primer cuaderno
luis alberto castillo
1. Introduccion
Hola yo soy Luis Alberto y este es mi primer R notebook donde manejare funciones basicas como las bibliotecas “simple features” (sf) y “tidyverse” (tidyverse). Tiene como proposito responder a la actividad de aprendizaje de las funcionalidades geoespaciales R en la clase de geomática básica.
para empezar toca instalar y llamar las siguientes librerias.
library(tidyverse)## -- Attaching packages ------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.2
## v tidyr 1.1.2 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 3.8.0, GDAL 3.0.4, PROJ 6.3.1Funciones para buscar archivos y saber la direccion del archivo selecionado.
getwd()## [1] "D:/Users/Janus/Documents/Geomatica basica"list.files()## [1] "18_CAQUETA" "Caqueta-R.Rmd" "Caqueta R.nb.html"
## [4] "Caqueta R.Rmd" "caqueta_municipios.pdf" "COL_adm"
## [7] "COL_adm.zip" "COL_adm0.cpg" "COL_adm0.csv"
## [10] "COL_adm0.dbf" "COL_adm0.prj" "COL_adm0.shp"
## [13] "COL_adm0.shx" "COL_adm1.cpg" "COL_adm1.csv"
## [16] "COL_adm1.dbf" "COL_adm1.prj" "COL_adm1.shp"
## [19] "COL_adm1.shx" "COL_adm2.cpg" "COL_adm2.csv"
## [22] "COL_adm2.dbf" "COL_adm2.prj" "COL_adm2.shp"
## [25] "COL_adm2.shx" "license.txt" "map_antioquia.png"list.files("Geomatica basica")## character(0)#2. En este paso creamos un vector, en este caso nuestro vector es la direccion del arvico escojido.
deptos<- read_sf("D:/Users/Janus/Documents/Geomatica basica/COL_adm1.shp")Esta funcion de “head” la utilizamos para saber el contenido del vector
head(deptos)La funcion “st_crs()” la utilizamos para conocer cual es el sistema de referencia de coordenadas de los datos vectoriales almacenados en el vector “deptos”
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. Visualizar datos geoespaciales usando 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_utmggplot() + geom_sf(data = deptos_utm)
#4. Filtrar los datos geoespaciales basados en atributos como solo nos interesa el departamento de caqueta
caqueta <- deptos %>% filter(NAME_1 == "Caquetá")trazamos el nuevo vector
ggplot() + geom_sf(data = caqueta)
Vamos a cargar los municipios colombianos y filtrar los del departamento de cacqueta
munic <- read_sf("D:/Users/Janus/Documents/Geomatica basica/COL_adm2.shp")
mun_caqueta <- munic %>% filter(NAME_1 == "Caquetá")
ggplot() + geom_sf(data = mun_caqueta)
mun_caquetaVamos a asignar un centroide acada poligono que representan a los municipios de Caqueta para poder ponerles una etiqueta de ID a los municipios.
caqueta_points<- st_centroid(mun_caqueta)## Warning in st_centroid.sf(mun_caqueta): 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 datacaqueta_points <- cbind(mun_caqueta,st_coordinates(st_centroid(mun_caqueta$geometry)))## Warning in st_centroid.sfc(mun_caqueta$geometry): st_centroid does not give
## correct centroids for longitude/latitude dataggplot(caqueta) +
geom_sf(data=deptos) +
geom_sf(data = caqueta_points, fill = "antiquewhite",) +
geom_text(data = caqueta_points, aes(x=X, y=Y,label = ID_2), size = 2,) +
coord_sf(xlim = c(-76.6, -70), ylim = c(-1, 3), 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(caqueta) +
geom_sf(data=caqueta_points, aes(x=X, y=Y, fill =ID_2), color = "black", size = 0.25) + geom_text(data = caqueta_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 Caqueta")## Warning: Ignoring unknown aesthetics: x, y
Podemos guadar nuestro proceso hasta el momento en PDF
ggsave("caqueta_municipios.pdf")## Saving 7 x 5 in imageggsave("map_antioquia.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
caq_ponits <- as(caqueta_points, "Spatial")Despues observamos que hay dentro del objeto
