IronistM

# sql.export.rf(): save a randomForest model as SQL
# v0.04
# Copyright (c) 2013-2014 Shane Butler <shane dot butler at gmail dot com>
# 
# sql.export.rf is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# sql.export.rf is distributed in the hope that it will be useful, but

IronistM

# You don't need Fog in Ruby or some other library to upload to S3 -- shell works perfectly fine
# This is how I upload my new Sol Trader builds (http://soltrader.net)
# Based on a modified script from here: http://tmont.com/blargh/2014/1/uploading-to-s3-in-bash

S3KEY="my aws key"
S3SECRET="my aws secret" # pass these in

function putS3
{
  path=$1

IronistM

#!/bin/bash
#
# Installs the latest RStudio daily desktop build for OSX/macOS and Ubuntu(amd64)
#
# https://support.rstudio.com/hc/en-us/articles/203842428-Getting-the-newest-RStudio-builds

set -e

install_macos_daily() {
    REDIRECT_URL="https://www.rstudio.org/download/latest/daily/desktop/mac/RStudio-latest.dmg"

IronistM

#Start with a dist upgrade
sudo apt dist-upgrade

# Get chrome
wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
sudo dpkg -i ./google-chrome*.deb
sudo apt-get install -f

# dev (php, docker, git, node, bower, electron, composer)
sudo apt install -y \

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

library("RGoogleAnalytics")
library("ggplot2")
library("dplyr")

#Google Auth Codes
clientid = "USE YOUR CLIENT ID HERE"
clientsecret = "CLIENT SECRET GOES HERE"

#Fire off an AUTH request to Google.  This will actually kick off a browser opening to do the OAuth2 transaction.
oauth_token <- Auth(clientid,clientsecret)

IronistM

library(Rcpp)
library(RGA)
profiles <- list_profiles()
webIDs <- unique(profiles$webPropertyId)
col <- c("id","accountId","webPropertyId","name","index","scope","active","working")
for(i in webIDs){
  assign("dims", list_custom_dimensions(accountId = unlist(profiles[which(profiles$webPropertyId==i)[1],2]),webPropertyId = i,token=token))
  for(j in dims$id){
    test<-get_ga(unlist(profiles[which(profiles$webPropertyId==i)[1],1]),
         start.date=start,

IronistM

#' Check a list of URLs for existence of the Google Tag Manager Container code
#'
#' @param containers
#' A character vector of the container IDs for Google Tag Manager
#' @param urlFile
#' A character vector providing the path to a csv file with 2 columns: url, the full url to the page to be checked; country, the country where the URL is hosted.
#'
#' @param outputdir
#' A character vector providing the path to the desired output directory for the csv of results. If not provided, the csv will be output to the current working directory.

IronistM

# *--------------------------------------------------------------------
#Adstock functions
adstock_calc_1<-function(media_var,dec,dim){
  length<-length(media_var)
  adstock<-rep(0,length)
  for(i in 2:length){
    adstock[i]<-(1-exp(-media_var[i]/dim)+dec*adstock[i-1])
  }
  adstock
}

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')