1. Introdducción

Este es un R Markdown Notebook. Mediante este sketch observaremos algunas funcionalidades geospaciales de R para lo cual usaremos algunas librerias como simple features (sf) y tidyverse.

Primero instalamos las librerias nescesarias, para ello usamos el siguiente comando:

#install.packages("tidyverse")
#install.packages("sf")

Luego, necesitamos cargar las dos bibliotecas:

library(tidyverse)
## -- Attaching packages ------------------------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.2     v purrr   0.3.4
## v tibble  3.0.2     v dplyr   1.0.0
## v tidyr   1.1.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0
## -- Conflicts ---------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(sf)
## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
  1. Lectura de datos vectoriales Una vez descargados los archivos nescesarios desde la pagina oficial del DANE, cargamos el shapefile que contiene los datos del departamento de Boyacá:
Deptos <- read_sf("C:/Users/JUAN PABLO/Documents/geo maps/COL_adm1.shp")

Para conocer los datos que contiene el archivo se usa la función head

head(Deptos)
## Simple feature collection with 6 features and 9 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -77.149 ymin: -4.228429 xmax: -69.36835 ymax: 11.10792
## geographic CRS: WGS 84
## Warning: `...` is not empty.
## 
## We detected these problematic arguments:
## * `needs_dots`
## 
## These dots only exist to allow future extensions and should be empty.
## Did you misspecify an argument?
## # A tibble: 6 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>     
## # ... with 1 more variable: geometry <MULTIPOLYGON [°]>

A continuación, para conocer el sistema de referencia que tienen los datos se debe usar el comando “st_crs” de la libería sf

st_crs(Deptos)
## Coordinate Reference System:
##   User input: WGS 84 
##   wkt:
## 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]]

3. Usamos ggplot para vizualizar los datos espaciales.

Para comprender los datas lo mejor es trazarlos, para ello es posible utilizar las funcionalidades de ggplot.

El primer paso es instalar la libreria:

#install.packages("ggplot2")

cargar la libreria instalada

library(ggplot2)

Ahora si utilizamos los datos del objeto deptos con las funciones de ggplot:

ggplot() + geom_sf(data = Deptos) 

Sepuede utilizar cualquier sistema de referencias de coordenadas para trazar datos, en el caso de CRS 3978 es el sistema de referencia de coordenadas usado en canada

ggplot() + geom_sf(data = Deptos) + coord_sf(crs = st_crs(3978))

las propiedades del CRS con el código EPSG 32618. Corresponde a UTM 18 N. Por lo cual, en caso de requerir usar dicho CRS, es necesario convertir el objeto espacial de EPSG4326 a EPSG: 32618.Para ello realizamos lo siguiente

deptos_utm <- st_transform(Deptos, crs = st_crs(32618))
deptos_utm
## Simple feature collection with 32 features and 9 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -245935.3 ymin: -469204.3 xmax: 1407491 ymax: 1763314
## projected CRS:  WGS 84 / UTM zone 18N
## Warning: `...` is not empty.
## 
## We detected these problematic arguments:
## * `needs_dots`
## 
## These dots only exist to allow future extensions and should be empty.
## Did you misspecify an argument?
## # 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 [m]>
ggplot() + geom_sf(data = deptos_utm)

4. Filtrar los datos geoespaciales basados en atributos como solo nos interesa el departamento de boyacá —

boyaca <- Deptos %>% filter(NAME_1 == "Boyacá")

trazamos el nuevo vector

ggplot() + geom_sf(data = boyaca)

Vamos a cargar los municipios colombianos y filtrar los del departamento de Boyacá

munic <- read_sf("C:/Users/JUAN PABLO/Documents/geo maps/COL_adm2.shp")
mun_boyaca <- munic %>% filter(NAME_1 == "Boyacá")
ggplot() + geom_sf(data = mun_boyaca)

