IronistM

function github() {
  username = "xxx"
  repo = "xxx"
  url = "https://api.github.com/repos/"+username+"/"+repo+"/issues"
  payload = {
    "title": "xxx",
    "body": "xxx",
    "assignee": "xxx"
  }
  sendToGithub(url, payload)

IronistM

#First we are going to set up probability distributions for our beliefs about the inputs
#We've been told ARPU is about £7 and it's very unlikely to be higher than £10 or lower than £4
#So we'll go for a normal distribution centred at 7 with 5% and 95% quantiles at 4 and 10

#Show how we get the variance

arpu.sd<-3/1.96
x<-seq(0, 15,by=0.5)
d<-dnorm(x, 7, arpu.sd)
plot(x, d, type='l')

IronistM

function uploadData() {
  var accountId = "xxxxxxxx";
  var webPropertyId = "UA-xxxxxxxx-x";
  var customDataSourceId = "xxxxxxxx";
  var ss = SpreadsheetApp.getActiveSpreadsheet().getActiveSheet();
  var maxRows = ss.getLastRow();
  var maxColumns = ss.getLastColumn();
  var data = [];
  for (var i = 1; i < maxRows;i++) {
    data.push(ss.getRange([i], 1,1, maxColumns).getValues());

IronistM

var SPREADSHEET_URL = "";
function facebookSpend() {
  var ss = SpreadsheetApp.openByUrl(SPREADSHEET_URL);
  var sheet = ss.getSheetByName('facebook');
  var values = [];
  Utilities.sleep(1000);
  var fullUrl = 'https://graph.facebook.com/v2.3/act_xxx/stats?access_token=xxx';
  var fetchRequest = UrlFetchApp.fetch(fullUrl);
  var results = JSON.parse(fetchRequest.getContentText());
  Logger.log(results.spent/100);

IronistM

#
# PREDICTING LONG TERM CUSTOMER VALUE WITH BTYD PACKAGE
# Pareto/NBD (negative binomial distribution) modeling of 
# repeat-buying behavior in a noncontractual setting
#
# Matthew Baggott, matt@baggott.net
#
# Accompanying slides at:
# http://www.slideshare.net/mattbagg/baggott-predict-customerinrpart1#
#

IronistM

SELECT orders.customerid, 
       orders.transactiondate, 
       orders.transactionamount, 
       cohorts.cohortdate 
FROM   orders 
       JOIN (SELECT customerid, 
                    Min(transactiondate) AS cohortDate 
             FROM   orders 
             GROUP  BY customerid) AS cohorts 
         ON orders.customerid = cohorts.customerid; 

IronistM

require(RMySQL)
require(ggplot2)
require(scales)

myusername = "peter"
mypassword = "sekret"

system('ssh -f pe@192.168.0.10  -L 3306:localhost:3306 -N -o ExitOnForwardFailure=yes')
con <- dbConnect(MySQL(),
                 user=myusername, password=mypassword,

IronistM

function pullJSON() {
  var ss = SpreadsheetApp.getActiveSpreadsheet();
  var sheets = ss.getSheets();
  var sheet = ss.getActiveSheet();


  var url="http://example.com/feeds?type=json"; // Paste your JSON URL here
  
   var response = UrlFetchApp.fetch(url); // get feed
  var dataAll = JSON.parse(response.getContentText()); //

IronistM

# http://learnr.wordpress.com/2009/05/18/ggplot2-three-variable-time-series-panel-chart/
# http://www.talkstats.com/showthread.php/21228-time-series-in-ggplot2
# http://had.co.nz/ggplot2/
# http://stats.stackexchange.com/questions/14513/align-multiple-ggplot2-plots-with-grid
# http://wiki.stdout.org/rcookbook/Graphs/Facets%20(ggplot2)
# colors: red: "730202", lightyellow: D5D95B, lightgreen: 4F7302 midgreed=274001 darkgreen=092601

setwd('~/repositories/uni/thesis-repos/Opad4lsssExperiments/r_scripts/')
library(ggplot2)
library(gridExtra)

IronistM

library(ggplot2)
library(ggmap)
library(plyr)
library(grid)
library(gridExtra)

# read in cleaned up data
dat <- read.table("quakes.dat", header=TRUE, stringsAsFactors=FALSE)

# map decimal magnitudes into an integer range