---
title: "Introduction to geoidep"
author: "Antony Barja"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{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.}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  dpi = 300,
  out.width = "100%" 
)
```

## 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>.**

<img src='../man/figures/testing.svg' width='100%'/>

If you want to support this project, you can support me with a coffee for my programming moments.

<center>
<script type='text/javascript' src='https://storage.ko-fi.com/cdn/widget/Widget_2.js'></script><script type='text/javascript'>kofiwidget2.init('Support Me on Ko-fi', '#ff0022', 'B0B56J9J9');kofiwidget2.draw();</script> 
<center/>

## 2. Package installation 

```r
install.packages("geoidep")
```
Also, you can install the development version as follows: 

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

```{r}
library(geoidep)
```

## 3. Basic usage 

```{r}
providers <- get_data_sources()
providers
```

```{r}
layers_available <- get_providers()
layers_available
```

## 4. Download Official Administrative Boundaries by INEI
```{r}
# Region boundaries download 
loreto_prov <- get_provinces(show_progress = FALSE) |> 
  subset(NOMBDEP == 'LORETO')
```

```{r out.width='100%', out.height=250}
library(leaflet)
library(sf)
loreto_prov |> 
  leaflet() |> 
  addTiles() |> 
  addPolygons()
```


```{r}
# Defined Ubigeo 
loreto_prov[["ubigeo"]] <- paste0(loreto_prov[["CCDD"]],loreto_prov[["CCPP"]])
```


```{r}
# The first five rows
head(loreto_prov)
```

## 5. Working with Geobosque data

```{r}
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)
```

```{r}
# The first five rows
head(historico_df)
```

## 6. Simple visualization with ggplot

```{r ,fig.align='center'}
library(ggplot2)
library(dplyr)
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 = "") 
```