Ejercicio 1

  1. Confirme y envie el paquete geografico creado recientemente
  2. Obtenga el enlance de Github para leer el geopaquete en R
  3. Usando la biblioteca sf en R, confirme las capas creadas (use st_layes) y abra cada mapa (read_sf). Dibuja las tres capas (como hicimos en Python) usando ggplot.
library(sf)
## Linking to GEOS 3.11.2, GDAL 3.6.2, PROJ 9.2.0; sf_use_s2() is TRUE
library(remotes)
linkWorld_gpkg<- "C:/Users/USER/Documents/GitHub/introgeodf/maps/worldMap.gpkg"
sf::st_layers(linkWorld_gpkg)
## Driver: GPKG 
## Available layers:
##       layer_name geometry_type features fields crs_name
## 1 countryBorders                    252      1   WGS 84
## 2     riverLines                     98      2   WGS 84
## 3     cityPoints         Point      610      3   WGS 84
countries=read_sf(linkWorld_gpkg,layer="countryBorders")
rivers=read_sf(linkWorld_gpkg,layer="riverLines")
cities=read_sf(linkWorld_gpkg,layer="cityPoints")
library(ggplot2)
baseLayer=ggplot(data=countries)  + geom_sf(fill='grey90') + theme_light()
final=baseLayer + geom_sf(data=rivers, color='blue') + geom_sf(data=cities, color='red') 
final

Ejercicio 2

  1. Sigue los mismos pasos de este ultimo apartado,pero utiliza Peru 2.Traza tus tres capas en R
Peru<- "C:/Users/USER/Documents/GitHub/introgeodf/maps/Peru_data.gpkg"
st_layers(Peru)
## Driver: GPKG 
## Available layers:
##   layer_name geometry_type features fields crs_name
## 1       peru       Polygon        1      1   WGS 84
## 2 cityPoints         Point        8      3   WGS 84
## 3 riverLines                      5      2   WGS 84
peru=read_sf(Peru,layer="peru")
peru_rivers=read_sf(Peru,layer="riverLines")
peru_cities=read_sf(Peru,layer="cityPoints")
baseLayer=ggplot(data=peru)  + geom_sf(fill='grey90') + theme_light()
final=baseLayer + geom_sf(data=peru_rivers, color='blue') + geom_sf(data=peru_cities, color='red') 
final

Ejercicio 3

Peru_air="C:/Users/USER/Documents/GitHub/introgeodf/maps/PeruMaps_8901.gpkg"
st_layers(Peru_air)
## Driver: GPKG 
## Available layers:
##   layer_name geometry_type features fields         crs_name
## 1    country       Polygon        1      1 RGWF96 (lon-lat)
## 2     cities         Point        8      3 RGWF96 (lon-lat)
## 3     rivers                      5      2 RGWF96 (lon-lat)
## 4   centroid         Point        1      0 RGWF96 (lon-lat)
## 5   airports         Point      203      7 RGWF96 (lon-lat)
peru=read_sf(Peru_air,layer="country")
peru_cities=read_sf(Peru_air,layer="cities")
peru_rivers=read_sf(Peru_air,layer="rivers")
peru_centroid=read_sf(Peru_air,layer="centroid")
peru_air=read_sf(Peru_air,layer="airports")
baseLayer=ggplot(data=peru)  + geom_sf(fill='grey90') + theme_light()
final=baseLayer + geom_sf(data=peru_rivers, color='blue')  + geom_sf(data=peru_air, color='black') + geom_sf(data=peru_cities, color='red') +
  coord_sf(datum = st_crs(peru))
final

Ejercicio 4 :

americ_rp_gpkg="C:/Users/USER/Documents/GitHub/introgeodf/maps/America_2023_prjed.gpkg"
sf::st_layers(americ_rp_gpkg)
## Driver: GPKG 
## Available layers:
##   layer_name geometry_type features fields                      crs_name
## 1  countries                     31      4 WGS 84 / Equal Earth Americas
## 2  centroids         Point       31      4 WGS 84 / Equal Earth Americas
ame=read_sf(americ_rp_gpkg,layer="countries")
ame_cen=read_sf(americ_rp_gpkg,layer="centroids")
library(ggplot2)
baseLayer=ggplot(data=ame)  + geom_sf(fill='grey90') + theme_light() 
final=baseLayer + geom_sf(data=ame_cen,aes(color=Total_ei5_cat),size=2+ame_cen$Total_ei5) +
    guides(size=NULL) +
  coord_sf(datum = st_crs(ame))

final