taxize is a taxonomic toolbelt for R. taxize wraps APIs for a large suite of taxonomic databases availab on the web.
First, install and load taxize into the R session.
install.packages("taxize")
library("taxize")
Advanced users can also download and install the latest development copy from GitHub.
This is a common task in biology. We often have a list of species names and we want to know a) if we have the most up to date names, b) if our names are spelled correctly, and c) the scientific name for a common name. One way to resolve names is via the Global Names Resolver (GNR) service provided by the Encyclopedia of Life. Here, we are searching for two misspelled names:
temp <- gnr_resolve(names = c("Helianthos annus", "Homo saapiens"))
head(temp)
#> user_supplied_name submitted_name matched_name
#> 1 Helianthos annus Helianthos annus Helianthus annus
#> 2 Helianthos annus Helianthos annus Helianthus annus L.
#> 3 Helianthos annus Helianthos annus Helianthus annus
#> 4 Helianthos annus Helianthos annus Helianthus annus
#> 5 Helianthos annus Helianthos annus Helianthus annuus L.
#> 6 Helianthos annus Helianthos annus Helianthus annuus L.
#> data_source_title score
#> 1 EOL 0.75
#> 2 EOL 0.75
#> 3 uBio NameBank 0.75
#> 4 Open Tree of Life Reference Taxonomy 0.75
#> 5 Catalogue of Life 0.75
#> 6 ITIS 0.75
The correct spellings are Helianthus annuus and Homo sapiens. Another approach uses the Taxonomic Name Resolution Service via the Taxosaurus API developed by iPLant and the Phylotastic organization. In this example, we provide a list of species names, some of which are misspelled, and we'll call the API with the tnrs function.
mynames <- c("Helianthus annuus", "Pinus contort", "Poa anua", "Abis magnifica",
"Rosa california", "Festuca arundinace", "Sorbus occidentalos","Madia sateva")
tnrs(query = mynames, source = "iPlant_TNRS")[ , -c(5:7)]
#> submittedname acceptedname sourceid score
#> 1 Sorbus occidentalos Sorbus occidentalis iPlant_TNRS 0.99
#> 2 Festuca arundinace Festuca arundinacea iPlant_TNRS 0.99
#> 3 Abis magnifica Abies magnifica iPlant_TNRS 0.96
#> 4 Pinus contort Pinus contorta iPlant_TNRS 0.98
#> 5 Poa anua Poa annua iPlant_TNRS 0.96
#> 6 Madia sateva Madia sativa iPlant_TNRS 0.97
#> 7 Helianthus annuus Helianthus annuus iPlant_TNRS 1
#> 8 Rosa california Rosa californica iPlant_TNRS 0.99
It turns out there are a few corrections: e.g., Madia sateva should be Madia sativa, and Rosa california should be Rosa californica. Note that this search worked because fuzzy matching was employed to retrieve names that were close, but not exact matches. Fuzzy matching is only available for plants in the TNRS service, so we advise using EOL's Global Names Resolver if you need to resolve animal names.
taxize takes the approach that the user should be able to make decisions about what resource to trust, rather than making the decision. Both the EOL GNR and the TNRS services provide data from a variety of data sources. The user may trust a specific data source, thus may want to use the names from that data source. In the future, we may provide the ability for taxize to suggest the best match from a variety of sources.
Another common use case is when there are many synonyms for a species. In this example, we have three synonyms of the currently accepted name for a species.
mynames <- c("Helianthus annuus ssp. jaegeri", "Helianthus annuus ssp. lenticularis", "Helianthus annuus ssp. texanus")
(tsn <- get_tsn(mynames, accepted = FALSE))
[1] "525928" "525929" "525930"
attr(,"match")
[1] "found" "found" "found"
attr(,"multiple_matches")
[1] FALSE FALSE FALSE
attr(,"pattern_match")
[1] FALSE FALSE FALSE
attr(,"uri")
[1] "http://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=525928"
[2] "http://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=525929"
[3] "http://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=525930"
attr(,"class")
[1] "tsn"
lapply(tsn, itis_acceptname)
[[1]]
submittedtsn acceptedname acceptedtsn author
1 525928 Helianthus annuus 36616 L.
[[2]]
submittedtsn acceptedname acceptedtsn author
1 525929 Helianthus annuus 36616 L.
[[3]]
submittedtsn acceptedname acceptedtsn author
1 525930 Helianthus annuus 36616 L.
