notebooks/watson_changepoint_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Change Point Detection in Time Series Sensor data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Environment Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Install dependent libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Package</th><th scope=col>Version</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>acepack</th><td>acepack   </td><td>1.4.1     </td></tr>\n",
       "\t<tr><th scope=row>arules</th><td>arules    </td><td>1.5-5     </td></tr>\n",
       "\t<tr><th scope=row>assertthat</th><td>assertthat</td><td>0.2.0     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & Package & Version\\\\\n",
       "\\hline\n",
       "\tacepack & acepack    & 1.4.1     \\\\\n",
       "\tarules & arules     & 1.5-5     \\\\\n",
       "\tassertthat & assertthat & 0.2.0     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Package | Version | \n",
       "|---|---|---|\n",
       "| acepack | acepack    | 1.4.1      | \n",
       "| arules | arules     | 1.5-5      | \n",
       "| assertthat | assertthat | 0.2.0      | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "           Package    Version\n",
       "acepack    acepack    1.4.1  \n",
       "arules     arules     1.5-5  \n",
       "assertthat assertthat 0.2.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Clear all objects from memory\n",
    " rm(list=ls())\n",
    "\n",
    "# Check for installed libraries\n",
    " inspkgs = as.data.frame(installed.packages()[,c(1,3:4)])\n",
    " inspkgs = inspkgs[is.na(inspkgs$Priority),1:2,drop=FALSE]\n",
    " inspkgs[1:3,] # Displying only 3 sample packages\n",
    "\n",
    "\n",
    "# Run the below commands for libraries installation only \n",
    "#    if they are not istalled already as indicated by the above command\n",
    "#    Uncomment and run the installation commands below\n",
    "\n",
    "# install.packages(\"sqldf\")\n",
    "# install.packages(\"ggplot2\")\n",
    "# install.packages(\"jsonlite\", repos=\"http://cran.r-project.org\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Load dependent libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: gsubfn\n",
      "Loading required package: proto\n",
      "Warning message in doTryCatch(return(expr), name, parentenv, handler):\n",
      "“unable to load shared object '/opt/conda/envs/R/lib/R/modules//R_X11.so':\n",
      "  libXt.so.6: cannot open shared object file: No such file or directory”Warning message:\n",
      "“no DISPLAY variable so Tk is not available”Loading required package: RSQLite\n",
      "Loading required package: bitops\n"
     ]
    }
   ],
   "source": [
    "library(sqldf)\n",
    "library(httr)\n",
    "library(RCurl)\n",
    "library(bitops)\n",
    "library(jsonlite)\n",
    "library(\"aws.s3\")\n",
    "library(stringr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Configure Parameters for Change Point Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Read DSX Configuration file and load all parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Complete below 2 steps before executing the rest of the cells\n",
    "1. Configure the parameters in JSON file and upload to Object storage\n",
    "2. Set the Configuration .json file name in the next section\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.1 Set the name of the .json configuration file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify file names for sample text and configuration files\n",
    "# Not required when reading data from database\n",
    "v_sampleConfigFileName = \"cpd_dsx_config.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.2 Insert the Object Storage file credentials to read the .json configuration file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @hidden_cell\n",
    "# The section below needs to be modified:\n",
    "#    Insert your credentials to read data from your data sources and replace \n",
