R Notebook
Nombre: Aparicio Aponte Erika
Fecha: 25 de marzo del 2020
Aprende a leer, filtrar y visualizar datos vectoriales geoespaciales en R
1. Introduccción
Este es un R Markdown Notebook que ilustra las funciones básicas proporcionadas por las funciones simples (sf) y las bibliotecas tidyverse, por tanto, el primer pasa sera instalar las bibliotecas necesarias
#install.packages(c("tidyverse", "sf"))Ahora es necesario cargar las dos librerias:
library(tidyverse)## -- Attaching packages --------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.0 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.4
## v tidyr 1.0.2 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 3.6.1, GDAL 2.2.3, PROJ 4.9.32. Lectura de datos de vectores
ya se puede leer un archivo de formas que representa a los departamentos colombiano, el cual se ha descargado previamente de DIVA-GIS.
deptos <- read_sf("C:/Users/Familia/Desktop/Curso/Datos/COL_adm/COL_adm1.shp")Para saber el contenido del objeto deptos
head(deptos)Por otro lado, si queremos saber cuál es el sistema de referencia de coordenadas de los datos vectoriales almacenados en el objeto deptos, se puede usar la función st_crs:
st_crs(deptos)## Coordinate Reference System:
## EPSG: 4326
## proj4string: "+proj=longlat +datum=WGS84 +no_defs"3. Usar ggplot para visualizar datos geoespaciales:
Primero es necesario comprender los datos, para ello, es trazar los datos. Sepuede usar las funcionalidades de ggplot:
ggplot() + geom_sf(data = deptos) 
##### Se puede utilizar cualquier sistema de referencia de coordenadas para trazar los datos. Sin embargo, no es correcto utilizar un sistema de referencia de coordenadas que se haya definido explícitamente para otro país o región. #### Por ejemplo, el CRS 3978 se usa en Canadá
ggplot() + geom_sf(data = deptos) + coord_sf(crs=st_crs(3978))
No obstante, 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:
deptos_utm <- st_transform(deptos, crs = st_crs(32618))deptos_utmggplot() + geom_sf(data = deptos_utm)
### 4. Filtrar datos geoespaciales basados en atributos ##### En este caso, se va a filtrar los datos del departamentode Meta, el cual es el de interes
meta <- deptos %>% filter(NAME_1 == "Meta")Procedemos a trazar el nuevo objeto
ggplot() + geom_sf(data = meta) 
Ahora se repetira los pasos anteriores para cargar los municipios colombianos y filtrar los del meta
munic <- read_sf("C:/Users/Familia/Desktop/Curso/Datos/COL_adm/COL_adm2.shp")
mun_meta <- munic %>% filter(NAME_1 == "Meta")
ggplot() + geom_sf(data = mun_meta) 
mun_metameta_points<- st_centroid(mun_meta)## Warning in st_centroid.sf(mun_meta): 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 datameta_points <- cbind(mun_meta, st_coordinates(st_centroid(mun_meta$geometry)))## Warning in st_centroid.sfc(mun_meta$geometry): st_centroid does not give correct
## centroids for longitude/latitude dataSe puede producir una mejor trama utilizando el siguiente fragmento:
ggplot(meta) +
geom_sf() +
geom_sf(data = meta_points, fill = "antiquewhite") +
geom_text(data = meta_points, aes(x=X, y=Y,label = ID_2), size = 2) +
coord_sf(xlim = c(-76, -70.5), ylim = c(1.5, 5), 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_factorggplot(meta) +
geom_sf(data=meta_points, aes(x=X, y=Y, fill =
ID_2), color = "black", size = 0.25) +
geom_text(data = meta_points, aes(x=X, y=Y,label = ID_2), size = 2) +
theme(aspect.ratio=1)+
scale_fill_distiller(name="ID_2", palette = "YlGn", breaks = pretty_breaks(n = 5))+
labs(title="Otro mapa de Meta")## Warning: Ignoring unknown aesthetics: x, y
Cabe recalcar que esta visualización no es un mapa real, Pero la salida se puede guardar como un pdf o como un png.
ggsave("meta_municipios.pdf")## Saving 7 x 5 in imageggsave("map_meta.png", width = 6, height = 6, dpi = "screen")5. Usar el folleto para visualizar datos
Primero, se instala “leaflet” que es la biblioteca requerida:
#install.packages("leaflet")Ahora se llama a la libreria
library(leaflet)## This version of Shiny is designed to work with 'htmlwidgets' >= 1.5.
