# parse_tax_data

0th

Percentile

##### Convert one or more data sets to taxmap

Parses taxonomic information and associated data and stores it in a taxa::taxmap() object. Taxonomic classifications must be present somewhere in the first input.

##### Usage
parse_tax_data(tax_data, datasets = list(), class_cols = 1,
class_sep = ";", sep_is_regex = FALSE, class_key = "taxon_name",
class_regex = "(.*)", include_match = TRUE, mappings = c(),
include_tax_data = TRUE)
##### Arguments
tax_data

A table, list, or vector that contains the names of taxa that represent taxonomic classifications. Accepted representations of classifications include:

• A list/vector or table with column(s) of taxon names: Something like "Animalia;Chordata;Mammalia;Primates;Hominidae;Homo". What separator(s) is used (";" in this example) can be changed with the class_sep option. For tables, the classification can be spread over multiple columns and the separator(s) will be applied to each column, although each column could just be single taxon names with no separator. Use the class_cols option to specify which columns have taxon names.

• A list in which each entry is a classifications. For example, list(c("Animalia", "Chordata", "Mammalia", "Primates", "Hominidae", "Homo"), ...).

• A list of data.frames where each represents a classification with one taxon per row. The column that contains taxon names is specified using the class_cols option. In this instance, it only makes sense to specify a single column.

datasets

Additional lists/vectors/tables that should be included in the resulting taxmap object. The mappings option is use to specify how these data sets relate to the tax_data and, by inference, what taxa apply to each item.

class_cols

(character or integer) The names or indexes of columns that contain classifications if the first input is a table. If multiple columns are specified, they will be combined in the order given.

class_sep

(character) One or more separators that delineate taxon names in a classification. For example, if one column had "Homo sapiens" and another had "Animalia;Chordata;Mammalia;Primates;Hominidae", then class_sep = c(" ", ";"). All separators are applied to each column so order does not matter.

sep_is_regex

(TRUE/FALSE) Whether or not class_sep should be used as a regular expression.

class_key

(character of length 1) The identity of the capturing groups defined using class_regex. The length of class_key must be equal to the number of capturing groups specified in class_regex. Any names added to the terms will be used as column names in the output. At least one "taxon_name" must be specified. Only "info" can be used multiple times. Each term must be one of those described below:

• taxon_name: The name of a taxon. Not necessarily unique, but are interpretable by a particular database. Requires an internet connection.

• info: Arbitrary taxon info you want included in the output. Can be used more than once.

class_regex

(character of length 1) A regular expression with capturing groups indicating the locations of data for each taxon in the class term in the key argument. The identity of the information must be specified using the class_key argument. The class_sep option can be used to split the classification into data for each taxon before matching. If class_sep is NULL, each match of class_regex defines a taxon in the classification.

include_match

(logical of length 1) If TRUE, include the part of the input matched by class_regex in the output object.

mappings

(named character) This defines how the taxonomic information in tax_data applies to data set in datasets. This option should have the same number of inputs as datasets, with values corresponding to each data set. The names of the character vector specify what information in tax_data is shared with info in each dataset, which is specified by the corresponding values of the character vector. If there are no shared variables, you can add NA as a placeholder, but you could just leave that data out since it is not benefiting from being in the taxmap object. The names/values can be one of the following:

• For tables, the names of columns can be used.

• "{{index}}" : This means to use the index of rows/items

• "{{name}}" : This means to use row/item names.

• "{{value}}" : This means to use the values in vectors or lists. Lists will be converted to vectors using unlist().

include_tax_data

(TRUE/FALSE) Whether or not to include tax_data as a dataset, like those in datasets.

Other parsers: extract_tax_data, lookup_tax_data

##### Aliases
• parse_tax_data
##### Examples
# NOT RUN {
# Make example data with taxonomic classifications
species_data <- data.frame(tax = c("Mammalia;Carnivora;Felidae",
"Mammalia;Carnivora;Felidae",
"Mammalia;Carnivora;Ursidae"),
species = c("Panthera leo",
"Panthera tigris",
"Ursus americanus"),
species_id = c("A", "B", "C"))

# Make example data associated with the taxonomic data
# Note how this does not contain classifications, but
# does have a varaible in common with "species_data" ("id" = "species_id")
abundance <- data.frame(id = c("A", "B", "C", "A", "B", "C"),
sample_id = c(1, 1, 1, 2, 2, 2),
counts = c(23, 4, 3, 34, 5, 13))

# Make another related data set named by species id
common_names <- c(A = "Lion", B = "Tiger", C = "Bear", "Oh my!")

# Make another related data set with no names
foods <- list(c("ungulates", "boar"),
c("ungulates", "boar"),
c("salmon", "fruit", "nuts"))

# Make a taxmap object with these three datasets
x = parse_tax_data(species_data,
datasets = list(counts = abundance,
my_names = common_names,
foods = foods),
mappings = c("species_id" = "id",
"species_id" = "{{name}}",
"{{index}}" = "{{index}}"),
class_cols = c("tax", "species"),
class_sep = c(" ", ";"))

# Note how all the datasets have taxon ids now
x\$data

# This allows for complex mappings between variables that other functions use
map_data(x, my_names, foods)
map_data(x, counts, my_names)

# }

Documentation reproduced from package taxa, version 0.1.0, License: MIT + file LICENSE

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