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

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

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coverage](https://codecov.io/gh/ambarja/geoidep/graph/badge.svg)](https://app.codecov.io/gh/ambarja/geoidep)
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status](https://circleci.com/gh/ambarja/geoidep.svg?style=svg)](https://circleci.com/gh/ambarja/geoidep)
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experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)

<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;">

The goal of geoidep📦 is to offers R users an easy and accessible
way to obtain official cartographic data on various topics, such as
society🏛️, transport🚗, environment🌱,
agriculture🌾, climate⛅️,among others.This includes
information provided by regional government entities and
technical-scientific institutions, managed by the Spatial Data
Infrastructure of Peru.

*⚠️ 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:

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

or also the official version available on CRAN:

``` r
install.packages('geoidep')
```

## Example 01: Introduction

``` r
library(geoidep)
```

``` r
── Welcome to geoidep ─────────────────────────────────────────────────────────────────
ℹ geoidep is a wrapper that enables you to download cartographic data for Peru directly from R.
ℹ Currently, `geoidep` supports data from the following providers:
• Geobosque
• INAIGEM
• INEI
• Midagri
• and more!
ℹ 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.

``` r
get_data_sources() |> 
 head()
#> # A tibble: 6 × 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

``` r
get_providers() 
#> # 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 4
```

## Example 02: Download official INEI administrative boundaries

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

``` r
dep <- get_departaments(show_progress = FALSE)
```

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

``` r
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 -...
```

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