Changelog

Source: NEWS.md

vaultkeepr 0.1.0

New functions

• get_taxon_names() - retrieve distinct taxon names from the Vault database
• get_age_uncertainty() - retrieve age uncertainty estimates for samples (wide and long format)
• explore_vault() - retrieve table names from a connected VegVault database
• get_classification_table() - retrieve classification data for inspecting and customising taxonomic classification

Improvements

• classify_taxa() - add a cli warning when automatic classification is used (i.e., to is not "original") to alert users about potential errors; add optional classification_data argument for user-supplied override tables
• open_vault() - add validation to ensure the supplied path points to a valid SQLite file

Refactoring

• assertthat_cli helper
  • replace assertthat::assert_that() with a new assertthat_cli() helper using cli and stringr for clearer error and warning messages across all exported functions; cli and stringr moved to Imports

vaultkeepr 0.0.6

• get_readable_column_names() - specify the link type for each table to improve the performance

• classify_taxa() - change the link type to “inner” to increase the performance.

tests

• update tests for extract_data()

• update the “helper database” (for testing) - make sure we do reference gridpoints in the dataset_source_type table

vaultkeepr 0.0.5

• get_references() - It now (optionally) outputs the source of the referecnes (i.e., the table where the references are stored)

• extract_data() - perform a check via get_references() and output reminder message to the user to cite mandatory references

vaultkeepr 0.0.4

• extract_data() - now returns human-readable column names (NAMES) by default (not IDs) and pack all data (samples) into several tibbles

• get_references() - the input is switched directly to a plan (no need to path and/or extracting data to get references)

vaultkeepr 0.0.3

• get_reference_data() - new function to get the references present in the data compilation extracted from the database

vaultkeepr 0.0.2

• get_abiotic_data()- now link with the distance data (i.e., needs to have both “gridpoints” samples and “non-gridpoints” samples to be present)
• removed select_abiotic_data_by_distance() as it is built within get_abiotic_data()
• Dummy database - add the AbioticDataReference data and adjust the creation of “gridpoints” data to have several random points around “non-gridpoints” data.
• Test - remove most hardcoded values and replace them with evaluations based on real size from the database