Of course, I realise that GBIF is not comprehensive in its coverage, and that there are often issues that need addressing with occurrence data and taxonomic precision; this code was written for a quick first assessment useful for comparing the presence/absence of potentially invasive species in countries in the context of horizon scanning. Use this code at your own risk!
Feedback/improvements welcome!
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| #### Function for retrieving GBIF country-based checklists based on existing records | |
| # 01.04.2018, OL Pescott | |
| ## v0, Script for retrieving lists of taxa per country from GBIF (not as straightforward as you might think!), O.Pescott, 22.03.2018 | |
| #rm(list = ls()) | |
| # Potential improvements: | |
| # Allow for more arguments to be fed through to rgbif functions | |
| # Allow for user to limit query to particular taxon groups | |
| # Overcome harcoded 99,999 hard coded facet limit from rgbif | |
| # Sys.sleep might stop the function failing to return when processing data for large countries/with lots of info (e.g. Colombia in the current example)? | |
| # Retrieves list for one country, but can be applied over a list of country names | |
| getSppLists <- function (country = country, ...) { | |
| # Establish the minimum number of records to examine in order to capture all phyla and orders across all countries (might need to restrict this approach to the smaller countries local to a target location) | |
| cntFacet <- rgbif::count_facet(by='country', countries = country, removezeros = TRUE) | |
| # create list of all keys (and record counts) for taxa within listed countries | |
| # limit = 0 only refers to record downloads, not to information inspected on GBIF; facetLimit refers to the number of rows returned by 'group by' facet clause | |
| out1 <- tryCatch(rgbif::occ_search(country = country, # sum(cntFacet$V1) -- sum not really needed since change from running this for all countries at once, but leave in for now | |
| facet=c("speciesKey"), facetMincount = sum(cntFacet$V1), limit = 0, speciesKey.facetLimit = 99999), # might not be not enough for large countries | |
| error = function(err) NULL) # if it fails, return NULL. Means that when applied across countries the whole process will not fail | |
| if (is.null(out1)) {NULL} # move on (e.g. to next country in list is being used with an apply function) if this step has failed | |
| else { | |
| # need to use speciesKey return in order to get the names and higher taxon information using rgbif::name_usage | |
| out2 <- as.data.frame(out1$facets$speciesKey) | |
| out3 <- list() # empty list for all the | |
| out3 <- tryCatch(lapply(out2[,'name'], function(x) {tmp <- rgbif::name_usage(key = x, rank = c("phylum", "order")) | |
| if("order" %in% colnames(tmp$data) & "phylum" %in% colnames(tmp$data)) {tmp} | |
| else {} | |
| }), error = function(err) NULL) # get higher taxon information for later filtering | |
| if (is.null(out3)) {NULL} # move on (e.g. to next country in list is being used with an apply function) if this step has failed | |
| else { # extract and simplify structure of data returned | |
| out4 <- do.call(rbind, lapply(lapply(out3, '[[', 'data'), function(x) as.data.frame(x[,c('scientificName','phylum','order')]))) # works even if list out4 has NULL elements | |
| out4$country <- rep(attr(out1, "args")$country, nrow(out4)) # add in country information to list item | |
| print(paste("Retrieved and processed:", unique(out4$country))) # add country info in terms of ISO 2 code | |
| return(out4) # return dataframe as long as no errors are picked up from GBIF | |
| } | |
| } | |
| } # END |
