splot (version 0.4.1)

splot.bench: splot benchmarker

Description

Time one or more expressions over several iteration, then plot the distributions of their times.

Usage

splot.bench(..., runs = 20, runsize = 200, cleanup = FALSE,
  print.names = FALSE, options = list())

Arguments

...

accepts any number of expressions to be timed. See examples.

runs

the number of overall iterations. Increase to stabilize estimates.

runsize

the number of times each expression is evaluated within each run. Increase to differentiate estimates (particularly for very fast operations).

cleanup

logical; if TRUE, garbage collection will be performed before each run. Garbage collection greatly increases run time, but may result in more stable timings.

print.names

logical; if FALSE, the entered expressions will be included in the plot as legend names. Otherwise, (and if the number of expressions is over 5 or the length of any expression is over 50 characters) expressions are replaced with numbers corresponding to their entered position.

options

a list of options to pass on to splot.

Examples

# NOT RUN {
# increase the number of runs for more stable estimates

# compare ways of looping through a vector
splot.bench(
  sapply(1:100,'*',10),
  mapply('*',1:100,10),
  vapply(1:100,'*',0,10),
  unlist(lapply(1:100,'*',10)),
  {a=numeric(100); for(i in 1:100) a[i]=i*10; a},
  runs = 20, runsize = 200
)

# compare ways of setting all but the maximum value of each row in a matrix to 0
# }
# NOT RUN {
mat = matrix(c(rep(1, 4), rep(0, 8)), 4, 3)
splot.bench(
  t(vapply(seq_len(4), function(r){
    mat[r, mat[r,] < max(mat[r,])] = 0
    mat[r,]
  }, numeric(ncol(mat)))),
  do.call(rbind, lapply(seq_len(4), function(r){
    mat[r, mat[r,] < max(mat[r,])] = 0
    mat[r,]
  })),
  do.call(rbind, lapply(seq_len(4), function(r){
    nr = mat[r,]
    nr[nr < max(nr)] = 0
    nr
  })),
  {nm = mat; for(r in seq_len(4)){
    nr = nm[r,]
    nm[r, nr < max(nr)] = 0
  }; nm},
  {nm = mat; for(r in seq_len(4)) nm[r, nm[r,] < max(nm[r,])] = 0; nm},
  {nm = matrix(0, dim(mat)[1], dim(mat)[2]); for(r in seq_len(4)){
    m = which.max(mat[r,])
    nm[r, m] = mat[r, m]
  }; nm},
  {ck = do.call(rbind, lapply(seq_len(4), function(r){
    nr = mat[r,]
    nr < max(nr)
  })); nm = mat; nm[ck] = 0; nm},
  t(apply(mat, 1, function(r){
    r[r < max(r)] = 0
    r
  })),
  runs = 50, runsize = 200
)
# }