taxa defines taxonomic classes and functions to manipulate them. The
goal is to use these classes as low level fundamental taxonomic classes
that other R packages can build on and use.
There are two distinct types of classes in taxa:
- Classes that are concerned only with taxonomic information:
taxon,taxonomy,hierarchy, etc. - A class called
taxmapthat is concerned with combining taxonomic data with user-defined data of any type (e.g. molecular sequences, abundance counts etc.)
Diagram of class concepts for taxa classes:
Relationship between classes implemented in the taxa package.
Diamond-tipped arrows indicate that objects of a one are used in another
class. For example, a database object can stored in the taxon_rank,
taxon_name, or taxon_id objects. A standard arrow indicates
inheritance. For example, the taxmap class inherits the taxonomy class.
* means that the object (e.g. a database object) can be replaced by a
simple character vector. ? means that the data is optional.
CRAN version
install.packages("taxa")
Development version from GitHub
devtools::install_github("ropensci/taxa")
library("taxa")
There are a few optional classes used to store information in other classes. In most cases, these can be replaced with simple character values but using them provides more information and potential functionality.
Taxonomic data usually comes from a database. A common example is the
NCBI Taxonomy Database used to
provide taxonomic classifications to sequences deposited in other NCBI
databases. The database
class stores the name of the database and associated information:
(ncbi <- taxon_database(
name = "ncbi",
url = "http://www.ncbi.nlm.nih.gov/taxonomy",
description = "NCBI Taxonomy Database",
id_regex = "*"
))
#> <database> ncbi
#> url: http://www.ncbi.nlm.nih.gov/taxonomy
#> description: NCBI Taxonomy Database
#> id regex: *
ncbi$name
#> [1] "ncbi"
ncbi$url
#> [1] "http://www.ncbi.nlm.nih.gov/taxonomy"
To save on memory, a selection of common databases is provided with the
package (database_list) and any in this list can be used by name
instead of making a new database object (e.g. "ncbi" instead of the
ncbi above).
database_list
#> $ncbi
#> <database> ncbi
#> url: http://www.ncbi.nlm.nih.gov/taxonomy
#> description: NCBI Taxonomy Database
#> id regex: .*
#>
#> $gbif
#> <database> gbif
#> url: http://www.gbif.org/developer/species
#> description: GBIF Taxonomic Backbone
#> id regex: .*
#>
#> $bold
#> <database> bold
#> url: http://www.boldsystems.org
#> description: Barcode of Life
#> id regex: .*
#>
#> $col
#> <database> col
#> url: http://www.catalogueoflife.org
#> description: Catalogue of Life
#> id regex: .*
#>
#> $eol
#> <database> eol
#> url: http://eol.org
#> description: Encyclopedia of Life
#> id regex: .*
#>
#> $nbn
#> <database> nbn
#> url: https://nbn.org.uk
#> description: UK National Biodiversity Network
#> id regex: .*
#>
#> $tps
#> <database> tps
#> url: http://www.tropicos.org/
#> description: Tropicos
#> id regex: .*
#>
#> $itis
#> <database> itis
#> url: http://www.itis.gov
#> description: Integrated Taxonomic Information System
#> id regex: .*
Taxa might have defined ranks (e.g. species, family, etc.), ambiguous ranks (e.g. "unranked", "unknown"), or no rank information at all. The particular selection and format of valid ranks varies with database, so the database can be optionally defined. If no database is defined, any ranks in any order are allowed.
taxon_rank(name = "species", database = "ncbi")
#> <TaxonRank> species
#> database: ncbi
The taxon name can be defined in the same way as rank.
taxon_name("Poa", database = "ncbi")
#> <TaxonName> Poa
#> database: ncbi
Each database has its set of unique taxon IDs. These IDs are better than using the taxon name directly because they are guaranteed to be unique, whereas there are often duplicates of taxon names (e.g. Orestias elegans is the name of both an orchid and a fish).
taxon_id(12345, database = "ncbi")
#> <TaxonId> 12345
#> database: ncbi
The taxon class combines the classes containing the name, rank, and ID
for the taxon. There is also a place to define an authority of the
taxon.
(x <- taxon(
name = taxon_name("Poa annua"),
rank = taxon_rank("species"),
id = taxon_id(93036),
authority = "Linnaeus"
))
#> <Taxon>
#> name: Poa annua
#> rank: species
#> id: 93036
#> authority: none
Instead of the name, rank, and ID classes, simple character vectors can be supplied.
(x <- taxon(
name = "Poa annua",
rank = "species",
id = 93036,
authority = "Linnaeus"
))
#> <Taxon>
#> name: Poa annua
#> rank: species
#> id: 93036
#> authority: none
The taxa class is just a list of taxon classes with some custom
print methods. It is meant to store an arbitrary list of taxon.
