## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE,
  fig.width = 8,
  fig.height = 6,
  out.width = "70%"
)

## ----setup--------------------------------------------------------------------
# library(EcoCleanR)
# library(rgbif)
# library(robis)
# library(ridigbio)
# library(dplyr)

## -----------------------------------------------------------------------------
# species_name <- "Mexacanthina lugubris"
# taxonkey <- name_backbone(species_name)$usageKey

## -----------------------------------------------------------------------------
# attribute_list <- c("source", "catalogNumber", "basisOfRecord", "occurrenceStatus", "occurrenceID", "institutionCode", "verbatimEventDate", "scientificName", "individualCount", "organismQuantity", "abundance", "decimalLatitude", "decimalLongitude", "coordinateUncertaintyInMeters", "locality", "verbatimLocality", "municipality", "county", "stateProvince", "country", "countryCode")

## -----------------------------------------------------------------------------
# gbif.occ <- occ_data(taxonKey = taxonkey, occurrenceStatus = NULL, limit = 10000L)$data
# 
# # refer article/cite_data.Rmd for instructions on how to cite the data from gbif- data providers
# 
# ## additional field added to know the source
# gbif.occ$source <- "gbif"
# for (field in attribute_list) {
#   if (!field %in% names(gbif.occ)) {
#     gbif.occ[[field]] <- NA # Add the missing field as NA
#   }
# }
# 
# ## we are making one column called abundance which should have values from individual count and organism Quantity
# gbif.occ$abundance <- ifelse(is.na(as.numeric(gbif.occ$individualCount)), as.numeric(gbif.occ$organismQuantity), as.numeric(gbif.occ$individualCount))
# ## additional field added to know the source
# gbif.occ$source <- "gbif"
# gbif.occ_temp <- gbif.occ[, attribute_list]
# str(gbif.occ_temp[, 1:3])

## -----------------------------------------------------------------------------
# obis.occ <- occurrence(species_name)
# for (field in attribute_list) {
#   if (!field %in% names(obis.occ)) {
#     obis.occ[[field]] <- NA # Add the missing field as NA
#   }
# }
# obis.occ$abundance <- ifelse(is.na(as.numeric(obis.occ$individualCount)), as.numeric(obis.occ$organismQuantity), as.numeric(obis.occ$individualCount))
# obis.occ$source <- "obis"
# obis.occ$municipality <- ""
# obis.occ_temp <- obis.occ[, attribute_list]
# str(obis.occ_temp[, 1:3])

## -----------------------------------------------------------------------------
# idig.occ <- idig_search_records(
#   type = "records",
#   rq = list("scientificname" = species_name),
#   field = "all",
#   max_items = 10000L,
#   limit = 10000L,
#   offset = 0
# )
# 
# idig.occ <- idig.occ %>%
#   mutate(
#     abundance = as.numeric(individualcount),
#     source = "idigbio",
#     occurrenceStatus = "",
#     organismQuantity = ""
#   ) %>%
#   rename(
#     decimalLatitude = geopoint.lat,
#     decimalLongitude = geopoint.lon,
#     basisOfRecord = basisofrecord,
#     catalogNumber = catalognumber,
#     scientificName = scientificname,
#     stateProvince = stateprovince,
#     coordinateUncertaintyInMeters = coordinateuncertainty,
#     individualCount = individualcount,
#     institutionCode = institutioncode,
#     verbatimLocality = verbatimlocality,
#     verbatimEventDate = verbatimeventdate,
#     countryCode = countrycode
#   )
# 
# idig.occ_temp <- idig.occ[, attribute_list]
# str(idig.occ_temp[, 1:3])

## -----------------------------------------------------------------------------
# sym.occ <- example_sp_invertebase
# sym.occ$abundance <- as.numeric(sym.occ$individualCount)
# 
# for (field in attribute_list) {
#   if (!field %in% names(sym.occ)) {
#     sym.occ[[field]] <- NA # Add the missing field as NA
#   }
# }
# 
# str(sym.occ[, 1:3])

## -----------------------------------------------------------------------------
# db_list <- list(gbif.occ_temp, obis.occ_temp, idig.occ_temp, sym.occ)
# Mixdb.occ <- ec_db_merge(db_list = db_list, datatype = "modern")
# 
# str(Mixdb.occ[, 1:3])
# ec_geographic_map(Mixdb.occ, "decimalLatitude", longitude = "decimalLongitude") # display records those has coordinate values

