R/to.unbalanced.R

In graemeleehickey/joineR: Joint Modelling of Repeated Measurements and Time-to-Event Data

#' Transform data to the longitudinal unbalanced format
#' 
#' @description Transforms a longitudinal data set in the balanced format to the
#'   unbalanced format.
#' 
#' @param data a data frame with longitudinal data in the balanced format. That 
#'   is, in the format of 'one row per subject'. \code{data}, where the patient
#'   identifications is.
#' @param times a vector with the unique time points where the patients are 
#'   observed. This is the study design time points in a balanced data set.
#' @inheritParams to.balanced
#'   
#' @author Ines Sousa
#' @seealso \code{\link{to.balanced}}.
#' @keywords data manip
#'   
#' @return A data frame with longitudinal data in the unbalanced format. The
#'   unbalanced format is considered in this context as the format where each
#'   row has data on each subject observation.
#' @export
#' 
#' @examples
#' simul <- data.frame(num = 1:10,
#'                     Y1.1 = rnorm(10), Y1.2 = rnorm(10),
#'                     Y2.1 = rnorm(10), Y2.2 = rnorm(10), 
#'                     age = rnorm(10))
#' to.unbalanced(simul, id.col = 1, times = c(1, 2), Y.col = 2:5,
#'               other.col = 6)
to.unbalanced <- function(data, id.col, times, Y.col, other.col = NA) {
  
  if (length(id.col) > 1) {
    stop("Only a single vector of subject identification is possible")
  }
  if (is.numeric(id.col)) {
    pat <- data[, id.col]
  } else {
    pat <- data[[id.col]]
  }
  tm <- as.vector(times)
  ltt <- length(tm)
  if (!is.numeric(Y.col)) {
    Y.col <- which(names(data) %in% Y.col)
  }
  Y.col <- as.vector(Y.col)
  nY <- length(Y.col) / length(tm)
  if ((nY %% 1) != 0) {
    stop("Number of longitudinal variables not consistent with the number of longitudinal time points")
  }
  time <- rep(tm, times = length(pat))
  indv <- rep(pat, each = ltt)
  Y <- as.matrix(data[, Y.col])
  data.trans <- as.data.frame(cbind(indv, time))
  names(data.trans)[1] <- names(data)[id.col]
  for (i in 1:nY) {
    Y.tt <- c(t(Y[, (ltt * (i - 1) + 1):(ltt * i)]))
    data.trans <- cbind(data.trans, Y.tt)
    names(data.trans)[dim(data.trans)[2]] <- (names(data)[Y.col])[(i - 1) * ltt + 1]
  }
  ddt <- dim(data.trans)[2]
  if (!identical(NA, other.col)) {
    if (!is.numeric(other.col)) {
      other.col <- which(names(data) %in% other.col)
    }
    other.col <- as.vector(other.col)
    other <- as.data.frame(data[, other.col])
    l.other <- dim(other)[2]
    for (i in 1:l.other) {
      data.trans <- cbind(data.trans, rep(other[, i], each = ltt))
    }
    data.trans <- as.data.frame(data.trans)
    names(data.trans)[(ddt + 1):(dim(data.trans)[2])] <- names(data)[other.col]
  }
  row.names(data.trans) <- 1:(dim(data.trans)[1])
  
  return(data.trans)
  
}