(x <- taxon(
name = taxon_name("Poa annua"),
rank = taxon_rank("species"),
id = taxon_id(93036)
))
#> <Taxon>
#> name: Poa annua
#> rank: species
#> id: 93036
#> authority: none
taxa(x, x, x)
#> <taxa>
#> no. taxa: 3
#> Poa annua / species / 93036
#> Poa annua / species / 93036
#> Poa annua / species / 93036
Taxonomic
classifications
are an ordered set of taxa, each at a different rank. The hierarchy
class stores a list of taxon classes like taxa, but hierarchy is
meant to store all of the taxa in a classification in the correct order.
x <- taxon(
name = taxon_name("Poaceae"),
rank = taxon_rank("family"),
id = taxon_id(4479)
)
y <- taxon(
name = taxon_name("Poa"),
rank = taxon_rank("genus"),
id = taxon_id(4544)
)
z <- taxon(
name = taxon_name("Poa annua"),
rank = taxon_rank("species"),
id = taxon_id(93036)
)
(hier1 <- hierarchy(z, y, x))
#> <Hierarchy>
#> no. taxon's: 3
#> Poaceae / family / 4479
#> Poa / genus / 4544
#> Poa annua / species / 93036
Multiple hierarchy classes are stored in the hierarchies class,
similar to how multiple taxon are stored in taxa.
a <- taxon(
name = taxon_name("Felidae"),
rank = taxon_rank("family"),
id = taxon_id(9681)
)
b <- taxon(
name = taxon_name("Puma"),
rank = taxon_rank("genus"),
id = taxon_id(146712)
)
c <- taxon(
name = taxon_name("Puma concolor"),
rank = taxon_rank("species"),
id = taxon_id(9696)
)
(hier2 <- hierarchy(c, b, a))
#> <Hierarchy>
#> no. taxon's: 3
#> Felidae / family / 9681
#> Puma / genus / 146712
#> Puma concolor / species / 9696
hierarchies(hier1, hier2)
#> <Hierarchies>
#> no. hierarchies: 2
#> Poaceae / Poa / Poa annua
#> Felidae / Puma / Puma concolor
The taxonomy class stores unique taxon objects in a tree structure.
Usually this kind of complex information would be the output of a file
parsing function, but the code below shows how to construct a taxonomy
object from scratch.
# define taxa
notoryctidae <- taxon(name = "Notoryctidae", rank = "family", id = 4479)
notoryctes <- taxon(name = "Notoryctes", rank = "genus", id = 4544)
typhlops <- taxon(name = "typhlops", rank = "species", id = 93036)
mammalia <- taxon(name = "Mammalia", rank = "class", id = 9681)
felidae <- taxon(name = "Felidae", rank = "family", id = 9681)
felis <- taxon(name = "Felis", rank = "genus", id = 9682)
catus <- taxon(name = "catus", rank = "species", id = 9685)
panthera <- taxon(name = "Panthera", rank = "genus", id = 146712)
tigris <- taxon(name = "tigris", rank = "species", id = 9696)
plantae <- taxon(name = "Plantae", rank = "kingdom", id = 33090)
solanaceae <- taxon(name = "Solanaceae", rank = "family", id = 4070)
solanum <- taxon(name = "Solanum", rank = "genus", id = 4107)
lycopersicum <- taxon(name = "lycopersicum", rank = "species", id = 49274)
tuberosum <- taxon(name = "tuberosum", rank = "species", id = 4113)
homo <- taxon(name = "homo", rank = "genus", id = 9605)
sapiens <- taxon(name = "sapiens", rank = "species", id = 9606)
hominidae <- taxon(name = "Hominidae", rank = "family", id = 9604)
# define hierarchies
tiger <- hierarchy(mammalia, felidae, panthera, tigris)
cat <- hierarchy(mammalia, felidae, felis, catus)
human <- hierarchy(mammalia, hominidae, homo, sapiens)
mole <- hierarchy(mammalia, notoryctidae, notoryctes, typhlops)
tomato <- hierarchy(plantae, solanaceae, solanum, lycopersicum)
potato <- hierarchy(plantae, solanaceae, solanum, tuberosum)
# make taxonomy
(tax <- taxonomy(tiger, cat, human, tomato, potato))
#> <Taxonomy>
#> 14 taxa: b. Mammalia, c. Plantae ... o. tuberosum
#> 14 edges: NA->b, NA->c, b->d, b->e ... i->m, j->n, j->o
Unlike the hierarchies class, each unique taxon object is only
represented once in the taxonomy object. Each taxon has a
corresponding entry in an edge
list that encode how it
is related to other taxa. This makes taxonomy more compact, but harder
to manipulate using standard indexing. To make manipulation easier,
there are methods for taxomomy that can provide indexes in a taxonomic
context.
