Compute count conversions — compute

Functions to convert species counts between different formats: raw abundance, relative abundance, and number concentration, using counts metadata.

Usage

compute_abundances(data, aggregate = TRUE)

compute_concentrations(data, aggregate = TRUE)

compute_frequencies(data, aggregate = TRUE)

Arguments

data: a data.frame. One obtained by read_*_data() functions.
aggregate: a logical of length 1. If FALSE counts will be derived for each subsample. If TRUE (default) subsample counts will be aggregated by sample_id.

Value

A data.frame in long format with two additional columns: taxa, the taxon name and counts_*, the number concentration (counts_n_conc) or the relative abundance (counts_rel_ab) or the raw abundance (counts_raw_ab).

Details

compute_concentrations() converts all counts to number concentrations (n specimens/m³).
compute_frequencies() converts all counts to relative abundances (% specimens per sampling unit).
compute_abundances() converts all counts to raw abundances (n specimens/sampling unit).

Examples

# Import example dataset ----
file_name <- system.file(file.path("extdata", "FORCIS_net_sample.csv"), 
                         package = "forcis")

net_data <- read.table(file_name, dec = ".", sep = ";")

# Add 'data_type' column ----
net_data$"data_type" <- "Net"

# Select a taxonomy ----
net_data <- select_taxonomy(net_data, taxonomy = "VT")

# Dimensions of the data.frame ----
dim(net_data)
#> [1] 2451   77

# Compute concentration ----
net_data_conc <- compute_concentrations(net_data)
#> Counts from 14 samples could not be converted because of missing volume data
#> Relative counts from 24 samples could not be converted because of missing data on total assemblage

# Dimensions of the data.frame ----
dim(net_data_conc)
#> [1] 39032    16