Another task biologists often face is getting higher taxonomic names for a taxa list. Having the higher taxonomy allows you to put into context the relationships of your species list. For example, you may find out that species A and species B are in Family C, which may lead to some interesting insight, as opposed to not knowing that Species A and B are closely related. This also makes it easy to aggregate/standardize data to a specific taxonomic level (e.g., family level) or to match data to other databases with different taxonomic resolution (e.g., trait databases).
A number of data sources in taxize provide the capability to retrieve higher taxonomic names, but we will highlight two of the more useful ones: Integrated Taxonomic Information System (ITIS) and National Center for Biotechnology Information (NCBI). First, we'll search for two species, Abies procera} and *Pinus contorta within ITIS.
specieslist <- c("Abies procera","Pinus contorta")
classification(specieslist, db = 'itis')
#> $`Abies procera`
#> name rank id
#> 1 Plantae kingdom 202422
#> 2 Viridiplantae subkingdom 954898
#> 3 Streptophyta infrakingdom 846494
#> 4 Embryophyta superdivision 954900
#> 5 Tracheophyta division 846496
#> 6 Spermatophytina subdivision 846504
#> 7 Pinopsida class 500009
#> 8 Pinidae subclass 954916
#> 9 Pinales order 500028
#> 10 Pinaceae family 18030
#> 11 Abies genus 18031
#> 12 Abies procera species 181835
#>
#> $`Pinus contorta`
#> name rank id
#> 1 Plantae kingdom 202422
#> 2 Viridiplantae subkingdom 954898
#> 3 Streptophyta infrakingdom 846494
#> 4 Embryophyta superdivision 954900
#> 5 Tracheophyta division 846496
#> 6 Spermatophytina subdivision 846504
#> 7 Pinopsida class 500009
#> 8 Pinidae subclass 954916
#> 9 Pinales order 500028
#> 10 Pinaceae family 18030
#> 11 Pinus genus 18035
#> 12 Pinus contorta species 183327
#>
#> attr(,"class")
#> [1] "classification"
#> attr(,"db")
#> [1] "itis"
It turns out both species are in the family Pinaceae. You can also get this type of information from the NCBI by doing classification(specieslist, db = 'ncbi').
Instead of a full classification, you may only want a single name, say a family name for your species of interest. The function *tax_name} is built just for this purpose. As with the classification function you can specify the data source with the db argument, either ITIS or NCBI.
tax_name(query = "Helianthus annuus", get = "family", db = "ncbi")
#> db query family
#> 1 ncbi Helianthus annuus Asteraceae
I may happen that a data source does not provide information on the queried species, than one could take the result from another source and union the results from the different sources.
As mentioned most databases use a numeric code to reference a species. A general workflow in taxize is: Retrieve Code for the queried species and then use this code to query more data/information.
Below are a few examples. When you run these examples in R, you are presented with a command prompt asking for the row that contains the name you would like back; that output is not printed below for brevity. In this example, the search term has many matches. The function returns a data frame of the matches, and asks for the user to input what row number to accept.
get_uid(sciname = "Pinus")
#> status rank division scientificname commonname uid genus
#> 1 active subgenus seed plants Pinus hard pines 139271
#> 2 active genus seed plants Pinus 3337
#> species subsp modificationdate
#> 1 2015/09/16 00:00
#> 2 2004/09/10 00:00
#> [1] "139271"
#> attr(,"class")
#> [1] "uid"
#> attr(,"match")
#> [1] "found"
#> attr(,"multiple_matches")
#> [1] TRUE
#> attr(,"pattern_match")
#> [1] FALSE
#> attr(,"uri")
#> [1] "https://www.ncbi.nlm.nih.gov/taxonomy/139271"
