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')
4. Download Official Administrative Boundaries by INEI
# Region boundaries download (done once in the setup chunk above)
head(loreto_prov, 3)
#> Simple feature collection with 3 features and 6 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -76.89454 ymin: -6.14773 xmax: -72.11719 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
#> geom ubigeo
#> 138 MULTIPOLYGON (((-75.24086 -... 1601
#> 139 MULTIPOLYGON (((-76.30752 -... 1602
#> 140 MULTIPOLYGON (((-75.74592 -... 1603
library(mapgl)
#> Warning: package 'mapgl' was built under R version 4.5.3
library(sf)
#> Linking to GEOS 3.14.1, GDAL 3.12.1, PROJ 9.7.1; sf_use_s2() is TRUE
maplibre_view(data = loreto_prov)
5. 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)
#> # A tibble: 6 × 8
#> anio perdida rango1 rango2 rango3 rango4 rango5 ubigeo
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 2001 4112. 2436. 1387. 289. 0 0 1601
#> 2 2002 2014. 1374. 539. 101. 0 0 1601
#> 3 2003 1448. 1000. 387. 60.7 0 0 1601
#> 4 2004 3741. 2257. 1344. 140. 0 0 1601
#> 5 2005 3749. 2269. 1213. 267. 0 0 1601
#> 6 2006 1405. 956. 347. 43.4 58.7 0 1601
6. Simple visualization with ggplot
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.5.3
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.5.3
#>
#> 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
historico_prov <- historico_df |>
inner_join(y = loreto_prov, by = "ubigeo")
promedio_loreto <- historico_prov |>
group_by(anio) |>
summarise(perdida = mean(perdida), .groups = "drop")
ggplot(historico_prov, aes(x = anio, y = perdida)) +
geom_line(aes(group = nombprov, color = "Provincia"), linewidth = 0.6) +
geom_line(
data = promedio_loreto,
aes(color = "Promedio Loreto"),
linewidth = 0.6,
linetype = "dashed",
) +
scale_color_manual(name = NULL, values = c("Provincia" = "red", "Promedio Loreto" = "black")) +
facet_wrap(nombprov ~ .) +
theme_minimal(base_size = 12) +
theme(legend.position = "bottom") +
labs(
title = "Pérdida de bosque 2001-2025: provincias de Loreto vs. promedio departamental",
caption = "Fuente: Geobosque",
x = "",
y = "")
