if (FALSE) {
# Look up taxon names in vector from NCBI
lookup_tax_data(c("homo sapiens", "felis catus", "Solanaceae"),
type = "taxon_name")
# Look up taxon names in list from NCBI
lookup_tax_data(list("homo sapiens", "felis catus", "Solanaceae"),
type = "taxon_name")
# Look up taxon names in table from NCBI
my_table <- data.frame(name = c("homo sapiens", "felis catus"),
decency = c("meh", "good"))
lookup_tax_data(my_table, type = "taxon_name", column = "name")
# Look up taxon names from NCBI with fuzzy matching
lookup_tax_data(c("homo sapienss", "feles catus", "Solanacese"),
type = "fuzzy_name")
# Look up taxon names from a different database
lookup_tax_data(c("homo sapiens", "felis catus", "Solanaceae"),
type = "taxon_name", database = "ITIS")
# Prevent asking questions for ambiguous taxon names
lookup_tax_data(c("homo sapiens", "felis catus", "Solanaceae"),
type = "taxon_name", database = "ITIS", ask = FALSE)
# Look up taxon IDs from NCBI
lookup_tax_data(c("9689", "9694", "9643"), type = "taxon_id")
# Look up sequence IDs from NCBI
lookup_tax_data(c("AB548412", "FJ358423", "DQ334818"),
type = "seq_id")
# Make up new taxon IDs instead of using the downloaded ones
lookup_tax_data(c("AB548412", "FJ358423", "DQ334818"),
type = "seq_id", use_database_ids = FALSE)
# --- Parsing multiple datasets at once (advanced) ---
# The rest is one example for how to classify multiple datasets at once.
# 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 = lookup_tax_data(species_data,
type = "taxon_name",
datasets = list(counts = abundance,
my_names = common_names,
foods = foods),
mappings = c("species_id" = "id",
"species_id" = "{{name}}",
"{{index}}" = "{{index}}"),
column = "species")
# 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)
}
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