Tarea 1 - Asignacion de poligonos
Luis Alberto Castillo Sanabria
2022-08-22
1) Read the file MGN_DPTO_POLITICO.shp in R using the function of rgdal to open SHP files. Name this object ORG_LEVEL_2. What is the class of this object? Plot ORG_LEVEL_2 using the color dark red. See the next pdf to find the typing of this color name:
Empezar a trabajar con un ambiente limpio en Rstudio
# To clean environment
rm(list = ls(all.names = TRUE))
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 518315 27.7 1158800 61.9 644200 34.5
## Vcells 924484 7.1 8388608 64.0 1634597 12.5Llamar todas las librerias que necesitamos
library("sp")
library("rgdal")## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
##
## rgdal: version: 1.5-32, (SVN revision 1176)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.4.3, released 2022/04/22
## Path to GDAL shared files: C:/Users/luisc/AppData/Local/R/win-library/4.2/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/luisc/AppData/Local/R/win-library/4.2/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.5-0
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.library("raster")
library("maptools")## Checking rgeos availability: TRUE
## Please note that 'maptools' will be retired by the end of 2023,
## plan transition at your earliest convenience;
## some functionality will be moved to 'sp'.library("tidyverse")## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::extract() masks raster::extract()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::select() masks raster::select()library ('gdalUtilities')
library ("landscapemetrics") library("rgeos")## rgeos version: 0.5-9, (SVN revision 684)
## GEOS runtime version: 3.9.1-CAPI-1.14.2
## Please note that rgeos will be retired by the end of 2023,
## plan transition to sf functions using GEOS at your earliest convenience.
## GEOS using OverlayNG
## Linking to sp version: 1.5-0
## Polygon checking: TRUElibrary("sf")## Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUE##
## Attaching package: 'sf'## The following object is masked from 'package:gdalUtilities':
##
## gdal_rasterizegetClass("Spatial") ## Class "Spatial" [package "sp"]
##
## Slots:
##
## Name: bbox proj4string
## Class: matrix CRS
##
## Known Subclasses:
## Class "SpatialPoints", directly
## Class "SpatialMultiPoints", directly
## Class "SpatialGrid", directly
## Class "SpatialLines", directly
## Class "SpatialPolygons", directly
## Class "SpatialPointsDataFrame", by class "SpatialPoints", distance 2
## Class "SpatialPixels", by class "SpatialPoints", distance 2
## Class "SpatialMultiPointsDataFrame", by class "SpatialMultiPoints", distance 2
## Class "SpatialGridDataFrame", by class "SpatialGrid", distance 2
## Class "SpatialLinesDataFrame", by class "SpatialLines", distance 2
## Class "SpatialPixelsDataFrame", by class "SpatialPoints", distance 3
## Class "SpatialPolygonsDataFrame", by class "SpatialPolygons", distance 2Leer el archivo MGN_DPTO_POLITICO. sp para abrir archivos shp.
crear objeto: ORG_LEVEL_2
library(rgdal)
ORG_LEVEL_2 = readOGR(dsn = "C:/Users/luisc/Downloads", layer = "MGN_DPTO_POLITICO") #no .shp required## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\luisc\Downloads", layer: "MGN_DPTO_POLITICO"
## with 33 features
## It has 10 fields
## Integer64 fields read as strings: DPTO_ANO_C DPTO_VGNCLa clase del objeto (ORG_LEVEL_2) es “SpatialPolygonsDataFrame”
summary(ORG_LEVEL_2)## Object of class SpatialPolygonsDataFrame
## Coordinates:
## min max
## x -81.735621 -66.84722
## y -4.229406 13.39473
## Is projected: FALSE
## proj4string : [+proj=longlat +datum=WGS84 +no_defs]
## Data attributes:
## DPTO_CCDGO DPTO_CNMBR DPTO_ANO_C DPTO_ACT_A
## Length:33 Length:33 Length:33 Length:33
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## DPTO_NAREA DPTO_CSMBL DPTO_VGNC SHAPE_AREA
## Min. : 49.42 Length:33 Length:33 Min. :0.00405
## 1st Qu.: 20619.01 Class :character Class :character 1st Qu.:1.67949
## Median : 24139.40 Mode :character Mode :character Median :1.96503
## Mean : 34574.78 Mean :2.81313
## 3rd Qu.: 48353.22 3rd Qu.:3.93974
## Max. :109497.05 Max. :8.87748
##
## SHAPE_LEN AREA
## Min. : 0.6853 Min. : NA
## 1st Qu.: 9.5497 1st Qu.: NA
## Median :12.5785 Median : NA
## Mean :12.7108 Mean :NaN
## 3rd Qu.:17.2926 3rd Qu.: NA
## Max. :25.3560 Max. : NA
## NA's :33class(ORG_LEVEL_2)## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"plot(ORG_LEVEL_2)
Traza ORG_LEVEL_2 usando el color rojo oscuro.