    "#    the credentials.1 <- list() section below\n",
    "credentials.1 <-list(\n",
    "    endpoint = \"https://s3-api.us-geo.objectstorage.service.networklayer.com\",\n",
    "    api.key = \"bT_623i_H_rIyjvo-DYiU59M-YQvB9hyaaIc-QS3Bu9m\",\n",
    "    iam.service.id = \"iam-ServiceId-bef1701c-ab8e-445f-8e68-af5da024ee91\",\n",
    "    bucket.name = \"iotchangepointcloudstorage-donotdelete-pr-fqyloftlgsbqoc\",\n",
    "    file.name = \"cpd_dsx_config.txt\",\n",
    "    access.key = \"ca6e393e8bbf42db9bb29bcbe9ccffd7\",\n",
    "    secret.key = \"8b691c9f809ecc79624c5a0363898085071ef342f3bc3977\",\n",
    "    iam.service.endpoint = \"https://iam.ng.bluemix.net/oidc/token\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A connection with                                    \n",
       "description \"rawToChar(obj$content)\"\n",
       "class       \"textConnection\"        \n",
       "mode        \"r\"                     \n",
       "text        \"text\"                  \n",
       "opened      \"opened\"                \n",
       "can read    \"yes\"                   \n",
       "can write   \"no\"                    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This function accesses a file in your Object Storage. The definition contains your credentials.\n",
    "url1 <- str_replace(credentials.1$endpoint, \"https://\", \"\")\n",
    "url1 <- str_replace(url1, \"http://\", \"\")\n",
    "\n",
    "obj <- s3HTTP(\n",
    "    verb = \"GET\",\n",
    "    bucket = credentials.1$bucket.name,\n",
    "    path = v_sampleConfigFileName,\n",
    "    key = credentials.1$access.key,\n",
    "    secret = credentials.1$secret.key,\n",
    "    check_region = FALSE,\n",
    "    base_url =url1)\n",
    "\n",
    "\n",
    "# Your data file was loaded into a textConnection object and you can process the data with your package of choice.\n",
    "configtxt = textConnection(rawToChar(obj$content))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.3 Read Configuration parametric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to Read json parametric values\n",
    "f_getconfigval <- function(injsonstr, invarname)\n",
    "{   # paramname, paramvalue\n",
    "    injsonstr$paramvalue[injsonstr$paramname==invarname]\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>paramname</th><th scope=col>paramvalue</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>coltimestamp     </td><td>TIMESTAMP        </td></tr>\n",
       "\t<tr><td>colsensorid      </td><td>SENSORID         </td></tr>\n",
       "\t<tr><td>colsensorvalue   </td><td>SENSORVALUE      </td></tr>\n",
       "\t<tr><td>sensorid         </td><td>3B1              </td></tr>\n",
       "\t<tr><td>datatimeformat   </td><td>%d-%m-%Y %H:%M:%S</td></tr>\n",
       "\t<tr><td>intimezone       </td><td>GMT              </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " paramname & paramvalue\\\\\n",
       "\\hline\n",
       "\t coltimestamp      & TIMESTAMP        \\\\\n",
       "\t colsensorid       & SENSORID         \\\\\n",
       "\t colsensorvalue    & SENSORVALUE      \\\\\n",
       "\t sensorid          & 3B1              \\\\\n",
       "\t datatimeformat                & \\%d-\\%m-\\%Y \\%H:\\%M:\\%S\\\\\n",
       "\t intimezone        & GMT              \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "paramname | paramvalue | \n",
       "|---|---|---|---|---|---|\n",
       "| coltimestamp      | TIMESTAMP         | \n",
       "| colsensorid       | SENSORID          | \n",
       "| colsensorvalue    | SENSORVALUE       | \n",
       "| sensorid          | 3B1               | \n",
       "| datatimeformat    | %d-%m-%Y %H:%M:%S | \n",
       "| intimezone        | GMT               | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  paramname      paramvalue       \n",
       "1 coltimestamp   TIMESTAMP        \n",
       "2 colsensorid    SENSORID         \n",
       "3 colsensorvalue SENSORVALUE      \n",
       "4 sensorid       3B1              \n",
