R/nonmissing_per_group.R

In pmartR/pmartR: Panomics Marketplace - Quality Control and Statistical Analysis for Panomics Data

#' Computes the Number of Non-Missing Data Points by Group
#'
#' This function computes the number of non-missing observations for samples,
#' based on a group designation, for every biomolecule in the dataset
#'
#' @param omicsData an optional object of one of the classes "pepData",
#'        "proData", "metabData",  "lipidData", or "nmrData", usually created by
#'        \code{\link{as.pepData}}, \code{\link{as.proData}},
#'        \code{\link{as.metabData}}, \code{\link{as.lipidData}}, or
#'        \code{\link{as.nmrData}}, respectively. Either omicsData or all of
#'        e_data, groupDF, cname_id, and samp_id must be provided.
#'
#' @return a list of length two. The first element giving the total number of
#'         possible samples for each group. The second element giving a
#'         data.frame with the first column giving the peptide and the second
#'         through kth columns giving the number of non-missing observations for
#'         each of the \code{k} groups.
#'
#' @author Lisa Bramer, Kelly Stratton
#'
nonmissing_per_group <- function(omicsData) {
  # Extract the ID column.
  id_col <- which(names(omicsData$e_data) == get_edata_cname(omicsData))

  # Make a copy of e_data so the sample columns can be reordered later.
  edata <- omicsData$e_data[, -id_col]

  # Fish out information from the group_DF attribute.
  # groupDF <- attr(omicsData, "group_DF")
  groupDF <- get_group_DF(omicsData)

  # Count the number of samples per group from the group_DF data frame.
  tot_samps <- groupDF %>%
    # Gather all samples from each group.
    dplyr::group_by(Group) %>%
    # Create a new column, n_group, that is the number of samples per group.
    dplyr::summarize(n_group = dplyr::n()) %>%
    # Convert to a data frame.
    data.frame()

  # Reorder the samples. This is done because the C++ function for counting
  # group sizes assumes the samples are ordered by group in the omicsData data
  # frames.

  # match the order of the groupDF$SampleID to the order of the columns in edata
  samp_ord = match(groupDF[,get_fdata_cname(omicsData)], colnames(edata))
  group_ord = order(groupDF$Group)
  group_dat <- as.character(groupDF$Group[group_ord])

  # Reorder the columns of e_data. This needs to be done so the e_data columns
  # will match the order of the group_data vector. These are ordered because the
  # following C++ function assumes the samples are in order.
  edata <- edata[, samp_ord[group_ord]]

  # Count the number of nonmissing values per group. The output is a matrix with
  # the number of observations down the rows and the number of unique groups
  # across the columns.
  nonmissing <- nonmissing_per_grp(
    as.matrix(edata),
    group_dat
  )

  # Add the biomolecule IDs to the group counts.
  nonmiss_totals <- data.frame(as.character(omicsData$e_data[, id_col]),
    nonmissing,
    stringsAsFactors = FALSE
  )

  # Rename the columns according to the edata_cname in omicsData and the names
  # in the group_DF attribute. The order of the names in group_dat will always
  # match the order of the columns in nonmiss_totals because they were
  # previously placed in the same order.
  names(nonmiss_totals) <- c(get_edata_cname(omicsData), unique(group_dat))


  return(list(
    group_sizes = tot_samps,
    nonmiss_totals = nonmiss_totals
  ))
}