plot(ORG_LEVEL_2, col ="dark red")
2) Show the Geographic Coordinate Systems of ORG_LEVEL_2. Built a vector called nombres with the names of ORG_LEVEL_2. What is the class of this object? Show the first five or six rows of the data.frame (attribute table) of ORG_LEVEL_2.
Coordenadas georgraficas de ORG_LEVEL_2
ORG_LEVEL_2@proj4string## Coordinate Reference System:
## Deprecated Proj.4 representation: +proj=longlat +datum=WGS84 +no_defs
## WKT2 2019 representation:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["latitude",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["longitude",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4326]]names(ORG_LEVEL_2)## [1] "DPTO_CCDGO" "DPTO_CNMBR" "DPTO_ANO_C" "DPTO_ACT_A" "DPTO_NAREA"
## [6] "DPTO_CSMBL" "DPTO_VGNC" "SHAPE_AREA" "SHAPE_LEN" "AREA"Vector nombres
Nombres <- c(names(ORG_LEVEL_2))
Nombres## [1] "DPTO_CCDGO" "DPTO_CNMBR" "DPTO_ANO_C" "DPTO_ACT_A" "DPTO_NAREA"
## [6] "DPTO_CSMBL" "DPTO_VGNC" "SHAPE_AREA" "SHAPE_LEN" "AREA"Clase del objeto
class(Nombres)## [1] "character"Primeras filas de los atributos
head(ORG_LEVEL_2)## DPTO_CCDGO DPTO_CNMBR DPTO_ANO_C DPTO_ACT_A DPTO_NAREA
## 0 05 ANTIOQUIA 1886 Constitucion Politica de 1886 62804.71
## 1 23 CÓRDOBA 1951 Ley 9 del 18 de Diciembre de 1951 25086.55
## 2 27 CHOCÓ 1947 Ley 13 del 3 de Noviembre de 1947 48353.22
## 3 70 SUCRE 1966 Ley 47 del 8 de Agosto de 1966 10591.85
## 4 08 ATLÁNTICO 1910 Ley 21 de 1910 3313.81
## 5 13 BOLÍVAR 1886 Constitucion Politica de 1886 26719.21
## DPTO_CSMBL DPTO_VGNC SHAPE_AREA SHAPE_LEN AREA
## 0 3 2020 5.1349154 21.443794 NA
## 1 3 2020 2.0575332 9.691516 NA
## 2 3 2020 3.9397431 20.634463 NA
## 3 3 2020 0.8708103 8.570869 NA
## 4 3 2020 0.2737698 2.544651 NA
## 5 3 2020 2.1955769 16.234817 NAORG_LEVEL_2@data## DPTO_CCDGO DPTO_CNMBR
## 0 05 ANTIOQUIA
## 1 23 CÓRDOBA
## 2 27 CHOCÓ
## 3 70 SUCRE
## 4 08 ATLÁNTICO
## 5 13 BOLÍVAR
## 6 47 MAGDALENA
## 7 20 CESAR
## 8 44 LA GUAJIRA
## 9 19 CAUCA
## 10 76 VALLE DEL CAUCA
## 11 41 HUILA
## 12 18 CAQUETÁ
## 13 50 META
## 14 15 BOYACÁ
## 15 25 CUNDINAMARCA
## 16 17 CALDAS
## 17 63 QUINDIO
## 18 66 RISARALDA
## 19 73 TOLIMA
## 20 52 NARIÑO
## 21 54 NORTE DE SANTANDER
## 22 68 SANTANDER
## 23 85 CASANARE
## 24 97 VAUPÉS
## 25 86 PUTUMAYO
## 26 94 GUAINÍA
## 27 99 VICHADA
## 28 91 AMAZONAS
## 29 95 GUAVIARE
## 30 88 ARCHIPIÉLAGO DE SAN ANDRÉS, PROVIDENCIA Y SANTA CATALINA
## 31 81 ARAUCA
## 32 11 BOGOTÁ, D.C.
