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 9 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -77.82596 ymin: -6.14773 xmax: -69.94904 ymax: -0.03860597
#> Geodetic CRS: WGS 84
#> id objectid ccdd ccpp nombdep nombprov shape_length
#> 136 136 136 16 02 LORETO ALTO AMAZONAS 9.955533
#> 137 137 137 16 07 LORETO DATEM DEL MARAÑON 11.493679
#> 138 138 138 16 03 LORETO LORETO 13.602250
#> 139 139 139 16 04 LORETO MARISCAL RAMON CASTILLA 15.113580
#> 140 140 140 16 01 LORETO MAYNAS 14.501777
#> 141 141 141 16 08 LORETO PUTUMAYO 20.443604
#> shape_area geom ubigeo
#> 136 1.569481 MULTIPOLYGON (((-76.30752 -... 1602
#> 137 3.881998 MULTIPOLYGON (((-76.49832 -... 1607
#> 138 5.574816 MULTIPOLYGON (((-75.74592 -... 1603
#> 139 3.054652 MULTIPOLYGON (((-72.08996 -... 1604
#> 140 6.197942 MULTIPOLYGON (((-75.24086 -... 1601
#> 141 3.725588 MULTIPOLYGON (((-75.10004 -... 16085. 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 2081.97 0 0.00 40.05 519.21 1522.71 1 1602
#> 2 2002 3923.46 0 0.00 136.71 1254.51 2532.24 1 1602
#> 3 2003 1843.92 0 0.00 23.94 584.73 1235.25 1 1602
#> 4 2004 4269.33 0 0.00 351.36 1818.00 2099.97 1 1602
#> 5 2005 4948.20 0 0.00 351.09 2020.50 2576.61 1 1602
#> 6 2006 2274.48 0 209.07 152.64 689.22 1223.55 1 16026. 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 = "") 