date_mat {algaeClassify}

Transform a phytoplankton timeseries into a matrix of abundances for ordination

Description

Transform a phytoplankton timeseries into a matrix of abundances for ordination

Usage

date_mat(
  phyto.df,
  abundance.var = "biovol_um3_ml",
  summary.type = "abundance",
  taxa.name = "phyto_name",
  date.name = "date_dd_mm_yy",
  format = "%d-%m-%y",
  time.agg = c("day", "month", "year", "monthyear"),
  fun = mean_naomit
)

Arguments

phyto.df

Name of data.frame object

abundance.var

Character string: field containing abundance data. Can be NA if the dataset only contains a species list for each sampling date.

summary.type

'abundance' for a matrix of aggregated abundance,'presence.absence' for 1 (present) and 0 (absent).

taxa.name

Character string: field containing taxonomic identifiers.

date.name

Character string: field containing date.

format

Character string: POSIX format string for formatting date column.

time.agg

Character string: time interval for aggregating abundance. default is day.

fun

function for aggregation. default is mean, excluding NA's.

Value

A matrix of phytoplankton abundance, with taxa in rows and time in columns. If time.agg = 'monthyear', returns a 3dimensional matrix (taxa,month,year). If abundance.var = NA, matrix cells will be 1 for present, 0 for absent

Examples

data(lakegeneva)
#example dataset with 50 rows

geneva.mat1<-date_mat(lakegeneva,time.agg='month',summary.type='presence.absence')
geneva.mat2<-date_mat(lakegeneva,time.agg='month',summary.type='abundance')

geneva.mat1
geneva.mat2