This tutorial explains how to use our reptile taxonomy functions to format and access structured information about reptile species in your research or applications.
Our package provides two main functions for working with reptile taxonomic data:
format_all_attributes()format_selected_attributes()These functions help researchers efficiently organize and access taxonomic information about reptile species from various sources into standardized formats.
format_all_attributes()The format_all_attributes() function processes a
complete reptile dataset and formats all available taxonomic attributes
into structured tibbles (data frames).
reptile_data: A dataframe or list containing raw
reptile taxonomic informationThe function returns a list of tibbles, with each tibble containing a specific category of taxonomic information:
distribution: Geographic distribution informationsynonyms: Scientific name synonyms throughout taxonomic
historyhigher_taxa: Taxonomic classification hierarchysubspecies: Information about recognized
subspeciescommon_names: Common names in different languagesreproduction: Reproductive biology informationtypes: Type specimen informationdiagnosis: Diagnostic morphological featurescomments: Additional taxonomic notesetymology: Origin and meaning of scientific namesreferences: Scientific literature referenceslibrary(reptiledbr)
#> Welcome to reptiledbr (0.0.1)
#> This package provides tools to access and query data from the Reptile Database:
#> https://reptile-database.reptarium.cz/
#> Type ?reptiledbr to get started or visit the documentation for examples and guidance.
species_list <- c(
"Lachesis muta",
"Python bivittatus",
"Crotalus atrox",
"Bothrops atrox insularis", # Trinomial (con subespecie) - debería dar error
"Lachesis sp"
)
#
reptile_data <- get_reptiledb_data(species_list)
#> Starting search for 5 species...
#> Maximum wait time per request: 10 seconds
#> ------------------------------------------------------------------------
#> Processing: Lachesis muta
#> SUCCESS: Data found for Lachesis muta
#> Processing: Python bivittatus
#> SUCCESS: Data found for Python bivittatus
#> Processing: Crotalus atrox
#> SUCCESS: Data found for Crotalus atrox
#> Processing: Bothrops atrox insularis
#> WARNING: Trinomial name detected: Bothrops atrox insularis
#> The database only supports binomial names (genus and species).
#> No search will be performed for trinomial names.
#> Processing: Lachesis sp
#> SUCCESS: Data found for Lachesis sp
#> WARNING: No data table found for Lachesis sp
#> This is considered an error and the URL will be removed.
#> ------------------------------------------------------------------------
#> Search summary:
#> Species found with data: 3
#> Species not found: 0
#> Total errors: 2
#> - Trinomial names (not supported): 1
#> - Pages without data tables: 1
#> ------------------------------------------------------------------------
#
reptile_data
#> # A tibble: 5 × 7
#> input_name genus species url status error_message data
#> <chr> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 Lachesis muta Lachesis muta http… succe… <NA> <tibble>
#> 2 Python bivittatus Python bivitta… http… succe… <NA> <tibble>
#> 3 Crotalus atrox Crotalus atrox http… succe… <NA> <tibble>
#> 4 Bothrops atrox insularis Bothrops atrox <NA> error Trinomial na… <tibble>
#> 5 Lachesis sp Lachesis sp <NA> error Page found b… <tibble>
# Format all attributes
all_attributes <- format_all_attributes(reptile_data)
#> Starting to format all attributes...
#> Attribute formatting successfully completed.