A "supertaxon" is a taxon of a coarser rank that encompasses the taxon
of interest (e.g. "Homo" is a supertaxon of "sapiens"). The supertaxa
function returns the supertaxa of all or a subset of the taxa in a
taxonomy object.
supertaxa(tax)
#> $b
#> named integer(0)
#>
#> $c
#> named integer(0)
#>
#> $d
#> b
#> 1
#>
#> $e
#> b
#> 1
#>
#> $f
#> c
#> 2
#>
#> $g
#> d b
#> 3 1
#>
#> $h
#> d b
#> 3 1
#>
#> $i
#> e b
#> 4 1
#>
#> $j
#> f c
#> 5 2
#>
#> $k
#> g d b
#> 6 3 1
#>
#> $l
#> h d b
#> 7 3 1
#>
#> $m
#> i e b
#> 8 4 1
#>
#> $n
#> j f c
#> 9 5 2
#>
#> $o
#> j f c
#> 9 5 2
By default, the taxon IDs for the supertaxa of all taxa are returned in
the same order they appear in the edge list. Taxon IDs (character) or
edge list indexes (integer) can be supplied to the subset option to
only return information for some taxa.
supertaxa(tax, subset = "m")
#> $m
#> i e b
#> 8 4 1
What is returned can be modified with the value option:
supertaxa(tax, subset = "m", value = "taxon_names")
#> $m
#> i e b
#> "homo" "Hominidae" "Mammalia"
supertaxa(tax, subset = "m", value = "taxon_ranks")
#> $m
#> i e b
#> "genus" "family" "class"
You can also subset based on a logical test:
supertaxa(tax, subset = taxon_ranks == "genus", value = "taxon_names")
#> $g
#> d b
#> "Felidae" "Mammalia"
#>
#> $h
#> d b
#> "Felidae" "Mammalia"
#>
#> $i
#> e b
#> "Hominidae" "Mammalia"
#>
#> $j
#> f c
#> "Solanaceae" "Plantae"
The subset and value work the same for most of the following
functions as well. See tax$all_names() for what can be used with
value.
The "subtaxa" of a taxon are all those of a finer rank encompassed by
that taxon. For example, sapiens is a subtaxon of Homo. The
subtaxa function returns all subtaxa for each taxon in a taxonomy
object.
subtaxa(tax, value = "taxon_names")
#> $b
#> d g k h l e
#> "Felidae" "Panthera" "tigris" "Felis" "catus" "Hominidae"
#> i m
#> "homo" "sapiens"
#>
#> $c
#> f j n o
#> "Solanaceae" "Solanum" "lycopersicum" "tuberosum"
#>
#> $d
#> g k h l
#> "Panthera" "tigris" "Felis" "catus"
#>
#> $e
#> i m
#> "homo" "sapiens"
#>
#> $f
#> j n o
#> "Solanum" "lycopersicum" "tuberosum"
#>
#> $g
#> k
#> "tigris"
#>
#> $h
#> l
#> "catus"
#>
#> $i
#> m
#> "sapiens"
#>
#> $j
#> n o
#> "lycopersicum" "tuberosum"
#>
#> $k
#> named character(0)
#>
#> $l
#> named character(0)
#>
#> $m
#> named character(0)
#>
#> $n
#> named character(0)
#>
#> $o
#> named character(0)
We call taxa that have no supertaxa "roots". The roots function
returns these taxa.
roots(tax, value = "taxon_names")
#> b c
#> "Mammalia" "Plantae"
We call taxa without any subtaxa "leaves". The leaves function returns
these taxa.
leaves(tax, value = "taxon_names")
#> $b
#> k l m
#> "tigris" "catus" "sapiens"
#>
#> $c
#> n o
#> "lycopersicum" "tuberosum"
#>
#> $d
#> k l
#> "tigris" "catus"
#>
#> $e
#> m
#> "sapiens"
#>
#> $f
#> n o
#> "lycopersicum" "tuberosum"
#>
#> $g
#> k
#> "tigris"
#>
#> $h
#> l
#> "catus"
#>
#> $i
#> m
#> "sapiens"
#>
#> $j
#> n o
#> "lycopersicum" "tuberosum"
#>
#> $k
#> named character(0)
#>
#> $l
#> named character(0)
#>
#> $m
#> named character(0)
#>
#> $n
#> named character(0)
#>
#> $o
#> named character(0)
There are many other functions to interact with taxonomy object, such
as stems and n_subtaxa, but these will not be described here for
now.
The taxmap class is used to store any number of tables, lists, or
vectors associated with taxa. It is basically the same as the taxonomy
class, but with the following additions:
- A list called
datathat stores arbitrary user data associated with taxa - A list called
funcsthat stores user defined functions
info <- data.frame(name = c("tiger", "cat", "mole", "human", "tomato", "potato"),
n_legs = c(4, 4, 4, 2, 0, 0),
dangerous = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE))
phylopic_ids <- c("e148eabb-f138-43c6-b1e4-5cda2180485a",
"12899ba0-9923-4feb-a7f9-758c3c7d5e13",
"11b783d5-af1c-4f4e-8ab5-a51470652b47",
"9fae30cd-fb59-4a81-a39c-e1826a35f612",
"b6400f39-345a-4711-ab4f-92fd4e22cb1a",
"63604565-0406-460b-8cb8-1abe954b3f3a")
foods <- list(c("mammals", "birds"),
c("cat food", "mice"),
c("insects"),
c("Most things, but especially anything rare or expensive"),
c("light", "dirt"),
c("light", "dirt"))
reaction <- function(x) {
ifelse(x$data$info$dangerous,
paste0("Watch out! That ", x$data$info$name, " might attack!"),
paste0("No worries; its just a ", x$data$info$name, "."))
