R Notebook
Laura Hernández Sánchez
10 de Septiembre
- Introducción
El siguiente cuaderno tiene como objetivo ilustrar funciones geospaciales básicas de R, las cuales se encuentran en las librerías de simple features (sf) y tidyverse.
Luego de tener las librerías descargadas es necesario llamarlas de la siguiente forma:
library(sf)## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1library(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()- Leer vectores
Inicialmente se debe ubicar el directorio de trabajo y cambiarlo de ser necesario. A continuación se debe descargar un archivo de tipo shapefile de DIVA-GIS del departamento o lugar de interés, en este caso será el departamento de Cesár. Posteriormente, se debe subir el archivo shapefile a R usando el código:
getwd()## [1] "C:/Users/JUAN PABLO/Downloads"setwd("C:/Users/JUAN PABLO/Documents/geo maps")getwd()## [1] "C:/Users/JUAN PABLO/Downloads"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)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]]De acuerdo con lo anterior, los datos utilizan el Sistema Geodésico Mundial de 1984 (WGS 84).
- Usar comando ggplot para visualizar datos geoespaciales:
Para visualizar los datos que se acaban de subir a R se emplea la función:
ggplot() + geom_sf(data = Deptos, fill= "mediumorchid1")
Es posible usar cualquier sistema de referencia además del WGS 84 de la siguiente forma:
ggplot() + geom_sf(data = Deptos, fill= "mediumpurple") + coord_sf(crs=st_crs(3978))
Para transformar al sistema de coordenadas UTM 18 N, se emplea el código 32168 de la siguiente forma:
Deptos_UTM <- st_transform(Deptos, crs = st_crs(32618))Para visualizar cómo quedó la transformación es necesario llamar los datos
Deptos_UTMSe grafica los datos luego de ser transformados, tal como se realizó previamente
ggplot() + geom_sf(data = Deptos_UTM, fill= "plum")
4. Filtrar datos geoespaciales de acuerdo a sus atributos
En caso de estar interesado en un solo departamento en específico, es necesario filtrar los datos para visualizar solo aquel departamento
Cesar <- Deptos %>% filter (NAME_1 == "Cesar")Visualizar el departamento filtrado
ggplot() + geom_sf(data = Cesar, fill= "violet")
El mapa anterior muestra únicamente el departamento de Cesar. En caso de tener interes en los municipios de este departamento es posible visualizarlos subiendo previamente el segundo archivo descargado
Munic <- read_sf("C:/Users/JUAN PABLO/Documents/geo maps/COL_adm2.shp")Mun_Cesar <- Munic %>% filter(NAME_1 == "Cesar")ggplot() + geom_sf(data = Mun_Cesar, fill= "orchid")
Mun_CesarCesar_points <- st_centroid(Mun_Cesar)## Warning in st_centroid.sf(Mun_Cesar): 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 dataCesar_points <- cbind(Mun_Cesar, st_coordinates(st_centroid(Mun_Cesar$geometry)))## Warning in st_centroid.sfc(Mun_Cesar$geometry): st_centroid does not give
## correct centroids for longitude/latitude dataggplot(Cesar) +
geom_sf() +
geom_sf(data = Cesar_points, fill = "pink") +
geom_text(data = Cesar_points, aes( x=X, y=Y, label = ID_2), size = 2) 
library(scales)##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factorlibrary(tidyverse)ggplot(Cesar) +
geom_sf(data=Cesar_points, aes(x=X, y=Y, fill =
ID_2), color = "black", size = 0.25) +
geom_text(data = Cesar_points, aes(x=X, y=Y, label= ID_2), size = 2) +
theme(aspect.ratio=1)+
scale_fill_distiller(name="ID_2", palette= "RdPu", breaks = pretty_breaks(n = 5)) +
labs(title= "Cesar")## Warning: Ignoring unknown aesthetics: x, y
Para guardar la imagen anterior como formato PDF o PNG se usa los siguientes comandos, respectivamente
ggsave("Cesar_municipios.pdf")## Saving 7 x 5 in imageggsave("Cesar_Municipios.png", width = 6, height = 6, dpi = "screen")- Usar la librería leaflet para visualizar datos
Luego de haber instalado la librería es necesario llamarla