mun_boyaca
## Simple feature collection with 123 features and 11 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -74.6743 ymin: 4.642001 xmax: -71.98309 ymax: 7.0275
## geographic CRS: WGS 84
## Warning: `...` is not empty.
## 
## We detected these problematic arguments:
## * `needs_dots`
## 
## These dots only exist to allow future extensions and should be empty.
## Did you misspecify an argument?
## # A tibble: 123 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~     6 Boyacá   199 Almei~ Munic~ Municipa~ <NA>     
##  2    53 COL   Colom~     6 Boyacá   200 Aquit~ Munic~ Municipa~ <NA>     
##  3    53 COL   Colom~     6 Boyacá   201 Arcab~ Munic~ Municipa~ <NA>     
##  4    53 COL   Colom~     6 Boyacá   202 Belén  <NA>   <NA>      <NA>     
##  5    53 COL   Colom~     6 Boyacá   203 Berbeo Munic~ Municipa~ <NA>     
##  6    53 COL   Colom~     6 Boyacá   204 Betei~ Munic~ Municipa~ <NA>     
##  7    53 COL   Colom~     6 Boyacá   205 Boavi~ Munic~ Municipa~ <NA>     
##  8    53 COL   Colom~     6 Boyacá   206 Boyacá Munic~ Municipa~ <NA>     
##  9    53 COL   Colom~     6 Boyacá   207 Brice~ Munic~ Municipa~ <NA>     
## 10    53 COL   Colom~     6 Boyacá   208 Buena~ Munic~ Municipa~ <NA>     
## # ... with 113 more rows, and 2 more variables: VARNAME_2 <chr>,
## #   geometry <MULTIPOLYGON [°]>

Vamos a asignar un centroide acada poligono que representan a los municipios de Caqueta para poder ponerles una etiqueta de ID a los municipios.

boyaca_points<- st_centroid(mun_boyaca)
## Warning in st_centroid.sf(mun_boyaca): st_centroid assumes attributes are
## constant over geometries of x
## Warning in st_centroid.sfc(st_geometry(x), of_largest_polygon =
## of_largest_polygon): st_centroid does not give correct centroids for longitude/
## latitude data
boyaca_points <- cbind(mun_boyaca,st_coordinates(st_centroid(mun_boyaca$geometry)))
## Warning in st_centroid.sfc(mun_boyaca$geometry): st_centroid does not give
## correct centroids for longitude/latitude data
ggplot(boyaca) +
    geom_sf() +
    geom_sf(data = boyaca_points, fill = "antiquewhite") + 
    geom_text(data = boyaca_points, aes(x=X, y=Y,label = ID_2), size = 2) +
    coord_sf(xlim = c(-74.8, -71.8), ylim = c(4.4, 7.1), expand = FALSE)

library(scales)
## 
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
## 
##     col_factor
ggplot(boyaca) +
  geom_sf(data=boyaca_points, aes(x=X, y=Y, fill =ID_2), color = "black", size = 0.25) + geom_text(data = boyaca_points, aes(x=X, y=Y,label = ID_2), size = 2) +
  theme(aspect.ratio=1)+
  scale_fill_distiller(name="ID_2", palette = "YlOrBr", breaks = pretty_breaks(n =4))+labs(title="Municipios del departamento de Boyacá")
## Warning: Ignoring unknown aesthetics: x, y

Podemos guadar nuestro proceso hasta el momento en PDF

ggsave("boyaca_municipios.pdf")
## Saving 7 x 5 in image
ggsave("map_boyaca.png", width = 6, height = 6, dpi = "screen")

5. Usamos “leaflt” para visualizar datos para esto necesitamos instalar libreria y llamarla

library(leaflet)

Necesitamos convertir de caracteristicas simples a puntos espaciales

boy_ponits <- as(boyaca_points, "Spatial")

Despues observamos que hay dentro del objeto

head(boy_ponits)
## An object of class "SpatialPolygonsDataFrame"
## Slot "data":
##   ID_0 ISO   NAME_0 ID_1 NAME_1 ID_2    NAME_2    TYPE_2    ENGTYPE_2 NL_NAME_2
## 1   53 COL Colombia    6 Boyacá  199   Almeida Municipio Municipality      <NA>
## 2   53 COL Colombia    6 Boyacá  200 Aquitania Municipio Municipality      <NA>
## 3   53 COL Colombia    6 Boyacá  201  Arcabuco Municipio Municipality      <NA>
## 4   53 COL Colombia    6 Boyacá  202     Belén      <NA>         <NA>      <NA>
## 5   53 COL Colombia    6 Boyacá  203    Berbeo Municipio Municipality      <NA>
## 6   53 COL Colombia    6 Boyacá  204 Beteitiva Municipio Municipality      <NA>
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## 3      <NA> -73.46782 5.723896
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## 
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## An object of class "Polygons"
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## An object of class "Polygon"
## Slot "labpt":
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## Slot "area":
## [1] 0.0051261
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## Slot "hole":
## [1] FALSE
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## An object of class "Polygon"
## Slot "labpt":
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## Slot "area":
## [1] 0.01047862
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## Slot "hole":
## [1] FALSE
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## Slot "ringDir":
## [1] 1
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## Slot "coords":
##           [,1]     [,2]
##  [1,] -72.8499 5.849000
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## [1] 0.01047862
## 
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## [1] 2 3 4 6 5 1
## 
## Slot "bbox":
##          min      max
## x -73.541603 -72.7061
## y   4.871301   6.0585
## 
## Slot "proj4string":
## CRS arguments: +proj=longlat +datum=WGS84 +no_defs

