Introduction to rbiodatacr

Overview

rbiodatacr is an R client for querying BIODATACR, the national biodiversity information platform of Costa Rica managed by the Technical Office of CONAGEBIO. The platform is built on the Atlas of Living Australia (ALA) API infrastructure.

library(rbiodatacr)
library(dplyr)
library(sf)
library(ggplot2)

2. Counting records

Use bdcr_count() to check how many occurrence records are available before downloading.

bdcr_count("Panthera onca")
#> [1] 313

For multiple species at once use bdcr_count_batch(), which returns a tidy tibble with one row per species.

species <- c(
  "Tapirus bairdii",
  "Panthera onca",
  "Ara ambiguus",
  "Bradypus variegatus"
)

conteos <- bdcr_count_batch(species)
conteos
#> # A tibble: 4 × 2
#>   taxon               n_records
#>   <chr>                   <int>
#> 1 Tapirus bairdii             1
#> 2 Panthera onca             313
#> 3 Ara ambiguus             1216
#> 4 Bradypus variegatus      4151

3. Downloading occurrence records

bdcr_occurrences() downloads records for a single species and returns a tibble with 15 fields relevant for biodiversity analysis.

df_jaguar <- bdcr_occurrences("Panthera onca", rows = 100)
glimpse(df_jaguar)
#> Rows: 100
#> Columns: 15
#> $ scientificName   <chr> "Panthera onca subsp. centralis (Mearns, 1901)", "Pan…
#> $ vernacularName   <chr> "Central American Jaguar", "Jaguar Panthera onca", "J…
#> $ decimalLatitude  <dbl> 10.91970, 9.95000, 10.47563, 10.68948, 10.48542, 10.5…
#> $ decimalLongitude <dbl> -85.01460, -84.00000, -83.46852, -84.14154, -83.81592…
#> $ year             <int> 1993, NA, 2021, 2013, 2013, NA, 2013, NA, 2022, 2013,…
#> $ month            <chr> "06", NA, "12", "06", "04", NA, "10", NA, "05", "09",…
#> $ basisOfRecord    <chr> "PreservedSpecimen", "PreservedSpecimen", "HumanObser…
#> $ dataResourceName <chr> "Modelado de la distribución geográfica de mamíferos …
#> $ country          <chr> "Costa Rica", "Costa Rica", "Costa Rica", "Costa Rica…
#> $ family           <chr> "Felidae", "Felidae", "Felidae", "Felidae", "Felidae"…
#> $ species          <chr> "Panthera onca", "Panthera onca", "Panthera onca", "P…
#> $ collector        <chr> "NO DISPONIBLE", "Ch. d'Eternod", "UACFel (SINAC-Pant…
#> $ license          <chr> "other", "other", "other", "other", "other", "other",…
#> $ geospatialKosher <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
#> $ taxonomicKosher  <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…

For multiple species use bdcr_occurrences_batch(), which returns a named list of tibbles — one per species.

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)
#>       Panthera onca        Ara ambiguus Bradypus variegatus 
#>                 100                 100                 100

4. Quality control

bdcr_quality_check() adds a quality_flag column to the occurrences tibble. Possible flags are:

Flag Condition
"ok" No issues detected
"no_coords" Missing coordinates
"geospatial_issue" geospatialKosher == FALSE
"taxonomic_issue" taxonomicKosher == FALSE
"old_record" Year before minimum threshold (default 1950) 
df_qc <- bdcr_quality_check(df_jaguar)

count(df_qc, quality_flag, sort = TRUE)
#> # A tibble: 1 × 2
#>   quality_flag     n
#>   <chr>        <int>
#> 1 ok             100

Keep only clean records:

df_clean <- filter(df_qc, quality_flag == "ok",
                         !is.na(decimalLatitude),
                         !is.na(decimalLongitude))
nrow(df_clean)
#> [1] 100

5. Mapping occurrence records

Convert the clean tibble to an sf object and plot the records over Costa Rica.

# 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()

6. Complete 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)
#> # A tibble: 3 × 2
#>   taxon               clean_records
#>   <chr>                       <int>
#> 1 Panthera onca                 200
#> 2 Ara ambiguus                  199
#> 3 Bradypus variegatus           199