Reptile Data Formatting Tutorial

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This tutorial explains how to use our reptile taxonomy functions to format and access structured information about reptile species in your research or applications.

Overview of Functions

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.

Using `format_all_attributes()`

The format_all_attributes() function processes a complete reptile dataset and formats all available taxonomic attributes into structured tibbles (data frames).

Syntax

format_all_attributes(reptile_data)

Parameters

reptile_data: A dataframe or list containing raw reptile taxonomic information

Return Value

The function returns a list of tibbles, with each tibble containing a specific category of taxonomic information:

distribution: Geographic distribution information
synonyms: Scientific name synonyms throughout taxonomic history
higher_taxa: Taxonomic classification hierarchy
subspecies: Information about recognized subspecies
common_names: Common names in different languages
reproduction: Reproductive biology information
types: Type specimen information
diagnosis: Diagnostic morphological features
comments: Additional taxonomic notes
etymology: Origin and meaning of scientific names
references: Scientific literature references

Example

library(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 Diamantes

Using `format_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.

Syntax

format_selected_attributes(reptile_data, attributes, quiet = FALSE)

Parameters

reptile_data: A dataframe or list containing raw reptile taxonomic information
attributes: A character vector listing the specific attributes to extract
quiet: Logical value. If TRUE, suppresses processing messages

Return Value

Returns a list containing only the requested attribute tibbles.

Example

# 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 Diamantes

Working with the Formatted Data

Once 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…

Data Structure

Each formatted attribute tibble includes at least these standard columns:

input_name: The original species name provided
genus: The genus name
species: The species epithet
Attribute-specific column (e.g., distribution, common_name, synonym, etc.)

This consistent structure allows for easy filtering, joining, and analysis across different attribute categories.

Tips for Effective Use

When working with large datasets, use format_selected_attributes() to improve performance
Consider that some species may have multiple entries in certain categories (e.g., multiple common names or distribution records)
The input_name column provides a reliable key for joining information across different attribute tibbles

By effectively using these formatting functions, you can streamline your reptile taxonomic research and data analysis workflows.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.

Reptile Data Formatting Tutorial

Overview of Functions

Using format_all_attributes()

Syntax

Parameters

Return Value

Example

Using format_selected_attributes()

Syntax

Parameters

Return Value

Example

Working with the Formatted Data

Data Structure

Tips for Effective Use

Using `format_all_attributes()`

Using `format_selected_attributes()`