In another example, you can pass in a long character vector of taxonomic names (although this one is rather short for demo purposes):
splist <- c("annona cherimola", 'annona muricata', "quercus robur")
get_tsn(searchterm = splist, searchtype = "scientific")
#> [1] "506198" "18098" "19405"
#> attr(,"match")
#> [1] "found" "found" "found"
#> attr(,"multiple_matches")
#> [1] FALSE FALSE TRUE
#> attr(,"pattern_match")
#> [1] FALSE FALSE TRUE
#> attr(,"uri")
#> [1] "http://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=506198"
#> [2] "http://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=18098"
#> [3] "http://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=19405"
#> attr(,"class")
#> [1] "tsn"
There are functions for many other sources
get_boldid()get_colid()get_eolid()get_gbifid()get_nbnid()get_tpsid()Sometimes with these functions you get a lot of data back. In these cases you may want to limit your choices. Soon we will incorporate the ability to filter using regex to limit matches, but for now, we have a new parameter, rows, which lets you select certain rows. For example, you can select the first row of each given name, which means there is no interactive component:
get_nbnid(c("Zootoca vivipara","Pinus contorta"), rows = 1)
#> [1] "NHMSYS0001706186" "NBNSYS0000004786"
#> attr(,"class")
#> [1] "nbnid"
#> attr(,"match")
#> [1] "found" "found"
#> attr(,"multiple_matches")
#> [1] TRUE TRUE
#> attr(,"pattern_match")
#> [1] FALSE FALSE
#> attr(,"uri")
#> [1] "https://data.nbn.org.uk/Taxa/NHMSYS0001706186"
#> [2] "https://data.nbn.org.uk/Taxa/NBNSYS0000004786"
Or you can select a range of rows
get_nbnid(c("Zootoca vivipara","Pinus contorta"), rows = 1:3)
#> nbnid scientificName rank
#> 1 NHMSYS0001706186 Zootoca vivipara species
#> 2 NHMSYS0020784960 Zootoca vivipara subsp. pannonica subspecies
#> 3 NHMSYS0000461615 Bistorta vivipara species
#> taxonomicStatus
#> 1 accepted
#> 2 accepted
#> 3 synonym
#> nbnid scientificName rank
#> 1 NBNSYS0000004786 Pinus contorta species
#> 2 NHMSYS0000494848 Pinus contorta subsp. contorta subspecies
#> 3 NHMSYS0000494858 Pinus contorta subsp. murreyana subspecies
#> taxonomicStatus
#> 1 accepted
#> 2 synonym
#> 3 synonym
#> [1] "NHMSYS0001706186" "NBNSYS0000004786"
#> attr(,"class")
#> [1] "nbnid"
#> attr(,"match")
#> [1] "found" "found"
#> attr(,"multiple_matches")
#> [1] TRUE TRUE
#> attr(,"pattern_match")
#> [1] FALSE FALSE
#> attr(,"uri")
#> [1] "https://data.nbn.org.uk/Taxa/NHMSYS0001706186"
#> [2] "https://data.nbn.org.uk/Taxa/NBNSYS0000004786"
In addition, in case you don't want to do interactive name selection in the case where there are a lot of names, you can get all data back with functions of the form, e.g., get_tsn_(), and likewise for other data sources. For example:
out <- get_nbnid_("Poa annua")
NROW(out$`Poa annua`)
#> [1] 25
That's a lot of data, so we can get only certain rows back
get_nbnid_("Poa annua", rows = 1:10)
#> $`Poa annua`
#> guid scientificName rank taxonomicStatus
#> 1 NBNSYS0000002544 Poa annua species accepted
#> 2 NBNSYS0000002739 Adonis annua species synonym
#> 3 NBNSYS0200001901 Bellis annua species accepted
#> 4 NHMSYS0000456951 Carrichtera annua species synonym
#> 5 NBNSYS0200003392 Triumfetta annua species accepted
#> 6 NBNSYS0000002918 Lunaria annua species accepted
#> 7 NBNSYS0200002555 Lonas annua species accepted
#> 8 NBNSYS0000033325 Artemisia annua species accepted
#> 9 NHMSYS0000456951 Carrichtera annua species accepted
#> 10 NHMSYS0000461806 Poa imbecilla species synonym
We've also introduced in v0.5 the ability to coerce numerics and alphanumerics to taxonomic ID classes that are usually only retrieved via get_*() functions.