Para obetner el areas de los municipios instalamos y llamamos las siguientes librerias

library(lwgeom)
## Linking to liblwgeom 3.0.0beta1 r16016, GEOS 3.8.0, PROJ 6.3.1
mun_boyaca$area <- st_area(mun_boyaca)

Creamos un campo paraalmacenar el area en kilometros cuadrados y verificamos la salida

mun_boyaca$km2 <- mun_boyaca$area/(100000)
mun_boyaca$km2
## Units: [m^2]
##   [1]   479.7334  9037.2455  1632.1342  1546.5223   628.4607  1283.1822
##   [7]  1785.9268   580.5340   540.3198   878.5452   158.5462  1870.7899
##  [13]   885.6730  3107.9600   546.4079   388.3464  1514.1470  1298.9509
##  [19]  7313.2186  5628.0458  1350.6323   609.0598  1081.5644   522.8278
##  [25]  1749.5417   635.9134   973.1592   463.3178 13135.6649   332.2368
##  [31]  2811.2784  1808.5505   796.0334   910.0209   986.7122  1280.2692
##  [37]   894.9661  2017.2954   684.0413   378.9154   722.8849  8799.5611
##  [43]   447.6551   459.6802  1329.7290   621.6379  1986.1630   460.2968
##  [49]  6335.8878  1874.0220  1622.5746  2459.3149  3609.3416   568.5137
##  [55]  2128.1716   651.8185  1708.7576   512.6003   398.5799   543.0010
##  [61]  5109.6286  3542.2549   625.5822  4162.6337  2722.1976   393.2367
##  [67]  2792.0117  5706.2090  1111.6939  2923.4229  3330.1586 14391.8169
##  [73]   955.2294  2077.4829  1398.7297  1868.9415   655.2678  2838.5552
##  [79]  1651.9258  1040.0139  1002.5682  2627.1646  1178.1661  1049.6359
##  [85]  1830.4830  4924.4555   746.9779   745.8023   723.4770  1416.1040
##  [91]   561.1386  1485.6929  1409.9821  1802.7338  6365.0190  2072.0233
##  [97]   788.2340   471.5936   303.5042  2817.5359  2135.0974  1056.1560
## [103]   331.3001  2075.1419   377.8645  1126.2569  1157.9385   729.6197
## [109]   710.0005  1564.3187   806.1687   340.4449  1824.9107  2212.0266
## [115]   279.5687   917.1484  1498.8357  1525.7119  1532.4558  1839.1159
## [121]  1129.0525   598.8663  2605.4623
boy_mun <- as(mun_boyaca, 'Spatial')
head(boy_mun)
## An object of class "SpatialPolygonsDataFrame"
## Slot "data":
##   ID_0 ISO   NAME_0 ID_1 NAME_1 ID_2    NAME_2    TYPE_2    ENGTYPE_2 NL_NAME_2
## 1   53 COL Colombia    6 Boyacá  199   Almeida Municipio Municipality      <NA>
## 2   53 COL Colombia    6 Boyacá  200 Aquitania Municipio Municipality      <NA>
## 3   53 COL Colombia    6 Boyacá  201  Arcabuco Municipio Municipality      <NA>
## 4   53 COL Colombia    6 Boyacá  202     Belén      <NA>         <NA>      <NA>
## 5   53 COL Colombia    6 Boyacá  203    Berbeo Municipio Municipality      <NA>
## 6   53 COL Colombia    6 Boyacá  204 Beteitiva Municipio Municipality      <NA>
##   VARNAME_2            area             km2
## 1      <NA>  47973340 [m^2]  479.7334 [m^2]
## 2      <NA> 903724548 [m^2] 9037.2455 [m^2]
## 3      <NA> 163213422 [m^2] 1632.1342 [m^2]
## 4      <NA> 154652234 [m^2] 1546.5223 [m^2]
## 5      <NA>  62846073 [m^2]  628.4607 [m^2]
## 6      <NA> 128318216 [m^2] 1283.1822 [m^2]
## 
## Slot "polygons":
## [[1]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] -73.411308   4.922605
## 
## Slot "area":
## [1] 0.00391144
## 
## Slot "hole":
## [1] FALSE
## 
## Slot "ringDir":
## [1] 1
## 
## Slot "coords":
##           [,1]     [,2]
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## Slot "area":
## [1] 0.00391144
## 
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## An object of class "Polygons"
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## An object of class "Polygon"
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## [1] 0.07373845
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## Slot "hole":
## [1] FALSE
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## Slot "ringDir":
## [1] 1
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## 
## 
## 
## Slot "plotOrder":
## [1] 1
## 
## Slot "labpt":
## [1] -72.90192   5.40341
## 
## Slot "ID":
## [1] "2"
## 
## Slot "area":
## [1] 0.07373845
## 
## 
## [[3]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] -73.467818   5.723896
## 
## Slot "area":
## [1] 0.01332427
## 
## Slot "hole":
## [1] FALSE
## 
## Slot "ringDir":
## [1] 1
## 
## Slot "coords":
##            [,1]     [,2]
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## Slot "area":
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## [1] 1
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## [1] 1
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## [1] -72.924811   5.976306
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## An object of class "Polygons"
## Slot "Polygons":
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## An object of class "Polygon"
## Slot "labpt":
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## Slot "area":
## [1] 0.0051261
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## [1] FALSE
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## Slot "ringDir":
## [1] 1
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## Slot "coords":
##           [,1]     [,2]
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## An object of class "Polygons"
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## An object of class "Polygon"
## Slot "labpt":
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## Slot "area":
## [1] 0.01047862
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## [1] FALSE
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## Slot "ringDir":
## [1] 1
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##           [,1]     [,2]
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## [1] 1
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## Slot "labpt":
## [1] -72.870782   5.894272
## 
## Slot "ID":
## [1] "6"
## 
## Slot "area":
## [1] 0.01047862
## 
## 
## 
## Slot "plotOrder":
## [1] 2 3 4 6 5 1
## 
## Slot "bbox":
##          min      max
## x -73.541603 -72.7061
## y   4.871301   6.0585
## 
## Slot "proj4string":
## CRS arguments: +proj=longlat +datum=WGS84 +no_defs

