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

## ----setup, eval=TRUE---------------------------------------------------------
library(rbiodatacr)
library(dplyr)
library(sf)
library(ggplot2)

## ----species-search-----------------------------------------------------------
# bdcr_species_search("Panthera onca")

## ----count-single-------------------------------------------------------------
# bdcr_count("Panthera onca")

## ----count-batch--------------------------------------------------------------
# species <- c(
#   "Tapirus bairdii",
#   "Panthera onca",
#   "Ara ambiguus",
#   "Bradypus variegatus"
# )
# 
# conteos <- bdcr_count_batch(species)
# conteos

## ----occurrences-single-------------------------------------------------------
# df_jaguar <- bdcr_occurrences("Panthera onca", rows = 100)
# glimpse(df_jaguar)

## ----occurrences-batch--------------------------------------------------------
# spp_with_data <- filter(conteos, n_records >= 10)
# 
# lista_occ <- bdcr_occurrences_batch(
#   taxa = spp_with_data$taxon,
#   rows = 100
# )
# 
# # Number of records per species
# purrr::map_int(lista_occ, nrow)

## ----quality-check------------------------------------------------------------
# df_qc <- bdcr_quality_check(df_jaguar)
# 
# count(df_qc, quality_flag, sort = TRUE)

## ----quality-filter-----------------------------------------------------------
# df_clean <- filter(df_qc, quality_flag == "ok",
#                          !is.na(decimalLatitude),
#                          !is.na(decimalLongitude))
# nrow(df_clean)

## ----map, fig.width = 7, fig.height = 6---------------------------------------
# # Convert to sf
# df_sf <- st_as_sf(
#   df_clean,
#   coords = c("decimalLongitude", "decimalLatitude"),
#   crs    = 4326
# )
# 
# # Load Costa Rica national boundary included in rbiodatacr
# # Source: GADM (gadm.org), level 0 = country boundary
# data(cr_outline)
# 
# # Map
# ggplot() +
#   geom_sf(data = cr_outline, fill = "gray95", color = "gray50") +
#   geom_sf(data = df_sf, color = "#E63946", size = 2, alpha = 0.7) +
#   labs(
#     title    = "Panthera onca — BIODATACR occurrence records",
#     subtitle = paste0(nrow(df_sf), " clean records"),
#     caption  = "Source: BIODATACR (biodiversidad.go.cr)",
#     x = "Longitude",
#     y = "Latitude"
#   ) +
#   theme_minimal()

## ----workflow-----------------------------------------------------------------
# # 1. Check availability
# species <- c("Tapirus bairdii", "Panthera onca",
#              "Ara ambiguus",    "Bradypus variegatus")
# 
# conteos <- bdcr_count_batch(species)
# 
# # 2. Download species with enough data
# con_datos <- filter(conteos, n_records >= 10)
# 
# lista_occ <- bdcr_occurrences_batch(
#   taxa = con_datos$taxon,
#   rows = 200
# )
# 
# # 3. Quality control
# lista_limpia <- purrr::map(lista_occ, bdcr_quality_check)
# 
# # 4. Consolidate and filter
# df_final <- bind_rows(lista_limpia, .id = "taxon") |>
#   filter(quality_flag == "ok",
#          !is.na(decimalLatitude),
#          !is.na(decimalLongitude))
# 
# # 5. Summary
# df_final |>
#   count(taxon, sort = TRUE) |>
#   rename(clean_records = n)