This is an R Markdown Notebook.It illustrates how to, make a map with polygons (representing departments in Colombia). The reference data set is a shapefile provided by DANE.
let´s start the loading libraries:
#install.packages(c("rgeos","sf"))
library(rgeos)## Loading required package: sp## rgeos version: 0.5-5, (SVN revision 640)
## GEOS runtime version: 3.8.0-CAPI-1.13.1
## Linking to sp version: 1.4-5
## Polygon checking: TRUElibrary(sf)## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1library(leaflet)let´s read the ciudades dataset, but this time as a table :
cities <- read.table(file="./ciudades.txt", header = FALSE, sep = ";")head(cities)names(cities)<- c("id", "country", "city", "latitude", "longitude", "altitude")Now, we convert the dataframe into a spatial object:
##el orden usual de las coordenadas (i) longitud:(ii) latitud (iii) altitud
m<- st_as_sf(cities, coords = c(5,4,6))m(coords <- st_coordinates(m))## 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 113lat = coords[, 2]
long = coords[, 1]
alt = coords[, 3]deptos <- sf::read_sf("./Municipios/MGN_DPTO_POLITICO.shp")head(deptos)deptos$DPTO_CNMBR## [1] "CAQUETÁ"
## [2] "CAUCA"
## [3] "PUTUMAYO"
## [4] "VALLE DEL CAUCA"
## [5] "GUAINÍA"
## [6] "VICHADA"
## [7] "CASANARE"
## [8] "AMAZONAS"
## [9] "VAUPÉS"
## [10] "GUAVIARE"
## [11] "CALDAS"
## [12] "QUINDIO"
## [13] "RISARALDA"
## [14] "ANTIOQUIA"
## [15] "CHOCÓ"
## [16] "NARIÑO"
## [17] "CÓRDOBA"
## [18] "BOLÍVAR"
## [19] "CESAR"
## [20] "LA GUAJIRA"
## [21] "MAGDALENA"
## [22] "SUCRE"
## [23] "ARCHIPIÉLAGO DE SAN ANDRÉS, PROVIDENCIA Y SANTA CATALINA"
## [24] "ARAUCA"
## [25] "BOYACÁ"
## [26] "CUNDINAMARCA"
## [27] "NORTE DE SANTANDER"
## [28] "BOGOTÁ, D.C."
## [29] "META"
## [30] "HUILA"
## [31] "SANTANDER"
## [32] "TOLIMA"
## [33] "ATLÁNTICO"deptos2 <- deptos %>% st_transform(3116)deptos3 <- sf::st_simplify(deptos2, preserveTopology = TRUE, dTolerance = 1000)object.size(deptos)## 16290112 bytesobject.size(deptos3)## 179568 bytesdeptos4 <- deptos3 %>% st_transform(4326)mapa <- leaflet(deptos4)mapa <- addTiles(mapa)labels<-sprintf(
"<strong>%s</strong><br/>%g unknow units</sup>",
deptos4$DPTO_CNMBR, deptos4$SHAPE_AREA
) %>% lapply(htmltools::HTML)mapa <- addPolygons(mapa, color = "#444444", weight = 1, smoothFactor = 0.5, opacity = 1.0, fillOpacity = 0.5,
fillColor = ~colorQuantile("yLOrRd", SHAPE_AREA)(SHAPE_AREA), highlightOptions = highlightOptions(color = "red", weight = 2, bringToFront = TRUE), label = labels, labelOptions = labelOptions( style = list("font-weight"="normal", padding = "3px 8px"), textsize = "15px", direction = "auto"))mapa <- addMarkers(mapa, lng=long, lat=lat, popup= m$city)mapa.