The goal of taxadb is to provide fast, consistent access to
taxonomic data, supporting common tasks such as resolving taxonomic
names to identifiers, looking up higher classification ranks of given
species, or returning a list of all species below a given rank. These
tasks are particularly common when synthesizing data across large
species assemblies, such as combining occurrence records with trait
records.
Existing approaches to these problems typically rely on web APIs, which
can make them impractical for work with large numbers of species or in
more complex pipelines. Queries and returned formats also differ across
the different taxonomic authorities, making tasks that query multiple
authorities particularly complex. taxadb creates a local database of
most readily available taxonomic authorities, each of which is
transformed into consistent, standard, and researcher-friendly tabular
formats.
To get started, install from CRAN
install.pacakges("taxadb")or install the development version directly from GitHub:
devtools::install_github("ropensci/taxadb")library(taxadb)
library(dplyr) # Used to illustrate how a typical workflow combines nicely with `dplyr`Create a local copy of the Catalogue of Life (2018) database:
td_create("col", overwrite=FALSE)Read in the species list used by the Breeding Bird Survey:
bbs_species_list <- system.file("extdata/bbs.tsv", package="taxadb")
bbs <- read.delim(bbs_species_list)Two core functions are get_ids() and get_names(). These functions
take a vector of names or ids (respectively), and return a vector of ids
or names (respectively). For instance, we can use this to attempt to
resolve all the bird names in the Breeding Bird Survey against the
Catalogue of Life:
birds <- bbs %>%
select(species) %>%
mutate(id = taxalight::get_ids(species, "col"))
head(birds, 10)
#> species id
#> 1 Dendrocygna autumnalis COL:3177882
#> 2 Dendrocygna bicolor COL:3177881
#> 3 Anser canagicus COL:3178026
#> 4 Anser caerulescens COL:3178024
#> 5 Chen caerulescens (blue form) <NA>
#> 6 Anser rossii COL:3178025
#> 7 Anser albifrons COL:3178017
#> 8 Branta bernicla COL:3178037
#> 9 Branta bernicla nigricans COL:3185200
#> 10 Branta hutchinsii COL:3178039Note that some names cannot be resolved to an identifier. This can occur
because of miss-spellings, non-standard formatting, or the use of a
synonym not recognized by the naming provider. Names that cannot be
uniquely resolved because they are known synonyms of multiple different
species will also return NA. The filter_name filtering functions can
help us resolve this last case (see below).
get_ids() returns the IDs of accepted names, that is
dwc:AcceptedNameUsageIDs. We can resolve the IDs into accepted names:
birds %>%
mutate(accepted_name = get_names(id, "col")) %>%
head()
#> species id accepted_name
#> 1 Dendrocygna autumnalis COL:3177882 Dendrocygna autumnalis
#> 2 Dendrocygna bicolor COL:3177881 Dendrocygna bicolor
#> 3 Anser canagicus COL:3178026 Chen canagica
#> 4 Anser caerulescens COL:3178024 Chen caerulescens
#> 5 Chen caerulescens (blue form) <NA> <NA>
#> 6 Anser rossii COL:3178025 Chen rossiiThis illustrates that some of our names, e.g. Dendrocygna bicolor are accepted in the Catalogue of Life, while others, Anser canagicus are known synonyms of a different accepted name: Chen canagica. Resolving synonyms and accepted names to identifiers helps us avoid the possible miss-matches we could have when the same species is known by two different names.
Local access to taxonomic data tables lets us do much more than look up
names and ids. A family of filter_* functions in taxadb help us work
directly with subsets of the taxonomic data. As we noted above, this can
be useful in resolving certain ambiguous names.
For instance, Trochalopteron henrici gucenense does not resolve to an identifier in ITIS:
get_ids("Trochalopteron henrici gucenense")
#> [1] NAUsing filter_name(), we find this is because the name resolves not to
zero matches, but to more than one match:
filter_name("Trochalopteron henrici gucenense")
#> # A tibble: 2 x 17
#> sort taxonID scientificName taxonRank acceptedNameUsa… taxonomicStatus
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 ITIS:924… Trochalopteron hen… subspeci… ITIS:916116 synonym
#> 2 1 ITIS:924… Trochalopteron hen… subspeci… ITIS:916117 synonym
#> # … with 11 more variables: update_date <chr>, kingdom <chr>, phylum <chr>,
#> # class <chr>, order <chr>, family <chr>, genus <chr>, specificEpithet <chr>,
#> # infraspecificEpithet <chr>, vernacularName <chr>, input <chr>filter_name("Trochalopteron henrici gucenense") %>%
mutate(acceptedNameUsage = get_names(acceptedNameUsageID)) %>%
select(scientificName, taxonomicStatus, acceptedNameUsage, acceptedNameUsageID)
#> # A tibble: 2 x 4
#> scientificName taxonomicStatus acceptedNameUsage acceptedNameUsag…
#> <chr> <chr> <chr> <chr>
#> 1 Trochalopteron henrici… synonym Trochalopteron elli… ITIS:916116
#> 2 Trochalopteron henrici… synonym Trochalopteron henr… ITIS:916117Similar functions filter_id, filter_rank, and filter_common take
IDs, scientific ranks, or common names, respectively. Here, we can get
taxonomic data on all bird names in the Catalogue of Life:
filter_rank(name = "Aves", rank = "class", provider = "col")
#> # A tibble: 36,336 x 21
#> sort taxonID scientificName acceptedNameUsag… taxonomicStatus taxonRank
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 COL:3148… Nisaetus nanus COL:3148416 accepted species
#> 2 1 COL:3148… Circaetus beaudo… COL:3148666 accepted species
#> 3 1 COL:3148… Cariama cristata COL:3148731 accepted species
#> 4 1 COL:3148… Chunga burmeiste… COL:3148732 accepted species
#> 5 1 COL:3148… Eurypyga helias COL:3148733 accepted species
#> 6 1 COL:3148… Rhynochetos juba… COL:3148734 accepted species
#> 7 1 COL:3148… Leptosomus disco… COL:3148735 accepted species
#> 8 1 COL:3148… Neotis heuglinii COL:3148736 accepted species
#> 9 1 COL:3148… Neotis ludwigii COL:3148737 accepted species
#> 10 1 COL:3148… Neotis denhami COL:3148738 accepted species
#> # … with 36,326 more rows, and 15 more variables: kingdom <chr>, phylum <chr>,
#> # class <chr>, order <chr>, family <chr>, genus <chr>, specificEpithet <chr>,
#> # infraspecificEpithet <chr>, taxonConceptID <chr>, isExtinct <chr>,
#> # nameAccordingTo <chr>, namePublishedIn <chr>,
#> # scientificNameAuthorship <chr>, vernacularName <chr>, input <chr>Combining these with dplyr functions can make it easy to explore this
data: for instance, which families have the most species?
