Getting started with sharkipediaR

Why this package exists

Sharkipedia is a curated, open database of shark and ray life-history traits and population abundance time series — the kind of comparative resource that underpins fisheries assessment, extinction-risk analysis, and macroecology (Dulvy et al., 2022). sharkipediaR brings those data into R as tidy tibbles so your workflows look like any other modern ecological analysis: %>% pipes, explicit provenance, ggplot-ready tables, and interactive plotly charts in the ecological vignette.

The package is deliberately not a bulk mirror of the website. It is a polite, reproducible client: rate-limited requests, in-session caching, and HTML parsing separated from cleaning and validation.

Installation

# install.packages("devtools")
devtools::install_github("ecologistpablo/SharkpediaR")

library(sharkipediaR)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

A thirty-second workflow

With an internet connection, you can pull data for a single species in three lines:

meta   <- sp_species("Carcharhinus acronotus")
traits <- sp_traits("Carcharhinus acronotus")
trends <- sp_trends("Carcharhinus acronotus")

Every user-facing table includes source_url and retrieved_at so you can cite exactly what was downloaded and when — essential for Red List workshops, stock assessments, and supplementary materials.

Offline example data

Vignettes and R CMD check use a frozen example dataset so documentation builds without hitting the live site:

ex <- example_carcharhinus()
names(ex)
#> [1] "species_meta"  "traits"        "trends"        "references"   
#> [5] "species_index"

List element	Description
`species_meta`	Taxonomy + provenance (one row)
`traits`	Long-format life-history trait measurements
`trends`	Long-format population trend observations
`references`	Reference IDs linked from the species page
`species_index`	Sample rows from the public species index

Exported functions at a glance

Function	Script	Purpose
`sp_species_urls()`	`R/sp_species_urls.R`	Discover species names and URLs from the index
`sp_search()`	`R/sp_search.R`	Filter the index by partial scientific name
`sp_species()`	`R/sp_species.R`	Taxonomy metadata for one species
`sp_traits()`	`R/sp_traits.R`	Life-history traits (long format)
`sp_trends()`	`R/sp_trends.R`	Population trends (long format)
`sp_references()`	`R/sp_references.R`	Bibliographic reference links
`fetch_page()`	`R/fetch.R`	Low-level HTML retrieval (advanced)
`example_carcharhinus()`	`R/example_data.R`	Offline example data

Internal parsing and cleaning live in R/parse.R, R/clean.R, and R/validate.R — see the Architecture and function reference vignette.

Where to go next

Ecological workflows — trait summaries, interactive plotly trend plots, %>% workflows.
Architecture and function reference — how every function in each script works, with pipeline examples.

Building the pkgdown site

install.packages(c("pkgdown", "ggplot2", "plotly", "htmltools"))
pkgdown::build_site()

The site will include these vignettes under Articles automatically.

Pablo Fuenzalida