       "5 datatimeformat %d-%m-%Y %H:%M:%S\n",
       "6 intimezone     GMT              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read json configuration file\n",
    "# Please read the documentation of 'jsonlite' to learn more about the possibilities \n",
    "# to adjust the data loading.\n",
    "# jsonlite documentation: https://cran.r-project.org/web/packages/jsonlite/jsonlite.pdf\n",
    "jsonstr <-  fromJSON(readLines(configtxt))\n",
    "\n",
    "head(jsonstr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read json configuration parametric values\n",
    "# \n",
    "# Name of the column which holds the Time stamp of data \n",
    "# recorded by Sensor\n",
    "v_coltimestamp <- f_getconfigval(jsonstr, \"coltimestamp\")\n",
    "# Name of the column which holds the Sensor identification\n",
    "v_colsensorid <- f_getconfigval(jsonstr, \"colsensorid\")\n",
    "# Name of the column that stores the values measured by sensor\n",
    "v_colsensorvalue <- f_getconfigval(jsonstr, \"colsensorvalue\")\n",
    "# Sensor ID for which the analysis needs to be applied\n",
    "v_sensorid <- f_getconfigval(jsonstr, \"sensorid\")\n",
    "# Time format of the data in the data frame\n",
    "v_datatimeformat <- f_getconfigval(jsonstr, \"datatimeformat\")\n",
    "# Time zone for the Time stamps\n",
    "v_intimezone <- f_getconfigval(jsonstr, \"intimezone\")\n",
    "# Time format which is used for specifying the\n",
    "# time ranges in the below paraneters\n",
    "v_rangetimeformat <- f_getconfigval(jsonstr, \"rangetimeformat\")\n",
    "# Start Time for first series Time range\n",
    "v_Pfrom <- f_getconfigval(jsonstr, \"Pfrom\")\n",
    "# End Time for first series Time range\n",
    "v_Pto <- f_getconfigval(jsonstr, \"Pto\")\n",
    "# Start Time for second series Time range\n",
    "v_Cfrom <- f_getconfigval(jsonstr, \"Cfrom\")\n",
    "# End Time for second series Time range\n",
    "v_Cto <- f_getconfigval(jsonstr, \"Cto\")\n",
    "# Set the threshold percentage of change if detected\n",
    "v_thresholdpercent <- as.numeric(f_getconfigval(jsonstr, \"thresholdpercent\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [1] \"TIMESTAMP\"         \"SENSORID\"          \"SENSORVALUE\"      \n",
      " [4] \"3B1\"               \"%d-%m-%Y %H:%M:%S\" \"GMT\"              \n",
      " [7] \"%Y%m%d %H:%M:%S\"   \"20160324 00:00:00\" \"20160325 00:00:00\"\n",
      "[10] \"20160325 00:00:00\" \"20160326 00:00:00\" \"25\"               \n"
     ]
    }
   ],
   "source": [
    "# Cross verify configuration parametric values\n",
    "print(c(v_coltimestamp, v_colsensorid, v_colsensorvalue, v_sensorid,\n",
    "        v_datatimeformat, v_intimezone, v_rangetimeformat, v_Pfrom,\n",
    "        v_Pto, v_Cfrom, v_Cto, v_thresholdpercent))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Read IoT Sensor data from database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: RODBC\n",
      "Loading required package: Matrix\n",
      "Loading required package: arules\n",
      "\n",
      "Attaching package: ‘arules’\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    abbreviate, write\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>SENSORID</th><th scope=col>TIMESTAMP</th><th scope=col>SENSORVALUE</th><th scope=col>SENSORUNITS</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>3B1                </td><td>24-03-2016 00:00:00</td><td>9.22500            </td><td>FAHRENHEIT         </td></tr>\n",
       "\t<tr><td>3B1                </td><td>24-03-2016 00:10:00</td><td>8.61000            </td><td>FAHRENHEIT         </td></tr>\n",
       "\t<tr><td>3B1                </td><td>24-03-2016 00:50:00</td><td>8.61000            </td><td>FAHRENHEIT         </td></tr>\n",
       "\t<tr><td>3B1                </td><td>24-03-2016 01:00:00</td><td>8.61000            </td><td>FAHRENHEIT         </td></tr>\n",
       "\t<tr><td>3B1                </td><td>24-03-2016 01:10:00</td><td>8.61000            </td><td>FAHRENHEIT         </td></tr>\n",