}
my_taxmap <- taxmap(tiger, cat, mole, human, tomato, potato,
data = list(info = info,
phylopic_ids = phylopic_ids,
foods = foods),
funcs = list(reaction = reaction))
In most functions that work with taxmap objects, the names of
list/vector datasets, table columns, or functions can be used as if they
were separate variables on their own. In the case of functions, instead
of returning the function itself, the results of the functions are
returned. To see what variables can be used this way, use all_names.
all_names(my_taxmap)
#> taxon_names taxon_ids taxon_indexes
#> "taxon_names" "taxon_ids" "taxon_indexes"
#> classifications n_supertaxa n_supertaxa_1
#> "classifications" "n_supertaxa" "n_supertaxa_1"
#> n_subtaxa n_subtaxa_1 taxon_ranks
#> "n_subtaxa" "n_subtaxa_1" "taxon_ranks"
#> is_root is_stem is_branch
#> "is_root" "is_stem" "is_branch"
#> is_leaf is_internode n_obs
#> "is_leaf" "is_internode" "n_obs"
#> n_obs_1 data$info$name data$info$n_legs
#> "n_obs_1" "name" "n_legs"
#> data$info$dangerous data$phylopic_ids data$foods
#> "dangerous" "phylopic_ids" "foods"
#> funcs$reaction
#> "reaction"
For example using my_taxmap$data$info$n_legs or n_legs will have the
same effect inside manipulation functions like filter_taxa described
below. To get the values of these variables, use get_data.
get_data(my_taxmap)
#> $taxon_names
#> b c d e f
#> "Mammalia" "Plantae" "Felidae" "Notoryctidae" "Hominidae"
#> g h i j k
#> "Solanaceae" "Panthera" "Felis" "Notoryctes" "homo"
#> l m n o p
#> "Solanum" "tigris" "catus" "typhlops" "sapiens"
#> q r
#> "lycopersicum" "tuberosum"
#>
#> $taxon_ids
#> b c d e f g h i j k l m n o p q r
#> "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r"
#>
#> $taxon_indexes
#> b c d e f g h i j k l m n o p q r
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
#>
#> $classifications
#> b
#> "Mammalia"
#> c
#> "Plantae"
#> d
#> "Mammalia;Felidae"
#> e
#> "Mammalia;Notoryctidae"
#> f
#> "Mammalia;Hominidae"
#> g
#> "Plantae;Solanaceae"
#> h
#> "Mammalia;Felidae;Panthera"
#> i
#> "Mammalia;Felidae;Felis"
#> j
#> "Mammalia;Notoryctidae;Notoryctes"
#> k
#> "Mammalia;Hominidae;homo"
#> l
#> "Plantae;Solanaceae;Solanum"
#> m
#> "Mammalia;Felidae;Panthera;tigris"
#> n
#> "Mammalia;Felidae;Felis;catus"
#> o
#> "Mammalia;Notoryctidae;Notoryctes;typhlops"
#> p
#> "Mammalia;Hominidae;homo;sapiens"
#> q
#> "Plantae;Solanaceae;Solanum;lycopersicum"
#> r
#> "Plantae;Solanaceae;Solanum;tuberosum"
#>
#> $n_supertaxa
#> b c d e f g h i j k l m n o p q r
#> 0 0 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3
#>
#> $n_supertaxa_1
#> b c d e f g h i j k l m n o p q r
#> 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#> $n_subtaxa
#> b c d e f g h i j k l m n o p q r
#> 11 4 4 2 2 3 1 1 1 1 2 0 0 0 0 0 0
#>
#> $n_subtaxa_1
#> b c d e f g h i j k l m n o p q r
#> 3 1 2 1 1 1 1 1 1 1 2 0 0 0 0 0 0
#>
#> $taxon_ranks
#> b c d e f g h
#> "class" "kingdom" "family" "family" "family" "family" "genus"
#> i j k l m n o
#> "genus" "genus" "genus" "genus" "species" "species" "species"
#> p q r
#> "species" "species" "species"
#>
#> $is_root
#> b c d e f g h i j k l m
#> TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> n o p q r
#> FALSE FALSE FALSE FALSE FALSE
#>
#> $is_stem
#> b c d e f g h i j k l m
#> FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> n o p q r
#> FALSE FALSE FALSE FALSE FALSE
#>
#> $is_branch
#> b c d e f g h i j k l m
#> FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
#> n o p q r
#> FALSE FALSE FALSE FALSE FALSE
#>
#> $is_leaf
#> b c d e f g h i j k l m
#> FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> n o p q r
#> TRUE TRUE TRUE TRUE TRUE
#>
#> $is_internode
#> b c d e f g h i j k l m
#> FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
#> n o p q r
#> FALSE FALSE FALSE FALSE FALSE
#>
#> $n_obs
#> b c d e f g h i j k l m n o p q r
#> 4 2 2 1 1 2 1 1 1 1 2 1 1 1 1 1 1
#>
#> $n_obs_1
#> b c d e f g h i j k l m n o p q r
#> 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1
#>
#> $name
#> m n o p q r
#> tiger cat mole human tomato potato
#> Levels: cat human mole potato tiger tomato
#>
#> $n_legs
#> m n o p q r
#> 4 4 4 2 0 0
#>
#> $dangerous
#> m n o p q r
#> TRUE FALSE FALSE TRUE FALSE FALSE
#>
#> $phylopic_ids
#> m
#> "e148eabb-f138-43c6-b1e4-5cda2180485a"
#> n
#> "12899ba0-9923-4feb-a7f9-758c3c7d5e13"
#> o
#> "11b783d5-af1c-4f4e-8ab5-a51470652b47"
#> p
#> "9fae30cd-fb59-4a81-a39c-e1826a35f612"
#> q
#> "b6400f39-345a-4711-ab4f-92fd4e22cb1a"
#> r
#> "63604565-0406-460b-8cb8-1abe954b3f3a"
#>
#> $foods
#> $foods$m
#> [1] "mammals" "birds"
#>
#> $foods$n
#> [1] "cat food" "mice"
#>
#> $foods$o
#> [1] "insects"
#>
#> $foods$p
#> [1] "Most things, but especially anything rare or expensive"
#>
#> $foods$q
#> [1] "light" "dirt"
#>
#> $foods$r
#> [1] "light" "dirt"
#>
#>
#> $reaction
#> [1] "Watch out! That tiger might attack!"
#> [2] "No worries; its just a cat."
#> [3] "No worries; its just a mole."
#> [4] "Watch out! That human might attack!"
#> [5] "No worries; its just a tomato."
#> [6] "No worries; its just a potato."
Note how "taxon_names" and "dangerous" are used below.
In addition to all of the functions like subtaxa that work with
taxonomy, taxmap has a set of functions to manipulate data in a
taxonomic context using functions based on dplyr. Like many
operations on taxmap objects, there are a pair of functions that
modify the taxa as well as the associated data, which we call
"observations". The filter_taxa and filter_obs functions are an
example of such a pair that can filter taxa and observations
respectively. For example, we can use filter_taxa to subset all taxa
with a name starting with "t":
filter_taxa(my_taxmap, startsWith(taxon_names, "t"))
#> <Taxmap>
#> 3 taxa: m. tigris, o. typhlops, r. tuberosum
#> 3 edges: NA->m, NA->o, NA->r
#> 3 data sets:
#> info:
#> # A tibble: 3 x 4
#> name n_legs dangerous taxon_id
#> <fct> <dbl> <lgl> <chr>
#> 1 tiger 4.00 T m
#> 2 mole 4.00 F o
#> 3 potato 0 F r
#> phylopic_ids: a named character with 3 items
#> m. e148eabb-f138-43[truncated] ... r. 63604565-0406-46[truncated]
#> foods: a list with 3 items with names:
#> m, o, r
#> 1 functions:
#> reaction
There can be any number of filters that resolve to TRUE/FALSE vectors, taxon ids, or edge list indexes.
filter_taxa(my_taxmap, startsWith(taxon_names, "t"), "r")
There are many options for filter_taxa that make it very flexible. For
example, the supertaxa option can make all the supertaxa of selected
taxa be preserved.
filter_taxa(my_taxmap, startsWith(taxon_names, "t"), supertaxa = TRUE)
#> <Taxmap>
#> 11 taxa: m. tigris, o. typhlops ... c. Plantae
#> 11 edges: h->m, j->o, l->r, d->h ... b->e, g->l, c->g, NA->c
#> 3 data sets:
#> info:
#> # A tibble: 6 x 4
#> name n_legs dangerous taxon_id
#> <fct> <dbl> <lgl> <chr>
#> 1 tiger 4.00 T m
#> 2 cat 4.00 F d
#> 3 mole 4.00 F o
#> # ... with 3 more rows
#> phylopic_ids: a named character with 6 items
#> m. e148eabb-f138-43[truncated] ... r. 63604565-0406-46[truncated]
#> foods: a list with 6 items with names:
#> m, d, o, b, l, r
#> 1 functions:
#> reaction
The filter_obs function works in a similar way, but subsets
observations in my_taxmap$data.
