Lectura y visualización de datos vectoriales geoespaciales en R
Laura Forero
14 de septiembre de 2020
En este Notebook abordaremos las funcionalidades geoespacioales R mediante el uso de funciones simples (sf) y otras bibliotecas, con el fin de brindar herramientas aplicables en el area de la geomática básica.
Para empezar debemos instalar las librerias necesarias para desarrollar nuestro trabajo:
#install.packages(c("tidyverse", "sf", "leaflet", "lwgeom"))Luego, cargaremos las librerías:
library(tidyverse)## ── Attaching packages ─────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(sf)## Linking to GEOS 3.5.1, GDAL 2.2.2, PROJ 4.9.2library(leaflet)library(lwgeom)## Linking to liblwgeom 3.0.0beta1 r16016, GEOS 3.5.1, PROJ 4.9.21. Lectura de datos vactoriales geoespaciales
A partir de una tabla que contiene coordenadas y nombres de algunas ciudades elaboraremos un mapa con dichas ciudades.
1.1. Leer datos en formato txt
cities<-read.table("Colombia_cities.txt", header = F, sep = ";")
head(cities)## V1 V2 V3 V4 V5 V6
## 1 2338 Colombi Bogota 4.600000 -74.08334 2620
## 2 2339 Colombi Cali 3.437222 -76.52250 758
## 3 2340 Colombi Medellin 6.291389 -75.53611 2076
## 4 2341 Colombi Barranquilla 10.963889 -74.79639 33
## 5 2342 Colombi Cartagena 10.399722 -75.51444 36
## 6 2343 Colombi Cucuta 7.883333 -72.50528 314Puede explorar el nombre de las columnas y cambiarlas si así lo desea
colnames(cities)## [1] "V1" "V2" "V3" "V4" "V5" "V6"names(cities)=c("cod", "Country", "Name", "Lat", "Lon", "Alt")
head(cities)## cod Country Name Lat Lon Alt
## 1 2338 Colombi Bogota 4.600000 -74.08334 2620
## 2 2339 Colombi Cali 3.437222 -76.52250 758
## 3 2340 Colombi Medellin 6.291389 -75.53611 2076
## 4 2341 Colombi Barranquilla 10.963889 -74.79639 33
## 5 2342 Colombi Cartagena 10.399722 -75.51444 36
## 6 2343 Colombi Cucuta 7.883333 -72.50528 3141.2. Convertir el dataframe en un objeto espacial
Verificar que el vector de las coordenadas este en el orden correspondiente
##m <- st_as_sf(cities, coords = c(Longitud,Latitud,Altura))
m <- st_as_sf(cities, coords = c(5,4,6))
