IUCN red list data visualisation"
In redlist: Interface to the IUCN Red List Data

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 to learn more about how to access the data.

Query Data

# Load the package
library(redlist)
# Get all data on Benin
benin_rl <- rl_countries(code = "BJ", page = NA)

benin_rl <- readRDS("benin_full_data.rds")

# 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.

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.

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.

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()