filter_obs(my_taxmap, "info", dangerous == TRUE)
#> <Taxmap>
#> 17 taxa: b. Mammalia, c. Plantae ... r. tuberosum
#> 17 edges: NA->b, NA->c, b->d, b->e ... k->p, l->q, l->r
#> 3 data sets:
#> info:
#> # A tibble: 2 x 4
#> name n_legs dangerous taxon_id
#> <fct> <dbl> <lgl> <chr>
#> 1 tiger 4.00 T m
#> 2 human 2.00 T p
#> phylopic_ids: a named character with 6 items
#> m. e148eabb-f138-43[truncated] ... r. 63604565-0406-46[truncated]
#> foods: a list with 6 items with names:
#> m, n, o, p, q, r
#> 1 functions:
#> reaction
You can choose to filter out taxa whose observations did not pass the filter as well:
filter_obs(my_taxmap, "info", dangerous == TRUE, drop_taxa = TRUE)
#> <Taxmap>
#> 7 taxa: b. Mammalia, d. Felidae ... m. tigris, p. sapiens
#> 7 edges: NA->b, b->d, b->f, d->h, f->k, h->m, k->p
#> 3 data sets:
#> info:
#> # A tibble: 2 x 4
#> name n_legs dangerous taxon_id
#> <fct> <dbl> <lgl> <chr>
#> 1 tiger 4.00 T m
#> 2 human 2.00 T p
#> phylopic_ids: a named character with 6 items
#> m. e148eabb-f138-43[truncated] ... r. 63604565-0406-46[truncated]
#> foods: a list with 6 items with names:
#> m, n, o, p, q, r
#> 1 functions:
#> reaction
The functions sample_n_obs and sample_n_taxa are similar to
filter_obs and filter_taxa, except taxa/observations are chosen
randomly. All of the options of the "filter_" functions are available
to the "sample_" functions
set.seed(1)
sample_n_taxa(my_taxmap, 3)
#> <Taxmap>
#> 3 taxa: g. Solanaceae, i. Felis, m. tigris
#> 3 edges: NA->g, NA->i, NA->m
#> 3 data sets:
#> info:
#> # A tibble: 4 x 4
#> name n_legs dangerous taxon_id
#> <fct> <dbl> <lgl> <chr>
#> 1 tiger 4.00 T m
#> 2 cat 4.00 F i
#> 3 tomato 0 F g
#> # ... with 1 more row
#> phylopic_ids: a named character with 4 items
#> m. e148eabb-f138-43[truncated] ... g. 63604565-0406-46[truncated]
#> foods: a list with 4 items with names:
#> m, i, g, g
#> 1 functions:
#> reaction
set.seed(1)
sample_n_taxa(my_taxmap, 3, supertaxa = TRUE)
#> <Taxmap>
#> 7 taxa: g. Solanaceae, i. Felis ... b. Mammalia, h. Panthera
#> 7 edges: c->g, d->i, h->m, NA->c, b->d, NA->b, d->h
#> 3 data sets:
#> info:
#> # A tibble: 6 x 4
#> name n_legs dangerous taxon_id
#> <fct> <dbl> <lgl> <chr>
#> 1 tiger 4.00 T m
#> 2 cat 4.00 F i
#> 3 mole 4.00 F b
#> # ... with 3 more rows
#> phylopic_ids: a named character with 6 items
#> m. e148eabb-f138-43[truncated] ... g. 63604565-0406-46[truncated]
#> foods: a list with 6 items with names:
#> m, i, b, b, g, g
#> 1 functions:
#> reaction
Adding columns to tabular datasets is done using mutate_obs.
mutate_obs(my_taxmap, "info",
new_col = "Im new",
newer_col = paste0(new_col, "er!"))
#> <Taxmap>
#> 17 taxa: b. Mammalia, c. Plantae ... r. tuberosum
#> 17 edges: NA->b, NA->c, b->d, b->e ... k->p, l->q, l->r
#> 3 data sets:
#> info:
#> # A tibble: 6 x 6
#> name n_legs dangerous taxon_id new_col newer_col
#> <fct> <dbl> <lgl> <chr> <chr> <chr>
#> 1 tiger 4.00 T m Im new Im newer!
#> 2 cat 4.00 F n Im new Im newer!
#> 3 mole 4.00 F o Im new Im newer!
#> # ... with 3 more rows
#> phylopic_ids: a named character with 6 items
#> m. e148eabb-f138-43[truncated] ... r. 63604565-0406-46[truncated]
#> foods: a list with 6 items with names:
#> m, n, o, p, q, r
#> 1 functions:
#> reaction
Subsetting columns in tabular datasets is done using select_obs.