head(caq_ponits)## An object of class "SpatialPolygonsDataFrame"
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## ID_0 ISO NAME_0 ID_1 NAME_1 ID_2 NAME_2 TYPE_2
## 1 53 COL Colombia 9 Caquetá 373 Albania Municipio
## 2 53 COL Colombia 9 Caquetá 374 Belén de los Andaquies Municipio
## 3 53 COL Colombia 9 Caquetá 375 Cartagena del Chairá Municipio
## 4 53 COL Colombia 9 Caquetá 376 Curillo Municipio
## 5 53 COL Colombia 9 Caquetá 377 El Doncello Municipio
## 6 53 COL Colombia 9 Caquetá 378 El Paujíl Municipio
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## CRS arguments: +proj=longlat +datum=WGS84 +no_defsPara 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 6.3.1mun_caqueta$area <- st_area(mun_caqueta)Creamos un campo paraalmacenar el area en kilometros cuadrados y verificamos la salida
mun_caqueta$km2 <- mun_caqueta$area/(100000)
mun_caqueta$km2## Units: [m^2]
## [1] 3235.200 9353.236 88418.345 4742.438 17352.031 7212.803
## [7] 20239.860 8915.957 11930.877 2233.164 27334.959 8359.698
## [13] 180402.569 496760.412 14926.313caq_mun <- as(mun_caqueta, 'Spatial')
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## Slot "data":
## ID_0 ISO NAME_0 ID_1 NAME_1 ID_2 NAME_2 TYPE_2
## 1 53 COL Colombia 9 Caquetá 373 Albania Municipio
## 2 53 COL Colombia 9 Caquetá 374 Belén de los Andaquies Municipio
## 3 53 COL Colombia 9 Caquetá 375 Cartagena del Chairá Municipio
## 4 53 COL Colombia 9 Caquetá 376 Curillo Municipio
## 5 53 COL Colombia 9 Caquetá 377 El Doncello Municipio
## 6 53 COL Colombia 9 Caquetá 378 El Paujíl Municipio
## ENGTYPE_2 NL_NAME_2 VARNAME_2 area km2
## 1 Municipality <NA> <NA> 323519987 [m^2] 3235.200 [m^2]
## 2 Municipality <NA> <NA> 935323567 [m^2] 9353.236 [m^2]
## 3 Municipality <NA> <NA> 8841834493 [m^2] 88418.345 [m^2]
## 4 Municipality <NA> <NA> 474243839 [m^2] 4742.438 [m^2]
## 5 Municipality <NA> <NA> 1735203063 [m^2] 17352.031 [m^2]
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## CRS arguments: +proj=longlat +datum=WGS84 +no_defsahora preparemos el plot
bins <- c(0, 50, 100, 200, 300, 500, 1000, 2000, Inf)
pal <- colorBin("OrRd", domain = caq_mun$km2, bins = bins)
labels <- mun_caqueta$NAME_2
labels## [1] "Albania" "Belén de los Andaquies" "Cartagena del Chairá"
## [4] "Curillo" "El Doncello" "El Paujíl"
## [7] "Florencia" "La Montañita" "Milán"
## [10] "Morelia" "Puerto Rico" "San José del Fragua"
## [13] "San Vicente del Caguán" "Solano" "Valparaíso"Ha llegado el momento de crear el plot
m <- leaflet(caq_mun) %>%
addProviderTiles(providers$CartoDB.PositronNoLabels) %>%
setView(-77, 2, 7.4) %>% addPolygons(
fillColor = ~pal(km2),
weight = 2,
opacity = 1,
color = "black",
dashArray = "3",
fillOpacity = 0.7,
highlight = highlightOptions(
weight = 5,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels) %>%
addLegend(pal = pal, values = ~km2, opacity = 0.7, title = NULL,
position = "bottomright")mNA## [1] NAPuede probar diferentes proveedores para mejorar su mapa. Aproveche la función de completar con pestañas para seleccionar el mapa base preferido simplemente desplazándose por la lista de proveedores con addProviderTiles()
cap_caqueta <- munic %>% filter(NAME_2 == "Florencia")
cap_caqueta$area <- st_area(cap_caqueta)
cap_caqueta$km2 <-cap_caqueta$area/(1000000)
cap_caqueta$km2## Units: [m^2]
## [1] 2023.98600 28.42409Podemos visualizar el departamento de caqueta en el mundo
caq_cap <- as(cap_caqueta, 'Spatial')leaflet(caq_mun) %>%
addProviderTiles(providers$Esri.WorldImagery, options = providerTileOptions(opacity = 0.99)) %>%
addPolygons(data = caq_mun, popup = caq_mun$NAME_2,
stroke = TRUE, fillOpacity = 0.25, smoothFactor = 0.25)