For example, adfafd
as.gbifid(get_gbifid("Poa annua")) # already a uid, returns the same
#> gbifid scientificname rank status matchtype
#> 1 2704179 Poa annua L. species ACCEPTED EXACT
#> 2 8422205 Poa annua Cham. & Schltdl. species SYNONYM EXACT
#> 3 7730008 Poa annua Steud. species DOUBTFUL EXACT
#> [1] "2704179"
#> attr(,"class")
#> [1] "gbifid"
#> attr(,"match")
#> [1] "found"
#> attr(,"multiple_matches")
#> [1] TRUE
#> attr(,"pattern_match")
#> [1] FALSE
#> attr(,"uri")
#> [1] "http://www.gbif.org/species/2704179"
as.gbifid(2704179) # numeric
#> [1] "2704179"
#> attr(,"class")
#> [1] "gbifid"
#> attr(,"match")
#> [1] "found"
#> attr(,"multiple_matches")
#> [1] FALSE
#> attr(,"pattern_match")
#> [1] FALSE
#> attr(,"uri")
#> [1] "http://www.gbif.org/species/2704179"
as.gbifid("2704179") # character
#> [1] "2704179"
#> attr(,"class")
#> [1] "gbifid"
#> attr(,"match")
#> [1] "found"
#> attr(,"multiple_matches")
#> [1] FALSE
#> attr(,"pattern_match")
#> [1] FALSE
#> attr(,"uri")
#> [1] "http://www.gbif.org/species/2704179"
as.gbifid(list("2704179","2435099","3171445")) # list, either numeric or character
#> [1] "2704179" "2435099" "3171445"
#> attr(,"class")
#> [1] "gbifid"
#> attr(,"match")
#> [1] "found" "found" "found"
#> attr(,"multiple_matches")
#> [1] FALSE FALSE FALSE
#> attr(,"pattern_match")
#> [1] FALSE FALSE FALSE
#> attr(,"uri")
#> [1] "http://www.gbif.org/species/2704179"
#> [2] "http://www.gbif.org/species/2435099"
#> [3] "http://www.gbif.org/species/3171445"
These as.*() functions do a quick check of the web resource to make sure it's a real ID. However, you can turn this check off, making this coercion much faster:
system.time( replicate(3, as.gbifid(c("2704179","2435099","3171445"), check=TRUE)) )
#> user system elapsed
#> 0.072 0.002 1.656
system.time( replicate(3, as.gbifid(c("2704179","2435099","3171445"), check=FALSE)) )
#> user system elapsed
#> 0.002 0.000 0.002
If someone is not a taxonomic specialist on a particular taxon he likely does not know what children taxa are within a family, or within a genus. This task becomes especially unwieldy when there are a large number of taxa downstream. You can of course go to a website like Wikispecies or Encyclopedia of Life to get downstream names. However, taxize provides an easy way to programatically search for downstream taxa, both for the Catalogue of Life (CoL) and the Integrated Taxonomic Information System. Here is a short example using the CoL in which we want to find all the species within the genus Apis (honey bees).
downstream("Apis", downto = "Species", db = "col")
#> name rank colid
#> 1 Apis genus 015be25f6b061ba517f495394b80f108
#> 2 Actinomadura apis species 1182a102a18b40aa19385bf5f1f53367
#> 3 Anisocentropus apis species 8891d18874dde14e44df52e931c44206
#> 4 Apis andreniformis species 7a4a38c5095963949d6d6ec917d471de
#> 5 Apis cerana species 39610a4ceff7e5244e334a3fbc5e47e5
#> 6 Apis dorsata species e1d4cbf3872c6c310b7a1c17ddd00ebc
#> 7 Apis florea species 92dca82a063fedd1da94b3f3972d7b22
#> 8 Apis koschevnikovi species 4bbc06b9dfbde0b72c619810b564c6e6
#> 9 Apis mellifera species 67cbbcf92cd60748759e58e802d98518
#> 10 Apis nigrocincta species 213668a26ba6d2aad9575218f10d422f
#> 11 Ascosphaera apis species 088549f2fb602367e84d5ffdb8c1d4fc
#> 12 Candida apis species 3219a9635d3438e8b76a645cecf87287
#> 13 Eristalis apis species 16d7c8023308d38f6bb831ed5fa82002
#> 14 Hister apis species d2d7483acf488b5ed932f49b0aa51d19
#> 15 Ifephylus apis species 9b4d00e009b58bbfc003b51bd3d0c6f0