ahora preparemos el plot

bins <- c(0, 50, 100, 200, 300, 500, 1000, 2000, Inf)
pal <- colorBin("OrRd", domain = boy_mun$km2, bins = bins)

labels <- mun_boyaca$NAME_2

labels
##   [1] "Almeida"               "Aquitania"             "Arcabuco"             
##   [4] "Belén"                 "Berbeo"                "Beteitiva"            
##   [7] "Boavita"               "Boyacá"                "Briceño"              
##  [10] "Buenavista"            "Busbanza"              "Cómbita"              
##  [13] "Caldas"                "Campohermoso"          "Cerinza"              
##  [16] "Chíquiza"              "Chinavita"             "Chiquinquirá"         
##  [19] "Chiscas"               "Chita"                 "Chitaraque"           
##  [22] "Chivatá"               "Chivor"                "Ciénaga"              
##  [25] "Coper"                 "Corrales"              "Covarachía"           
##  [28] "Cuítiva"               "Cubará"                "Cucaita"              
##  [31] "Duitama"               "El Cocuy"              "El Espino"            
##  [34] "Firavitoba"            "Floresta"              "Gámeza"               
##  [37] "Gachantivá"            "Garagoa"               "Guacamayas"           
##  [40] "Guateque"              "Guayatá"               "Guicán"               
##  [43] "Izá"                   "Jenesano"              "Jericó"               
##  [46] "La Capilla"            "La Uvita"              "La Victoria"          
##  [49] "Labranzagrande"        "Macanal"               "Maripí"               
##  [52] "Miraflores"            "Mongua"                "Monguí"               
##  [55] "Moniquirá"             "Motavita"              "Muzo"                 
##  [58] "Nobsa"                 "Nuevo Colón"           "Oicatá"               
##  [61] "Otanche"               "Páez"                  "Pachavita"            
##  [64] "Paipa"                 "Pajarito"              "Panqueba"             
##  [67] "Pauna"                 "Paya"                  "Paz de Río"           
##  [70] "Pesca"                 "Pisba"                 "Puerto Boyacá"        
##  [73] "Quípama"               "Ráquira"               "Ramiriquí"            
##  [76] "Rondón"                "Sáchica"               "Saboyá"               
##  [79] "Samacá"                "San Eduardo"           "San José de Pare"     
##  [82] "San Luis de Gaceno"    "San Mateo"             "San Miguel de Sema"   
##  [85] "San Pablo de Borbur"   "Santa María"           "Santa Rosa de Viterbo"
##  [88] "Santa Sofía"           "Santana"               "Sativanorte"          
##  [91] "Sativasur"             "Siachoque"             "Soatá"                
##  [94] "Socha"                 "Socotá"                "Sogamoso"             
##  [97] "Somondoco"             "Soracá"                "Sora"                 
## [100] "Sotaquirá"             "Susacón"               "Sutamarchán"          
## [103] "Sutatenza"             "Tasco"                 "Tenza"                
## [106] "Tibaná"                "Tibasosa"              "Tinjacá"              
## [109] "Tipacoque"             "Toca"                  "Toguí"                
## [112] "Topagá"                "Tota"                  "Tunja"                
## [115] "Tunungua"              "Turmequé"              "Tuta"                 
## [118] "Tutazá"                "Umbita"                "Ventaquemada"         
## [121] "Villa de Leyva"        "Viracachá"             "Zetaquirá"