# Selecting a column by name
select_obs(my_taxmap, "info", dangerous)
#> <Taxmap>
#> 17 taxa: b. Mammalia, c. Plantae ... r. tuberosum
#> 17 edges: NA->b, NA->c, b->d, b->e ... k->p, l->q, l->r
#> 3 data sets:
#> info:
#> # A tibble: 6 x 2
#> taxon_id dangerous
#> <chr> <lgl>
#> 1 m T
#> 2 n F
#> 3 o F
#> # ... with 3 more rows
#> phylopic_ids: a named character with 6 items
#> m. e148eabb-f138-43[truncated] ... r. 63604565-0406-46[truncated]
#> foods: a list with 6 items with names:
#> m, n, o, p, q, r
#> 1 functions:
#> reaction
# Selecting a column by index
select_obs(my_taxmap, "info", 3)
#> <Taxmap>
#> 17 taxa: b. Mammalia, c. Plantae ... r. tuberosum
#> 17 edges: NA->b, NA->c, b->d, b->e ... k->p, l->q, l->r
#> 3 data sets:
#> info:
#> # A tibble: 6 x 2
#> taxon_id dangerous
#> <chr> <lgl>
#> 1 m T
#> 2 n F
#> 3 o F
#> # ... with 3 more rows
#> phylopic_ids: a named character with 6 items
#> m. e148eabb-f138-43[truncated] ... r. 63604565-0406-46[truncated]
#> foods: a list with 6 items with names:
#> m, n, o, p, q, r
#> 1 functions:
#> reaction
# Selecting a column by regular expressions
select_obs(my_taxmap, "info", matches("^dange"))
#> <Taxmap>
#> 17 taxa: b. Mammalia, c. Plantae ... r. tuberosum
#> 17 edges: NA->b, NA->c, b->d, b->e ... k->p, l->q, l->r
#> 3 data sets:
#> info:
#> # A tibble: 6 x 2
#> taxon_id dangerous
#> <chr> <lgl>
#> 1 m T
#> 2 n F
#> 3 o F
#> # ... with 3 more rows
#> phylopic_ids: a named character with 6 items
#> m. e148eabb-f138-43[truncated] ... r. 63604565-0406-46[truncated]
#> foods: a list with 6 items with names:
#> m, n, o, p, q, r
#> 1 functions:
#> reaction
Sorting the edge list and observations is done using arrage_taxa and
arrange_obs.
arrange_taxa(my_taxmap, taxon_names)
#> <Taxmap>
#> 17 taxa: n. catus, d. Felidae ... r. tuberosum, o. typhlops
#> 17 edges: i->n, b->d, d->i, b->f ... g->l, h->m, l->r, j->o
#> 3 data sets:
#> info:
#> # A tibble: 6 x 4
#> name n_legs dangerous taxon_id
#> <fct> <dbl> <lgl> <chr>
#> 1 tiger 4.00 T m
#> 2 cat 4.00 F n
#> 3 mole 4.00 F o
#> # ... with 3 more rows
#> phylopic_ids: a named character with 6 items
#> m. e148eabb-f138-43[truncated] ... r. 63604565-0406-46[truncated]
#> foods: a list with 6 items with names:
#> m, n, o, p, q, r
#> 1 functions:
#> reaction
arrange_obs(my_taxmap, "info", name)
#> <Taxmap>
#> 17 taxa: b. Mammalia, c. Plantae ... r. tuberosum
#> 17 edges: NA->b, NA->c, b->d, b->e ... k->p, l->q, l->r
#> 3 data sets:
#> info:
#> # A tibble: 6 x 4
#> name n_legs dangerous taxon_id
#> <fct> <dbl> <lgl> <chr>
#> 1 cat 4.00 F n
#> 2 human 2.00 T p
#> 3 mole 4.00 F o
#> # ... with 3 more rows
#> phylopic_ids: a named character with 6 items
#> m. e148eabb-f138-43[truncated] ... r. 63604565-0406-46[truncated]
#> foods: a list with 6 items with names:
#> m, n, o, p, q, r
#> 1 functions:
#> reaction
The taxmap class has the ability to contain and manipulate very
complex data. However, this can make it difficult to parse the data into
a taxmap object. For this reason, there are three functions to help
creating taxmap objects from nearly any kind of data that a taxonomy
can be associated with or derived from. The figure below shows
simplified versions of how to create taxmap objects from different
types of data in different formats.
The parse_tax_data and lookup_tax_data have, in addition to the
functionality above, the ability to include additional data sets that
are somehow associated with the source datasets (e.g. share a common
identifier). Elements in these datasets will be assigned the taxa
defined in the source data, so functions like filter_taxa and
filter_obs will work on all of the dataset at once.
A set of functions are available for parsing objects of class
Hierarchy and hierarchies. These functions are being ported from the
CRAN package binomen.
