metacoder (version 0.3.7)

calc_group_rsd: Relative standard deviations of groups of columns

Description

For a given table in a taxmap object, split columns by a grouping factor and return the relative standard deviation for each row in a table. The relative standard deviation is the standard deviation divided by the mean of a set of numbers. It is useful for comparing the variation when magnitude of sets of number are very different.

Usage

calc_group_rsd(
  obj,
  data,
  groups,
  cols = NULL,
  other_cols = FALSE,
  out_names = NULL,
  dataset = NULL
)

Value

A tibble

Arguments

obj

A taxmap object

data

The name of a table in obj$data.

groups

Group multiple columns per treatment/group. This should be a vector of group IDs (e.g. character, integer) the same length as cols that defines which samples go in which group. When used, there will be one column in the output for each unique value in groups.

cols

The columns in data to use. By default, all numeric columns are used. Takes one of the following inputs:

TRUE/FALSE:

All/No columns will used.

Character vector:

The names of columns to use

Numeric vector:

The indexes of columns to use

Vector of TRUE/FALSE of length equal to the number of columns:

Use the columns corresponding to TRUE values.

other_cols

Preserve in the output non-target columns present in the input data. New columns will always be on the end. The "taxon_id" column will be preserved in the front. Takes one of the following inputs:

NULL:

No columns will be added back, not even the taxon id column.

TRUE/FALSE:

All/None of the non-target columns will be preserved.

Character vector:

The names of columns to preserve

Numeric vector:

The indexes of columns to preserve

Vector of TRUE/FALSE of length equal to the number of columns:

Preserve the columns corresponding to TRUE values.

out_names

The names of count columns in the output. Must be the same length and order as cols (or unique(groups), if groups is used).

dataset

DEPRECIATED. use "data" instead.

See Also

Other calculations: calc_diff_abund_deseq2(), calc_group_mean(), calc_group_median(), calc_group_stat(), calc_n_samples(), calc_obs_props(), calc_prop_samples(), calc_taxon_abund(), compare_groups(), counts_to_presence(), rarefy_obs(), zero_low_counts()

Examples

if (FALSE) {
# Parse data for examples
x = parse_tax_data(hmp_otus, class_cols = "lineage", class_sep = ";",
                   class_key = c(tax_rank = "taxon_rank", tax_name = "taxon_name"),
                   class_regex = "^(.+)__(.+)$")

# Calculate the RSD for each group
calc_group_rsd(x, "tax_data", hmp_samples$sex)

# Use only some columns
calc_group_rsd(x, "tax_data", hmp_samples$sex[4:20],
                cols = hmp_samples$sample_id[4:20])

# Including all other columns in ouput
calc_group_rsd(x, "tax_data", groups = hmp_samples$sex,
                other_cols = TRUE)

# Inlcuding specific columns in output
calc_group_rsd(x, "tax_data", groups = hmp_samples$sex,
                other_cols = 2)
calc_group_rsd(x, "tax_data", groups = hmp_samples$sex,
                other_cols = "otu_id")

# Rename output columns
calc_group_rsd(x, "tax_data", groups = hmp_samples$sex,
               out_names = c("Women", "Men"))

}