vegvault: Providing the interface to access the VegVault database • vaultkeepr

The goal of vaultkeepr is to providing the interface to access the VegVault database.

Installation

You can install the development version of vaultkeepr from GitHub with:

# install.packages("remotes")
remotes::install_github("OndrejMottl/vaultkeepr")

Overview

vaultkeepr provides a pipe-friendly interface to the VegVault database. Functions are designed to be chained together to build a lazy query plan that is executed in full with a single call to extract_data().

Connection & exploration

Function	Description
`open_vault()`	Open a connection to a VegVault SQLite file
`explore_vault()`	List all table names in the connected database

Building a query plan

Function	Description
`get_datasets()`	Add dataset metadata to the plan
`select_dataset_by_type()`	Filter by dataset type
`select_dataset_by_geo()`	Filter by geographic bounding box
`get_samples()`	Add sample metadata
`select_samples_by_age()`	Filter samples by age range
`get_age_uncertainty()`	Retrieve age uncertainty estimates for samples
`get_taxa()`	Add taxon occurrence data
`select_taxa_by_name()`	Filter taxa by name
`select_taxa_by_id()`	Filter taxa by ID
`get_taxon_names()`	Retrieve distinct taxon names from the current plan
`get_classification_table()`	Retrieve the taxonomic classification table
`get_traits()`	Add functional trait data
`select_traits_by_domain_name()`	Filter traits by domain
`get_abiotic_data()`	Add spatially matched abiotic (climate/soil) data
`select_abiotic_var_by_name()`	Filter abiotic variables by name
`select_abiotic_var_by_id()`	Filter abiotic variables by ID

Executing the plan

Function	Description
`extract_data()`	Execute the query plan and return a tibble
`get_references()`	Retrieve data-source references

Usage

library(vaultkeepr)

# Open the database
con <-
  open_vault(path = "path/to/VegVault.sqlite")

# Inspect available tables
explore_vault(con = con)

# Build and execute a query plan
data_betula <-
  con |>
  get_datasets() |>
  select_dataset_by_type(
    sel_dataset_type = c("vegetation_plot", "fossil_pollen_archive")
  ) |>
  get_samples() |>
  get_taxa() |>
  select_taxa_by_name(sel_taxa = "Betula") |>
  extract_data()

For detailed worked examples, see the package website.