## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = F,
  warning = FALSE
)

## ----eval=T, echo=F, fig.align='center', out.width='80%', fig.cap="Araucaria forest in southern Brazil"----
knitr::include_graphics("vignettes_img/paranapine2.jpeg")

## ----eval = T-----------------------------------------------------------------
# Load RuHere package
library(RuHere)

## -----------------------------------------------------------------------------
# set_gbif_credentials(
#   gbif_username = "your_username",
#   gbif_email = "your_email@domain.com",
#   gbif_password = "your_password",
#   verbose = FALSE
# )

## -----------------------------------------------------------------------------
# set_specieslink_credentials(specieslink_key = "your_api_key", verbose = FALSE)

## -----------------------------------------------------------------------------
# # Store downloads in a temporary directory
# # In your own project, replace this with a permanent directory
# output_dir <- file.path(tempdir(), "occ_data")
# dir.create(output_dir)

## -----------------------------------------------------------------------------
# # Prepare the taxonomic query
# gbif_prep <- prepare_gbif_download(species = "Araucaria angustifolia")

## -----------------------------------------------------------------------------
# gbif_prep
# #> with_coordinates n_records                species usageKey                          scientificName          canonicalName
# #> 1             3056     15696 Araucaria angustifolia  2684940 Araucaria angustifolia (Bertol.) Kuntze Araucaria angustifolia
# #>     rank   status confidence matchType kingdom       phylum   order        family     genus kingdomKey phylumKey classKey
# #> 1 SPECIES ACCEPTED         97     EXACT Plantae Tracheophyta Pinales Araucariaceae Araucaria          6   7707728      194
# #>   orderKey familyKey genusKey speciesKey     class          verbatim_name
# #> 1      640      3924  2684910    2684940 Pinopsida Araucaria angustifolia

## -----------------------------------------------------------------------------
# # Submit the request to GBIF
# gbif_req <- request_gbif(
#   gbif_info = gbif_prep,
#   hasCoordinate = TRUE,      # Retrieve only records with coordinates
#   hasGeospatialIssue = FALSE # Exclude records with geospatial issues
# )

## -----------------------------------------------------------------------------
# rgbif::occ_download_wait(gbif_req)
# #> status: succeeded
# #> download is done, status: succeeded

## -----------------------------------------------------------------------------
# # Import the processed file
# occ_gbif <- import_gbif(request_key = gbif_req)
# #> Download file size: 2.04 MB

## -----------------------------------------------------------------------------
# head(occ_gbif)
# #> # A tibble: 6 × 25
# #>   scientificName                acceptedScientificName occurrenceID collectionCode catalogNumber decimalLongitude #> decimalLatitude
# #>   <chr>                         <chr>                  <chr>        <chr>          <chr>                    <dbl>           #> <dbl>
# #> 1 Araucaria angustifolia (Bert… Araucaria angustifoli… urn:catalog… ALTA-VP        74703                    -52.8          #> -26.4
# #> 2 Araucaria angustifolia (Bert… Araucaria angustifoli… https://www… Observations   329390275                -49.2          #> -25.4
# #> 3 Araucaria angustifolia (Bert… Araucaria angustifoli… https://www… Observations   329483122                -49.5          #> -28.0
# #> 4 Araucaria angustifolia (Bert… Araucaria angustifoli… https://www… Observations   329455309                -74.0            #> 4.68
# #> 5 Araucaria angustifolia (Bert… Araucaria angustifoli… https://www… Observations   329576016                -45.6          #> -22.7
# #> 6 Araucaria angustifolia (Bert… Araucaria angustifoli… https://www… Observations   329805643                175.           #> -39.9
# #> # ℹ 18 more variables: coordinateUncertaintyInMeters <dbl>, elevation <dbl>, continent <chr>, countryCode <chr>,
# #> #   stateProvince <chr>, municipality <chr>, locality <chr>, verbatimLocality <chr>, year <int>, eventDate <chr>,
# #> #   recordedBy <chr>, identifiedBy <chr>, basisOfRecord <chr>, occurrenceRemarks <chr>, habitat <chr>, datasetName <chr>,
# #> #   datasetKey <chr>, speciesKey <int>

## -----------------------------------------------------------------------------
# # SpeciesLink: Filtering by Species
# occ_sl <- get_specieslink(species = "Araucaria angustifolia", verbose = FALSE)
# 
# # BIEN: Natives only, excluding cultivated records
# occ_bien <- get_bien(species = "Araucaria angustifolia",
#                      cultivated = FALSE,
#                      natives.only = TRUE,
#                      verbose = FALSE)
# #> Getting page 1 of records
# 
# # iDigBio:
# occ_idig <- get_idigbio(species = "Araucaria angustifolia")

## -----------------------------------------------------------------------------
# all_occ <- bind_here(occ_gbif, occ_sl, occ_bien, occ_idig)
# #> Error: All datasets must have the same columns.

## -----------------------------------------------------------------------------
# # Standardizing GBIF
# gbif_std <- format_columns(occ_gbif, metadata = "gbif")
# 
# # Standardizing SpeciesLink (checking for encoding issues)
# sl_std <- format_columns(occ_sl, metadata = "specieslink", check_encoding = TRUE)
# #> Warning: NAs introduced by coercion>
# 
# # Standardizing BIEN
# bien_std <- format_columns(occ_bien, metadata = "bien")
# 
# # Standardizing iDigBio
# idig_std <- format_columns(occ_idig, metadata = "idigbio")

## -----------------------------------------------------------------------------
# all_occ <- bind_here(gbif_std, sl_std, bien_std, idig_std)
# # Number of records by database
# table(all_occ$data_source)
# #>       bien        gbif     idigbio specieslink
# #>        137        3000        1271        1224

## ----eval = TRUE--------------------------------------------------------------
# Import data example
data("puma_atlanticr", package = "RuHere")

# Create metadata to standardize the occurrences
puma_metadata <- create_metadata(occ = puma_atlanticr,
                                 scientificName = "actual_species_name",
                                 decimalLongitude = "longitude",
                                 decimalLatitude = "latitude",
                                 elevation = "altitude",
                                 country = "country",
                                 stateProvince = "state",
                                 municipality = "municipality",
                                 locality = "study_location",
                                 year = "year_finish",
                                 habitat = "vegetation_type",
                                 datasetName = "reference")

# Now, we can use this metadata to standardize the columns
puma_occ <- format_columns(occ = puma_atlanticr, metadata = puma_metadata,
                           binomial_from = "actual_species_name",
                           data_source = "atlanticr")

head(puma_occ[, 1:5])

## -----------------------------------------------------------------------------
# occ_araucaria_cougar <- bind_here(all_occ, #Occurrences of Araucaria
#                                   puma_occ) #Occurrences of cougar
# # Number of records per species
# table(occ_araucaria_cougar$species)
# #> Araucaria angustifolia          Puma concolor
# #>                  5632                    139

## -----------------------------------------------------------------------------
# # Convert to spatial object
# occ_spatial <- spatialize(occ = occ_araucaria_cougar)
# 
# # Load mapview
# library(mapview)
# 
# # Plot the distribution using mapview
# mapview(occ_spatial, zcol = "species", layer.name = "Species", cex = 4)

## ----IMG01, eval=T, echo=F, fig.align='center', out.width='80%'---------------
knitr::include_graphics("vignettes_img/IMG01.jpeg")