## DPTO_ANO_C DPTO_ACT_A
## 0 1886 Constitucion Politica de 1886
## 1 1951 Ley 9 del 18 de Diciembre de 1951
## 2 1947 Ley 13 del 3 de Noviembre de 1947
## 3 1966 Ley 47 del 8 de Agosto de 1966
## 4 1910 Ley 21 de 1910
## 5 1886 Constitucion Politica de 1886
## 6 1964 1964
## 7 1967 Ley 25 21 de junio de 1967
## 8 1964 Acto Legislativo No. 1 de Diciembre 28 de 1964
## 9 1857 15 de junio de 1857
## 10 1910 Decreto No 340 de 16 de Abril de 1910
## 11 1905 Ley 46 de 1905
## 12 1981 Ley 78 del 29 de Diciembre de 1981
## 13 1959 Ley 118 del 16 de Diciembre de 1959
## 14 1886 Constitucion Politica de 1886
## 15 1886 Constitucion Politica de 1886
## 16 1905 11 de Abril de 1905
## 17 1966 Ley 2 TM de 1966
## 18 1966 Ley 70 del 1 de Diciembre de 1966
## 19 1909 Ley 65 de Noviembre de 1909
## 20 1904 Ley 1 de 1904
## 21 1910 Ley 25 de 1910
## 22 1910 Ley 25 14 de Julio de 1910
## 23 1991 5 de Julio Constitucion Politica de 1991
## 24 1991 Articulo 309 Constitucion Politica de 1991
## 25 1991 Articulo 309 Constitucion Politica de 1991
## 26 1991 Articulo 309 Constitucion Politica de 1991
## 27 1991 5 de Julio Constitucion Politica de 1991
## 28 1991 Dcto. 2274 del 4 de Octubre de la Constitución Política 1991
## 29 1991 5 de Julio Constitucion Politica de 1991
## 30 1991 Artículo 310 Constitucion Politica de 1991
## 31 1991 5 de Julio Constitucion Politica de 1991
## 32 1538 Constitucion Politica de 1886
## DPTO_NAREA DPTO_CSMBL DPTO_VGNC SHAPE_AREA SHAPE_LEN AREA
## 0 62804.71025 3 2020 5.134915412 21.4437936 NA
## 1 25086.54697 3 2020 2.057533219 9.6915160 NA
## 2 48353.21903 3 2020 3.939743093 20.6344626 NA
## 3 10591.84594 3 2020 0.870810349 8.5708693 NA
## 4 3313.81015 3 2020 0.273769829 2.5446507 NA
## 5 26719.21141 3 2020 2.195576862 16.2348170 NA
## 6 23135.93870 3 2020 1.909266273 10.8159579 NA
## 7 22565.30721 3 2020 1.858204407 12.5784592 NA
## 8 20619.00959 3 2020 1.706874490 10.7869049 NA
## 9 31242.91479 3 2020 2.534419222 13.9502629 NA
## 10 20665.54452 3 2020 1.679486591 12.6508699 NA
## 11 18141.66055 3 2020 1.474219177 10.3355646 NA
## 12 92831.12119 3 2020 7.540227855 21.2158385 NA
## 13 82799.17670 3 2020 6.733634872 18.1909537 NA
## 14 23138.04813 3 2020 1.888390832 15.9064914 NA
## 15 22370.48873 3 2020 1.823631133 13.1189510 NA
## 16 7425.22167 3 2020 0.605497805 6.6558442 NA
## 17 1933.57085 3 2020 0.157423729 2.5548254 NA
## 18 3556.77445 3 2020 0.289790632 4.8436149 NA
## 19 24139.40123 3 2020 1.965026841 9.5497261 NA
## 20 31497.57257 3 2020 2.548474605 12.8320974 NA
## 21 21856.75425 3 2020 1.792325441 10.7079284 NA
## 22 30561.51495 3 2020 2.499105532 11.8777470 NA
## 23 44394.23977 3 2020 3.615063148 12.1327536 NA
## 24 53299.28001 3 2020 4.313810177 20.1298337 NA
## 25 25976.28311 3 2020 2.107964833 12.7079225 NA
## 26 71289.35468 3 2020 5.747937395 21.1790507 NA
## 27 100063.37060 3 2020 8.100680492 17.2926130 NA
## 28 109497.05379 3 2020 8.877479989 25.3559774 NA
## 29 55575.23316 3 2020 4.511457243 19.3967890 NA
## 30 49.42425 3 2020 0.004049898 0.6852594 NA
## 31 23851.25706 3 2020 1.944156904 9.1245856 NA
## 32 1622.85260 3 2020 0.132207854 