# taxify

*offline taxonomic name resolution*

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**Offline taxonomic name matching against local Darwin Core backbones, with matching done in C.**

Hand it a column of messy species names. `taxify` cleans them, matches them against
a backbone you already have on disk, resolves synonyms to accepted names, and returns
one standardized data.frame. Every step runs locally against a versioned snapshot, so
there are no API calls, no rate limits, and the same input gives the same output on any
machine. The matching engine is written in C through the [vectra](https://github.com/gcol33/vectra)
columnar engine.

```r
library(taxify)

# match against WFO (downloads the backbone on first use, ~120 MB)
taxify(c(
  "Quercus robur",
  "Pinus abies",        # synonym, resolved to Picea abies
  "Quercus robus",      # typo, fuzzy-corrected to Q. robur
  "Taraxacum officinale"
))
```

## Local, not over the wire

The usual route for name resolution, `taxize`, calls out to around twenty web services
(NCBI, ITIS, GBIF, EOL, IUCN, WoRMS, Tropicos, ...). That covers everything, but it ties
each run to network latency, service uptime, and rate limits, and the answer can change
between runs as upstream services update. `taxify` ships the backbones as pre-built local
snapshots and matches against them in C, so a list of thousands resolves in seconds and a
result is reproducible from the recorded backbone version.

The closest local analogue is [taxadb](https://docs.ropensci.org/taxadb/), which also stores
backbone snapshots on disk; the [migration vignette](https://gillescolling.com/taxify/articles/migration.html)
walks through the differences in matching strategy, output schema, and enrichment.

## Thirteen backbones, one call

`taxify` ships thirteen backbones as compressed `.vtr` files, downloaded once and matched
locally. Pass several and they form a fallback chain: a name unmatched by the first
backbone cascades to the next.

```r
# WFO first (plants), then GBIF for whatever WFO doesn't cover
taxify(
  c("Quercus robur", "Panthera leo", "Amanita muscaria"),
  backend = c("wfo", "gbif")
)
```

| Backend | Scope | Approx. names |
|---------|-------|---------------|
| [WFO](https://www.worldfloraonline.org/) | Vascular plants | ~400k |
| [COL](https://www.catalogueoflife.org/) | All kingdoms | ~4.5M |
| [GBIF](https://www.gbif.org/) | All kingdoms | ~10M |
| [ITIS](https://www.itis.gov) | US focus, freshwater/marine | ~900k |
| [NCBI Taxonomy](https://www.ncbi.nlm.nih.gov/taxonomy) | All life | ~2.5M |
| [Open Tree of Life](https://opentreeoflife.github.io/) | All life (synthetic) | ~4M |
| [WoRMS](https://www.marinespecies.org/) | Marine/aquatic | ~600k |
| [Euro+Med](https://europlusmed.org/) | European/Mediterranean plants | ~132k |
| [Species Fungorum](https://www.speciesfungorum.org/) | Fungi | ~329k |
| [AlgaeBase](https://www.algaebase.org/) | Algae | ~172k |
| [FishBase](https://www.fishbase.org/) | Fishes | ~100k |
| [SeaLifeBase](https://www.sealifebase.org/) | Non-fish marine/aquatic | ~134k |
| [Reptile Database](http://www.reptile-database.org/) | Reptiles | ~50k |

## Names are cleaned before matching

Input names are normalized first, so the fuzzy pass only runs on names that genuinely
differ from the backbone rather than on names that just carry extra authorship or
qualifiers:

```r
"Quercus robur L."            ->  "Quercus robur"      # authorship stripped
"Pinus cf. sylvestris"        ->  "Pinus sylvestris"   # qualifier removed
"Nothofagus x alpina"         ->  "Nothofagus alpina"  # hybrid marker normalized
"Betula pendula (Roth) Doll"  ->  "Betula pendula"     # parenthesized author stripped
```

Fuzzy matching is configurable (Damerau-Levenshtein, Levenshtein, or Jaro-Winkler, with a
distance threshold), and runs genus-blocked so a typo only competes against names in the
same genus.

On the same WFO backbone and the same 5,000 plant names (Windows, R 4.5.2), matching
against the local snapshot in C avoids the per-name cost of the CSV-into-RAM approach:

| | taxify | WorldFlora |
|---|---|---|
| Exact match (1,000 names) | 0.1 s | 1.3 s |
| Fuzzy match (1,000 names) | 1.0 s | 1,862 s (31 min) |
| Fuzzy match (5,000 names) | 1.1 s | ~83 min (extrapolated) |
| Backbone load | ~3 s (first call) | 33 s (CSV into RAM) |

