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Geoidep

πŸ“₯ Download geographic data on various topics provided and managed by the Spatial Data Infrastructure of Peru 🌎🌐.

Install / Use

/learn @ambarja/Geoidep
About this skill

Quality Score

0/100

Category

Operations

Supported Platforms

Universal

README

<!-- README.md is generated from README.Rmd. Please edit that file -->

geoidep: Download Geographic Data Managed by Peru’s Spatial Data Infrastructure

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status Lifecycle:
experimental

<!-- badges: end --> <img align="right" src="https://raw.githubusercontent.com/ambarja/geoidep/refs/heads/main/man/figures/geoidep_logo_b.png" alt="logo" width="124" style="margin-left: 5px;margin-right: 5px;"> <img align="right" src="https://raw.githubusercontent.com/ambarja/geoidep/refs/heads/main/man/figures/geoidep_logo_o.png" alt="logo" width="124" style="margin-left: 5px;margin-right: 5px;"> <p align="justify"> The goal of <b>geoidep</b>πŸ“¦ is to offers R users an easy and accessible way to obtain official cartographic data on various topics, such as <b>society</b>πŸ›οΈ,<b> transport</b>πŸš—, <b>environment</b>🌱, <b>agriculture</b>🌾, <b>climate</b>⛅️,among others.This includes information provided by regional government entities and technical-scientific institutions, managed by the <b>Spatial Data Infrastructure of Peru</b>. </p>

⚠️ The package accesses these datasets dynamically from official public servers, without redistributing data locally.

The package is currently available in R and Python (coming soon).

<hr>

Installation R

You can install the development version of geoidep like so:

install.packages('pak')
pak::pkg_install('ambarja/geoidep')

or also the official version available on CRAN:

install.packages('geoidep')

Example 01: Introduction

library(geoidep)
── Welcome to geoidep ─────────────────────────────────────────────────────────────────
β„Ή geoidep is a wrapper that enables you to download cartographic data for Peru directly from R.
β„Ή Currently, `geoidep` supports data from the following providers:
β€’ Geobosque
β€’ INAIGEM
β€’ INEI
β€’ Midagri
β€’ and more!
β„Ή For more information, please use the `get_data_sources()` function.

In this example, we can identify the list of providers available in geoidep and the layers they present.

get_data_sources() |> 
  head()
#> # A tibble: 6 Γ— 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   Agriculture agri… TRUE                 Ministr… 2024  https://siea.…
#> 5 Midagri   Agriculture oil_… TRUE                 Ministr… 2016… https://siea.…
#> 6 Geobosque Forest      stoc… FALSE                Ministr… 2001… https://geobo…

In summary the suppliers and the number of available layers

get_providers() 
#> # A tibble: 9 Γ— 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              4

Example 02: Download official INEI administrative boundaries

This is a simple example of how to download Peru’s official administrative boundaries:

dep <- get_departaments(show_progress = FALSE)

The first 10 rows of the original data are displayed here:

head(dep)
#> Simple feature collection with 6 features and 6 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -79.45857 ymin: -17.28501 xmax: -70.80408 ymax: -2.986125
#> Geodetic CRS:  WGS 84
#>   id objectid ccdd   nombdep shape_length shape_area
#> 1  1        1   01  AMAZONAS    13.059047   3.199147
#> 2  2        2   02    ANCASH    11.788249   2.954697
#> 3  3        3   03  APURIMAC     7.730154   1.765933
#> 4  4        4   04  AREQUIPA    17.459435   5.330125
#> 5  5        5   05  AYACUCHO    17.127166   3.643705
#> 6  6        6   06 CAJAMARCA    12.540288   2.688386
#>                             geom
#> 1 MULTIPOLYGON (((-77.81399 -...
#> 2 MULTIPOLYGON (((-77.64697 -...
#> 3 MULTIPOLYGON (((-73.74655 -...
#> 4 MULTIPOLYGON (((-71.98109 -...
#> 5 MULTIPOLYGON (((-74.34843 -...
#> 6 MULTIPOLYGON (((-78.70034 -...
View on GitHub
GitHub Stars15
CategoryOperations
Updated17d ago
Forks2

Languages

R

Security Score

95/100

Audited on Mar 19, 2026

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