3.7604533 NA3) Create a vector with the foundation date of the departments (variable DPTO_ANO_C), called FUNDACION. What are the foundation years of the youngest and oldest departments? I recommend the use of the functions max() and min() to know these two years. Create a SpatialPolygonsDataFrame with the youngest departments of Colombia (using ORG_LEVEL_2) and called JOVENSITOS. Also, you must plot JOVENSITOS. In this new object, JOVENSITOS, subset the largest and smallest departments and plot them (the variable or column SHAPE_AREA is the area of the departments).
Vector FUNDACION
FUNDACION <- c(ORG_LEVEL_2$DPTO_ANO_C)Bogota D.C es el departamento mas antiguo y el Guaviare es el departamento mas joven
max(FUNDACION)## [1] "1991"min(FUNDACION)## [1] "1538"Se filtro por los departamentos mas jovenes
Se creao un SpatialPolygonsDataFrame con los departamentos más jóvenes de Colombia (usando ORG_LEVEL_2) y llamado JOVENSITOS
JOVENCITOS <- subset(ORG_LEVEL_2, ORG_LEVEL_2$DPTO_ANO_C >=1991)
plot(JOVENCITOS)
Size = mean(JOVENCITOS$SHAPE_AREA)
Size## [1] 4.358067En este nuevo objeto, JOVENSITOS, subdivide los departamentos más grandes y más pequeños y graficalos (la variable o columna SHAPE_AREA. es el área de los departamentos).
Grupo 1, departamentos mayores o iguales a la media
Grupo_1 = subset(JOVENCITOS, SHAPE_AREA >= mean(JOVENCITOS$SHAPE_AREA))
plot(Grupo_1)
Grupo 2, departamentos menores o iguales a la media
Grupo_2 = subset(JOVENCITOS, SHAPE_AREA <= mean(JOVENCITOS$SHAPE_AREA))
plot(Grupo_2)
4) Project ORG_LEVEL_2 to Transversal Mercator, called ORG_LEVEL_2_MER this new SpatialPolygonsDataFrame. The CRSobj of Transversal Mercator to use the function spTransform() is : “+proj=tmerc +lat_0=4.596200416666666 +lon_0=-74.07750791666666 +k=1 +x_0=1000000 +y_0=1000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs” Now create the centroids for the departments of ORG_LEVEL_2_MER (plot these centroids) and the matrix of their distances. Which one are the mean, median, standard deviation of the values of this matrix?
ORG_LEVEL_2_MER = spTransform(ORG_LEVEL_2, CRSobj="+proj=tmerc +lat_0=4.596200416666666 +lon_0=-74.07750791666666
+k=1 +x_0=1000000 +y_0=1000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs")## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definitionproj4string(ORG_LEVEL_2_MER)## Warning in proj4string(ORG_LEVEL_2_MER): CRS object has comment, which is lost in output; in tests, see
## https://cran.r-project.org/web/packages/sp/vignettes/CRS_warnings.html## [1] "+proj=tmerc +lat_0=4.59620041666667 +lon_0=-74.0775079166667 +k=1 +x_0=1000000 +y_0=1000000 +ellps=GRS80 +units=m +no_defs"Se creo los centroides para los departamentos de ORG_LEVEL_2_MER (trazar estos centroides) y la matriz de sus distancias.