#> 16 Impatiens nidus-apis species 6aecf448e6aa0cb46387066db94426d1
#> 17 Kirkaldykra apis species 70a68f13454abd937aabf56746f4a6ad
#> 18 Mallota apis species 10c3c3921d2ea9f9425ef9fd41914520
#> 19 Melanosella mors-apis species 4ac238f1597847dbc7998d97b8d45a0e
#> 20 Microdon apis species 9be92242562eb923e711dc24b7bbab9a
#> 21 Nosema apis species 5b2838dfd0ec15844fc6f659f7580322
#> 22 Scutirodes apis species 164ab3ac910547bc945cdbb994be1ee5
#> 23 Spiroplasma apis species 789f91571ce55de4df9821f2d05efab0
#> 24 Trichomonascus apis species 17dc4d840323e2c5b87e67a952f6dff3
#> 25 Pericystis apis species 088549f2fb602367e84d5ffdb8c1d4fc
#> 26 Pericystis apis species 088549f2fb602367e84d5ffdb8c1d4fc
#> 27 Torulopsis apis species 3219a9635d3438e8b76a645cecf87287
#> 28 Torulopsis apis species 3219a9635d3438e8b76a645cecf87287
#> 29 Apis aestuans species a517bc572c3c2697fe3bbfabc46a1493
#> 30 Apis alpina species f2781627115e4212ddab5979cdd425d2
#> 31 Apis bicornis species e67e82d00faae69da173bb31f9914056
#> 32 Apis canescens species d6b8850db971d65d6079e3a22f35e10e
#> 33 Apis clypeata species 706060924801130f6c3abf454087c100
#> 34 Apis cunicularia species ebc3c5166ce2cabf419c4c6dc332cf3b
#> 35 Apis etrusca species 6d27fd39a1d8b10050ba4e331987f3c9
#> 36 Apis globosa species 254c8e847ca4ff128bba57fe94deb98d
#> 37 Apis hispanica species e8d2057a3efeb2cfdaebe27ea8191cd5
#> 38 Apis hypnorum species dfb743f54f50b9b9dbee378473542821
#> 39 Apis ichneumonea species 13c35287e20ab9373fa445dbc44981ea
#> 40 Apis lapidaria species f8da5667af3562ebc0f6a83e1ec408f0
#> 41 Apis muscorum species 5bbfe59da5ce7fe59eb9ca3a7a45916c
#> 42 Apis mystacea species fba8e4752a7fa5939a7eae293ba633ec
#> 43 Apis obsoleta species da42bcb6cc0267903fb175f8a215aecb
#> 44 Apis rostrata species e155a4277b66d1114182cafd875afbe3
#> 45 Apis rostrata species e155a4277b66d1114182cafd875afbe3
#> 46 Apis rufa species e67e82d00faae69da173bb31f9914056
#> 47 Apis signata species 551f101ad3b9bc17b24575585b2500c1
#> 48 Apis smaragdula species 4bc5c886b061e17e9aecb537a04c616d
#> 49 Apis spinulosa species 56e7e9f854c9ed31ea6d0a06567607d0
#> 50 Apis subterranea species 3d2adff364a87bf7dd30524aa8071807
#> name_status kingdom family acc_name
#> 1 accepted name Animalia Apidae <NA>
#> 2 accepted name Bacteria Thermomonosporaceae <NA>
#> 3 accepted name Animalia Calamoceratidae <NA>
#> 4 accepted name Animalia Apidae <NA>
#> 5 accepted name Animalia Apidae <NA>
#> 6 accepted name Animalia Apidae <NA>
#> 7 accepted name Animalia Apidae <NA>
#> 8 accepted name Animalia Apidae <NA>
#> 9 accepted name Animalia Apidae <NA>
#> 10 accepted name Animalia Apidae <NA>
#> 11 accepted name Fungi Ascosphaeraceae <NA>
#> 12 accepted name Fungi Not assigned <NA>
#> 13 accepted name Animalia Syrphidae <NA>
#> 14 accepted name Animalia Histeridae <NA>
#> 15 accepted name Animalia Miridae <NA>
#> 16 accepted name Plantae Balsaminaceae <NA>
#> 17 accepted name Animalia Cicadellidae <NA>
#> 18 accepted name Animalia Syrphidae <NA>
#> 19 accepted name Fungi Not assigned <NA>
#> 20 accepted name Animalia Syrphidae <NA>
#> 21 accepted name Protozoa Nosematidae <NA>
#> 22 accepted name Animalia Noctuidae <NA>
#> 23 accepted name Bacteria Spiroplasmataceae <NA>
#> 24 accepted name Fungi Trichomonascaceae <NA>
#> 25 ambiguous synonym <NA> <NA> Ascosphaera apis
#> 26 ambiguous synonym <NA> <NA> Ascosphaera apis
#> 27 ambiguous synonym <NA> <NA> Candida apis
#> 28 ambiguous synonym <NA> <NA> Candida apis
#> 29 synonym <NA> <NA> Xylocopa aestuans
#> 30 synonym <NA> <NA> Bombus alpinus