       "\t<tr><td>3B1                </td><td>24-03-2016 01:20:00</td><td>8.61000            </td><td>FAHRENHEIT         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " SENSORID & TIMESTAMP & SENSORVALUE & SENSORUNITS\\\\\n",
       "\\hline\n",
       "\t 3B1                 & 24-03-2016 00:00:00 & 9.22500             & FAHRENHEIT         \\\\\n",
       "\t 3B1                 & 24-03-2016 00:10:00 & 8.61000             & FAHRENHEIT         \\\\\n",
       "\t 3B1                 & 24-03-2016 00:50:00 & 8.61000             & FAHRENHEIT         \\\\\n",
       "\t 3B1                 & 24-03-2016 01:00:00 & 8.61000             & FAHRENHEIT         \\\\\n",
       "\t 3B1                 & 24-03-2016 01:10:00 & 8.61000             & FAHRENHEIT         \\\\\n",
       "\t 3B1                 & 24-03-2016 01:20:00 & 8.61000             & FAHRENHEIT         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "SENSORID | TIMESTAMP | SENSORVALUE | SENSORUNITS | \n",
       "|---|---|---|---|---|---|\n",
       "| 3B1                 | 24-03-2016 00:00:00 | 9.22500             | FAHRENHEIT          | \n",
       "| 3B1                 | 24-03-2016 00:10:00 | 8.61000             | FAHRENHEIT          | \n",
       "| 3B1                 | 24-03-2016 00:50:00 | 8.61000             | FAHRENHEIT          | \n",
       "| 3B1                 | 24-03-2016 01:00:00 | 8.61000             | FAHRENHEIT          | \n",
       "| 3B1                 | 24-03-2016 01:10:00 | 8.61000             | FAHRENHEIT          | \n",
       "| 3B1                 | 24-03-2016 01:20:00 | 8.61000             | FAHRENHEIT          | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  SENSORID TIMESTAMP           SENSORVALUE SENSORUNITS\n",
       "1 3B1      24-03-2016 00:00:00 9.22500     FAHRENHEIT \n",
       "2 3B1      24-03-2016 00:10:00 8.61000     FAHRENHEIT \n",
       "3 3B1      24-03-2016 00:50:00 8.61000     FAHRENHEIT \n",
       "4 3B1      24-03-2016 01:00:00 8.61000     FAHRENHEIT \n",
       "5 3B1      24-03-2016 01:10:00 8.61000     FAHRENHEIT \n",
       "6 3B1      24-03-2016 01:20:00 8.61000     FAHRENHEIT "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "432"
      ],
      "text/latex": [
       "432"
      ],
      "text/markdown": [
       "432"
      ],
      "text/plain": [
       "[1] 432"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read data from DB2 Warehouse in BMX\n",
    "library(ibmdbR)\n",
    "library(RODBC)\n",
    "library(Matrix)\n",
    "\n",
    "# Call function to read data for specific sensor\n",
    "# @hidden_cell\n",
    "# The section below needs to be modified:\n",
    "#    Insert your credentials to read data from your data sources and replace \n",
    "#    the idaConnect() section below\n",
    "# This connection object is used to access your data and contains your credentials.\n",
    "\n",
    "con_cpd <- idaConnect(\"DASHDB;DATABASE=BLUDB;HOSTNAME=dashdb-entry-yp-dal09-09.services.dal.bluemix.net;PORT=50000;PROTOCOL=TCPIP;\",\n",
    "                      uid = \"dash10720\", pwd = \"32uf_R_giSXX\", conType = \"odbc\")\n",
    "idaInit(con_cpd)\n",
    "\n",
    "alarmdf <- ida.data.frame('DASH10720.CHANGEPOINTIOT')\n",
    "\n",
    "\n",
    "head(alarmdf)\n",
    "nrow(alarmdf)\n",
    "\n",
    "\n",
    "# You can close the connection with the following code:\n",
    "# idaClose(con_cpd)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to translate from one datetime format to another datetime format\n",
    "# Returns character strings in the converted format NOT in posix or datetime format <br/>\n",
    "# DateTime passed in also should be in character string format\n",
    "\n",
    "dtformatconvert <- function(indatetimes, fromdatetimeformat=\"%Y-%m-%d %H:%M:%S %p\", \n",
    "                            todatetimeformat=\"%d-%m-%Y %H:%M:%S\",\n",
    "                            fromtz=\"GMT\", totz=\"\", usetz=FALSE)\n",
    "    {\n",
