1. Introduction
This package aims to provide R users with a new way of accessing official Peruvian cartographic data on various topics that are managed by the country’s Spatial Data Infrastructure.
By offering a new approach to accessing this official data, both from technical-scientific entities and from regional and local governments, it facilitates the automation of processes, thereby optimizing the analysis and use of geospatial information across various fields.
However, this project is still under construction, for more information you can visit the GitHub official repository https://github.com/ambarja/geoidep.
If you want to support this project, you can support me with a coffee for my programming moments.
2. Package installation
install.packages("geoidep")Also, you can install the development version as follows:
install.packages('pak')
pak::pkg_install('ambarja/geoidep')3. Basic usage
providers <- get_data_sources()
providers
#> # A tibble: 82 × 7
#> provider category layer layer_can_be_actived admin_en year link_geoportal
#> <chr> <chr> <chr> <lgl> <chr> <chr> <chr>
#> 1 INEI General depa… TRUE Nationa… 2019 https://ide.i…
#> 2 INEI General prov… TRUE Nationa… 2019 https://ide.i…
#> 3 INEI General dist… TRUE Nationa… 2019 https://ide.i…
#> 4 Midagri Agricultu… agri… TRUE Ministr… 2024 https://siea.…
#> 5 Midagri Agricultu… oil_… TRUE Ministr… 2016… https://siea.…
#> 6 Geobosque Forest stoc… FALSE Ministr… 2001… https://geobo…
#> 7 Geobosque Forest stoc… TRUE Ministr… 2001… https://geobo…
#> 8 Geobosque Forest stoc… TRUE Ministr… 2001… https://geobo…
#> 9 Geobosque Forest stoc… TRUE Ministr… 2001… https://geobo…
#> 10 Geobosque Forest warn… TRUE Ministr… last… https://geobo…
#> # ℹ 72 more rows
layers_available <- get_providers()
layers_available
#> # A tibble: 9 × 2
#> provider layer_count
#> <fct> <int>
#> 1 Geobosque 5
#> 2 INAIGEM 5
#> 3 INEI 7
#> 4 Midagri 2
#> 5 MTC 26
#> 6 Senamhi 1
#> 7 Serfor 1
#> 8 Sernanp 31
#> 9 SIGRID 44. Download Official Administrative Boundaries by INEI
# Region boundaries download
loreto_prov <- get_provinces(show_progress = FALSE) |>
subset(nombdep == 'LORETO')
library(leaflet)
#>
#> Attaching package: 'leaflet'
#> The following object is masked _by_ '.GlobalEnv':
#>
#> providers
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
loreto_prov |>
leaflet() |>
addTiles() |>
addPolygons()
# Defined Ubigeo
loreto_prov[["ubigeo"]] <- paste0(loreto_prov[["ccdd"]],loreto_prov[["ccpp"]])
# The first five rows
head(loreto_prov)
#> Simple feature collection with 6 features and 6 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -76.89454 ymin: -8.715191 xmax: -69.94904 ymax: -0.63937
#> Geodetic CRS: WGS 84
#> ccdd ccpp nombprov fuente nombdep
#> 138 16 01 MAYNAS V Censo Nacional Economico LORETO
#> 139 16 02 ALTO AMAZONAS V Censo Nacional Economico LORETO
#> 140 16 03 LORETO V Censo Nacional Economico LORETO
#> 141 16 04 MARISCAL RAMON CASTILLA V Censo Nacional Economico LORETO
#> 142 16 05 REQUENA V Censo Nacional Economico LORETO
#> 143 16 06 UCAYALI V Censo Nacional Economico LORETO
#> geom ubigeo
#> 138 MULTIPOLYGON (((-75.24086 -... 1601
#> 139 MULTIPOLYGON (((-76.30752 -... 1602
#> 140 MULTIPOLYGON (((-75.74592 -... 1603
#> 141 MULTIPOLYGON (((-72.08996 -... 1604
#> 142 MULTIPOLYGON (((-73.42087 -... 1605
#> 143 MULTIPOLYGON (((-74.82778 -... 16065. Working with Geobosque data
my_fun <- function(x){
data <- get_forest_loss_data(
layer = 'stock_bosque_perdida_provincia',
ubigeo = loreto_prov[["ubigeo"]][x],
show_progress = FALSE )
return(data)
}
historico_list <- lapply(X = 1:nrow(loreto_prov),FUN = my_fun)
historico_df <- do.call(rbind.data.frame,historico_list)
# The first five rows
head(historico_df)
#> anio perdida rango1 rango2 rango3 rango4 rango5 tipobosque ubigeo
#> 1 2001 4111.92 0 0 288.54 1387.08 2436.30 1 1601
#> 2 2002 2014.02 0 0 101.34 538.83 1373.85 1 1601
#> 3 2003 1448.01 0 0 60.66 386.91 1000.44 1 1601
#> 4 2004 3741.39 0 0 139.59 1344.51 2257.29 1 1601
#> 5 2005 3749.40 0 0 266.94 1213.38 2269.08 1 1601
#> 6 2006 1405.44 0 59 43.38 347.31 955.75 1 16016. Simple visualization with ggplot
library(ggplot2)
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
historico_df |>
inner_join(y = loreto_prov,by = "ubigeo") |>
ggplot(aes(x = anio,y = perdida)) +
geom_point(size = 1) +
geom_line() +
facet_wrap(nombprov~.,ncol = 3) +
theme_minimal(base_size = 5) +
labs(
title = "Pérdida de bosque histórico del 2001-2023 \npara las provincias de Loreto",
caption = "Fuente: Geobosque",
x = "",
y = "") 