## Please upgrade via install.packages('htmlwidgets').Sin embargo, para usar la biblioteca se necesita convertir de características simples a puntos espaciales.
ant_points <- as(meta_points, 'Spatial')Descomente el siguiente comando para ver qué hay dentro del objeto:
#head(ant_points)head(ant_points)## An object of class "SpatialPolygonsDataFrame"
## Slot "data":
## ID_0 ISO NAME_0 ID_1 NAME_1 ID_2 NAME_2 TYPE_2 ENGTYPE_2
## 1 53 COL Colombia 20 Meta 683 Acacías Municipio Municipality
## 2 53 COL Colombia 20 Meta 684 Barranca de Upía Municipio Municipality
## 3 53 COL Colombia 20 Meta 685 Cabuyaro Municipio Municipality
## 4 53 COL Colombia 20 Meta 686 Castilla la Nueva Municipio Municipality
## 5 53 COL Colombia 20 Meta 687 Cumaral Municipio Municipality
## 6 53 COL Colombia 20 Meta 688 El Calvario Municipio Municipality
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##
##
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## [1] 1
##
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## [1] -73.744336 4.354279
##
## Slot "ID":
## [1] "6"
##
## Slot "area":
## [1] 0.01611318
##
##
##
## Slot "plotOrder":
## [1] 1 3 4 2 5 6
##
## Slot "bbox":
## min max
## x -74.090500 -72.760803
## y 3.681109 4.666399
##
## Slot "proj4string":
## CRS arguments: +proj=longlat +datum=WGS84 +no_defsPara obtener el area de los municipios
#install.packages("lwgeom")Cargar la libreria
library(lwgeom)## Linking to liblwgeom 3.0.0beta1 r16016, GEOS 3.6.1, PROJ 4.9.3Ahora, para calcular el área de cada municipio (en metros cuadrados):
mun_meta$area <- st_area(mun_meta)Posteriormente, se crea un nuevo campo para almacenar el área en kilómetros cuadrados:
mun_meta$km2 <- mun_meta$area/(1000000)Despues se Verifica la salida:
mun_meta$km2## Units: [m^2]
## [1] 975.4030 424.6030 808.6122 561.5467 379.1041 197.7810
## [7] 719.5302 105.7460 754.1116 229.6312 764.3242 11025.7283
## [13] 6298.7609 969.2610 12455.3204 2119.5518 918.9151 16788.4326
## [19] 5576.2151 2084.8685 4061.3990 566.5750 1437.4442 834.0937
## [25] 202.6045 1686.2457 6511.0136 1369.7743 4498.9691Nuevamente, se necesita una conversión de características simples a polígonos espaciales:
ant_mun <- as(mun_meta, 'Spatial')Se Descomenta el siguiente código para ver qué hay dentro del objeto espacial:
#head(ant_mun)head(ant_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
## 1 53 COL Colombia 20 Meta 683 Acacías Municipio Municipality
## 2 53 COL Colombia 20 Meta 684 Barranca de Upía Municipio Municipality
## 3 53 COL Colombia 20 Meta 685 Cabuyaro Municipio Municipality
## 4 53 COL Colombia 20 Meta 686 Castilla la Nueva Municipio Municipality
## 5 53 COL Colombia 20 Meta 687 Cumaral Municipio Municipality
## 6 53 COL Colombia 20 Meta 688 El Calvario Municipio Municipality
## NL_NAME_2 VARNAME_2 area km2
## 1 <NA> <NA> 975403018 [m^2] 975.4030 [m^2]
## 2 <NA> <NA> 424602991 [m^2] 424.6030 [m^2]
## 3 <NA> <NA> 808612218 [m^2] 808.6122 [m^2]
## 4 <NA> <NA> 561546739 [m^2] 561.5467 [m^2]
## 5 <NA> <NA> 379104067 [m^2] 379.1041 [m^2]
## 6 <NA> <NA> 197780984 [m^2] 197.7810 [m^2]
##
## Slot "polygons":
## [[1]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] -73.780200 4.009281
##
## Slot "area":
## [1] 0.07943183
##
## Slot "hole":
## [1] FALSE
##
## Slot "ringDir":
## [1] 1
##
## Slot "coords":
## [,1] [,2]
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##
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## [1] "1"
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## Slot "area":
## [1] 0.07943183
##
##
## [[2]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] -72.993668 4.474491
##
## Slot "area":
## [1] 0.03459735
##
## Slot "hole":
## [1] FALSE
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## Slot "ringDir":
## [1] 1
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## Slot "coords":
## [,1] [,2]
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## [2,] -72.8542 4.343099
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## [24,] -72.9332 4.475200
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## [27,] -72.8773 4.418300
## [28,] -72.8715 4.409600
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## [30,] -72.8698 4.397500
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## [38,] -72.8213 4.356799
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##