# Access specific attribute categories
all_attributes$distribution
#> # A tibble: 11 × 4
#> input_name genus species distribution
#> <chr> <chr> <chr> <chr>
#> 1 Lachesis muta Lachesis muta "Colombia, E Ecuador, Brazil (Minas Ge…
#> 2 Lachesis muta Lachesis muta "rhombeata: Brazil (Alagoas, Bahia, Ri…
#> 3 Lachesis muta Lachesis muta "Type locality: Suriname; restricted t…
#> 4 Python bivittatus Python bivittatus "SE Nepal, India (Assam, Tripura, Sikk…
#> 5 Python bivittatus Python bivittatus "Introduced to Florida (USA)"
#> 6 Python bivittatus Python bivittatus "progschai: Sulawesi; Type locality: S…
#> 7 Python bivittatus Python bivittatus "Type locality: Java (designated by ME…
#> 8 Crotalus atrox Crotalus atrox "USA (SE California, S Nevada [A Heind…
#> 9 Crotalus atrox Crotalus atrox "Mexico (Mexico [HR 35: 190], Baja Cal…
#> 10 Crotalus atrox Crotalus atrox "Type locality: \"Indianola\" [Indiano…
#> 11 Crotalus atrox Crotalus atrox "tortugensis: Mexico (Isla Tortuga in …
all_attributes$common_names
#> # A tibble: 13 × 4
#> input_name genus species common_name
#> <chr> <chr> <chr> <chr>
#> 1 Lachesis muta Lachesis muta E: South American Bushmaster
#> 2 Lachesis muta Lachesis muta G: Südamerikanischer Buschmeister
#> 3 Lachesis muta Lachesis muta rhombeata: Atlantic Forest bushmaster
#> 4 Lachesis muta Lachesis muta NL: Bosmeester
#> 5 Lachesis muta Lachesis muta Portuguese: Bico-de-Jaca, Cobra-Topete…
#> 6 Lachesis muta Lachesis muta S: Cuaima; Guayma concha de piña
#> 7 Python bivittatus Python bivittatus E: Burmese Python
#> 8 Python bivittatus Python bivittatus G: Dunkler Tigerpython
#> 9 Python bivittatus Python bivittatus Chinese: 蟒
#> 10 Crotalus atrox Crotalus atrox E: Western Diamond-backed Rattlesnake
#> 11 Crotalus atrox Crotalus atrox G: Texas-Klapperschlange, Westliche Di…
#> 12 Crotalus atrox Crotalus atrox E: Tortuga Island Rattlesnake (tortuge…
#> 13 Crotalus atrox Crotalus atrox S: Cascabel de Diamantesformat_selected_attributes()If you only need specific taxonomic attributes, the
format_selected_attributes() function allows you to extract
only the categories you’re interested in.
reptile_data: A dataframe or list containing raw
reptile taxonomic informationattributes: A character vector listing the specific
attributes to extractquiet: Logical value. If TRUE, suppresses processing
messagesReturns a list containing only the requested attribute tibbles.
# Extract only synonyms, higher taxa, and common names
selected_info <- format_selected_attributes(
reptile_data = reptile_data,
attributes = c("Synonym", "Higher Taxa", "Common Names"),
quiet = TRUE
)
# Access the selected information
selected_info$Synonym
#> # A tibble: 78 × 4
#> input_name genus species synonym
#> <chr> <chr> <chr> <chr>
#> 1 Lachesis muta Lachesis muta Crotalus mutus LINNAEUS 1766: 373
#> 2 Lachesis muta Lachesis muta Coluber crotalinus GMELIN 1789: 1094
#> 3 Lachesis muta Lachesis muta Scytale catenatus LATREILLE in SONNINI and LA…
#> 4 Lachesis muta Lachesis muta Scytale ammodytes LATREILLE in SONNINI and LA…
#> 5 Lachesis muta Lachesis muta Coluber alecto SHAW 1802: 405
#> 6 Lachesis muta Lachesis muta Lachesis mutus — DAUDIN 1803: 351
#> 7 Lachesis muta Lachesis muta Lachesis ater — DAUDIN 1803: 354
#> 8 Lachesis muta Lachesis muta Trigonocephalus ammodytes — OPPEL 1811: 390
#> 9 Lachesis muta Lachesis muta Cophias crotalinus — MERREM 1820: 144
#> 10 Lachesis muta Lachesis muta Trigonocephalus crotalinus — SCHINZ 1822: 144
#> # ℹ 68 more rows
selected_info$`Higher Taxa`
#> # A tibble: 20 × 4
#> input_name genus species taxon
