Get the species names of all GBIF records from Malaysia, Indonesia and Brunei

SE_asian_bat_species.R

#### Header ####
## Project: NA
## Script purpose: Get all the species of bats with GBIF records from Malaysia,
## Indonesia and Brunei
## Date:
## Author: Dave Hemprich-Bennett (hemprich.bennett@gmail.com)
## Notes
##################################################

# Code repurposed from the fantastic lesson at https://ourcodingclub.github.io/2016/01/01/data-synthesis.html

library(dplyr)
library(tidyr)
library(ggplot2)
library(ggthemes)
library(rgbif)
library(purrr)

# Make a custom function function to use in the map call below
gbif_querying <- function(desired_country, desired_taxa) {
  cat(as.character(Sys.time()), desired_country, desired_taxa, "starting\n")
  output <- occ_search(
    scientificName = desired_taxa, limit = 10000,
    country = desired_country,
    hasCoordinate = T, return = "data"
  )
  # if there are no matches, occ_search returns a gbif object rather than a list
  # as gbif objects break the select call and subsequent dataframe making,
  # check the type of 'output'
  if (typeof(output) == "list") {
    # Some records don't have species names etc. These records break the select
    # call, are no use to us and should be discarded
    required_cols <- c("species", "name", "decimalLongitude",
                       "decimalLatitude", "year",
                       "individualCount", "country")
    # if statement translates to 'if all of the required_cols are in 
    # names(output)
    if(!FALSE %in% (is.element(required_cols, names(output)))){
      
      # Simplify occurrence dataframe, get rid of superfluous columns. This
      # is necessary as GBIF won't return empty columns, and this can mess 
      # with any attempts at combining dataframes later
      output <- dplyr::select(output,
                              species, name, decimalLongitude,
                              decimalLatitude, year,
                              individualCount, country
      ) %>%
        mutate(taxon = desired_taxa)
      
      cat(as.character(Sys.time()), desired_country, desired_taxa, "done\n")
      return(output) 
    }
    
  }
}

# Make a vector of shortnames for all African countries (GBIF doesn't accept
# the full name of a country, but instead their 2-letter ISO-3166-1 code, see
# http://en.wikipedia.org/wiki/ISO_3166-1_alpha-2 for details)

bornean_countries <- c("MY", "BN", "ID")

# Make a vector of desired taxonomic groups

taxa_vec <- c("Chiroptera")

# Make a dataframe with all possible combinations of the two desired variables.
# This makes it easier to use purrr later
combinations <- crossing(bornean_countries, taxa_vec)

# Use our custom function on the above dataframe to query for every combination
# of country and taxonomic group
occurrence_df <- purrr::map2_df(
  combinations$bornean_countries,
  combinations$taxa_vec,
  gbif_querying
)

occurrence_df <-  occurrence_df %>%
  filter(!is.na(species)) %>% # get rid of the NAs
  select(species, country) %>% # delete superfluous columns
  distinct() %>% # filter out the non-distinct rows
  arrange(species) # order the rows alphabetically by species

#Save the output
write.csv(occurrence_df,
         "se_asian_bats/bat_species.csv",
         row.names = F)

Are you familiar with the species list format now available from GBIF? I believe it makes what you're doing a lot easier ;)

Ha, I am not! This sounds potentially much easier...