# Get IUCN red list data with redlist R package

# What is IUCN red list ?

The IUCN Red List of Threatened Species is a comprehensive global inventory that assesses the conservation status of plant, animal, and fungi species. Managed by the International Union for Conservation of Nature (IUCN), it evaluates species based on factors like population size, rate of decline, and geographic range to classify them into categories such as Least Concern, Vulnerable, Endangered, and Critically Endangered. The Red List serves as a critical tool for conservation planning, policy-making, and raising awareness about the risk of extinction facing species worldwide. In this article, I demonstrate how to access Red List data using the redlist R package.

# Install redlist R package

The package can be installed from github with pak package manager. The pak package in R offers a faster, more reliable, and efficient way to install packages compared to traditional methods. It handles dependencies automatically, supports parallel downloads. Alternately is {devtool} or remotes. I prefered to use pak.

    # Install pak if not available
    if (!requireNamespace("pak", quietly = TRUE)) {
      install.packages("pak")
    }

    # Then install redlist
    pak::pkg_install("stangandaho/redlist")
    # Load the package
    library(redlist)

# Set up an API key

If you’re using this package for the first time, you’ll likely need an IUCN Red List API key. You can check whether it’s set by running `rl_check_api()`. If this throws an error like ‘! No Redlist API key found… ’, you’ll need to set an API key before using any of the package functions. Just follow these two simple steps:
1. Click here to create an account if you don’t already have one. Once logged in, you can generate your API key.
2. Copy your API key and set it using the `rl_set_api()` function, like this `rl_set_api("2GoWiThmYrEDlitApiThatWorkS4me")`.
You can then run `rl_check_api()` again to confirm that your API key is set successfully.

# Get data

The IUCN provides extensive data through the Red List across various categories, including assessments, biogeographical realms, comprehensive groups, conservation actions, habitats, population trends, red list categories, taxonomic levels, and more. A complete overview, along with the corresponding access functions, can be found here. In this article, we will focus specifically on data related to threatened species that have been assessed in Benin.

    benin_redlist <- rl_countries(code = "BJ")
    head(benin_redlist)

country_description_en

country_code

assessments_year_published

assessments_latest

assessments_possibly_extinct

assessments_possibly_extinct_in_the_wild

assessments_sis_taxon_id

assessments_url

assessments_taxon_scientific_name

assessments_red_list_category_code

assessments_assessment_id

assessments_code

assessments_code_type

assessments_scopes_description_en

assessments_scopes_code

Benin | BJ | 2013 | FALSE | FALSE | FALSE | 137286 | https://www.iucnredlist.org/species/137286/522738 | Caccobius ferrugineus | LC | 522738 | BJ | country | Global | 1
Benin | BJ | 2025 | TRUE | FALSE | FALSE | 137829 | https://www.iucnredlist.org/species/137829/531737 | Garreta laetus | LC | 531737 | BJ | country | Global | 1
Benin | BJ | 2014 | TRUE | FALSE | FALSE | 137859 | https://www.iucnredlist.org/species/137859/532227 | Pedaria estellae | LC | 532227 | BJ | country | Global | 1
Benin | BJ | 2013 | TRUE | FALSE | FALSE | 137957 | https://www.iucnredlist.org/species/137957/533937 | Trichonthophagus juvencus | LC | 533937 | BJ | country | Global | 1
Benin | BJ | 2014 | TRUE | FALSE | FALSE | 138000 | https://www.iucnredlist.org/species/138000/534595 | Pedaria criberrima | LC | 534595 | BJ | country | Global | 1
Benin | BJ | 2013 | TRUE | FALSE | FALSE | 138301 | https://www.iucnredlist.org/species/138301/539328 | Latodrepanus laticollis | LC | 539328 | BJ | country | Global | 1

By default, the function retrieves the first page of results, which includes the first 100 records across various years. You can specify one or more pages, as well as specific publication years, to filter the data accordingly. If you want to retrieve all available data without restricting by page, you can set the page argument to `NA` or `NULL`.

    # Get data from five first pages
    benin_redlist <- rl_countries(code = "BJ", page = 1:5)
    # Get data from two first pages specifically for 2023
    benin_redlist <- rl_countries(code = "BJ", page = 1:5, year_published = 2023)
    # Get all data on Benin
    benin_redlist <- rl_countries(code = "BJ", page = NA)

As displayed, each output from redlist functions is returned as a tibble, allowing us to easily filter observations for analytical purposes. In the article on data visulisation, I’ll present a few examples of plots to illustrate how the data can be explored visually.

To get detailed information about each species or observation, you can use one of two functions by passing data from this output. For example, you can use the function `rl_sis()` and pass a species `sis_id` (e.g., `137286` for the first row). Alternatively, you can use the `rl_assessment_id()` function, which accepts a species `assessment_id` (e.g., `522738`). The `rl_assessment_id()` function returns more detailed data than `rl_sis()`.

If you need information for multiple species, you can simply loop through the `assessments_assessment_id` column and combine the data row by row.

    all_species_details <- lapply(benin_redlist$assessments_assessment_id, function(x){
      rl_assessment_id(id)
    }) %>%
      dplyr::bind_rows()