Ha llegado el momento de crear el plot

m <- leaflet(boy_mun) %>% 
  
  addProviderTiles(providers$CartoDB.PositronNoLabels) %>%
   
  setView(-77, 2, 7.4)  %>% addPolygons(
  fillColor = ~pal(km2),
  weight = 2,
  opacity = 1,
  color = "black",
  dashArray = "3",
  fillOpacity = 0.7,
  highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label = labels) %>%
  addLegend(pal = pal, values = ~km2, opacity = 0.7, title = NULL,
    position = "bottomright")
m
NA
## [1] NA

Puede probar diferentes proveedores para mejorar su mapa. Aproveche la función de completar con pestañas para seleccionar el mapa base preferido simplemente desplazándose por la lista de proveedores con addProviderTiles()

cap_boyaca <- munic %>% filter(NAME_2 == "Tuta")
cap_boyaca$area <- st_area(cap_boyaca)
cap_boyaca$km2 <-cap_boyaca$area/(1000000)
cap_boyaca$km2
## 149.8836 [m^2]

Podemos visualizar el departamento de caqueta en el mundo

boy_cap <- as(cap_boyaca, 'Spatial')
leaflet(boy_mun) %>%
  addProviderTiles(providers$Esri.WorldImagery, options = providerTileOptions(opacity = 0.99)) %>%
  addPolygons(data = boy_mun, popup = boy_mun$NAME_2,
              stroke = TRUE, fillOpacity = 0.25, smoothFactor = 0.25)
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 8.1 x64 (build 9600)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Colombia.1252  LC_CTYPE=Spanish_Colombia.1252   
## [3] LC_MONETARY=Spanish_Colombia.1252 LC_NUMERIC=C                     
## [5] LC_TIME=Spanish_Colombia.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] lwgeom_0.2-5    leaflet_2.0.3   scales_1.1.1    sf_0.9-6       
##  [5] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.0     purrr_0.3.4    
##  [9] readr_1.3.1     tidyr_1.1.0     tibble_3.0.2    ggplot2_3.3.2  
## [13] tidyverse_1.3.0
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.5              lattice_0.20-41         lubridate_1.7.9        
##  [4] leaflet.providers_1.9.0 class_7.3-17            assertthat_0.2.1       
##  [7] digest_0.6.25           utf8_1.1.4              R6_2.4.1               
## [10] cellranger_1.1.0        backports_1.1.7         reprex_0.3.0           
## [13] evaluate_0.14           e1071_1.7-3             httr_1.4.1             
## [16] pillar_1.4.6            rlang_0.4.6             readxl_1.3.1           
## [19] rstudioapi_0.11         blob_1.2.1              rmarkdown_2.3          
## [22] htmlwidgets_1.5.1       munsell_0.5.0           broom_0.7.0            
## [25] compiler_4.0.2          modelr_0.1.8            xfun_0.15              
## [28] pkgconfig_2.0.3         htmltools_0.5.0         tidyselect_1.1.0       
## [31] fansi_0.4.1             crayon_1.3.4            dbplyr_1.4.4           
## [34] withr_2.2.0             grid_4.0.2              jsonlite_1.7.0         
## [37] gtable_0.3.0            lifecycle_0.2.0         DBI_1.1.0              
## [40] magrittr_1.5            units_0.6-7             KernSmooth_2.23-17     
## [43] cli_2.0.2               stringi_1.4.6           farver_2.0.3           
## [46] fs_1.4.2                sp_1.4-2                xml2_1.3.2             
## [49] ellipsis_0.3.1          generics_0.0.2          vctrs_0.3.1            
## [52] RColorBrewer_1.1-2      tools_4.0.2             glue_1.4.1             
## [55] hms_0.5.3               crosstalk_1.1.0.1       yaml_2.2.1             
## [58] colorspace_1.4-1        classInt_0.4-3          rvest_0.3.5            
## [61] knitr_1.29              haven_2.3.1