IronistM

var https = require('https'),
	key = 'key',
	url = 'http://www.igvita.com/',
	strategy = 'desktop';

https.get({
	host: 'www.googleapis.com', 
	path: '/pagespeedonline/v1/runPagespeed?url=' + encodeURIComponent(url) + 
		  '&key='+key+'&strategy='+strategy
	}, function(res) {

IronistM

var SPREADSHEET_URL = "[YOUR_URL]";

var DAYS = ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'];

function main() {
  var spreadsheet = SpreadsheetApp.openByUrl(SPREADSHEET_URL);
  spreadsheet.getRangeByName("date").setValue(new Date());
  spreadsheet.getRangeByName("account_id").setValue(AdWordsApp.currentAccount().getCustomerId());
  var impressionsThreshold = parseField(spreadsheet.getRangeByName("impressions").getValue());
  var clicksThreshold = parseField(spreadsheet.getRangeByName("clicks").getValue());

IronistM

/* 

    Email Tracking with Google Analytics
    ====================================
    
    Written by Amit Agarwal on 09/24/2013
    
    Tutorial: http://labnol.org/?p=8082
    YouTube : http://youtu.be/9T_EqwQnZY0
    Support : http://twitter.com/labnol

IronistM

ip2Int <- function(ip){
  split <- as.numeric(strsplit(ip, "\\.")[[1]])
  out <- (split[1] * 256^3) + (split[2] * 256^2) + (split[3] * 256) + (split[4])
  return(out)
}


int2Ip <- function(int){
  split <- NULL
  split[1] <- as.integer(int/256^3)

IronistM

#Plot data using ggplot2
library(ggplot2)

#Combine year/month/day together into POSIX
pageviews_w_forecast$date <- ISOdate(pageviews_w_forecast$year, pageviews_w_forecast$month, pageviews_w_forecast$day)

#Convert columns to numeric
pageviews_w_forecast$pageviews <- as.numeric(pageviews_w_forecast$pageviews)
pageviews_w_forecast$pageviews_upper <- as.numeric(pageviews_w_forecast$pageviews_upper)
pageviews_w_forecast$pageviews_lower <- as.numeric(pageviews_w_forecast$pageviews_lower)

IronistM

# Load requirements to pull data from Google Analytics.
 
library(RGoogleAnalytics)
library(RColorBrewer)
library(scales)
library(lubridate)
library(ProjectTemplate)
library(ggplot2)
library(gridExtra)
library(plyr)

IronistM

// in this case we want to load a 10% sampling set of the data
data:
LOAD ….<your fields>…
FROM <your QVD file>
WHERE ceil(rand() * 100) <= 10;  // or: rand() <= 0.1

// SAMPLE n LOAD is much faster but allocates the memory of the full data set during the LOAD
// Where rand() <= n allocates only the memory of the smaller result set which could be crucial with Big Data..

IronistM

LOAD
if(age<16, 'Under 16',
     if(age>65, 'Over 65',
          text(16+(Div(age-16,10)*10)) & ' - ' & text(25+(Div(age-16,10)*10)))) as age_group

IronistM

postDailyCustomDataSource = function(filename = "./YandexUpload/cost.data.splits.2013_02_11.csv", 
                                     accountId=GA.profiles$accountId[52], #uploading to 52nd accountId 
                                     webPropertyId=GA.profiles$webPropertyId[52], 
                                     customDataSourceId="XXXXX", 
                                     date = '2013-02-11',
                                     access_token = MEtoken) {

# Verified this is correct URL as it works in curl
  url2 <- paste0('https://www.googleapis.com/upload/analytics/v3/management/accounts/', accountId ,
               '/webproperties/', webPropertyId, 

IronistM

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console