R/good_measurement.R

In fomotis/cyanoFilter: Phytoplankton Population Identification using Cell Pigmentation and/or Complexity

#' indicates if measurement from a flowfile is good or bad.
#'
#' @param metafile associated metafile to the supplied fcsfile. This is a csv 
#' file containig computed stats from the flow cytometer.
#' @param col_cpml column name or column number in metafile containing cell 
#' per microlitre measurements.
#' @param mxd_cellpML maximal accepted cell per microlitre. Flowfiles with 
#' larger cell per microlitre are termed bad. Defaults to 1000.
#' @param mnd_cellpML minimum accepted cell per microlitre. Flowfiles with 
#'lesser cell per microlitre are termed bad. Defaults to 50.
#'
#' @return character vector with length same as the number of rows in the 
#' metafile whose entries are \strong{good} for good files and 
#' \strong{bad} for bad files.
#'
#' @description This function examines the column containig \eqn{cells/\mu L}  
#' and determins if the measurement can be used for further analysis or not 
#' based on a supplied range.
#'
#' @details Most flow cytometer makers will always inform clients within which 
#' range can measurements from the machine be trusted. The machines normally 
#' stores the amount of \eqn{cells/\mu L} it counted in a sample. Too large 
#' value could mean possible doublets and too low value could mean too little 
#' cells.
#'
#' @examples
#' require("stringr")
#'  metadata <- system.file("extdata", "2019-03-25_Rstarted.csv", 
#'  package = "cyanoFilter",
#'               mustWork = TRUE)
#'  metafile <- read.csv(metadata, skip = 7, stringsAsFactors = FALSE,
#'                      check.names = TRUE, encoding = "UTF-8")
#'  metafile <- metafile[, seq_len(65)] #first 65 columns contains useful information
#'  #extract the part of the Sample.ID that corresponds to BS4 or BS5
#'  metafile$Sample.ID2 <- stringr::str_extract(metafile$Sample.ID, "BS*[4-5]")
#'  #clean up the Cells.muL column
#'  names(metafile)[which(stringr::str_detect(names(metafile), 
#'  "Cells."))] <- "CellspML"
#'  goodFcs(metafile = metafile, col_cpml = "CellspML", mxd_cellpML = 1000, 
#'  mnd_cellpML = 50)
#'
#' @export goodFcs

goodFcs <- function(metafile, col_cpml = "CellspML", mxd_cellpML = 1000, 
                    mnd_cellpML = 50) {
    if (!is.null(metafile) & !is.null(mxd_cellpML) & !is.null(mnd_cellpML)) {

        goodfile <- ifelse((metafile[, col_cpml] < mxd_cellpML & metafile[, 
                            col_cpml] > mnd_cellpML), "good", "bad")

    } else stop("At least metafile is empty")
    return(goodfile)
}