#> 31 synonym <NA> <NA> Osmia rufa
#> 32 synonym <NA> <NA> Bembix canescens
#> 33 synonym <NA> <NA> Lestica clypeata
#> 34 synonym <NA> <NA> Colletes cunicularius
#> 35 synonym <NA> <NA> Tachytes etruscus
#> 36 synonym <NA> <NA> Exomalopsis similis
#> 37 synonym <NA> <NA> Tachytes freygessneri
#> 38 synonym <NA> <NA> Bombus hypnorum
#> 39 synonym <NA> <NA> Sphex ichneumoneus
#> 40 synonym <NA> <NA> Bombus lapidarius
#> 41 synonym <NA> <NA> Bombus muscorum
#> 42 synonym <NA> <NA> Argogorytes mystaceus
#> 43 synonym <NA> <NA> Tachytes obsoletus
#> 44 synonym <NA> <NA> Bembix rostrata
#> 45 synonym <NA> <NA> Bembix rostrata
#> 46 synonym <NA> <NA> Osmia rufa
#> 47 synonym <NA> <NA> Stictia signata
#> 48 synonym <NA> <NA> Ceratina smaragdula
#> 49 synonym <NA> <NA> Hoplosmia spinulosa
#> 50 synonym <NA> <NA> Bombus subterraneus
#> $Apis
#> childtaxa_id childtaxa_name childtaxa_rank
#> 1 7a4a38c5095963949d6d6ec917d471de Apis andreniformis species
#> 2 39610a4ceff7e5244e334a3fbc5e47e5 Apis cerana species
#> 3 e1d4cbf3872c6c310b7a1c17ddd00ebc Apis dorsata species
#> 4 92dca82a063fedd1da94b3f3972d7b22 Apis florea species
#> 5 4bbc06b9dfbde0b72c619810b564c6e6 Apis koschevnikovi species
#> 6 67cbbcf92cd60748759e58e802d98518 Apis mellifera species
#> 7 213668a26ba6d2aad9575218f10d422f Apis nigrocincta species
#>
#> attr(,"class")
#> [1] "downstream"
#> attr(,"db")
#> [1] "col"
We can also request data from ITIS
downstream("Apis", downto = "Species", db = "itis")
#> $Apis
#> tsn parentname parenttsn taxonname rankid rankname
#> 1 154396 Apis 154395 Apis mellifera 220 species
#> 2 763550 Apis 154395 Apis andreniformis 220 species
#> 3 763551 Apis 154395 Apis cerana 220 species
#> 4 763552 Apis 154395 Apis dorsata 220 species
#> 5 763553 Apis 154395 Apis florea 220 species
#> 6 763554 Apis 154395 Apis koschevnikovi 220 species
#> 7 763555 Apis 154395 Apis nigrocincta 220 species
#>
#> attr(,"class")
#> [1] "downstream"
#> attr(,"db")
#> [1] "itis"
You may sometimes only want the direct children. We got you covered on that front, with methods for ITIS, NCBI, and Catalogue of Life. For example, let's get direct children (species in this case) of the bee genus Apis using COL data:
children(get_colid("Apis"))
#> name rank colid
#> 1 Apis genus 015be25f6b061ba517f495394b80f108
#> 2 Actinomadura apis species 1182a102a18b40aa19385bf5f1f53367
#> 3 Anisocentropus apis species 8891d18874dde14e44df52e931c44206
#> 4 Apis andreniformis species 7a4a38c5095963949d6d6ec917d471de
#> 5 Apis cerana species 39610a4ceff7e5244e334a3fbc5e47e5
#> 6 Apis dorsata species e1d4cbf3872c6c310b7a1c17ddd00ebc
#> 7 Apis florea species 92dca82a063fedd1da94b3f3972d7b22
#> 8 Apis koschevnikovi species 4bbc06b9dfbde0b72c619810b564c6e6
#> 9 Apis mellifera species 67cbbcf92cd60748759e58e802d98518
#> 10 Apis nigrocincta species 213668a26ba6d2aad9575218f10d422f
#> 11 Ascosphaera apis species 088549f2fb602367e84d5ffdb8c1d4fc
#> 12 Candida apis species 3219a9635d3438e8b76a645cecf87287
#> 13 Eristalis apis species 16d7c8023308d38f6bb831ed5fa82002
#> 14 Hister apis species d2d7483acf488b5ed932f49b0aa51d19
#> 15 Ifephylus apis species 9b4d00e009b58bbfc003b51bd3d0c6f0
#> 16 Impatiens nidus-apis species 6aecf448e6aa0cb46387066db94426d1
#> 17 Kirkaldykra apis species 70a68f13454abd937aabf56746f4a6ad
#> 18 Mallota apis species 10c3c3921d2ea9f9425ef9fd41914520
#> 19 Melanosella mors-apis species 4ac238f1597847dbc7998d97b8d45a0e
#> 20 Microdon apis species 9be92242562eb923e711dc24b7bbab9a
#> 21 Nosema apis species 5b2838dfd0ec15844fc6f659f7580322
