PietrH

#' Get NUTS data for Belgium
#'
#' Downloads, caches, and parses the official Nomenclature of Territorial Units
#' for Statistics (NUTS) and Local Administrative Units (LAU) data for Belgium
#' from Statbel. The data includes Dutch descriptions and National Institute of
#' Statistics (NIS) codes for various administrative levels.
#'
#' @param level A character string specifying the administrative level to
#'   retrieve. Must be one of `"municipalities"` (Level 4/LAU),
#'   `"arrodissements"` (Level 3), `"provinces"` (Level 2), `"regions"` (Level

PietrH

readr_lazy_colnames <- function(path, delim = "|") {
  readr::read_delim(
    path,
    delim = delim,
    lazy = TRUE,
    progress = FALSE,
    show_col_types = FALSE
  ) |>
    colnames()
}

PietrH

library(etn)

# Creating a connection object will result in a warning and not result in a
# connection object
my_con <- connect_to_etn()
#> Warning: `connect_to_etn()` was deprecated in etn 2.3.0.
#> ℹ You will be prompted for credentials instead.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

PietrH

library(etn)

# Creating a connection object will result in a warning and not result in a
# connection object
my_con <- connect_to_etn()
#> Warning: `connect_to_etn()` was deprecated in etn 2.3.0.
#> ℹ You will be prompted for credentials instead.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
class(my_con)

PietrH

# Finding species that are only a single dataset and nowhere else: human
# observations edition

if(file.exists("query_results/0024071-251009101135966.zip")) {
  species_per_dataset <-
    readr::read_delim("query_results/0024071-251009101135966.zip")
} else {
  species_per_dataset <-
    rgbif::occ_download_get("0024071-251009101135966") |>
    rgbif::occ_download_import()

PietrH

# Passerines in South America

# Data read from GBIF occurrence download (SQL QUERY)
pas_sa <- 
  rgbif::occ_download_get("0028483-251009101135966") |>
  rgbif::occ_download_import() |>
  dplyr::filter(!is.na(month))

# Identify the 3 most common genera -------------------------------------
common_genera <- pas_sa |>

PietrH

# Get all rato data as a flat csv

if(!rlang::is_installed("ratatouille", version = "1.0.4")){
  pak::pak("inbo/ratatouille")
}
library(ratatouille)

# To run this function you need to store your RATO database credentials as env variables, as RATO_USER and RATO_PWD, otherwise you'll get an error.

all_rato_data <- ratatouille() 

PietrH

return_default <- function(my_param = 50){
  rlang::eval_bare(formals(rlang::caller_fn(0))[["my_param"]])
}

PietrH

object_name_message <- function(x){
  cli::cli_inform("The object you passed is called {substitute(x)}")
}

object_name_message(letters)

PietrH

repo <- "my_org/my_repo"
zenodo_token <- "token from url in webhook page of repo"

repo_response <- 
  httr2::request("https://api.github.com/repos") %>% 
  httr2::req_url_path_append(repo) %>% 
  httr2::req_perform() %>% 
  httr2::resp_body_json()

release_response <-