m## Simple feature collection with 100 features and 3 fields
## geometry type: POINT
## dimension: XYZ
## bbox: xmin: -81.70055 ymin: 0.8302778 xmax: -70.76167 ymax: 12.58472
## CRS: NA
## First 10 features:
## cod Country Name geometry
## 1 2338 Colombi Bogota POINT Z (-74.08334 4.6 2620)
## 2 2339 Colombi Cali POINT Z (-76.5225 3.437222 ...
## 3 2340 Colombi Medellin POINT Z (-75.53611 6.291389...
## 4 2341 Colombi Barranquilla POINT Z (-74.79639 10.96389...
## 5 2342 Colombi Cartagena POINT Z (-75.51444 10.39972...
## 6 2343 Colombi Cucuta POINT Z (-72.50528 7.883333...
## 7 2344 Colombi Bucaramanga POINT Z (-73.12583 7.129722...
## 8 2345 Colombi Pereira POINT Z (-75.69611 4.813333...
## 9 2346 Colombi Santa Marta POINT Z (-74.20167 11.24722 9)
## 10 2347 Colombi Ibague POINT Z (-75.23222 4.438889...¿Qué clase de objeto es m?
class(m)## [1] "sf" "data.frame"Para saber como R interpreta los datos utiliaremos el siguiente fragmento de codigo
plot(st_geometry(m))
1.3. Creación del mapa leaflet de cities
coords=st_coordinates(m)
coords## X Y Z
## 1 -74.08334 4.6000000 2620
## 2 -76.52250 3.4372222 758
## 3 -75.53611 6.2913889 2076
## 4 -74.79639 10.9638889 33
## 5 -75.51444 10.3997222 36
## 6 -72.50528 7.8833333 314
## 7 -73.12583 7.1297222 967
## 8 -75.69611 4.8133333 1445
## 9 -74.20167 11.2472222 9
## 10 -75.23222 4.4388889 1082
## 11 -75.56223 6.3388889 1489
## 12 -77.28111 1.2136111 2569
## 13 -75.52055 5.0700000 1935
## 14 -75.33028 2.9305556 507
## 15 -74.76667 10.9172222 10
## 16 -73.63500 4.1533333 529
## 17 -75.68111 4.5338889 1349
## 18 -74.22139 4.5872222 2413
## 19 -73.25056 10.4769444 163
## 20 -75.61139 6.1719444 1637
## 21 -75.89000 8.7575000 25
## 22 -75.39778 9.3047222 219
## 23 -73.08972 7.0647222 1026
## 24 -76.30361 3.5394444 863
## 25 -77.06973 3.8933333 1
## 26 -73.85472 7.0652778 45
## 27 -75.67250 4.8347222 1500
## 28 -76.20000 4.0866667 766
## 29 -75.56389 6.1730556 1784
## 30 -75.91167 4.7463889 722
## 31 -72.24416 11.3841667 44
## 32 -75.61750 1.6175000 579
## 33 -74.80083 4.3030556 328
## 34 -72.92973 5.7205556 2419
## 35 -76.30278 3.9022222 722
## 36 -73.36777 5.5352778 2717
## 37 -73.17306 7.0708333 712
## 38 -74.77306 10.8588889 5
## 39 -74.75333 9.2413889 18
## 40 -74.36667 4.8166667 2816
## 41 -72.90722 11.5444444 5
## 42 -73.02028 5.8269444 2501
## 43 -74.00584 5.0283333 2656
## 44 -74.36777 4.3438889 1745
## 45 -74.25417 11.0094444 12
## 46 -78.81556 1.7986111 1
## 47 -76.63472 7.8855556 57
## 48 -73.05361 6.9894444 1080
## 49 -73.35778 8.2363889 1272
## 50 -74.66917 5.4522222 152
## 51 -77.64445 0.8302778 2954
## 52 -76.66111 5.6947222 67
## 53 -73.62695 8.3125000 151
## 54 -76.49583 3.5850000 991
## 55 -70.76167 7.0902778 119
## 56 -74.92278 10.6377778 88
## 57 -75.60361 4.9825000 1488
## 58 -75.63750 6.0900000 1929
## 59 -72.47417 7.8338889 381
## 60 -74.06667 4.8666667 2325
## 61 -75.38889 6.1552778 2058
## 62 -75.64361 4.5325000 1459
## 63 -72.39417 5.3394444 426
## 64 -75.44778 8.9494444 72
## 65 -74.18667 10.5213889 55
## 66 -72.51334 7.8383333 385
## 67 -81.70055 12.5847222 -9999
## 68 -75.19722 7.9869444 65
## 69 -75.62139 4.8680556 1827
## 70 -74.88861 4.1527778 329
## 71 -75.40833 10.3294444 129
## 72 -75.79667 8.8855556 15
## 73 -73.97806 9.0047222 21
## 74 -74.21667 4.7166667 2276
## 75 -76.05639 1.8675000 1319
## 76 -76.41944 3.2336111 847
## 77 -72.65250 7.3780556 2516
## 78 -73.23889 10.0358333 183
## 79 -76.73167 8.0980556 7
## 80 -74.26833 4.7344444 2400
## 81 -75.34389 10.2544444 58
## 82 -75.64694 6.1605556 1887
## 83 -75.51305 6.3488889 1498
## 84 -74.78722 9.7919444 18
## 85 -76.68639 7.6769444 109
## 86 -75.13306 9.7222222 154
## 87 -76.23861 3.3275000 894
## 88 -73.82000 5.6188889 2713
## 89 -76.54444 3.2638889 890
## 90 -76.24472 3.4211111 912
## 91 -74.91833 10.7997222 124
## 92 -75.93611 4.2688889 1394
## 93 -76.48666 3.0130556 1061
## 94 -74.81194 7.5941667 51
## 95 -73.75667 3.9875000 579
## 96 -75.81973 9.2316667 7
## 97 -74.70361 7.0833333 638
## 98 -75.29583 9.3177778 154
## 99 -76.31667 3.6880556 851
## 100 -75.58833 8.4147222 113Creación de los vectores de coordenadas
#Longitud [Fila, columna]
long=coords[,1]
long## 1 2 3 4 5 6 7 8
## -74.08334 -76.52250 -75.53611 -74.79639 -75.51444 -72.50528 -73.12583 -75.69611
## 9 10 11 12 13 14 15 16
## -74.20167 -75.23222 -75.56223 -77.28111 -75.52055 -75.33028 -74.76667 -73.63500
## 17 18 19 20 21 22 23 24
## -75.68111 -74.22139 -73.25056 -75.61139 -75.89000 -75.39778 -73.08972 -76.30361
## 25 26 27 28 29 30 31 32
## -77.06973 -73.85472 -75.67250 -76.20000 -75.56389 -75.91167 -72.24416 -75.61750
## 33 34 35 36 37 38 39 40
## -74.80083 -72.92973 -76.30278 -73.36777 -73.17306 -74.77306 -74.75333 -74.36667
## 41 42 43 44 45 46 47 48
## -72.90722 -73.02028 -74.00584 -74.36777 -74.25417 -78.81556 -76.63472 -73.05361
## 49 50 51 52 53 54 55 56
## -73.35778 -74.66917 -77.64445 -76.66111 -73.62695 -76.49583 -70.76167 -74.92278
## 57 58 59 60 61 62 63 64
## -75.60361 -75.63750 -72.47417 -74.06667 -75.38889 -75.64361 -72.39417 -75.44778
## 65 66 67 68 69 70 71 72
## -74.18667 -72.51334 -81.70055 -75.19722 -75.62139 -74.88861 -75.40833 -75.79667
## 73 74 75 76 77 78 79 80
## -73.97806 -74.21667 -76.05639 -76.41944 -72.65250 -73.23889 -76.73167 -74.26833
## 81 82 83 84 85 86 87 88
## -75.34389 -75.64694 -75.51305 -74.78722 -76.68639 -75.13306 -76.23861 -73.82000
## 89 90 91 92 93 94 95 96
## -76.54444 -76.24472 -74.91833 -75.93611 -76.48666 -74.81194 -73.75667 -75.81973
## 97 98 99 100
## -74.70361 -75.29583 -76.31667 -75.58833#Latitud
lat=coords[,2]
lat## 1 2 3 4 5 6 7
## 4.6000000 3.4372222 6.2913889 10.9638889 10.3997222 7.8833333 7.1297222
## 8 9 10 11 12 13 14
## 4.8133333 11.2472222 4.4388889 6.3388889 1.2136111 5.0700000 2.9305556
## 15 16 17 18 19 20 21
## 10.9172222 4.1533333 4.5338889 4.5872222 10.4769444 6.1719444 8.7575000
## 22 23 24 25 26 27 28
## 9.3047222 7.0647222 3.5394444 3.8933333 7.0652778 4.8347222 4.0866667
## 29 30 31 32 33 34 35
## 6.1730556 4.7463889 11.3841667 1.6175000 4.3030556 5.7205556 3.9022222
## 36 37 38 39 40 41 42
## 5.5352778 7.0708333 10.8588889 9.2413889 4.8166667 11.5444444 5.8269444
## 43 44 45 46 47 48 49
## 5.0283333 4.3438889 11.0094444 1.7986111 7.8855556 6.9894444 8.2363889
## 50 51 52 53 54 55 56
## 5.4522222 0.8302778 5.6947222 8.3125000 3.5850000 7.0902778 10.6377778
## 57 58 59 60 61 62 63
## 4.9825000 6.0900000 7.8338889 4.8666667 6.1552778 4.5325000 5.3394444
## 64 65 66 67 68 69 70
## 8.9494444 10.5213889 7.8383333 12.5847222 7.9869444 4.8680556 4.1527778
## 71 72 73 74 75 76 77
## 10.3294444 8.8855556 9.0047222 4.7166667 1.8675000 3.2336111 7.3780556
## 78 79 80 81 82 83 84
## 10.0358333 8.0980556 4.7344444 10.2544444 6.1605556 6.3488889 9.7919444
## 85 86 87 88 89 90 91
## 7.6769444 9.7222222 3.3275000 5.6188889 3.2638889 3.4211111 10.7997222
## 92 93 94 95 96 97 98
## 4.2688889 3.0130556 7.5941667 3.9875000 9.2316667 7.0833333 9.3177778
## 99 100
## 3.6880556 8.4147222#Altitud
alt=coords[,3]