library(rgeos)
CENT_ORG_LEVEL_2_MER = gCentroid(ORG_LEVEL_2_MER, byid = T)
plot(ORG_LEVEL_2_MER)
plot(CENT_ORG_LEVEL_2_MER, add= T, col = "blue3")
Matriz de distancia de la cuatro primeras filas
dist_matrix <- gDistance(spgeom1 = CENT_ORG_LEVEL_2_MER, spgeom2 = NULL, byid=T)
dist_matrix[1:4, 1:4]## 0 1 2 3
## 0 0.0 160792.0 186296.6 242240.7
## 1 160792.0 0.0 295467.1 108453.1
## 2 186296.6 295467.1 0.0 399800.9
## 3 242240.7 108453.1 399800.9 0.0Matriz de distancia completa
dist_matrix## 0 1 2 3 4 5 6
## 0 0.0 160792.0 186296.6 242240.73 420526.76 232796.49 394664.51
## 1 160792.0 0.0 295467.1 108453.13 272135.53 147838.06 268163.68
## 2 186296.6 295467.1 0.0 399800.86 566857.20 409665.27 559795.50
## 3 242240.7 108453.1 399800.9 0.00 179035.18 75218.75 160431.42
## 4 420526.8 272135.5 566857.2 179035.18 0.00 219700.02 90514.05
## 5 232796.5 147838.1 409665.3 75218.75 219700.02 0.00 168409.25
## 6 394664.5 268163.7 559795.5 160431.42 90514.05 168409.25 0.00
## 7 366569.8 281985.1 547631.1 182413.01 202737.77 139557.31 113345.52
## 8 610285.1 504937.4 787572.3 396649.15 290747.70 378449.55 242148.22
## 9 519957.1 669577.3 392976.9 761868.14 938961.90 747642.25 913524.94
## 10 355154.3 504380.7 235497.9 596762.69 773658.17 584495.05 749382.48
## 11 481507.5 640764.3 402457.8 720359.23 899376.14 693191.06 861587.83
## 12 696832.1 856922.7 655024.4 919498.08 1094627.21 877204.25 1041915.27
## 13 488321.3 634604.0 527730.0 672350.08 836532.53 617130.73 772205.45
## 14 300512.0 412105.2 424952.4 425813.16 579506.25 362903.14 510619.21
## 15 283334.8 433497.4 338126.4 482155.90 654361.27 435815.31 599986.70
## 16 177138.8 337929.0 192370.4 412348.06 591303.04 386452.33 554561.52
## 17 273291.2 431934.2 215069.5 513915.50 692862.17 492017.52 659668.26
## 18 205765.8 362352.3 154181.6 447810.98 626288.12 430911.59 597121.40
## 19 321298.4 481964.8 281748.8 556639.59 735431.45 527378.16 695787.22
## 20 645408.8 786000.7 495472.9 884156.60 1057955.03 877049.64 1039903.60
## 21 323334.4 322090.2 507188.4 267732.20 366037.95 192862.30 282306.17
## 22 231210.6 314152.0 390600.8 317526.52 469677.43 253135.83 402155.03
## 23 469780.8 566832.3 594020.5 560357.96 691072.06 489318.44 610734.49
## 24 889475.6 1031939.3 919611.5 1059229.26 1212233.61 997147.39 1138693.92
## 25 716452.3 874624.2 619508.0 956233.40 1135268.04 929141.84 1097550.79
## 26 881749.8 993578.6 970010.7 989383.05 1112462.09 917590.00 1028041.05
## 27 724410.0 813129.0 845520.8 792975.11 900673.05 718755.38 813620.41
## 28 1040025.9 1194748.5 1025695.1 1240217.41 1405590.55 1186185.36 1340052.80
## 29 671567.5 819285.4 694829.7 855940.09 1017573.64 798876.10 950235.64
## 30 936212.7 810162.2 926512.8 826015.89 762517.22 901096.31 851263.14
## 31 510115.0 568439.3 664417.2 533980.89 632697.25 458858.65 545284.28
## 32 326507.1 481293.0 354620.5 535112.96 708793.03 490946.18 655932.98
## 7 8 9 10 11 12 13
## 0 366569.8 