Map viewer and editor

The following workflow demonstrates how to manually edit occurrence data generated through LCr using a custom map tool. This workflow is useful for cases when you need more control than is provided by the automated cleaning.

We start with the same steps as in Rapid LC - plants workflow
The LCr and rCAT packages are not on CRAN, but can be installed via the github repository:

# install from github for first time use
remotes::install_github("stevenpbachman/LCr")
remotes::install_github('matildabrown/rWCVPdata')

# load libraries
library(LCr)
library(dplyr)
library(rCAT)
library(leaflet)
library(leaflet.extras)
library(htmlwidgets)
library(tidyr)

Get name keys from a species list

# dataframe of species you want to run through LCr
lc_species <-
  data.frame(sp = c(
    "Crabbea acaulis", "Crabbea nana", "Crabbea velutina"
  ))

Run the get_name_keys function using your dataframe of names as input.

# get the GBIF and WCVP identification keys
lc_keys <-
  get_name_keys(
    df = lc_species,
    name_column = "sp",
    match = "single",
    kingdom = "plantae"
  )

Clean keys

LCr is designed to work at species level and where taxonomic status is accepted. The clean_keys will remove any problematic records.

# remove any problematic records
lc_keys_clean <- LCr::clean_keys(lc_keys)

Get GBIF occurrence data

Note that we will use the rGBIF package to obtain the occurrence data. You will need to set up your GBIF credentials to obtain the downloads. After you have set up an account at GBIF you need to register your credentials in the R environment - see this post for an explanation. You may need to restart R after updating the R environment.

# get the raw GBIF occs - with timer
start_time <- Sys.time()
gbif_occs <- LCr::get_gbif_occs(lc_keys_clean, mode = "search")
end_time <- Sys.time()
check <-  end_time - start_time
print(check)

Get native ranges

We now need to extract native range information for our species from the World Checklist of Vascular Plants dataset.

# get native ranges from WCVP- used for cleaning occs, and also for country list for SIS
native_ranges <- LCr::get_native_range(keys = lc_keys_clean)

Data quality checks

# run the occurrence quality checks
# if you don't want to run native range cleaning check, just leave out native_ranges
# you can also adjust the buffer to account for the coarse WGSRPD polygons.
# the default is a 2 km buffer
checked_occs <- LCr::check_occs(gbif_occs$points, native_ranges, buffer = 2000)

# flagged occurrences and summary table
flagged_occs <- checked_occs$checked_data
flagged_sum <- checked_occs$summary

Map individual species

We can now step through each species and map the occurrence data. We use the GBIF ID field to map species:

# filter by species key
spkey <- "5573640" # Crabbea acaulis
sp <- flagged_occs %>% dplyr::filter(taxonKey == spkey)
sp_range <- native_ranges %>% dplyr::filter(internal_taxon_id == spkey)
single_map <- LCr::map_species_single(sp, sp_range, show_flags = TRUE)
single_map

The leaflet map is interactive and shows several elements: the native range polygon, valid points in green, problematic points in red and yellow borders for points to indicate multiple points in the same place.

You can turn on/off these layers to see which points are flagged with different errors.

Note that the default clean will remove all red points and leave you with only the valid green points. You may want to adjust this for two reasons: i) You decide some points flagged as errors are ok and you want to include them in the analysis, and ii) You may disagree with some points that are marked as valid, and want to label them as errors. You can adjust the clean_occs function to achieve these edits.

Use the rectangle and polygon tools on the left of the map to highlight groups of points that you want to keep/remove for each species. A sensible workflow would be to build up the list of GBIF IDs across all species before running the clean_occs function so that you only have to do this once.

Some examples:

# clean using the default options, but keep GBIF ID "6185525998" 
cleaned_result <- clean_occs(flagged_occs, keep_gbifids = "6185525998")

# keep multiple occurrences using GBIF IDs
cleaned_result <- clean_occs(flagged_occs, keep_gbifids = c("6185525998", 
                                                            "3708204613"))

# remove occurrences using GBIF IDs
cleaned_result <- clean_occs(flagged_occs, 
                             remove_gbifids = "4606839903")

# You can also combine both the `keep_gbifids` and `remove_gbifids` parameters
cleaned_result <- clean_occs(flagged_occs, 
                             keep_gbifids = "6185525998",
                             remove_gbifids = "4606839903")

At this stage you may want to save both the valid and problem data files.

# Create objects for clean and problem records
valid_data <- cleaned_result$clean_occs
problem_data <- cleaned_result$problem_occs

# For reference you may want to save these down as csv files
write.csv(valid_data, file = "valid_occs.csv")
write.csv(problem_data, file = "problem_occs.csv")

Note that you can also save the map as a self-contained HTML file which may be useful to share with collaborators or reviewers. You can save a single map with the saveWidget function or use map_species_batch to process and save maps for all species in a file

# save a single map
saveWidget(single_map, 
           file = "my_species_occs.html", 
           selfcontained = TRUE)