# INBO CODING CLUB

25 September 2025

Welcome!

## Share your code snippet

If you want to share your code snippet, copy paste your snippet within a section of three backticks (```):

As an **example**:

library(tidyverse)

(*you can copy paste this example and add your code further down*)

## Yellow sticky notes

No yellow sticky notes online. Put your name + " | " and add a "*" each time you solve a challenge (see below).

## Participants

Name | Challenges
--- | ---
Damiano Oldoni | ***
Cécile Herr |
Ward Langeraert |
Emma Cartuyvels| *
Charlotte Van Moorleghem |

## Challenge 1

### Damiano's solution (example)

Copy paste this section to show your solutions.

# dummy code
print("This is how to insert code.")

### Ward's solution

## 1.1 ####
names(data_nl) <-  species_df$species
names(data_nl)

## 1.2 ####
map(data_nl, function(taxon) {
  nrow(taxon$data)
})

## 1.3 ####
map_int(data_nl, function(taxon) {
  nrow(taxon$data)
})

## 1.4 ####
map(data_nl, function(taxon) {
  taxon$data
}) %>%
  list_rbind() # why not bind_rows?

## 1.5 ####
countries <- c("NL", "AT", "ES", "DK")

get_gbif_occurrences <- function(taxon_keys, country_code) {
  rgbif::occ_search(
    taxonKey = taxon_keys,
    country = country_code,
    year = "1950,2025",
    hasCoordinate = TRUE,
    occurrenceStatus = "PRESENT",
    limit = 100000 # High enough
  )
}

data_countries <- map(
  countries,
  get_gbif_occurrences,
  taxon_keys = species_df$taxonKey
)

data_nl_og <- rgbif::occ_search(
  taxonKey = species_df$taxonKey,
  country = "NL",
  year = "1950,2025",
  hasCoordinate = TRUE,
  occurrenceStatus = "PRESENT",
  limit = 100000 # High enough
)

waldo::compare(data_countries[[1]], data_nl_og)

## 1.6 ####
# The input was not named
names(data_countries) <- countries

# Also rename second level
data_countries <- map(data_countries, function(country){
  names(country) <- species_df$species
  country
})

head(data_countries$NL$`Procyon lotor`$data)

### Emma's solution

## 1.1 ####
names(data_nl) <- c("Procyon lotor", "Nasua nasua", "Muntiacus reevesi")

## 1.2 ####
map(data_nl, function(x) nrow(x$data))

## 1.3 ####
map_int(data_nl, function(x) nrow(x$data))

## 1.4 ####
map(data_nl, "data") |> bind_rows()

## 1.5 and 1.6 ####
countries <- c("NL" = "NL",
               "AT" = "AT",
               "ES" = "ES",
               "DK" = "DK")

retrieve_data <- function(country){
  sub_dat <- rgbif::occ_search(
    taxonKey = species_df$taxonKey,
    country = country,
    year = "1950,2025",
    hasCoordinate = TRUE,
    occurrenceStatus = "PRESENT",
    limit = 100000 # High enough
  )
  
  names(sub_dat) <- c("Procyon lotor", "Nasua nasua", "Muntiacus reevesi")
  
  return(sub_dat)
}

data_countries <- map(countries, retrieve_data)

## Challenge 2

### Ward's solution

## 2.1 ####
spec_records <- map(data_countries, function(country){
  map_vec(country, function(taxon) {
    data <- taxon$data
    if (is.null(data)) return(0)
    nrow(data)
  })
})
spec_records

## 2.2 ####
reduce(spec_records, sum)

## 2.3 ####
plot_records <- function(data, country, species) {
  if (is.null(data)) return(paste("No data for", species, "in", country))
  p <- ggplot2::ggplot(data = data) +
    ggplot2::geom_bar(ggplot2::aes(x = year)) +
    ggplot2::xlab("Year") +
    ggplot2::ylab("Number of records") +
    ggplot2::ggtitle(paste(species, country, sep = " - "))

  return(p)
}

map(data_list, function(country) {
  map(country, function(taxon) {
    data <- taxon$data
    species <- unique(data$species)
    country <- unique(data$countryCode)
    plot_records(data, species, country)
  })
})

# Use for loop (much cleaner)
plot_records2 <- function(data_list, country, species) {
  plot_data <- data_list[[country]][[species]]$data
  if (is.null(plot_data)) return(paste("No data for", species, "in", country))
  p <- ggplot2::ggplot(data = plot_data) +
    ggplot2::geom_bar(ggplot2::aes(x = year)) +
    ggplot2::xlab("Year") +
    ggplot2::ylab("Number of records") +
    ggplot2::ggtitle(paste(species, country, sep = " - "))

  return(p)
}

for (country in names(data_list)) {
  for (species in names(data_list[[country]])) {
    print(plot_records2(data_list, country, species))
  }
}

## 2.4 ####
data_path <- file.path("src", "20250925")
map(data_list, function(country) {
  map(country, function(taxon) {
    data <- taxon$data
    if (!is.null(data)) {
      key <- unique(data$speciesKey)
      species <- unique(data$species)
      country <- unique(data$countryCode)
      file_name <- paste0(
        "20250925_gbif_", key, "_", species, "_", country, ".csv"
      )

      paste0("Writing '", file_name, "'.")
      write_csv(data, file.path(data_path, file_name))
    }
  })
})

## 2.5 ####
data_countries_df <- map(data_list, function(country) {
  map(country, function(taxon) {
    if (!is.null(taxon$data)) return(taxon$data)
  })
}) %>%
  list_c() %>%
  list_rbind()

head(data_countries_df)

## Challenge 3