---
title: "IUCN red list data visualisation"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{IUCN red list data visualisation}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.align = "center",
  fig.width = 8,
  fig.height = 5
)
library(dplyr)
library(ggplot2)
library(redlist)
```


# Introduction
In this vignette, I will examine assessments specific to **Benin**. I will walk through three types of visualizations:

* Total number of assessments per year
* Proportional breakdown of IUCN categories
* Trends over time for **threatened** categories (CR, EN, VU)

Refer to [this vignette](https://stangandaho.github.io/redlist/articles/get_data.html) to learn more about how to access the data.

# Query Data
```{r eval=FALSE}
# Load the package
library(redlist)
# Get all data on Benin
benin_rl <- rl_countries(code = "BJ", page = NA)

```

```{r echo=FALSE}
benin_rl <- readRDS("benin_full_data.rds")
```


```{r}
# Basic overview
glimpse(benin_rl)
```
The dataset includes **all species** assessed in Benin across various taxonomic groups — including **plants, animals, fungi**, and other organisms.

# Number of Assessments per Year

Understanding the volume of assessments over time gives insight into conservation attention and effort.

```{r plot-assessments-per-year}
benin_rl %>%
  count(assessments_year_published) %>%
  ggplot(aes(x = assessments_year_published, y = n)) +
  geom_line(color = "steelblue") +
  geom_point(color = "darkblue") +
  labs(
    title = "Number of assessments per year in Benin",
    x = "Year",
    y = "Number of assessments"
  ) +
  theme_minimal()
```


# Proportion of Red List Categories

Most species in Benin fall under **Least Concern (LC)**, but some are classified as threatened. This chart highlights the proportion of assessments by category.

```{r plot-category-proportions}
benin_rl %>%
  filter(!is.na(assessments_red_list_category_code)) %>%
  count(assessments_red_list_category_code) %>%
  mutate(prop = n / sum(n)) %>%
  ggplot(aes(x = reorder(assessments_red_list_category_code, -prop), y = prop)) +
  geom_col(fill = "salmon") +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(
    title = "Proportion of red list categories in Benin",
    x = "Red List Category",
    y = "Proportion"
  ) +
  theme_minimal()
```


# Trends in Threatened Categories Over Time

Focusing on **Critically Endangered (CR)**, **Endangered (EN)**, and **Vulnerable (VU)** species helps track biodiversity risk.

```{r plot-threatened-trends}
benin_rl %>%
  filter(assessments_red_list_category_code %in% c("CR", "EN", "VU")) %>%
  count(assessments_year_published, assessments_red_list_category_code) %>%
  ggplot(aes(x = assessments_year_published, y = n,
             color = assessments_red_list_category_code)) +
  geom_line() +
  geom_point() +
  labs(
    title = "Trends of Threatened Categories (CR, EN, VU) Over Time",
    x = "Year",
    y = "Number of Assessments",
    color = "Category"
  ) +
  theme_minimal()
```