library(leaflet)Para poder usar la librería es necesario convertir los datos de objetos simples a puntos espaciales
Ces_points <- as(Cesar_points, "Spatial")Para ver qué hay en el objeto acabo de crear:
head(Ces_points)## An object of class "SpatialPolygonsDataFrame"
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## ID_0 ISO NAME_0 ID_1 NAME_1 ID_2 NAME_2 TYPE_2 ENGTYPE_2
## 1 53 COL Colombia 12 Cesar 445 Aguachica Municipio Municipality
## 2 53 COL Colombia 12 Cesar 446 Agustín Codazzi Municipio Municipality
## 3 53 COL Colombia 12 Cesar 447 Astrea Municipio Municipality
## 4 53 COL Colombia 12 Cesar 448 Becerril Municipio Municipality
## 5 53 COL Colombia 12 Cesar 449 Bosconia Municipio Municipality
## 6 53 COL Colombia 12 Cesar 450 Chimichagua Municipio Municipality
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## CRS arguments: +proj=longlat +datum=WGS84 +no_defsPara visualizar el área de cada munipio es necesario instalar y llamar la librería “lwgeom”
library(lwgeom)## Linking to liblwgeom 3.0.0beta1 r16016, GEOS 3.8.0, PROJ 6.3.1Para calcular el área en metros cuadrados
Mun_Cesar$area <- st_geod_area(Mun_Cesar)Para convertir lo anterior a kilómetros cuadrados
Mun_Cesar$km2 <- Mun_Cesar$area/(1000000)Observar cómo quedó
Mun_Cesar$km2## Units: [m^2]
## [1] 915.64403 1645.91395 572.30780 1397.17529 593.38209 1476.35547
## [7] 1151.51736 806.73777 996.39927 882.51184 391.02047 83.09969
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## [19] 349.00526 908.24133 218.05999 784.71916 678.21668 4970.08120Nuevamente es necesario convertir los datos a polígonos espaciales
Ces_mun <- as(Mun_Cesar, "Spatial")head(Ces_mun)## An object of class "SpatialPolygonsDataFrame"
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## ID_0 ISO NAME_0 ID_1 NAME_1 ID_2 NAME_2 TYPE_2 ENGTYPE_2
## 1 53 COL Colombia 12 Cesar 445 Aguachica Municipio Municipality
## 2 53 COL Colombia 12 Cesar 446 Agustín Codazzi Municipio Municipality
## 3 53 COL Colombia 12 Cesar 447 Astrea Municipio Municipality
## 4 53 COL Colombia 12 Cesar 448 Becerril Municipio Municipality
## 5 53 COL Colombia 12 Cesar 449 Bosconia Municipio Municipality
## 6 53 COL Colombia 12 Cesar 450 Chimichagua Municipio Municipality
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## CRS arguments: +proj=longlat +datum=WGS84 +no_defsPara graficar los datos
bins <- c(0, 50, 100, 200, 300, 500, 1000, 2000, Inf)pal <-colorBin("PuRd", domain = Ces_mun$km2, bins=bins)labels <- Mun_Cesar$NAME_2labels## [1] "Aguachica" "Agustín Codazzi" "Astrea"
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## [10] "El Paso" "Gamarra" "González"
## [13] "La Gloria" "La Jagua de Ibirico" "La Paz"
## [16] "Manaure" "Pailitas" "Pelaya"
## [19] "Río de Oro" "San Alberto" "San Diego"
## [22] "San Martín" "Tamalameque" "Valledupar"library(leaflet)m <- leaflet(Ces_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 = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels) %>%
addLegend(pal = pal, values = ~km2, opacity = 0.7, title = NULL,
position = "bottomright")mleaflet() %>%
addProviderTiles(providers$Esri.WorldImagery, options = providerTileOptions(opacity = 0.99)) %>%
addPolygons(data = Ces_mun, popup = Ces_mun$NAME_2,
stroke = TRUE, fillOpacity = 0.25, smoothFactor = 0.25
)leaflet() %>%
addProviderTiles(providers$Esri.WorldStreetMap, options = providerTileOptions(opacity = 0.99)) %>%
addPolygons(data = Ces_mun, popup = Ces_mun$NAME_2,
stroke = TRUE, fillOpacity = 0.25, smoothFactor = 0.25
)leaflet() %>%
addProviderTiles(providers$Esri.NatGeoWorldMap, options = providerTileOptions(opacity = 0.99)) %>%
addPolygons(data = Ces_mun, popup = Ces_mun$NAME_2,
stroke = TRUE, fillOpacity = 0.25, smoothFactor = 0.25
)leaflet() %>%
addProviderTiles(providers$Esri.WorldPhysical, options = providerTileOptions(opacity = 0.99)) %>%
addPolygons(data = Ces_mun, popup = Ces_mun$NAME_2,
stroke = TRUE, fillOpacity = 0.25, smoothFactor = 0.25
)