#> <chr> <chr> <chr> <chr>
#> 1 Lachesis muta Lachesis muta Viperidae
#> 2 Lachesis muta Lachesis muta Crotalinae
#> 3 Lachesis muta Lachesis muta Colubroidea
#> 4 Lachesis muta Lachesis muta Caenophidia
#> 5 Lachesis muta Lachesis muta Alethinophidia
#> 6 Lachesis muta Lachesis muta Serpentes
#> 7 Lachesis muta Lachesis muta Squamata (snakes)
#> 8 Python bivittatus Python bivittatus Pythonidae
#> 9 Python bivittatus Python bivittatus Henophidia
#> 10 Python bivittatus Python bivittatus Pythonoidea
#> 11 Python bivittatus Python bivittatus Alethinophidia
#> 12 Python bivittatus Python bivittatus Serpentes
#> 13 Python bivittatus Python bivittatus Squamata (snakes)
#> 14 Crotalus atrox Crotalus atrox Viperidae
#> 15 Crotalus atrox Crotalus atrox Crotalinae
#> 16 Crotalus atrox Crotalus atrox Colubroidea
#> 17 Crotalus atrox Crotalus atrox Caenophidia
#> 18 Crotalus atrox Crotalus atrox Alethinophidia
#> 19 Crotalus atrox Crotalus atrox Serpentes
#> 20 Crotalus atrox Crotalus atrox Squamata (snakes)
selected_info$`Common Names`
#> # A tibble: 13 × 4
#> input_name genus species common_name
#> <chr> <chr> <chr> <chr>
#> 1 Lachesis muta Lachesis muta E: South American Bushmaster
#> 2 Lachesis muta Lachesis muta G: Südamerikanischer Buschmeister
#> 3 Lachesis muta Lachesis muta rhombeata: Atlantic Forest bushmaster
#> 4 Lachesis muta Lachesis muta NL: Bosmeester
#> 5 Lachesis muta Lachesis muta Portuguese: Bico-de-Jaca, Cobra-Topete…
#> 6 Lachesis muta Lachesis muta S: Cuaima; Guayma concha de piña
#> 7 Python bivittatus Python bivittatus E: Burmese Python
#> 8 Python bivittatus Python bivittatus G: Dunkler Tigerpython
#> 9 Python bivittatus Python bivittatus Chinese: 蟒
#> 10 Crotalus atrox Crotalus atrox E: Western Diamond-backed Rattlesnake
#> 11 Crotalus atrox Crotalus atrox G: Texas-Klapperschlange, Westliche Di…
#> 12 Crotalus atrox Crotalus atrox E: Tortuga Island Rattlesnake (tortuge…
#> 13 Crotalus atrox Crotalus atrox S: Cascabel de DiamantesOnce you have formatted your reptile data, you can use standard R data manipulation techniques to analyze the information:
# Find all venomous species
all_attributes$comments |>
dplyr::filter(stringr::str_detect(comment_detail, "Venomous"))
#> # A tibble: 2 × 4
#> input_name genus species comment_detail
#> <chr> <chr> <chr> <chr>
#> 1 Lachesis muta Lachesis muta Venomous!
#> 2 Crotalus atrox Crotalus atrox Venomous! Crotalus atrox is responsible for m…
# Extract distribution information for a specific species
all_attributes$distribution |>
dplyr::filter(input_name == "Crotalus atrox")
#> # A tibble: 4 × 4
#> input_name genus species distribution
#> <chr> <chr> <chr> <chr>
#> 1 Crotalus atrox Crotalus atrox "USA (SE California, S Nevada [A Heindl, pers…
#> 2 Crotalus atrox Crotalus atrox "Mexico (Mexico [HR 35: 190], Baja California…
#> 3 Crotalus atrox Crotalus atrox "Type locality: \"Indianola\" [Indianola, Cal…
#> 4 Crotalus atrox Crotalus atrox "tortugensis: Mexico (Isla Tortuga in the Gul…Each formatted attribute tibble includes at least these standard columns:
input_name: The original species name providedgenus: The genus namespecies: The species epithetdistribution,
common_name, synonym, etc.)This consistent structure allows for easy filtering, joining, and analysis across different attribute categories.
format_selected_attributes() to improve performanceinput_name column provides a reliable key for
joining information across different attribute tibblesBy effectively using these formatting functions, you can streamline your reptile taxonomic research and data analysis workflows.