MarkEdmondson1234

library(shiny)

## test dataframe
df <- data.frame(option1 = c("A","A","B","B","C","C"),
                 option2 = c("A1","A2","B3","B1","C2","C3"),
                 option3 = c(T,F,T,F,T,F),
                 option4 = c("a","b","c","d","e","f"))

shinyServer(function(input, output, session) {
  

MarkEdmondson1234

#HWplot.R

library(ggplot2)
library(reshape)


HWplot<-function(ts_object, n.ahead=4, CI=.95, error.ribbon='green', line.size=1){
  
  hw_object<-HoltWinters(ts_object)
  

MarkEdmondson1234

# Refer to https://rtweet.info/

MarkEdmondson1234

### tweetdata.r
### Fetch Twitter data and Data Transformations
### Requires you have authenticated in auth.R
### 15th June 2014
### Mark Edmondson @HoloMarkeD
###

### For use with Twitter API, processTweets() takes the df$tweetDF data.frame as input
### And calculates/transforms data for the plots
processTweets <- function(tweetDF,

MarkEdmondson1234

### plotTweets.r
### Plot out tweets
### Requires you have authenticated in auth.R and processed in tweetdata.R
### 15th June 2014
### Mark Edmondson @HoloMarkeD
###

library(ggplot2)

######################################################################

MarkEdmondson1234

### r-installation-debain-wheezy
#### setup commands to setup R, RStudio and OpenCPU on a Google Compute Engine Wheezy instance
#### Mark Edmondson 29 June 2014
##
## No original work, all taken from these sources:
## https://github.com/jeroenooms/opencpu-deb/blob/master/build-on-debian.md
## https://support.rstudio.com/hc/communities/public/questions/200651456-RStudio-server-not-installable-on-Debian-Wheezy-just-released-this-week-
## http://cran.r-project.org/bin/linux/debian/README.html

# need to be sudo for all below

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#!/bin/bash

INPUT=
OUTPUT="output.txt"
COL=1

while [ $# -gt 0 ]
do
    case "$1" in
        -f)  INPUT="$2"; shift;;

MarkEdmondson1234

## Setup

## below only needed first time if you haven't the packages installed
# install.packages("devtools")
# library(devtools)
# devtools::install_github("google/CausalImpact")

## load the libraries
library(CausalImpact)
library(rga)

MarkEdmondson1234

## pull in the GA data

# get list of profiles
profiles <- ga$getProfiles()

# View the profiles table, and pick the profile you want and put in the profile ID below
UA <- "XXXXXXX"

# choose the dates you want to pull in data for
date_before = "2014-09-01"

MarkEdmondson1234

## do the causalImpact 
#  here the effect took place on the 200th day of the data imported, so the pre.period = 200
#  Data is examined and compared in the post perid to day 244
impact <- CausalImpact(data = gadata$visits, 
                       pre.period = c(1,200),
                       post.period = c(201,244), 
                       model.args = NULL,
                       bsts.model = NULL, 
                       post.period.response = NULL,
                       alpha = 0.05)