accumulation.R

# benchmarks from http://www.win-vector.com/blog/2015/07/efficient-accumulation-in-r/#more-3295

mkRow <- function(nCol) {
  x <- as.list(rnorm(nCol))
  # make row mixed types by changing first column to string
  x[[1]] <- ifelse(x[[1]]>0,'pos','neg')
  names(x) <- paste('x',seq_len(nCol),sep='.')
  x
}

mkFrameForLoop <- function(nRow,nCol) {
  set.seed(0)
  d <- c()
  for(i in seq_len(nRow)) {
    ri <- mkRow(nCol)
    di <- data.frame(ri,
                     stringsAsFactors=FALSE)
    d <- rbind(d,di)
  }
  d
}

mkFrameList <- function(nRow,nCol) {
  set.seed(0)
  d <- lapply(seq_len(nRow),function(i) {
    ri <- mkRow(nCol)
    data.frame(ri,
               stringsAsFactors=FALSE)
  })
  do.call(rbind,d)
}

mkFrameBindRows <- function(nRow,nCol) {
  set.seed(0)
  d <- lapply(seq_len(nRow),function(i) {
    ri <- mkRow(nCol)
    data.frame(ri,
               stringsAsFactors=FALSE)
  })
  dplyr::bind_rows(d)
}

mkFrameVector <- function(nRow,nCol) {
  set.seed(0)
  res <- c(list(character(nRow)), replicate(nCol - 1, numeric(nRow), simplify = FALSE))
  for(i in seq_len(nRow)) {
    ri <- mkRow(nCol)
    for(j in seq_along(ri)) {
      res[[j]][i] <- ri[[j]]
    }
  }
  # fix the names
  names(res) <- paste('x',
     seq_len(nCol),sep='.')

  data.frame(res, stringsAsFactors = FALSE)
}

mkRow2 <- function(nCol, fileloc) {
  x <- as.list(rnorm(nCol))
  # make row mixed types by changing first column to string
  x[[1]] <- ifelse(x[[1]]>0,'pos','neg')
  writeLines(paste(x, collapse=" "), con = fileloc)
}

avoidAggregate <- function(nRow,nCol) {
  set.seed(0)
  some.file <- tempfile()
  file.create(some.file)
  cf <- file(some.file, open="a")
  lapply(seq_len(nRow),function(i) {mkRow2(nCol, cf)})
  close(cf)
  numnumer <- rep("numeric", nCol - 1)
  res <- read.table(some.file, nrows=nRow,
    colClasses= c("character", numnumer))

  names(res) <- paste('x',
     seq_len(nCol),sep='.')
  res
}

mkRowC <- function(nCol) {
  xN <- rnorm(nCol)
  x <- as.character(xN)
  x[[1]] <- ifelse(xN[[1]]>0,'pos','neg')
  x
}

mkFrameMat <- function(nRow,nCol) {
  set.seed(0)
  d <- matrix(data="",
     nrow=nRow,ncol=nCol)
  for(i in seq_len(nRow)) {
    ri <- mkRowC(nCol)
    d[i,] <- ri
  }
  d <- data.frame(d,
     stringsAsFactors=FALSE)
  if(nCol>1) {
    for(i in 2:nCol) {
      d[[i]] <- as.numeric(d[[i]])
    }
  }
  names(d) <- paste('x',
     seq_len(nCol),sep='.')
  d
}

library(microbenchmark)
library(compile)

rows = 10000
columns = 100

microbenchmark(a <- mkFrameForLoop(rows, columns),
               b <- mkFrameList(rows, columns),
               d <- mkFrameBindRows(rows, columns),
               e <- avoidAggregate(rows, columns),
               ec <- cmpfun(avoidAggregate)(rows, columns),
               f <- mkFrameVector(rows, columns),
               fc <- cmpfun(mkFrameVector)(rows, columns),
               g <- mkFrameMat(rows, columns),
               gc <- cmpfun(mkFrameMat)(rows, columns),
               times = 1)
# Unit: milliseconds
#                                         expr         min          lq        mean      median          uq         max neval
#           a <- mkFrameForLoop(rows, columns) 145496.8312 145496.8312 145496.8312 145496.8312 145496.8312 145496.8312     1
#              b <- mkFrameList(rows, columns)  63811.0009  63811.0009  63811.0009  63811.0009  63811.0009  63811.0009     1
#          d <- mkFrameBindRows(rows, columns)  42721.5881  42721.5881  42721.5881  42721.5881  42721.5881  42721.5881     1
#           e <- avoidAggregate(rows, columns)   3618.5261   3618.5261   3618.5261   3618.5261   3618.5261   3618.5261     1
#  ec <- cmpfun(avoidAggregate)(rows, columns)   3666.2685   3666.2685   3666.2685   3666.2685   3666.2685   3666.2685     1
#            f <- mkFrameVector(rows, columns)   2809.1274   2809.1274   2809.1274   2809.1274   2809.1274   2809.1274     1
#   fc <- cmpfun(mkFrameVector)(rows, columns)    689.0486    689.0486    689.0486    689.0486    689.0486    689.0486     1
#               g <- mkFrameMat(rows, columns)   1182.1522   1182.1522   1182.1522   1182.1522   1182.1522   1182.1522     1
#      gc <- cmpfun(mkFrameMat)(rows, columns)   1932.6838   1932.6838   1932.6838   1932.6838   1932.6838   1932.6838     1

all.equal(a, b)
all.equal(a, d, check.attributes = FALSE) # dplyr adds a few class names to the data frame
all.equal(a, e)
all.equal(a, ec)
all.equal(a, f)
all.equal(a, fc)
all.equal(a, g)
all.equal(a, gc)

# run the fast ones a few times to get more accurate timings
# Unit: milliseconds
#                                         expr       min        lq      mean   median       uq      max neval  cld
#           e <- avoidAggregate(rows, columns) 3460.6368 3492.7273 3572.8147 3556.015 3675.985 3678.709     5    d
#  ec <- cmpfun(avoidAggregate)(rows, columns) 3424.0553 3492.4201 3548.3598 3570.829 3596.581 3657.914     5    d
#            f <- mkFrameVector(rows, columns) 2115.1614 2400.8824 2813.8144 2710.366 3155.541 3687.122     5   c
#   fc <- cmpfun(mkFrameVector)(rows, columns)  575.5463  631.7447  708.3951  635.345  665.834 1033.506     5 a
#               g <- mkFrameMat(rows, columns) 1279.0403 1286.2203 1328.8007 1322.776 1343.263 1412.704     5  b
#      gc <- cmpfun(mkFrameMat)(rows, columns) 1228.0720 1299.3356 1419.9762 1330.457 1423.105 1818.911     5  b

You may mean library(compiler) rather than library(compile) or is compile a non-CRAN package?