610285.1 519957.1 355154.3 481507.5 696832.1 488321.3
## 1 281985.1 504937.4 669577.3 504380.7 640764.3 856922.7 634604.0
## 2 547631.1 787572.3 392976.9 235497.9 402457.8 655024.4 527730.0
## 3 182413.0 396649.2 761868.1 596762.7 720359.2 919498.1 672350.1
## 4 202737.8 290747.7 938961.9 773658.2 899376.1 1094627.2 836532.5
## 5 139557.3 378449.6 747642.2 584495.1 693191.1 877204.2 617130.7
## 6 113345.5 242148.2 913524.9 749382.5 861587.8 1041915.3 772205.4
## 7 0.0 245628.2 870349.8 710192.9 803589.6 964018.0 682361.2
## 8 245628.2 0.0 1115548.1 955801.0 1044945.4 1189865.4 895697.7
## 9 870349.8 1115548.1 0.0 165324.8 139290.7 362384.0 448537.3
## 10 710192.9 955801.0 165324.8 0.0 176442.2 439247.6 403728.7
## 11 803589.6 1044945.4 139290.7 176442.2 0.0 263621.5 310509.5
## 12 964018.0 1189865.4 362384.0 439247.6 263621.5 0.0 306843.7
## 13 682361.2 895697.7 448537.3 403728.7 310509.5 306843.7 0.0
## 14 418228.7 634962.4 557355.3 434430.5 449164.6 555432.9 264213.1
## 15 524959.2 758504.5 404877.5 289306.7 299074.6 441929.9 205412.2
## 16 504041.8 748856.1 366932.0 212251.7 308201.7 520813.7 341806.1
## 17 610927.5 855714.6 260405.6 113374.0 208897.9 444432.4 329677.3
## 18 555241.3 800832.2 316731.4 154998.9 279533.2 514611.6 374750.4
## 19 637972.6 880192.6 251499.0 141688.7 166070.6 382353.3 269473.4
## 20 1004539.6 1250167.8 148142.8 294618.0 277522.6 442574.5 587151.1
## 21 174100.9 377540.5 766961.7 617857.9 680484.5 812378.8 519780.3
## 22 314331.8 541779.6 602623.7 459784.3 512046.7 650624.6 370215.9
## 23 503590.9 678232.4 668846.2 571970.0 542264.2 570253.0 265926.2
## 24 1036952.0 1216834.0 723792.9 752290.3 598439.7 379817.7 401657.3
## 25 1037392.4 1276853.8 240640.9 384010.3 236132.4 214590.0 461490.3
## 26 916602.4 1048793.5 892568.0 866339.2 754006.1 611333.7 462704.1
## 27 700707.1 819803.0 863201.2 795264.3 726219.8 664720.2 416078.3
## 28 1246320.0 1445013.2 736037.0 818260.6 643515.2 380127.2 569066.4
## 29 855782.7 1057463.8 525235.6 533117.5 391165.7 237767.0 184744.0
## 30 956739.6 1008510.7 1279656.6 1148946.6 1323989.3 1581210.3 1422125.1
## 31 432309.5 566635.0 797541.1 685000.9 677208.1 717233.8 412594.6
## 32 581831.6 815258.5 362536.0 264584.6 248261.8 386270.7 173590.9
## 14 15 16 17 18 19 20
## 0 300512.0 283334.8 177138.76 273291.21 205765.81 321298.40 645408.8
## 1 412105.2 433497.4 337929.05 431934.23 362352.32 481964.76 786000.7
## 2 424952.4 338126.4 192370.44 215069.50 154181.58 281748.83 495472.9
## 3 425813.2 482155.9 412348.06 513915.50 447810.98 556639.59 884156.6
## 4 579506.3 654361.3 591303.04 692862.17 626288.12 735431.45 1057955.0
## 5 362903.1 435815.3 386452.33 492017.52 430911.59 527378.16 877049.6
## 6 510619.2 599986.7 554561.52 659668.26 597121.40 695787.22 1039903.6
## 7 418228.7 524959.2 504041.81 610927.48 555241.33 637972.64 1004539.6
## 8 634962.4 758504.5 748856.05 855714.55 800832.19 880192.56 1250167.8