    "        return(strftime(as.POSIXct(indatetimes, format=fromdatetimeformat, tz=fromtz, usetz=FALSE), \n",
    "                            format=todatetimeformat, tz=totz, usetz=FALSE))\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Read data for 1 sensor for analysis\n",
    "You can investigate the set of unique sensor ids in the data using the below command <br/>\n",
    "[unique(alarmdf$SensorID)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to Standardise the Dataset with standard column names\n",
    "# <Timestamp, SensorID, SensorValue>\n",
    "f_readsensordata <- function(sensordf, sensorid, coltimestamp, colsensorid, colsensorval)\n",
    "{\n",
    "  # sensordf <- read.csv(paste(dirname, filename, sep=\"\"), sep=\",\", as.is=TRUE, header=TRUE) ##as.is=TRUE to ensure Timestamp read in as character type\n",
    "  sensordf1 <- sensordf[,c(coltimestamp, colsensorid, colsensorval)]\n",
    "  sensordf1 <- as.data.frame(sensordf1)\n",
    "  names(sensordf1) <- c(\"TimeStamp\",\"SensorID\",\"SensorValue\")\n",
    "  if (sensorid != \"ALL\")\n",
    "  {\n",
    "    sensordf1 <- sensordf1[sensordf1$SensorID==sensorid,]\n",
    "  }\n",
    "  #View(alarmdf1)\n",
    "  rm(sensordf)\n",
    "  return(sensordf1)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>TimeStamp</th><th scope=col>SensorID</th><th scope=col>SensorValue</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>24-03-2016 00:00:00</td><td>3B1                </td><td>9.225              </td></tr>\n",
       "\t<tr><td>24-03-2016 00:10:00</td><td>3B1                </td><td>8.610              </td></tr>\n",
       "\t<tr><td>24-03-2016 00:50:00</td><td>3B1                </td><td>8.610              </td></tr>\n",
       "\t<tr><td>24-03-2016 01:00:00</td><td>3B1                </td><td>8.610              </td></tr>\n",
       "\t<tr><td>24-03-2016 01:10:00</td><td>3B1                </td><td>8.610              </td></tr>\n",
       "\t<tr><td>24-03-2016 01:20:00</td><td>3B1                </td><td>8.610              </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " TimeStamp & SensorID & SensorValue\\\\\n",
       "\\hline\n",
       "\t 24-03-2016 00:00:00 & 3B1                 & 9.225              \\\\\n",
       "\t 24-03-2016 00:10:00 & 3B1                 & 8.610              \\\\\n",
       "\t 24-03-2016 00:50:00 & 3B1                 & 8.610              \\\\\n",
       "\t 24-03-2016 01:00:00 & 3B1                 & 8.610              \\\\\n",
       "\t 24-03-2016 01:10:00 & 3B1                 & 8.610              \\\\\n",
       "\t 24-03-2016 01:20:00 & 3B1                 & 8.610              \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "TimeStamp | SensorID | SensorValue | \n",
       "|---|---|---|---|---|---|\n",
       "| 24-03-2016 00:00:00 | 3B1                 | 9.225               | \n",
       "| 24-03-2016 00:10:00 | 3B1                 | 8.610               | \n",
       "| 24-03-2016 00:50:00 | 3B1                 | 8.610               | \n",
       "| 24-03-2016 01:00:00 | 3B1                 | 8.610               | \n",
       "| 24-03-2016 01:10:00 | 3B1                 | 8.610               | \n",
       "| 24-03-2016 01:20:00 | 3B1                 | 8.610               | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  TimeStamp           SensorID SensorValue\n",
       "1 24-03-2016 00:00:00 3B1      9.225      \n",
       "2 24-03-2016 00:10:00 3B1      8.610      \n",
       "3 24-03-2016 00:50:00 3B1      8.610      \n",
       "4 24-03-2016 01:00:00 3B1      8.610      \n",
       "5 24-03-2016 01:10:00 3B1      8.610      \n",
       "6 24-03-2016 01:20:00 3B1      8.610      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read data and store in R data frame\n",
    "alarmdf <- f_readsensordata(sensordf = alarmdf, \n",
    "                            sensorid = v_sensorid, \n",
    "                            coltimestamp = v_coltimestamp, colsensorid=v_colsensorid, \n",
    "                            colsensorval=v_colsensorvalue)\n",