## What you get back

`taxify()` returns one row per input name with a fixed schema: the matched and accepted
names with their IDs and authorship, rank, family, genus, epithet, synonym / hybrid /
ambiguity flags, any taxonomic qualifier, the match type (`exact`, `exact_ci`, `fuzzy`,
`abbrev`, `out_of_scope`, or `none`), the fuzzy distance, a coarse kingdom / taxon-group
label, the backend, and the backbone version used. `summary()` prints a compact digest of
how the batch resolved.

```r
result <- taxify(c("Quercus robur", "Pinus abies", "Quercus robus", "Taraxacum officinale"))
summary(result)
#> -- taxify results ----------------------------------------------------
#>   backend: WFO  |  4 names submitted
#>
#>   matched         4  (exact: 2, case-insensitive: 0, fuzzy: 2, abbrev: 0)
#>   --------------------------------------------------------------
#>   taxon groups: vascular plant: 4
```

## Trait and status enrichment

More than eighty enrichment layers join published trait and status data to your results through
the backbone-resolved accepted name, so synonyms in either dataset land on the same key:

```r
# plants
taxify(plant_names) |>
  add_iucn() |>                  # IUCN Red List
  add_griis("AT") |>             # GRIIS
  add_zanne() |>                 # Zanne et al. woodiness
  add_eive()                     # EIVE indicator values

# fish
taxify(fish_names, backend = "col") |>
  add_fishbase() |>              # FishBase morphology & ecology
  add_fishmorph()                # FISHMORPH functional traits

# one trait, gathered across every source that has it
taxify(plant_names) |>
  add_trait("seed_mass")         # Diaz + GIFT, harmonized to mg
```

Sources span all kingdoms: IUCN, GRIIS, GBIF common names, WCVP, EIVE, Diaz et al., LEDA,
GIFT, FungalTraits, FUNGuild, AlgaeTraits, EltonTraits, AVONET, PanTHERIA, AmphiBIO, FISHMORPH,
FishBase, AnAge, GloNAF, LepTraits, AnimalTraits, and regional plant-trait sets for France
(Baseflor), Britain (Ecoflora), and Germany (FloraWeb), and more. The
[enrichments vignette](https://gillescolling.com/taxify/articles/enrichments.html) lists
the full set with references and licenses.

To join your own table, `add_data()` auto-detects the species column, matches it through
the same backbone(s) used in the original call, and left-joins. It accepts data.frames,
CSV, CSV.GZ, XLSX, SQLite, and `.vtr`.

```r
result |> add_data("TRY_traits.csv")
result |> add_data("TRY_traits.csv", cols = c("LeafArea", "SLA", "PlantHeight"))
```

## Check a list before you trust it

`inspect()` returns only the names that look wrong, each labelled with what
stands out and the name to use instead. It catches typos, retired synonyms,
made-up genera, near-duplicate spellings, and the lone animal in a list of
plants, and ranks them by whether they need a decision, a second look, or just
optional cleanup.

```r
inspect(field_names)                  # offline register and list checks
inspect(field_names, backbones = TRUE) # also typos, synonyms, ambiguity
```

For regional field lists, `region` steers a fuzzy correction toward species
that actually occur where you work, so a misspelling resolves to the plant that
grows there even when a one-letter neighbour from another continent sits just as
close in spelling. Pass a region name, a TDWG code, or coordinates.

```r
taxify(field_names, region = "Belgium")
taxify(field_names, coords = c(4.35, 50.85))
```

## Installation

```r
install.packages("taxify")
```

Or the development version from GitHub:

```r
install.packages("pak")
pak::pak("gcol33/taxify")          # vectra is installed automatically
```

## Documentation

- [Getting started](https://gillescolling.com/taxify/articles/quickstart.html)
- [Choosing and combining backends](https://gillescolling.com/taxify/articles/backends.html)
- [Fuzzy matching](https://gillescolling.com/taxify/articles/fuzzy-matching.html)
- [Constraining matches to a region](https://gillescolling.com/taxify/articles/regions.html)
- [Enrichments](https://gillescolling.com/taxify/articles/enrichments.html)
- [Custom data](https://gillescolling.com/taxify/articles/custom-data.html)
- [Inspecting a name list](https://gillescolling.com/taxify/articles/inspecting-names.html)
- [Hybrid names](https://gillescolling.com/taxify/articles/hybrid-names.html)
- [Migrating from taxize, WorldFlora, and related tools](https://gillescolling.com/taxify/articles/migration.html)
- [Large-scale workflows](https://gillescolling.com/taxify/articles/large-scale.html)

## Support

> "Software is like sex: it's better when it's free." — Linus Torvalds

I'm a PhD student who builds R packages in my free time because I believe good tools
should be free and open. I started these projects for my own work and figured others
might find them useful too.

If this package saved you some time, buying me a coffee is a nice way to say thanks.
It helps with my coffee addiction.

[![Buy Me A Coffee](https://img.shields.io/badge/-Buy%20me%20a%20coffee-FFDD00?logo=buymeacoffee&logoColor=black)](https://buymeacoffee.com/gcol33)

## License

MIT (see the LICENSE.md file)