alt## 1 2 3 4 5 6 7 8 9 10 11 12 13
## 2620 758 2076 33 36 314 967 1445 9 1082 1489 2569 1935
## 14 15 16 17 18 19 20 21 22 23 24 25 26
## 507 10 529 1349 2413 163 1637 25 219 1026 863 1 45
## 27 28 29 30 31 32 33 34 35 36 37 38 39
## 1500 766 1784 722 44 579 328 2419 722 2717 712 5 18
## 40 41 42 43 44 45 46 47 48 49 50 51 52
## 2816 5 2501 2656 1745 12 1 57 1080 1272 152 2954 67
## 53 54 55 56 57 58 59 60 61 62 63 64 65
## 151 991 119 88 1488 1929 381 2325 2058 1459 426 72 55
## 66 67 68 69 70 71 72 73 74 75 76 77 78
## 385 -9999 65 1827 329 129 15 21 2276 1319 847 2516 183
## 79 80 81 82 83 84 85 86 87 88 89 90 91
## 7 2400 58 1887 1498 18 109 154 894 2713 890 912 124
## 92 93 94 95 96 97 98 99 100
## 1394 1061 51 579 7 638 154 851 113- Crar el mapa leaflet
library(leaflet)
mapa=leaflet()%>%
addTiles()%>%
addMarkers(lng=long, lat=lat, popup=cities$Name)
mapaUsaremos la biblioteca sf para leer un shapefile de departamentos descargados del Dane o DIVA-GIS. Revise el siguiente fragmento de código y cámbielo de acuerdo con su departamento.
Antes de empezar instale la libreria necesaria
2.1. Shapefile de departamentos
Primero, podemos leer un shapefile que representa a los departamentos colombianos que se descargó previamente de DIVA-GIS.
deptos <- read_sf("COL_adm1.shp")
deptos## Simple feature collection with 32 features and 9 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -81.84153 ymin: -4.228429 xmax: -66.87033 ymax: 15.91247
## CRS: 4326
## # A tibble: 32 x 10
## ID_0 ISO NAME_0 ID_1 NAME_1 TYPE_1 ENGTYPE_1 NL_NAME_1 VARNAME_1
## <dbl> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 53 COL Colom… 1 Amazo… Comis… Commissi… <NA> <NA>
## 2 53 COL Colom… 2 Antio… Depar… Departme… <NA> <NA>
## 3 53 COL Colom… 3 Arauca Inten… Intendan… <NA> <NA>
## 4 53 COL Colom… 4 Atlán… Depar… Departme… <NA> <NA>
## 5 53 COL Colom… 5 Bolív… Depar… Departme… <NA> <NA>
## 6 53 COL Colom… 6 Boyacá Depar… Departme… <NA> <NA>
## 7 53 COL Colom… 7 Córdo… Depar… Departme… <NA> <NA>
## 8 53 COL Colom… 8 Caldas Depar… Departme… <NA> <NA>
## 9 53 COL Colom… 9 Caque… Inten… Intendan… <NA> <NA>
## 10 53 COL Colom… 10 Casan… Inten… Intendan… <NA> <NA>
## # … with 22 more rows, and 1 more variable: geometry <MULTIPOLYGON [°]>Para saber el sistema de referencia de coordenadas de los datos vectoriales almacenados en el objeto deptos, aplicamos el siguiente codigo
st_crs(deptos)## Coordinate Reference System:
## User input: 4326
## wkt:
## GEOGCS["GCS_WGS_1984",
## DATUM["WGS_1984",
## SPHEROID["WGS_84",6378137.0,298.257223563]],
## PRIMEM["Greenwich",0.0],
## UNIT["Degree",0.0174532925199433],
## AUTHORITY["EPSG","4326"]]Un ggplot nos permite visualizar los datos geoespaciales
ggplot() + geom_sf(data = deptos, fill="gold2")
2.2. Mapa de Antioquía por filtrado de datos
Como solo trabajaremos con un departamento, podemos filtrar los datos de DIVA-GIS o subir los datos obtenidos del DANE.