## 9 557355.3 404877.5 366932.00 260405.62 316731.37 251498.96 148142.8
## 10 434430.5 289306.7 212251.68 113374.02 154998.95 141688.72 294618.0
## 11 449164.6 299074.6 308201.71 208897.92 279533.25 166070.60 277522.6
## 12 555432.9 441929.9 520813.69 444432.44 514611.55 382353.30 442574.5
## 13 264213.1 205412.2 341806.10 329677.25 374750.41 269473.43 587151.1
## 14 0.0 152564.7 248981.48 321978.11 313191.06 306916.11 705200.6
## 15 152564.7 0.0 145848.69 181310.10 195615.06 155430.97 552822.0
## 16 248981.5 145848.7 0.00 106888.07 65872.17 144752.49 505470.1
## 17 321978.1 181310.1 106888.07 0.00 71519.21 66968.01 401044.9
## 18 313191.1 195615.1 65872.17 71519.21 0.00 133023.69 449573.7
## 19 306916.1 155431.0 144752.49 66968.01 133023.69 0.00 398736.6
## 20 705200.6 552822.0 505470.14 401044.93 449573.75 398736.58 0.0
## 21 257512.2 385909.6 405681.38 508477.74 467022.86 520292.93 909519.6
## 22 109918.5 217555.9 250955.98 347680.39 315884.96 353492.56 747225.7
## 23 171368.1 284200.4 410844.12 465509.44 471539.27 432935.47 815763.2
## 24 634298.6 607046.1 740581.55 709319.79 765472.02 643019.75 820552.4
## 25 663597.8 521497.7 544284.30 443169.61 512292.91 401772.29 256341.2
## 26 584330.9 631886.6 777676.69 788284.73 823735.97 731185.41 1016711.4
## 27 426174.1 520496.9 657953.30 697764.50 714933.33 653593.62 1003210.1
## 28 829644.1 761680.9 871726.91 811200.29 878131.08 745525.39 788938.1
## 29 439617.8 388240.0 517287.27 484842.99 540710.13 418807.00 640540.9
## 30 1221443.4 1216832.3 1086319.72 1140184.93 1073409.13 1205774.12 1324700.7
## 31 252369.5 397202.6 499644.76 573466.79 564678.23 552230.32 945557.7
## 32 201007.9 56917.7 168753.56 168168.30 202907.56 123362.04 510599.4
## 21 22 23 24 25 26 27
## 0 323334.4 231210.6 469780.8 889475.6 716452.3 881749.8 724410.0
## 1 322090.2 314152.0 566832.3 1031939.3 874624.2 993578.6 813129.0
## 2 507188.4 390600.8 594020.5 919611.5 619508.0 970010.7 845520.8
## 3 267732.2 317526.5 560358.0 1059229.3 956233.4 989383.0 792975.1
## 4 366038.0 469677.4 691072.1 1212233.6 1135268.0 1112462.1 900673.1
## 5 192862.3 253135.8 489318.4 997147.4 929141.8 917590.0 718755.4
## 6 282306.2 402155.0 610734.5 1138693.9 1097550.8 1028041.0 813620.4
## 7 174100.9 314331.8 503590.9 1036952.0 1037392.4 916602.4 700707.1
## 8 377540.5 541779.6 678232.4 1216834.0 1276853.8 1048793.5 819803.0
## 9 766961.7 602623.7 668846.2 723792.9 240640.9 892568.0 863201.2
## 10 617857.9 459784.3 571970.0 752290.3 384010.3 866339.2 795264.3
## 11 680484.5 512046.7 542264.2 598439.7 236132.4 754006.1 726219.8
## 12 812378.8 650624.6 570253.0 379817.7 214590.0 611333.7 664720.2
## 13 519780.3 370215.9 265926.2 401657.3 461490.3 462704.1 416078.3
## 14 257512.2 109918.5 171368.1 634298.6 663597.8 584330.9 426174.1
## 15 385909.6 217555.9 284200.4 607046.1 521497.7 631886.6 520496.9
## 16 405681.4 250956.0 410844.1 740581.6 544284.3 777676.7 657953.3
