brockf

library(lme4)
library(dplyr)
library(broom)

p_vals <- lapply(1:1000, function (x) {
  df <- expand.grid(
    subject = 1:30,
    trial = 1:8,
    condition = c('Control','Experimental')
  )

brockf

<?php

$map = directory_map('/path/to/folder/');
$file_paths = concatenate_filepaths($map);

function concatenate_filepaths ($map, $prefix = '') {
	$return = array();
	
	foreach ($map as $key => $file) {
		if (is_array($file)) {

brockf

# create demo dataframe
data <- data.frame(
              obs = 1:20,
              value = rbinom(20, 2, .5)
            )

# calculate runs of duplicate values
runs <- rle(data$value)

# create cluster vector use runs

brockf

# load *eyetrackingR* and set data options
library(eyetrackingR)

data_options = set_data_options(item_columns = "Trial", trial_column = "Trial", time_column = "TimeFromPhaseOnset", trackloss_column = "TrackLoss", aoi_columns = c('Animate','Inanimate'), participant_column = "ParticipantName")

# verify colums in dataset
master_clean <- verify_dataset(master_clean, data_options)

# analyze and remove trackloss-ridden trials
master_clean <- convert_non_aoi_to_trackloss(master_clean, data_options)

brockf

library(beepr)

# ...run something that takes a long time...

beep(8);

brockf

<?php

// webpage_filesize()
//
// returns the total number of Bytes downloaded
// when a single webpage is accessed without any caching
//
// requires: cURL
//
// @param string $url (e.g., 'http://www.cbc.ca')