library("readxl")
ruta_excel <- "C:\\Users\\jdom3\\Downloads\\Coordenadas Lote 4.1.xlsx"
5*144/100
## [1] 7.2
coor_feijoa <- read_excel(ruta_excel)
coor_feijoa
## # A tibble: 144 × 4
## Num Latitud Longitud Altitud
## <dbl> <chr> <chr> <chr>
## 1 1 4.6822953079999996 -74.216115018 2541.7
## 2 2 4.682320988 -74.216084307 2541.8
## 3 3 4.682347243 -74.216052276 2541.7
## 4 4 4.682373635 -74.216028939 2541.7
## 5 5 4.682398892 -74.216004481 2541.6
## 6 6 4.682423163 -74.215968565 2541.5
## 7 7 4.682444857 -74.215947723 2541.4
## 8 8 4.682492204 -74.215896737 2541.2
## 9 9 4.682517382 -74.215869985 2541.2
## 10 10 4.682537920 -74.215843297 2541.2
## # … with 134 more rows
x <- unlist(coor_feijoa[,c(3)])
y <- unlist(coor_feijoa[,c(2)])
options(digits = 10)
x_num <- as.numeric(x)
y_num <- as.numeric(y)
df_feijoa <- data.frame("x" = x_num, "y" = y_num)
df_feijoa
## x y
## 1 -74.21611502 4.682295308
## 2 -74.21608431 4.682320988
## 3 -74.21605228 4.682347243
## 4 -74.21602894 4.682373635
## 5 -74.21600448 4.682398892
## 6 -74.21596856 4.682423163
## 7 -74.21594772 4.682444857
## 8 -74.21589674 4.682492204
## 9 -74.21586998 4.682517382
## 10 -74.21584330 4.682537920
## 11 -74.21581834 4.682566102
## 12 -74.21578881 4.682590751
## 13 -74.21576286 4.682613129
## 14 -74.21573266 4.682633330
## 15 -74.21570681 4.682663052
## 16 -74.21568379 4.682681468
## 17 -74.21565453 4.682707467
## 18 -74.21562431 4.682733799
## 19 -74.21559503 4.682756254
## 20 -74.21557134 4.682776860
## 21 -74.21554223 4.682803939
## 22 -74.21551941 4.682831304
## 23 -74.21546397 4.682879487
## 24 -74.21543907 4.682901114
## 25 -74.21541267 4.682930555
## 26 -74.21538402 4.682952409
## 27 -74.21536343 4.682975484
## 28 -74.21533206 4.683003290
## 29 -74.21609341 4.682269956
## 30 -74.21606202 4.682293959
## 31 -74.21602996 4.682321949
## 32 -74.21600686 4.682344384
## 33 -74.21598062 4.682370332
## 34 -74.21595373 4.682392985
## 35 -74.21592468 4.682416648
## 36 -74.21589350 4.682444094
## 37 -74.21587175 4.682470557
## 38 -74.21584540 4.682491300
## 39 -74.21579374 4.682538187
## 40 -74.21576762 4.682564331
## 41 -74.21573964 4.682590488
## 42 -74.21571046 4.682612973
## 43 -74.21565622 4.682659228
## 44 -74.21562776 4.682683291
## 45 -74.21560032 4.682708420
## 46 -74.21557866 4.682732301
## 47 -74.21554840 4.682756843
## 48 -74.21552425 4.682777776
## 49 -74.21549437 4.682804514
## 50 -74.21546447 4.682831303
## 51 -74.21544151 4.682854611
## 52 -74.21541375 4.682881363
## 53 -74.21539205 4.682902254
## 54 -74.21535921 4.682928710
## 55 -74.21533357 4.682950976
## 56 -74.21530881 4.682977162
## 57 -74.21605639 4.682250213
## 58 -74.21600177 4.682301536
## 59 -74.21597883 4.682321617
## 60 -74.21595060 4.682347805
## 61 -74.21592235 4.682368209
## 62 -74.21589580 4.682393678
## 63 -74.21586932 