##
##
## Slot "plotOrder":
## [1] 1
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## Slot "labpt":
## [1] -72.993668 4.474491
##
## Slot "ID":
## [1] "2"
##
## Slot "area":
## [1] 0.03459735
##
##
## [[3]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] -72.980284 4.285326
##
## Slot "area":
## [1] 0.06587133
##
## Slot "hole":
## [1] FALSE
##
## Slot "ringDir":
## [1] 1
##
## Slot "coords":
## [,1] [,2]
## [1,] -73.0633 4.163800
## [2,] -73.0605 4.181101
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##
##
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## Slot "plotOrder":
## [1] 1
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## Slot "labpt":
## [1] -72.980284 4.285326
##
## Slot "ID":
## [1] "3"
##
## Slot "area":
## [1] 0.06587133
##
##
## [[4]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] -73.535484 3.793866
##
## Slot "area":
## [1] 0.04571812
##
## Slot "hole":
## [1] FALSE
##
## Slot "ringDir":
## [1] 1
##
## Slot "coords":
## [,1] [,2]
## [1,] -73.51823 3.862573
## [2,] -73.51450 3.857201
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##
##
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## [1] 1
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## Slot "labpt":
## [1] -73.535484 3.793866
##
## Slot "ID":
## [1] "4"
##
## Slot "area":
## [1] 0.04571812
##
##
## [[5]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] -73.322518 4.220168
##
## Slot "area":
## [1] 0.03088037
##
## Slot "hole":
## [1] FALSE
##
## Slot "ringDir":
## [1] 1
##
## Slot "coords":
## [,1] [,2]
## [1,] -73.06330 4.163800
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## [25,] -73.41620 4.284801
## [26,] -73.38800 4.278399
## [27,] -73.36260 4.270700
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## [29,] -73.29050 4.255401
## [30,] -73.25190 4.248900
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## [33,] -73.15910 4.197700
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##
##
##
## Slot "plotOrder":
## [1] 1
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## Slot "labpt":
## [1] -73.322518 4.220168
##
## Slot "ID":
## [1] "5"
##
## Slot "area":
## [1] 0.03088037
##
##
## [[6]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] -73.744336 4.354279
##
## Slot "area":
## [1] 0.01611318
##
## Slot "hole":
## [1] FALSE
##
## Slot "ringDir":
## [1] 1
##
## Slot "coords":
## [,1] [,2]
## [1,] -73.6382 4.371801
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##
##
##
## Slot "plotOrder":
## [1] 1
##
## Slot "labpt":
## [1] -73.744336 4.354279
##
## Slot "ID":
## [1] "6"
##
## Slot "area":
## [1] 0.01611318
##
##
##
## Slot "plotOrder":
## [1] 1 3 4 2 5 6
##
## Slot "bbox":
## min max
## x -74.090500 -72.760803
## y 3.681109 4.666399
##
## Slot "proj4string":
## CRS arguments: +proj=longlat +datum=WGS84 +no_defsAhora, se prepara la trama:
bins <- c(0, 50, 100, 200, 300, 500, 1000, 2000, Inf)
pal <- colorBin("YlOrRd", domain = ant_mun$km2, bins = bins)
labels <- mun_meta$NAME_2
labels## [1] "Acacías" "Barranca de Upía" "Cabuyaro"
## [4] "Castilla la Nueva" "Cumaral" "El Calvario"
## [7] "El Castillo" "El Dorado" "Fuente de Oro"
## [10] "Granada" "Guamal" "La Macarena"
## [13] "La Uribe" "Lejanías" "Mapiripán"
## [16] "Mesetas" "Puerto Concordia" "Puerto Gaitán"
## [19] "Puerto López" "Puerto Lleras" "Puerto Rico"
## [22] "Restrepo" "San Carlos de Guaroa" "San Juan de Arama"
## [25] "San Juanito" "San Luis de Cubarral" "San Martín"
## [28] "Villavicencio" "Vista Hermosa"Entonces, se crea el plot:
m <- leaflet(ant_mun) %>%
setView(-72.7, 3.5, 6.9) %>% addPolygons(
fillColor = ~pal(km2),
weight = 2,
opacity = 1,
color = "white",
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")Visualización del plot:
mOtra forma que puede utilizar para trazar, la cual Puede ser más simple es:
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
)Por último, si se quiere visualizar la capital del departamento puede descomente el siguiente fragmento y completar el código
#leaflet() %>%
# addProviderTiles(providers$leaflet() %>%
addProviderTiles(providers$Esri.WorldImagery, options= providerTileOptions(opacity = 0.99)) %>%
addPolygons(data = ant_mun, popup= ant_mun$NAME_2,
stroke = TRUE, fillOpacity = 0.25, smoothFactor = 0.25)%>% setView(lng = -73.6226403, lat = 4.1220002, zoom = 14.5) %>%
addMarkers(lng = -73.6226403, lat = 4.1220002, popup = "Villavicencio")