683814
), name = c("Geranium Close to Hylton Road", "Geranium Close to Hylton Road"
), walk = c(0, 0), leaving = c("2016-09-06 15:23:07", "2016-09-06 15:23:07"
), arriving = c("2016-09-06 16:11:48", "2016-09-06 16:20:09"
), coordinates = c("-1.294832,54.610240 -1.294883,54.610080 -1.294262,54.610016 -1.294141,54.610006 -1.293710,54.610038 -1.293726,54.610142 -1.293742,54.610247 -1.293510,54.610262 -1.293368,54.610258 -1.292816,54.610195 -1.292489,54.610152 -1.292298,54.610667 -1.292205,54.610951 -1.292182,54.611063 -1.292183,54.611153 -1.292239,54.611341 -1.292305,54.611447 -1.292375,54.611534 -1.292494,54.611639 -1.292739,54.611830 -1.292909,54.611980 -1.293010,54.612107 -1.293111,54.612262 -1.293192,54.612423 -1.293235,54.612546 -1.293267,54.612684 -1.293279,54.612818 -1.293510,54.612813 -1.293732,54.612790 -1.294324,54.612691 -1.295086,54.612568 -1.295313,54.612539 -1.295379,54.612543 -1.295889,54.612645 -1.295945,54.612648 -1.296006,54.612642 -1.297154,54.612414 -1.297502,54.612895 -1.297733,54.612847 -1.297990,54.612796 -1.298292,54.612747 -1.298515,54.612727 -1.299088,54.612681 -1.299564,54.612669 -1.299798,54.612663 -1.300006,54.612660 -1.300057,54.612809 -1.300056,54.613335 -1.300071,54.613693 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"-1.294832,54.610240 -1.294883,54.610080 -1.294262,54.610016 -1.294141,54.610006 -1.293710,54.610038 -1.293726,54.610142 -1.293742,54.610247 -1.293510,54.610262 -1.293368,54.610258 -1.292816,54.610195 -1.292489,54.610152 -1.292298,54.610667 -1.292167,54.610651 -1.291371,54.610562 -1.291240,54.610556 -1.291107,54.610564 -1.290983,54.610581 -1.290467,54.610665 -1.290253,54.610690 -1.290017,54.610689 -1.289770,54.610665 -1.289500,54.610620 -1.289281,54.610570 -1.289124,54.610514 -1.288957,54.610440 -1.288611,54.610277 -1.288420,54.610222 -1.287445,54.610110 -1.287259,54.610664 -1.286758,54.610611 -1.285446,54.610462 -1.285308,54.610459 -1.283356,54.610475 -1.283159,54.610475 -1.283156,54.610324 -1.283153,54.610119 -1.282818,54.610118 -1.282560,54.610114 -1.282110,54.610131 -1.281962,54.610153 -1.281788,54.610200 -1.281639,54.610257 -1.281298,54.609964 -1.281196,54.609851 -1.280586,54.610008 -1.280272,54.610054 -1.279816,54.610091 -1.279480,54.610104 -1.279112,54.610121 -1.278953,54.610146 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-1.233336,54.658428 -1.233507,54.658699 -1.233592,54.658803 -1.234197,54.659389 -1.234690,54.659825 -1.234979,54.660119 -1.235153,54.660314 -1.235343,54.660572 -1.235566,54.661037 -1.235656,54.661355 -1.235690,54.661638 -1.235677,54.661902 -1.235677,54.661984 -1.235683,54.663215 -1.235656,54.663632 -1.235639,54.664273 -1.235613,54.664639 -1.235593,54.664822 -1.235566,54.664957 -1.235508,54.665351 -1.235197,54.667327 -1.235120,54.668542 -1.235100,54.668897 -1.235199,54.669535 -1.235358,54.670231 -1.235437,54.670482 -1.235756,54.671241 -1.236144,54.672181 -1.236375,54.672971 -1.236309,54.673562 -1.236286,54.673704 -1.236127,54.674365 -1.235918,54.675272 -1.235827,54.675620 -1.235749,54.675960 -1.235735,54.676152 -1.235740,54.676328 -1.235754,54.676598 -1.235770,54.676987 -1.235771,54.677013 -1.235793,54.677480 -1.235758,54.677760 -1.235607,54.678134 -1.235470,54.678420 -1.235167,54.678875 -1.234263,54.679929 -1.234207,54.680065 -1.234175,54.680201 -1.234204,54.680465 -1.234300,54.681119 -1.234362,54.681549 -1.234427,54.681771 -1.234560,54.682172 -1.234782,54.682824 -1.236530,54.682837 -1.236725,54.682829 -1.237133,54.682813 -1.238813,54.683143 -1.241021,54.683814 -1.241819,54.683771 -1.242854,54.683717 -1.242946,54.683718 -1.243082,54.683716 -1.244694,54.683772 -1.244658,54.683077 -1.245038,54.682805 -1.245047,54.681990 -1.245011,54.681238 -1.245220,54.680975 -1.247056,54.680601 -1.248019,54.680404 -1.249478,54.680691"
), grammesCO2saved = c(2359, 2406), calories = c(211, 201
), type = c("route", "route")), .Names = c("rid", "start_id",
"start_code", "end_id", "end_code", "strategy", "distance", "time_seconds",
"calories", "document.id", "array.index", "start", "finish",
"startBearing", "startSpeed", "start_longitude", "start_latitude",
"finish_longitude", "finish_latitude", "crow_fly_distance", "event",
"whence", "speed", "itinerary", "clientRouteId", "plan", "note",
"length", "time", "busynance", "quietness", "signalledJunctions",
"signalledCrossings", "west", "south", "east", "north", "name",
"walk", "leaving", "arriving", "coordinates", "grammesCO2saved",
"calories", "type"), row.names = 1:2, class = "data.frame")
>
df1 <- data.frame(id= c(1,2), dist =c(45,23), speed = c(6,24) ,do.call(rbind,strsplit(df$cord,split = " ")))
library(reshape2)
df1 <- melt(df1,id=c("id","dist","speed"))
df2<- data.frame(do.call(rbind,strsplit(df1$value, split=",")))
df1$value <- NULL
df1 <- cbind(df1,df2)
names(df1)[5:6] <- c("x","y")
id dist speed variable x y
1 1 45 6 X1 1.294832 54.610240
2 2 23 24 X1 2.788732 34.787940
3 1 45 6 X2 -1.294883 54.610080
4 2 23 24 X2 6.294883 24.567080
5 1 45 6 X3 -1.294262 54.6482757
6 2 23 24 X3 -5.564262 -45.7676757
我相信您最终想要的是数据框中的一列，每一行都是一个包含 x.coord 和 y.coord 列的列表（或数据框）。为此，我们可以使用 tidyr 中的 unnest 和 nest 以及 dplyr:
library(dplyr)
library(tidyr)
result <- finalsub %>% mutate(coordinates = strsplit(coordinates,split=" ",fixed=TRUE)) %>%
unnest(coordinates) %>%
mutate(coordinates = strsplit(coordinates,split=",",fixed=TRUE),
x.coord = as.numeric(unlist(coordinates)[c(TRUE,FALSE)]),
y.coord = as.numeric(unlist(coordinates)[c(FALSE,TRUE)])) %>%
select(-coordinates) %>%
nest(x.coord,y.coord,.key=coordinates)
备注：
- 第一个
mutate将coordinates列中的字符向量拆分为" "以分隔每个坐标x,y，从而得到其中的list。 unnest将此列表分成几行。- 在第二个
mutate中，我们首先拆分每个坐标x,y，这次通过","将每个坐标拆分为x和y。然后我们创建单独的x.coord和y.coord列来保存这些。注意此处转换为数字。 - 最后，我们使用
nest将x.coord和y.coord列收集为名为coordinates的列下的列表。请注意，我们首先必须删除原始的coordinates列。
结果使用您的 dput 数据，仅打印 coordinates 列：
print(result$coordinates)
##[[1]]
### A tibble: 284 x 2
## x.coord y.coord
## <dbl> <dbl>
##1 -1.294832 54.61024
##2 -1.294883 54.61008
##3 -1.294262 54.61002
##4 -1.294141 54.61001
##5 -1.293710 54.61004
##6 -1.293726 54.61014
##7 -1.293742 54.61025
##8 -1.293510 54.61026
##9 -1.293368 54.61026
##10 -1.292816 54.61019
### ... with 274 more rows
##
##[[2]]
### A tibble: 322 x 2
## x.coord y.coord
## <dbl> <dbl>
##1 -1.294832 54.61024
##2 -1.294883 54.61008
##3 -1.294262 54.61002
##4 -1.294141 54.61001
##5 -1.293710 54.61004
##6 -1.293726 54.61014
##7 -1.293742 54.61025
##8 -1.293510 54.61026
##9 -1.293368 54.61026
##10 -1.292816 54.61019
### ... with 312 more rows