mrdwab

stratified <- function(df, group, size, select = NULL, 
                       replace = FALSE, bothSets = FALSE) {
  if (is.null(select)) {
    df <- df
  } else {
    if (is.null(names(select))) stop("'select' must be a named list")
    if (!all(names(select) %in% names(df)))
      stop("Please verify your 'select' argument")
    temp <- sapply(names(select),
                   function(x) df[[x]] %in% select[[x]])

mrdwab

padNA <- function (mydata, rowsneeded, first = TRUE) 
{
  temp1 = colnames(mydata)
  rowsneeded = rowsneeded - nrow(mydata)
  temp2 = setNames(
    data.frame(matrix(rep(NA, length(temp1) * rowsneeded), 
                      ncol = length(temp1))), temp1)
  if (isTRUE(first)) rbind(mydata, temp2)
  else rbind(temp2, mydata)
}

mrdwab

#' Split concatenated cells in a \code{data.frame} or a \code{data.table}
#' 
#' A variation of the \code{concat.split} family of functions designed for
#' large rectangular datasets.
#' 
#' While the general \code{concat.split} functions are able to handle
#' "unbalanced" datasets (for example, where the number of fields in a given
#' column might differ from row to row) because of the nature of \code{fread}
#' from the "data.table" package, this function does not support such data
#' types.

mrdwab

#' Factor vectors with multiple levels
#'
#' \code{\link{factor}} does not let you use duplicated levels nicely. It results
#' in an ugly warning message and you need to use \code{\link{droplevels}} to get
#' the desired output.
#'
#' The "solution" is to first factor the vector, and then use a named \code{list}
#' with the \code{\link{levels}} function. This function is a wrapper around
#' those steps.
#'

mrdwab

moveme <- function(invec, movecommand) {
  movecommand <- lapply(strsplit(strsplit(movecommand, ";")[[1]], ",|\\s+"),
                        function(x) x[x != ""])
  movelist <- lapply(movecommand, function(x) {
    Where <- x[which(x %in% c("before", "after", "first", "last")):length(x)]
    ToMove <- setdiff(x, Where)
    list(ToMove, Where)
  })
  myVec <- invec
  for (i in seq_along(movelist)) {

mrdwab

CUT <- function (x, breaks, labels = NULL, include.lowest = FALSE, right = TRUE, 
    dig.lab = 3L, ordered_result = FALSE, ...) 
{
    if (!is.numeric(x)) 
        stop("'x' must be numeric")
    if (length(breaks) == 1L) {
        if (is.na(breaks) || breaks < 2L) 
            stop("invalid number of intervals")
        nb <- as.integer(breaks + 1)
        dx <- diff(rx <- range(x, na.rm = TRUE))

mrdwab

Interleave <- function(myList, append.source = TRUE, sep = ": ", drop = FALSE) {
  sources <- myList
  sources[sapply(sources, is.null)] <- NULL
  sources <- lapply(sources, function(x) if (is.matrix(x) || 
                                               is.data.frame(x)) 
    x
                    else t(x))
  nrows <- sapply(sources, nrow)
  mrows <- max(nrows)
  if (any(nrows != mrows & nrows != 1)) 

mrdwab

WeekDays <- function(startOn = "Monday", abbreviate = FALSE) {
  WD <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")
  x <- match(startOn, WD)
  WD <- WD[c(x:7, setdiff(1:7, x:7))]
  if (isTRUE(abbreviate)) {
    substring(WD, 0, 3)
  } else WD
}

mrdwab

helpExtract <- function(Function, section = "Usage", type = "m_code") {
  A <- deparse(substitute(Function))
  x <- capture.output(tools:::Rd2txt(utils:::.getHelpFile(utils::help(A)),
                                     options = list(sectionIndent = 0)))
  B <- grep("^_", x)                    ## section start lines
  x <- gsub("_\b", "", x, fixed = TRUE) ## remove "_\b"
  X <- rep(FALSE, length(x))
  X[B] <- 1
  out <- split(x, cumsum(X))
  out <- out[[which(sapply(out, function(x) 

mrdwab

library(microbenchmark)


## Change "n" to experiment with different sized `data.frame`s
set.seed(1)
n = 100000

## I couldn't think of other constants off the top of my head
## This should give us 26+26+50+50 "dummy variable" columns.
example <- data.frame(strcol = sample(