4.682415836
## 64 -74.21584329 4.682441323
## 65 -74.21581761 4.682466413
## 66 -74.21578849 4.682492098
## 67 -74.21576621 4.682516719
## 68 -74.21573615 4.682540537
## 69 -74.21568313 4.682590710
## 70 -74.21565833 4.682613786
## 71 -74.21562745 4.682635532
## 72 -74.21560622 4.682660007
## 73 -74.21557793 4.682685871
## 74 -74.21554844 4.682710804
## 75 -74.21551812 4.682732380
## 76 -74.21549536 4.682758518
## 77 -74.21546938 4.682783680
## 78 -74.21544232 4.682804538
## 79 -74.21541548 4.682830674
## 80 -74.21538768 4.682854211
## 81 -74.21536119 4.682878041
## 82 -74.21533744 4.682899408
## 83 -74.21530773 4.682920324
## 84 -74.21597433 4.682277113
## 85 -74.21595405 4.682291689
## 86 -74.21592494 4.682321101
## 87 -74.21589416 4.682347702
## 88 -74.21587655 4.682368338
## 89 -74.21584327 4.682393600
## 90 -74.21582138 4.682419517
## 91 -74.21579419 4.682444470
## 92 -74.21576665 4.682469535
## 93 -74.21574062 4.682492988
## 94 -74.21571047 4.682514656
## 95 -74.21568433 4.682536462
## 96 -74.21566212 4.682556721
## 97 -74.21563371 4.682580652
## 98 -74.21560407 4.682602822
## 99 -74.21552238 4.682681296
## 100 -74.21549765 4.682700588
## 101 -74.21546822 4.682730739
## 102 -74.21543909 4.682755690
## 103 -74.21541228 4.682781363
## 104 -74.21538338 4.682805503
## 105 -74.21533312 4.682852318
## 106 -74.21530848 4.682875955
## 107 -74.21528478 4.682900353
## 108 -74.21597525 4.682227635
## 109 -74.21595448 4.682253103
## 110 -74.21592592 4.682272189
## 111 -74.21589322 4.682297570
## 112 -74.21587199 4.682319158
## 113 -74.21584426 4.682345014
## 114 -74.21581916 4.682371916
## 115 -74.21581756 4.682373060
## 116 -74.21578933 4.682399146
## 117 -74.21576480 4.682422010
## 118 -74.21574073 4.682443270
## 119 -74.21571011 4.682468742
## 120 -74.21568406 4.682488842
## 121 -74.21565468 4.682517623
## 122 -74.21563233 4.682543961
## 123 -74.21560166 4.682557726
## 124 -74.21544738 4.682707829
## 125 -74.21541621 4.682730025
## 126 -74.21538579 4.682754339
## 127 -74.21536220 4.682778372
## 128 -74.21533618 4.682802509
## 129 -74.21530608 4.682825100
## 130 -74.21528503 4.682853637
## 131 -74.21525286 4.682872018
## 132 -74.21589623 4.682254193
## 133 -74.21587198 4.682272589
## 134 -74.21584662 4.682300840
## 135 -74.21581753 4.682326022
## 136 -74.21579073 4.682343916
## 137 -74.21575727 4.682375833
## 138 -74.21568485 4.682441913
## 139 -74.21552290 4.682582405
## 140 -74.21549356 4.682612719
## 141 -74.21530887 4.682782353
## 142 -74.21528053 4.682805114
## 143 -74.21522869 4.682850630
## 144 -74.21519722 4.682873078
library(clhs)
arboles_sel <- clhs(df_feijoa[,c(1,2)], size = 7, progress = FALSE, simple = TRUE)
arboles_sel
## [1] 49 133 71 5 67 81 96
plot(df_feijoa)
points(df_feijoa$x[arboles_sel],df_feijoa$y[arboles_sel],col = "red",pch=16)
