Lectura de datos espaciales
Lo primero que haremos serĂĄ instalar las librerĂas necesarias para
este cuaderno
library(sf)
## Linking to GEOS 3.14.1, GDAL 3.12.1, PROJ 9.7.1; sf_use_s2() is TRUE
library(dplyr)
##
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(ggspatial)
getwd()
## [1] "C:/Users/isabe/OneDrive/Documentos/GB2/proyecto3"
El sigiente paso es definir el directorio de trabajo
list.files("D:\\GB2\\proyecto3\\MGN2023_MPIO_POLITICO")
## [1] "MGN_ADM_MPIO_GRAFICO.cpg" "MGN_ADM_MPIO_GRAFICO.dbf"
## [3] "MGN_ADM_MPIO_GRAFICO.prj" "MGN_ADM_MPIO_GRAFICO.sbn"
## [5] "MGN_ADM_MPIO_GRAFICO.sbx" "MGN_ADM_MPIO_GRAFICO.shp"
## [7] "MGN_ADM_MPIO_GRAFICO.shp.xml" "MGN_ADM_MPIO_GRAFICO.shx"
colombia <- st_read("D:\\GB2\\proyecto3\\MGN2023_MPIO_POLITICO\\MGN_ADM_MPIO_GRAFICO.shp")
## Reading layer `MGN_ADM_MPIO_GRAFICO' from data source
## `D:\GB2\proyecto3\MGN2023_MPIO_POLITICO\MGN_ADM_MPIO_GRAFICO.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 1121 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -81.73562 ymin: -4.229406 xmax: -66.84722 ymax: 13.39473
## Geodetic CRS: MAGNA-SIRGAS
head(colombia)
Escogemos la extensiĂłn de los archivos que utiizaremos
st_write(colombia, "municipios.gpkg", driver = "GPKG", append = F)
## Deleting layer `municipios' using driver `GPKG'
## Writing layer `municipios' to data source `municipios.gpkg' using driver `GPKG'
## Writing 1121 features with 11 fields and geometry type Multi Polygon.
list.files(pattern = "gpkg")
## [1] "municipios.gpkg" "municipios__9377.gpkg"
## [3] "MunicipiosSantander.gpkg" "stder_munic.gpkg"
colombia2 <- st_read(".\\municipios__9377.gpkg")
## Reading layer `muncipios9377' from data source
## `C:\Users\isabe\OneDrive\Documentos\GB2\proyecto3\municipios__9377.gpkg'
## using driver `GPKG'
## Simple feature collection with 87 features and 12 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 4828392 ymin: 2188513 xmax: 5053451 ymax: 2455407
## Projected CRS: MAGNA-SIRGAS 2018 / Origen-Nacional
Ahora seleccionaremos objetos, en este caso, los datos del
departamento de Santander
(santander <- dplyr::filter(colombia, dpto_cnmbr=="SANTANDER"))
Con la siguiente funciĂłn visualizaremos un mapa rudimentario de los
municipios de Santander
plot(st_geometry(santander), col= sf.colors(12, categorical = TRUE), border = 'grey', axes = TRUE)
plot(st_geometry(st_centroid(santander)), pch = 3, col = 'red', add = TRUE)
## Warning: st_centroid assumes attributes are constant over geometries
st_write(santander, "stder_munic.gpkg", driver = "GPKG", append = F)
## Deleting layer `stder_munic' using driver `GPKG'
## Writing layer `stder_munic' to data source `stder_munic.gpkg' using driver `GPKG'
## Writing 87 features with 11 fields and geometry type Multi Polygon.
Vamos a subir un archivo que contenga los nombres de ciudades a
nivel mundial
cities = read.csv("D:\\GB2\\proyecto3\\Datos\\worldcities.csv") %>% st_as_sf(coords=c("lng", "lat"), crs= 4326)
cities
Convertimos el sistema de coordenadas
st_crs(cities)$epsg
## [1] 4326
st_crs(santander)$epsg
## [1] 4686
Filtranos las ciudades de Santander
ncities <- st_transform(cities,crs = st_crs(santander))
stder_cities <- ncities[santander, , op = st_within]
Repetimos el mapa
plot(st_geometry(santander), col = sf.colors(12, categorical = TRUE), border = 'grey', axes = TRUE)
plot(st_geometry(stder_cities), pch = 20, col = 'red' , add = TRUE)
Vamos a âplottear con eleganciaâ
ggplot() + geom_sf(data = santander) + geom_sf(data = stder_cities, aes(color = city, label = city), size = 3) + labs(x = "Longitud", y = "Latitud", title = "Ciudades de Santander") + theme_bw()
## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown aesthetics: label
Por Ășltimo, le ponemos una flecha norte a nuetro mapa
ggplot() +
geom_sf(data = santander) +
geom_sf(data = stder_cities, aes(color = city, label = city), size = 3) +
annotation_scale(location = "tr", height = unit(.25, "cm"), width = unit(1, "cm"), pad_x = unit(0.3, "in"), pad_y = unit(0.5, "in")) + annotation_north_arrow(height = unit(1, "cm"), width = unit(1, "cm"),
which_north = "true", location = "tr",
pad_x = unit(0.5, "in"), pad_y = unit(0.05, "in")) +
labs(x = "Longitud", y = "Latitud", title = "Ciudades de Santander") +
theme_bw()
## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown aesthetics: label
## Warning in annotation_scale(location = "tr", height = unit(0.25, "cm"), :
## Ignoring unknown parameters: `width`
sessionInfo()
## R version 4.6.0 (2026-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=Spanish_Colombia.utf8 LC_CTYPE=Spanish_Colombia.utf8
## [3] LC_MONETARY=Spanish_Colombia.utf8 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Colombia.utf8
##
## time zone: America/Bogota
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggspatial_1.1.10 ggplot2_4.0.3 dplyr_1.2.1 sf_1.1-1
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 jsonlite_2.0.0 compiler_4.6.0 tidyselect_1.2.1
## [5] Rcpp_1.1.1-1.1 jquerylib_0.1.4 scales_1.4.0 yaml_2.3.12
## [9] fastmap_1.2.0 R6_2.6.1 generics_0.1.4 classInt_0.4-11
## [13] s2_1.1.9 knitr_1.51 tibble_3.3.1 units_1.0-1
## [17] DBI_1.3.0 bslib_0.10.0 pillar_1.11.1 RColorBrewer_1.1-3
## [21] rlang_1.2.0 cachem_1.1.0 xfun_0.57 sass_0.4.10
## [25] S7_0.2.2 cli_3.6.6 withr_3.0.2 magrittr_2.0.5
## [29] wk_0.9.5 class_7.3-23 digest_0.6.39 grid_4.6.0
## [33] rstudioapi_0.18.0 lifecycle_1.0.5 vctrs_0.7.3 KernSmooth_2.23-26
## [37] proxy_0.4-29 evaluate_1.0.5 glue_1.8.1 farver_2.1.2
## [41] e1071_1.7-17 rmarkdown_2.31 tools_4.6.0 pkgconfig_2.0.3
## [45] htmltools_0.5.9