Para visualizar el mapa del Departamento aplicar el siguiente codigo
antioquia <- deptos %>% filter(NAME_1 == "Antioquia")
ggplot() + geom_sf(data = antioquia, fill="gold4") 
También podemos filtrar por municipio según el departamento
munic <- read_sf("COL_adm2.shp")
mun_antioquia <- munic %>% filter(NAME_1 == "Antioquia")
ggplot() + geom_sf(data = mun_antioquia, fill="khaki1") 
Veamos que hay en mun_antioquia
mun_antioquia## Simple feature collection with 124 features and 11 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -77.149 ymin: 5.456801 xmax: -73.8962 ymax: 9.000139
## CRS: 4326
## # A tibble: 124 x 12
## ID_0 ISO NAME_0 ID_1 NAME_1 ID_2 NAME_2 TYPE_2 ENGTYPE_2 NL_NAME_2
## * <dbl> <chr> <chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 53 COL Colom… 2 Antio… 9 Abejo… Munic… Municipa… <NA>
## 2 53 COL Colom… 2 Antio… 10 Abria… Munic… Municipa… <NA>
## 3 53 COL Colom… 2 Antio… 11 Aleja… Munic… Municipa… <NA>
## 4 53 COL Colom… 2 Antio… 12 Amagá Munic… Municipa… <NA>
## 5 53 COL Colom… 2 Antio… 13 Amalfi Munic… Municipa… <NA>
## 6 53 COL Colom… 2 Antio… 14 Andes Munic… Municipa… <NA>
## 7 53 COL Colom… 2 Antio… 15 Angel… Munic… Municipa… <NA>
## 8 53 COL Colom… 2 Antio… 16 Angos… Munic… Municipa… <NA>
## 9 53 COL Colom… 2 Antio… 17 Anorí Munic… Municipa… <NA>
## 10 53 COL Colom… 2 Antio… 18 Anzá Munic… Municipa… <NA>
## # … with 114 more rows, and 2 more variables: VARNAME_2 <chr>,
## # geometry <MULTIPOLYGON [°]>Para construir un mejor terminado del mapa en formato png o pdf, descomentar el siguiente fragmento de código
#antioquia_points<- st_centroid(mun_antioquia)
#antioquia_points <- cbind(mun_antioquia, st_coordinates(st_centroid(mun_antioquia$geometry)))
#ggplot(antioquia) +
#geom_sf(data=antioquia_points, aes(x=X, y=Y, fill =ID_2), color = "black", size = 0.25) +
#geom_text(data = antioquia_points, aes(x=X, y=Y,label = ID_2), size = 2) +
#theme(aspect.ratio=1)+
#scale_fill_distiller(name="ID_2", palette = "YlGn")+
#labs(title="Another Map of Antioquia")2.3. Visualización de datos mediante leaflet
Para obtener el área de los municipios cargamos primero la siguiente librería (lwgeom) previamente intalada
library(lwgeom)Calculemos el área de cada municipio (m2)
mun_antioquia$area <- st_area(mun_antioquia)
mun_antioquia$area## Units: [m^2]
## [1] 507604080 281746587 131995406 102161347 1181139427 540102630
## [7] 87932214 364579608 1412775529 222173985 573793070 482786408
## [13] 233750426 130729354 221367480 150389375 309021433 225723968
## [19] 255264509 409749329 358611915 308970970 2034382489 465211833
## [25] 222977132 152670264 212665113 309890464 78874429 246308794
## [31] 170913544 1320498637 673713930 39000787 262909428 155605622
## [37] 201448673 66265470 1831296444 183027832 220889086 1927631274
## [43] 423670642 64551669 192706031 46776861 225715166 1315083049
## [49] 387271775 