The functions below are "taxonomically aware" so that you can use for
example > and < operators to filter your taxonomic names data.
pick() - Pick out specific taxa, while others are dropped
ex_hierarchy1
#> <Hierarchy>
#> no. taxon's: 3
#> Poaceae / family / 4479
#> Poa / genus / 4544
#> Poa annua / species / 93036
# specific ranks by rank name
pick(ex_hierarchy1, ranks("family"))
#> <Hierarchy>
#> no. taxon's: 1
#> Poaceae / family / 4479
# two elements by taxonomic name
pick(ex_hierarchy1, nms("Poaceae", "Poa"))
#> <Hierarchy>
#> no. taxon's: 2
#> Poaceae / family / 4479
#> Poa / genus / 4544
# two elements by taxonomic identifier
pick(ex_hierarchy1, ids(4479, 4544))
#> <Hierarchy>
#> no. taxon's: 2
#> Poaceae / family / 4479
#> Poa / genus / 4544
# combine types
pick(ex_hierarchy1, ranks("family"), ids(4544))
#> <Hierarchy>
#> no. taxon's: 2
#> Poaceae / family / 4479
#> Poa / genus / 4544
pop() - Pop out taxa, that is, drop them
ex_hierarchy1
#> <Hierarchy>
#> no. taxon's: 3
#> Poaceae / family / 4479
#> Poa / genus / 4544
#> Poa annua / species / 93036
# specific ranks by rank name
pop(ex_hierarchy1, ranks("family"))
#> <Hierarchy>
#> no. taxon's: 2
#> Poa / genus / 4544
#> Poa annua / species / 93036
# two elements by taxonomic name
pop(ex_hierarchy1, nms("Poaceae", "Poa"))
#> <Hierarchy>
#> no. taxon's: 1
#> Poa annua / species / 93036
# two elements by taxonomic identifier
pop(ex_hierarchy1, ids(4479, 4544))
#> <Hierarchy>
#> no. taxon's: 1
#> Poa annua / species / 93036
# combine types
pop(ex_hierarchy1, ranks("family"), ids(4544))
#> <Hierarchy>
#> no. taxon's: 1
#> Poa annua / species / 93036
span() - Select a range of taxa, either by two names, or relational
operators
ex_hierarchy1
#> <Hierarchy>
#> no. taxon's: 3
#> Poaceae / family / 4479
#> Poa / genus / 4544
#> Poa annua / species / 93036
# keep all taxa between family and genus
# - by rank name, taxonomic name or ID
span(ex_hierarchy1, nms("Poaceae", "Poa"))
#> <Hierarchy>
#> no. taxon's: 2
#> Poaceae / family / 4479
#> Poa / genus / 4544
# keep all taxa greater than genus
span(ex_hierarchy1, ranks("> genus"))
#> <Hierarchy>
#> no. taxon's: 1
#> Poaceae / family / 4479
# keep all taxa greater than or equal to genus
span(ex_hierarchy1, ranks(">= genus"))
#> <Hierarchy>
#> no. taxon's: 2
#> Poaceae / family / 4479
#> Poa / genus / 4544
# keep all taxa less than Felidae
span(ex_hierarchy2, nms("< Felidae"))
#> <Hierarchy>
#> no. taxon's: 2
#> Puma / genus / 146712
#> Puma concolor / species / 9696
## Multiple operator statements - useful with larger classifications
ex_hierarchy3
#> <Hierarchy>
#> no. taxon's: 6
#> Chordata / phylum / 158852
#> Vertebrata / subphylum / 331030
#> Teleostei / class / 161105
#> Salmonidae / family / 161931
#> Salmo / genus / 161994
#> Salmo salar / species / 161996
span(ex_hierarchy3, ranks("> genus"), ranks("< phylum"))
#> <Hierarchy>
#> no. taxon's: 3
#> Vertebrata / subphylum / 331030
#> Teleostei / class / 161105
#> Salmonidae / family / 161931
This vignettte is meant to be just an outline of what taxa can do. In
the future, we plan to release additional, in-depth vignettes for
specific topics. More informaiton for specific functions and examples
can be found on their man pages by typing the name of the function
prefixed by a ? in an R session. For example, ?filter_taxa will pull
up the help page for filter_taxa.
- use in binomen:
- if this pkg does classes,
binomencan focus on verbs, e.g., manipulating taxonomic classes, doingsplit-apply-combinetype things
- if this pkg does classes,
- use in taxize:
- as we don't want to break things, probably ideal to have
coercion fxns, e.g.,
as.taxon(), which will convert e.g., the output ofget_uid()to ataxataxonomic class, which we can then go downstream and do things with (i.e., whatever we build on top of the classes) - Or we could even have output of
get_*()functions do coercion totaxaclasses on output since they are just simple S3 classes without print methods right now
- as we don't want to break things, probably ideal to have
coercion fxns, e.g.,
- use in metacoder: This will eventually replace the similar classes used in metacoder.
We welcome comments, criticisms, and especially contributions! GitHub issues are the preferred way to report bugs, ask questions, or request new features. You can submit issues here:
https://github.com/ropensci/taxa/issues
- Please report any issues or bugs.
- License: MIT
- Get citation information for
taxain R doingcitation(package = 'taxa') - 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.