#> 22 Scutirodes apis species 164ab3ac910547bc945cdbb994be1ee5
#> 23 Spiroplasma apis species 789f91571ce55de4df9821f2d05efab0
#> 24 Trichomonascus apis species 17dc4d840323e2c5b87e67a952f6dff3
#> 25 Pericystis apis species 088549f2fb602367e84d5ffdb8c1d4fc
#> 26 Pericystis apis species 088549f2fb602367e84d5ffdb8c1d4fc
#> 27 Torulopsis apis species 3219a9635d3438e8b76a645cecf87287
#> 28 Torulopsis apis species 3219a9635d3438e8b76a645cecf87287
#> 29 Apis aestuans species a517bc572c3c2697fe3bbfabc46a1493
#> 30 Apis alpina species f2781627115e4212ddab5979cdd425d2
#> 31 Apis bicornis species e67e82d00faae69da173bb31f9914056
#> 32 Apis canescens species d6b8850db971d65d6079e3a22f35e10e
#> 33 Apis clypeata species 706060924801130f6c3abf454087c100
#> 34 Apis cunicularia species ebc3c5166ce2cabf419c4c6dc332cf3b
#> 35 Apis etrusca species 6d27fd39a1d8b10050ba4e331987f3c9
#> 36 Apis globosa species 254c8e847ca4ff128bba57fe94deb98d
#> 37 Apis hispanica species e8d2057a3efeb2cfdaebe27ea8191cd5
#> 38 Apis hypnorum species dfb743f54f50b9b9dbee378473542821
#> 39 Apis ichneumonea species 13c35287e20ab9373fa445dbc44981ea
#> 40 Apis lapidaria species f8da5667af3562ebc0f6a83e1ec408f0
#> 41 Apis muscorum species 5bbfe59da5ce7fe59eb9ca3a7a45916c
#> 42 Apis mystacea species fba8e4752a7fa5939a7eae293ba633ec
#> 43 Apis obsoleta species da42bcb6cc0267903fb175f8a215aecb
#> 44 Apis rostrata species e155a4277b66d1114182cafd875afbe3
#> 45 Apis rostrata species e155a4277b66d1114182cafd875afbe3
#> 46 Apis rufa species e67e82d00faae69da173bb31f9914056
#> 47 Apis signata species 551f101ad3b9bc17b24575585b2500c1
#> 48 Apis smaragdula species 4bc5c886b061e17e9aecb537a04c616d
#> 49 Apis spinulosa species 56e7e9f854c9ed31ea6d0a06567607d0
#> 50 Apis subterranea species 3d2adff364a87bf7dd30524aa8071807
#> name_status kingdom family acc_name
#> 1 accepted name Animalia Apidae <NA>
#> 2 accepted name Bacteria Thermomonosporaceae <NA>
#> 3 accepted name Animalia Calamoceratidae <NA>
#> 4 accepted name Animalia Apidae <NA>
#> 5 accepted name Animalia Apidae <NA>
#> 6 accepted name Animalia Apidae <NA>
#> 7 accepted name Animalia Apidae <NA>
#> 8 accepted name Animalia Apidae <NA>
#> 9 accepted name Animalia Apidae <NA>
#> 10 accepted name Animalia Apidae <NA>
#> 11 accepted name Fungi Ascosphaeraceae <NA>
#> 12 accepted name Fungi Not assigned <NA>
#> 13 accepted name Animalia Syrphidae <NA>
#> 14 accepted name Animalia Histeridae <NA>
#> 15 accepted name Animalia Miridae <NA>
#> 16 accepted name Plantae Balsaminaceae <NA>
#> 17 accepted name Animalia Cicadellidae <NA>
#> 18 accepted name Animalia Syrphidae <NA>
#> 19 accepted name Fungi Not assigned <NA>
#> 20 accepted name Animalia Syrphidae <NA>
#> 21 accepted name Protozoa Nosematidae <NA>
#> 22 accepted name Animalia Noctuidae <NA>
#> 23 accepted name Bacteria Spiroplasmataceae <NA>
#> 24 accepted name Fungi Trichomonascaceae <NA>
#> 25 ambiguous synonym <NA> <NA> Ascosphaera apis
#> 26 ambiguous synonym <NA> <NA> Ascosphaera apis
#> 27 ambiguous synonym <NA> <NA> Candida apis
#> 28 ambiguous synonym <NA> <NA> Candida apis
#> 29 synonym <NA> <NA> Xylocopa aestuans
#> 30 synonym <NA> <NA> Bombus alpinus
#> 31 synonym <NA> <NA> Osmia rufa
#> 32 synonym <NA> <NA> Bembix canescens
#> 33 synonym <NA> <NA> Lestica clypeata
#> 34 synonym <NA> <NA> Colletes cunicularius
#> 35 synonym <NA> <NA> Tachytes etruscus
#> 36 synonym <NA> <NA> Exomalopsis similis
#> 37 synonym <NA> <NA> Tachytes freygessneri
#> 38 synonym <NA> <NA> Bombus hypnorum
#> 39 synonym <NA> <NA> Sphex ichneumoneus
