Sequence Through Taxonomic Metrics

This function is a wrapper for the other "taxa_" functions that sequences through unique values within specified columns. The intent is to provide the ability to quickly calculate metrics for a large number of taxa without having to call a "taxa_" function for each taxon of interest.

taxa_seq(
  .dataframe,
  .key_col,
  .counts_col,
  .filter_cols_vec,
  .group_col = NULL,
  .unnest_col = NULL,
  .job,
  .base_log = 2,
  .q = NULL
)

Arguments

.dataframe	A data frame where each row should represent the number of individuals enumerated for a single taxon collected during a single sampling event.
.key_col	One unquoted column name that represents a key (i.e., unique ID) for a sampling event for which to group (i.e., aggregate) the data.
.counts_col	One unquoted column name that represents taxonomic counts.
.filter_cols_vec	A quoted vector of column names for which the function will sequence through each unique value to perform the specified .job.
.group_col	One unquoted column name that represents a taxomic rank or group of interest.
.unnest_col	One unqouted column name that represents nested data. If this column is NULL (default), then the data will not be unnested.
.job	A character string specifying the metric of interest. Below is a list of exceptable inputs: "abund"Description needed "pct"Description needed "rich"Description needed "pct_rich"Description needed "shannon"Description needed "effective_shannon"Description needed "simpson""Description needed "invsimpson"Description needed "gini_simpson"Description needed "effective_simpson"Description needed "pielou"Description needed "margalef"Description needed "menhinick"Description needed "hill"Description needed "renyi"Description needed
.base_log	The base log value used during the calculation of Shannon Diversity index ("shannon") or Effective Shannon Diversity ("effective_shannon"). The default value is two.
.q	The exponent used during the calculation of Hill Numbers ("hill") and Renyi Entropy ("renyi").

Value

A data frame where each column represents numeric metric values.