Tarea 2-ASIES

Ricardo Bardales

2024-05-20

##1. Vamos a leer el paquete GPKG

#install.packages("sf")

library(ggplot2)

Cargar la librería sf

library(sf)

## Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE

#importamos geopackage desde git hub
linkWorld_gpkg <- "https://raw.githubusercontent.com/Richmaninoff/introgeodf/main/maps/worldMap.gpkg"

#usamos st_layers para lista de las capas presentes 
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")

# Dibujar las capas usando 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

Follow the same steps in this last section, but use Peru. Plot your three layers in R.

#Importando paises
Archivo = "C:/Users/Ricardo/Desktop/ASIES-Herramientas cuantitativas/S4. DATA/maps/World_Countries/World_Countries.shp"
countries=st_read(Archivo)

## Reading layer `World_Countries' from data source 
##   `C:\Users\Ricardo\Desktop\ASIES-Herramientas cuantitativas\S4. DATA\maps\World_Countries\World_Countries.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 252 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.6236
## Geodetic CRS:  WGS 84

#importando rios
Archivorios = "C:/Users/Ricardo/Desktop/ASIES-Herramientas cuantitativas/S4. DATA/maps/World_Hydrography/World_Hydrography.shp"

rios=st_read(Archivorios)

## Reading layer `World_Hydrography' from data source 
##   `C:\Users\Ricardo\Desktop\ASIES-Herramientas cuantitativas\S4. DATA\maps\World_Hydrography\World_Hydrography.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 98 features and 2 fields
## Geometry type: MULTILINESTRING
## Dimension:     XY
## Bounding box:  xmin: -164.6246 ymin: -36.90435 xmax: 161.0658 ymax: 72.23418
## Geodetic CRS:  WGS 84

#importando ciudades
Archivocit = "C:/Users/Ricardo/Desktop/ASIES-Herramientas cuantitativas/S4. DATA/maps/World_Cities/World_Cities.shp"

ciudades=st_read(Archivocit)

## Reading layer `World_Cities' from data source 
##   `C:\Users\Ricardo\Desktop\ASIES-Herramientas cuantitativas\S4. DATA\maps\World_Cities\World_Cities.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 610 features and 3 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -165.27 ymin: -53.15 xmax: 177.1302 ymax: 78.2
## Geodetic CRS:  WGS 84

Creaando el objeto peru

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

peru <- countries %>% filter(COUNTRY == 'Peru')

citiesperu_clipped <- st_intersection(ciudades, peru)

## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries

riversperu_clipped <- st_intersection(rios, peru)

## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries

graficoperu=ggplot(data=peru)  + geom_sf(fill='grey90') + theme_light()
visual1=graficoperu + geom_sf(data=riversperu_clipped, color='blue') + geom_sf(data=citiesperu_clipped, color='red') 
visual1

ejercicio 3.

library(rio)
data=import("pe_airports.csv")

#Importando los aeropuertos 
Archivo4 = "C:/Users/Ricardo/Desktop/ASIES-Herramientas cuantitativas/S4. DATA/maps/peruoair_4326.shp"
peruoair_4326=st_read(Archivo4)

## Reading layer `peruoair_4326' from data source 
##   `C:\Users\Ricardo\Desktop\ASIES-Herramientas cuantitativas\S4. DATA\maps\peruoair_4326.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 203 features and 7 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -81.27556 ymin: -18.0533 xmax: -68.99421 ymax: -0.119056
## Geodetic CRS:  WGS 84

#creamos la intersección 
peru_air <- st_intersection(peruoair_4326, peru)

## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries

baseLayer=ggplot(data=peru)  + geom_sf(fill='grey90') + theme_light()
final=baseLayer + geom_sf(data=riversperu_clipped, color='blue') + geom_sf(data=peru_air, color='black') + geom_sf(data=citiesperu_clipped, color='red') + 
  coord_sf(datum = st_crs(peru))
final

## ejercicio 4.

#install.packages("remotes")

library(remotes)

america="C:/Users/Ricardo/Desktop/ASIES-Herramientas cuantitativas/S4. DATA/maps/America_2023_prjed.gpkg"
sf::st_layers(america)

## 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(america,layer="countries")
ame_cen=read_sf(america,layer="centroids")

library(ggplot2)
baseLayer=ggplot(data=ame)  + geom_sf(fill='grey90') + theme_light() 
E4=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))

E4