#> 40 synonym <NA> <NA> Bombus lapidarius
#> 41 synonym <NA> <NA> Bombus muscorum
#> 42 synonym <NA> <NA> Argogorytes mystaceus
#> 43 synonym <NA> <NA> Tachytes obsoletus
#> 44 synonym <NA> <NA> Bembix rostrata
#> 45 synonym <NA> <NA> Bembix rostrata
#> 46 synonym <NA> <NA> Osmia rufa
#> 47 synonym <NA> <NA> Stictia signata
#> 48 synonym <NA> <NA> Ceratina smaragdula
#> 49 synonym <NA> <NA> Hoplosmia spinulosa
#> 50 synonym <NA> <NA> Bombus subterraneus
#> $`015be25f6b061ba517f495394b80f108`
#> childtaxa_id childtaxa_name childtaxa_rank
#> 1 7a4a38c5095963949d6d6ec917d471de Apis andreniformis species
#> 2 39610a4ceff7e5244e334a3fbc5e47e5 Apis cerana species
#> 3 e1d4cbf3872c6c310b7a1c17ddd00ebc Apis dorsata species
#> 4 92dca82a063fedd1da94b3f3972d7b22 Apis florea species
#> 5 4bbc06b9dfbde0b72c619810b564c6e6 Apis koschevnikovi species
#> 6 67cbbcf92cd60748759e58e802d98518 Apis mellifera species
#> 7 213668a26ba6d2aad9575218f10d422f Apis nigrocincta species
#>
#> attr(,"class")
#> [1] "children"
#> attr(,"db")
#> [1] "col"
The direct children (genera in this case) of Pinaceae using NCBI data:
children("Pinaceae", db = "ncbi")
#> $Pinaceae
#> childtaxa_id childtaxa_name childtaxa_rank
#> 1 123600 Nothotsuga genus
#> 2 64685 Cathaya genus
#> 3 3358 Tsuga genus
#> 4 3356 Pseudotsuga genus
#> 5 3354 Pseudolarix genus
#> 6 3337 Pinus genus
#> 7 3328 Picea genus
#> 8 3325 Larix genus
#> 9 3323 Keteleeria genus
#> 10 3321 Cedrus genus
#> 11 3319 Abies genus
#>
#> attr(,"class")
#> [1] "children"
#> attr(,"db")
#> [1] "ncbi"
With accession numbers
genbank2uid(id = 'AJ748748')
#> [1] "282199"
#> attr(,"class")
#> [1] "uid"
#> attr(,"match")
#> [1] "found"
#> attr(,"multiple_matches")
#> [1] FALSE
#> attr(,"pattern_match")
#> [1] FALSE
#> attr(,"uri")
#> [1] "http://www.ncbi.nlm.nih.gov/taxonomy/282199"
With gi numbers
genbank2uid(id = 62689767)
#> [1] "282199"
#> attr(,"class")
#> [1] "uid"
#> attr(,"match")
#> [1] "found"
#> attr(,"multiple_matches")
#> [1] FALSE
#> attr(,"pattern_match")
#> [1] FALSE
#> attr(,"uri")
#> [1] "http://www.ncbi.nlm.nih.gov/taxonomy/282199"
Biologist often need to match different sets of data tied to species. For example, trait-based approaches are a promising tool in ecology. One problem is that abundance data must be matched with trait databases. These two data tables may contain species information on different taxonomic levels and possibly data must be aggregated to a joint taxonomic level, so that the data can be merged. taxize can help in this data-cleaning step, providing a reproducible workflow:
We can use the mentioned classification-function to retrieve the taxonomic hierarchy and then search the hierarchies up- and downwards for matches. Here is an example to match a species with names on three different taxonomic levels.
A <- "gammarus roeseli"
B1 <- "gammarus roeseli"
B2 <- "gammarus"
B3 <- "gammaridae"
A_clas <- classification(A, db = 'ncbi')
B1_clas <- classification(B1, db = 'ncbi')
B2_clas <- classification(B2, db = 'ncbi')
B3_clas <- classification(B3, db = 'ncbi')
B1[match(A, B1)]
#> [1] "gammarus roeseli"
A_clas[[1]]$rank[tolower(A_clas[[1]]$name) %in% B2]
#> [1] "genus"
A_clas[[1]]$rank[tolower(A_clas[[1]]$name) %in% B3]
#> [1] "family"
If we find a direct match (here Gammarus roeseli), we are lucky. But we can also match Gammaridae with Gammarus roeseli, but on a lower taxonomic level. A more comprehensive and realistic example (matching a trait table with an abundance table) is given in the vignette on matching.