filter_rank(name = "Aves", rank = "class", provider = "col") %>%
filter(taxonomicStatus == "accepted", taxonRank=="species") %>%
group_by(family) %>%
count(sort = TRUE) %>%
head()
#> # A tibble: 6 x 2
#> # Groups: family [6]
#> family n
#> <chr> <int>
#> 1 Tyrannidae 401
#> 2 Thraupidae 374
#> 3 Psittacidae 370
#> 4 Columbidae 344
#> 5 Trochilidae 338
#> 6 Muscicapidae 314filter_* functions by default return in-memory data frames. Because
they are filtering functions, they return a subset of the full data
which matches a given query (names, ids, ranks, etc), so the returned
data.frames are smaller than the full record of a naming provider.
Working directly with the SQL connection to the MonetDBLite database
gives us access to all the data. The taxa_tbl() function provides this
connection:
taxa_tbl("col")
#> # Source: table<2020_dwc_col> [?? x 19]
#> # Database: duckdb_connection
#> taxonID scientificName acceptedNameUsa… taxonomicStatus taxonRank kingdom
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 COL:3738 Lobesia triacant… COL:3738 accepted species Animal…
#> 2 COL:4116 Syncollesis tril… COL:4116 accepted species Animal…
#> 3 COL:4118 Anisodes anablem… COL:4118 accepted species Animal…
#> 4 COL:4122 Cyclophora carol… COL:4122 accepted species Animal…
#> 5 COL:4127 Morchella magnis… COL:4127 accepted species Fungi
#> 6 COL:4128 Streptothrix eff… COL:4128 accepted species Fungi
#> 7 COL:4344 Aplosporella fau… COL:4344 accepted species Fungi
#> 8 COL:9466 Synalus angustus COL:9466 accepted species Animal…
#> 9 COL:9467 Synalus terrosus COL:9467 accepted species Animal…
#> 10 COL:9468 Synema spinosum COL:9468 accepted species Animal…
#> # … with more rows, and 13 more variables: phylum <chr>, class <chr>,
#> # order <chr>, family <chr>, genus <chr>, specificEpithet <chr>,
#> # infraspecificEpithet <chr>, taxonConceptID <chr>, isExtinct <chr>,
#> # nameAccordingTo <chr>, namePublishedIn <chr>,
#> # scientificNameAuthorship <chr>, vernacularName <chr>We can still use most familiar dplyr verbs to perform common tasks.
For instance: which species has the most known synonyms?
taxa_tbl("col") %>%
count(acceptedNameUsageID, sort=TRUE)
#> # Source: lazy query [?? x 2]
#> # Database: duckdb_connection
#> # Ordered by: desc(n)
#> acceptedNameUsageID n
#> <chr> <dbl>
#> 1 COL:274062 456
#> 2 COL:353741 373
#> 3 COL:3778950 329
#> 4 COL:2535424 328
#> 5 COL:2921616 322
#> 6 COL:2532677 307
#> 7 COL:3779182 302
#> 8 COL:353740 296
#> 9 COL:2531203 253
#> 10 COL:1585420 252
#> # … with more rowsHowever, unlike the filter_* functions which return convenient
in-memory tables, this is still a remote connection. This means that
direct access using the taxa_tbl() function (or directly accessing the
database connection using td_connect()) is more low-level and requires
greater care. For instance, we cannot just add a %>% mutate(acceptedNameUsage = get_names(acceptedNameUsageID)) to the
above, because get_names does not work on a remote collection.
Instead, we would first need to use a collect() to pull the summary
table into memory. Users familiar with remote databases in dplyr will
find using taxa_tbl() directly to be convenient and fast, while other
users may find the filter_* approach to be more intuitive.
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See richer examples the package Tutorial.
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Learn about the underlying data sources and formats in Data Sources
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Get better performance by selecting an alternative database backend engines.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