101544194 82686209 220662295 82197491 159732255
## [55] 81437619 113263619 45629700 23479693 2129961667 237635109
## [61] 177933656 134626147 37046194 200996395 241062299 423522380
## [67] 113759123 361372217 80117178 1574108207 1200382035 303970842
## [73] 491462592 1160824515 118439371 148436329 392588911 116915089
## [79] 1140186485 543644719 354299204 1923549049 267507032 215622357
## [85] 252586428 21554042 448135674 169303920 764538247 356872303
## [91] 151178175 177611752 310801185 443691309 215791030 715293115
## [97] 369051467 432297142 204964758 245728904 444902343 871822946
## [103] 262356515 1216274998 1302794296 188183801 253151556 1748808006
## [109] 139974164 145784102 140095786 3566771011 242289889 2642135198
## [115] 534073867 121026645 531755944 144635975 1495856879 487295551
## [121] 734335803 896467192 1825954162 1021941883Luego obtenemos el área en km2 mediante la creación de un nuevo objeto
mun_antioquia$km2 <- mun_antioquia$area/(1000000)Veamos que hay en mun_antioquia$km2
mun_antioquia$km2## Units: [m^2]
## [1] 507.60408 281.74659 131.99541 102.16135 1181.13943 540.10263
## [7] 87.93221 364.57961 1412.77553 222.17398 573.79307 482.78641
## [13] 233.75043 130.72935 221.36748 150.38938 309.02143 225.72397
## [19] 255.26451 409.74933 358.61191 308.97097 2034.38249 465.21183
## [25] 222.97713 152.67026 212.66511 309.89046 78.87443 246.30879
## [31] 170.91354 1320.49864 673.71393 39.00079 262.90943 155.60562
## [37] 201.44867 66.26547 1831.29644 183.02783 220.88909 1927.63127
## [43] 423.67064 64.55167 192.70603 46.77686 225.71517 1315.08305
## [49] 387.27178 101.54419 82.68621 220.66230 82.19749 159.73225
## [55] 81.43762 113.26362 45.62970 23.47969 2129.96167 237.63511
## [61] 177.93366 134.62615 37.04619 200.99640 241.06230 423.52238
## [67] 113.75912 361.37222 80.11718 1574.10821 1200.38203 303.97084
## [73] 491.46259 1160.82451 118.43937 148.43633 392.58891 116.91509
## [79] 1140.18649 543.64472 354.29920 1923.54905 267.50703 215.62236
## [85] 252.58643 21.55404 448.13567 169.30392 764.53825 356.87230
## [91] 151.17818 177.61175 310.80119 443.69131 215.79103 715.29312
## [97] 369.05147 432.29714 204.96476 245.72890 444.90234 871.82295
## [103] 262.35652 1216.27500 1302.79430 188.18380 253.15156 1748.80801
## [109] 139.97416 145.78410 140.09579 3566.77101 242.28989 2642.13520
## [115] 534.07387 121.02665 531.75594 144.63597 1495.85688 487.29555
## [121] 734.33580 896.46719 1825.95416 1021.941882.3. Creación del plot
Necesitamos una conversión de características simples a polígonos espaciales
ant_mun <- as(mun_antioquia, 'Spatial')Exploramos el objeto espacial (desomentar el diguiente codigo)
#head(ant_mun)Ahora, organicemos el plot
bins <- c(0, 50, 100, 200, 300, 500, 1000, 2000, Inf)
pal <- colorBin("RdPu", domain = ant_mun$km2, bins = bins)
labels <- mun_antioquia$NAME_2
labels## [1] "Abejorral" "Abriaquí"