## 17 508477.7 347680.4 465509.4 709319.8 443169.6 788284.7 697764.5
## 18 467022.9 315885.0 471539.3 765472.0 512292.9 823736.0 714933.3
## 19 520292.9 353492.6 432935.5 643019.8 401772.3 731185.4 653593.6
## 20 909519.6 747225.7 815763.2 820552.4 256341.2 1016711.4 1003210.1
## 21 0.0 168728.2 329545.2 863741.3 907306.6 746481.5 536422.4
## 22 168728.2 0.0 252747.8 744016.2 738689.1 679966.3 502123.2
## 23 329545.2 252747.8 0.0 539534.5 723703.0 429301.7 254986.3
## 24 863741.3 744016.2 539534.5 0.0 590183.9 302209.1 468921.6
## 25 907306.6 738689.1 723703.0 590183.9 0.0 823955.9 858341.8
## 26 746481.5 679966.3 429301.7 302209.1 823955.9 0.0 230422.2
## 27 536422.4 502123.2 254986.3 468921.6 858341.8 230422.2 0.0
## 28 1077745.9 937978.2 769554.8 264462.1 533110.4 560697.3 731856.5
## 29 687597.8 548577.3 389501.9 224801.2 445860.3 379962.5 432326.3
## 30 1091621.7 1123892.6 1376059.6 1823755.9 1519996.0 1803705.7 1617282.4
## 31 270652.2 279175.9 146998.2 657728.4 867837.4 488675.4 268403.6
## 32 441921.7 273949.5 310509.1 571990.8 466137.1 622527.5 531644.6
## 28 29 30 31 32
## 0 1040025.9 671567.5 936212.7 510115.0 326507.1
## 1 1194748.5 819285.4 810162.2 568439.3 481293.0
## 2 1025695.1 694829.7 926512.8 664417.2 354620.5
## 3 1240217.4 855940.1 826015.9 533980.9 535113.0
## 4 1405590.5 1017573.6 762517.2 632697.2 708793.0
## 5 1186185.4 798876.1 901096.3 458858.6 490946.2
## 6 1340052.8 950235.6 851263.1 545284.3 655933.0
## 7 1246320.0 855782.7 956739.6 432309.5 581831.6
## 8 1445013.2 1057463.8 1008510.7 566635.0 815258.5
## 9 736037.0 525235.6 1279656.6 797541.1 362536.0
## 10 818260.6 533117.5 1148946.6 685000.9 264584.6
## 11 643515.2 391165.7 1323989.3 677208.1 248261.8
## 12 380127.2 237767.0 1581210.3 717233.8 386270.7
## 13 569066.4 184744.0 1422125.1 412594.6 173590.9
## 14 829644.1 439617.8 1221443.4 252369.5 201007.9
## 15 761680.9 388240.0 1216832.3 397202.6 56917.7
## 16 871726.9 517287.3 1086319.7 499644.8 168753.6
## 17 811200.3 484843.0 1140184.9 573466.8 168168.3
## 18 878131.1 540710.1 1073409.1 564678.2 202907.6
## 19 745525.4 418807.0 1205774.1 552230.3 123362.0
## 20 788938.1 640540.9 1324700.7 945557.7 510599.4
## 21 1077745.9 687597.8 1091621.7 270652.2 441921.7
## 22 937978.2 548577.3 1123892.6 279175.9 273949.5
## 23 769554.8 389501.9 1376059.6 146998.2 310509.1
## 24 264462.1 224801.2 1823755.9 657728.4 571990.8
## 25 533110.4 445860.3 1519996.0 867837.4 466137.1
## 26 560697.3 379962.5 1803705.7 488675.4 622527.5
## 27 731856.5 432326.3 1617282.4 268403.6 531644.6
## 28 0.0 390538.3 1951285.3 900654.5 713871.3
## 29 390538.3 0.0 1602553.3 530118.2 349345.7
## 30 1951285.3 1602553.3 0.0 1359953.2 1253249.2
## 31 900654.5 530118.2 1359953.2 0.0 435017.4
## 32 713871.3 349345.7 1253249.2 435017.4 0.0media, mediana y desviacion estandar
mean(dist_matrix)## [1] 574993.6median(dist_matrix)## [1] 531644.6sd(dist_matrix)## [1] 333192.5