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#' Apply a function over groups of columns.
grouped_columns_apply = function (data, width, fun) {
    # Generate the group indices for the columns (see below).
    group_indices = group_indices(ncol(data), width)
    # For each group, apply `fun` to the columns of its indices.
    results = lapply(group_indices, function (indices) fun(data[, indices, drop = FALSE]))
    bind_cols(results)
}

# If the number of columns is always evenly divisible by the width, the following

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Homebrew build logs for glibc on Ubuntu 12.04.5 LTS
Build date: 2017-10-03 18:51:18

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#include <algorithm>
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <iterator>
#include <string>
#include <vector>

auto main(int argc, char** argv) -> int {
    if (argc < 2) {

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context('Same as the basic tests, just in an interactive R shell')

play_back_results = function (record_events, onto) {
    for (event in record_events) {
        switch(event$type,
               start_test = onto$start_test(event$context, event$test),
               add_result = onto$add_result(event$context, event$test, event$result),
               end_test = onto$end_test(event$context, event$test))
    }
}

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Who is touching the .Random.seed?

Inspired by a Stack Overflow question, here’s a way of tracking what’s been modifying the .Random.seed.

Since R makes static analysis impossible in general, the following is a runtime tracer that injects itself into the .Random.seed variable via an active binding:

NOTE: This will only work properly if called directly by the user at the top level. In particular, it won’t work inside an RMarkdown report or similar, where the top-level call is rmarkdown::render(), or knitr::knit(), or similar; rather than, say, sample().

trace_random_seed()

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#!/usr/bin/env perl

use strict;
use warnings;

my $state = 0; # 0 = ID; 1 = sequence; 2 = repeat ID; 3 = quality
my $seqlen = 0;

while (<>) {
    chomp;

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#!/usr/bin/env bash

while getopts ":n:c:q:" option; do
    case "$option" in
        n)
            cores="$OPTARG"
            ;;
        m)
            mem="$OPTARG"
            ;;

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library(dplyr)
library(tidyr)
library(ggplot2)

stages = c('Ordered', '?', 'In transit', 'Arrived', 'Returned')

make_stages = function (status)
    factor(status, levels = stages, ordered = TRUE)

pillows = tibble::tribble(

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Homebrew build logs for util-linux on Ubuntu 12.04.5 LTS
Build date: 2017-02-14 14:50:53

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Homebrew build logs for msgpack on Ubuntu 12.04.5 LTS
Build date: 2017-02-14 14:29:12