## [3] "Alejandría" "Amagá"
## [5] "Amalfi" "Andes"
## [7] "Angelópolis" "Angostura"
## [9] "Anorí" "Anzá"
## [11] "Apartadó" "Arboletes"
## [13] "Argelia" "Armenia"
## [15] "Barbosa" "Bello"
## [17] "Belmira" "Betania"
## [19] "Betulia" "Bolívar"
## [21] "Briceño" "Buriticá"
## [23] "Cáceres" "Cañasgordas"
## [25] "Caicedo" "Caldas"
## [27] "Campamento" "Caracolí"
## [29] "Caramanta" "Carepa"
## [31] "Carolina del Principe" "Caucasia"
## [33] "Chigorodó" "Cisneros"
## [35] "Cocorná" "Concepción"
## [37] "Concordia" "Copacabana"
## [39] "Dabeiba" "Don Matías"
## [41] "Ebéjico" "El Bagre"
## [43] "El Carmen de Viboral" "El Santuario"
## [45] "Entrerríos" "Envigado"
## [47] "Fredonia" "Frontino"
## [49] "Gómez Plata" "Giraldo"
## [51] "Girardota" "Granada"
## [53] "Guadalupe" "Guarne"
## [55] "Guatapé" "Heliconia"
## [57] "Hispania" "Itagüí"
## [59] "Ituango" "Jardín"
## [61] "Jericó" "La Ceja"
## [63] "La Estrella" "La Unión de Sucre"
## [65] "Liborina" "Maceo"
## [67] "Marinilla" "Medellín"
## [69] "Montebello" "Murindó"
## [71] "Mutatá" "Nariño"
## [73] "Nechí" "Necoclí"
## [75] "Olaya" "Peñol"
## [77] "Pequé" "Pueblorrico"
## [79] "Puerto Berrío" "Puerto Nare"
## [81] "Puerto Triunfo" "Remedios"
## [83] "Retiro" "Rionegro"
## [85] "Sabanalarga" "Sabaneta"
## [87] "Salgar" "San Andrés de Cuerquia"
## [89] "San Carlos" "San Francisco"
## [91] "San Jerónimo" "San José de la Montaña"
## [93] "San Juan de Urabá" "San Luis"
## [95] "San Pedro de los Milagros" "San Pedro de Urabá"
## [97] "San Rafael" "San Roque"
## [99] "San Vicente" "Santa Bárbara"
## [101] "Santa Fe de Antioquia" "Santa Rosa de Osos"
## [103] "Santo Domingo" "Segovia"
## [105] "Sonsón" "Sopetrán"
## [107] "Támesis" "Tarazá"
## [109] "Tarso" "Titiribí"
## [111] "Toledo" "Turbo"
## [113] "Uramita" "Urrao"
## [115] "Valdivia" "Valparaíso"
## [117] "Vegachí" "Venecia"
## [119] "Vigía del Fuerte" "Yalí"
## [121] "Yarumal" "Yolombó"
## [123] "Yondó" "Zaragoza"Luego, crea el plot
mapa1 <- leaflet(ant_mun) %>%
setView(-75.5, 7, 8) %>% addPolygons(
fillColor = ~pal(km2),
weight = 2,
opacity = 1,
color = "white",
dashArray = "3",
fillOpacity = 0.7,
highlight = highlightOptions(
weight = 5,
color = "RdPu",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels) %>%
addLegend(pal = pal, values = ~km2, opacity = 0.7, title = NULL,
position = "bottomright")Visualicemos como quedó
mapa1Exploremos otras formas de diagramar el mapa
leaflet() %>%
addProviderTiles(providers$Esri.WorldImagery, options= providerTileOptions(opacity = 0.99)) %>%
addPolygons(data = ant_mun, popup= ant_mun$NAME_2,
stroke = TRUE, fillOpacity = 0.25, smoothFactor = 0.25)leaflet() %>%
addProviderTiles(providers$Esri.WorldStreetMap, options= providerTileOptions(opacity = 0.9)) %>%
addPolygons(data = ant_mun, popup= ant_mun$NAME_2,
stroke = TRUE, fillOpacity = 0.05, smoothFactor = 0.05)