    "\n",
    "alarmdf$SensorValue <- as.numeric(alarmdf$SensorValue)\n",
    "head(alarmdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 Prepare data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Sort the data by Time stamp in ascending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>TimeStamp</th><th scope=col>SensorID</th><th scope=col>SensorValue</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>24-03-2016 00:00:00</td><td>3B1                </td><td>9.225              </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>24-03-2016 00:10:00</td><td>3B1                </td><td>8.610              </td></tr>\n",
       "\t<tr><th scope=row>10</th><td>24-03-2016 00:20:00</td><td>3B1                </td><td>8.610              </td></tr>\n",
       "\t<tr><th scope=row>39</th><td>24-03-2016 00:30:00</td><td>3B1                </td><td>8.610              </td></tr>\n",
       "\t<tr><th scope=row>135</th><td>24-03-2016 00:40:00</td><td>3B1                </td><td>8.610              </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>24-03-2016 00:50:00</td><td>3B1                </td><td>8.610              </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       "  & TimeStamp & SensorID & SensorValue\\\\\n",
       "\\hline\n",
       "\t1 & 24-03-2016 00:00:00 & 3B1                 & 9.225              \\\\\n",
       "\t2 & 24-03-2016 00:10:00 & 3B1                 & 8.610              \\\\\n",
       "\t10 & 24-03-2016 00:20:00 & 3B1                 & 8.610              \\\\\n",
       "\t39 & 24-03-2016 00:30:00 & 3B1                 & 8.610              \\\\\n",
       "\t135 & 24-03-2016 00:40:00 & 3B1                 & 8.610              \\\\\n",
       "\t3 & 24-03-2016 00:50:00 & 3B1                 & 8.610              \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | TimeStamp | SensorID | SensorValue | \n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 24-03-2016 00:00:00 | 3B1                 | 9.225               | \n",
       "| 2 | 24-03-2016 00:10:00 | 3B1                 | 8.610               | \n",
       "| 10 | 24-03-2016 00:20:00 | 3B1                 | 8.610               | \n",
       "| 39 | 24-03-2016 00:30:00 | 3B1                 | 8.610               | \n",
       "| 135 | 24-03-2016 00:40:00 | 3B1                 | 8.610               | \n",
       "| 3 | 24-03-2016 00:50:00 | 3B1                 | 8.610               | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "    TimeStamp           SensorID SensorValue\n",
       "1   24-03-2016 00:00:00 3B1      9.225      \n",
       "2   24-03-2016 00:10:00 3B1      8.610      \n",
       "10  24-03-2016 00:20:00 3B1      8.610      \n",
       "39  24-03-2016 00:30:00 3B1      8.610      \n",
       "135 24-03-2016 00:40:00 3B1      8.610      \n",
       "3   24-03-2016 00:50:00 3B1      8.610      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort the data by Time stamp\n",
    "alarmdf <- alarmdf[with(alarmdf, \n",
    "                        order(SensorID, \n",
    "                              as.POSIXct(TimeStamp,format=v_datatimeformat, tz=v_intimezone))), ];\n",
    "\n",
    "head(alarmdf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Split data into 2 divergent sets for detecting changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to split data into 2 datasets: Previous, Current\n",
    "# IN: Standard Data Frame, SensorID,\n",
    "#     Previous From Time stamp, Previous To Time stamp, \n",
    "#     Current From Time Stamp, Current To Time Stamp\n",
    "# OUT: Data series <br/>\n",
    "#     series 1 (SensorID, TimeStamp, SensorValue),\n",
    "#     series 2 (SensorID, TimeStamp, SensorValue)\n",
    "\n",
    "f_splitdataseries <- function(SensorID, Intimeformat, Datatimeformat, PFrom, PTo, CFrom, CTo)\n",
    "{\n",
    "  PFromPOSIX = as.POSIXct(PFrom, format=Intimeformat, tz=\"GMT\", usetz=FALSE);\n",
    "  PToPOSIX = as.POSIXct(PTo, format=Intimeformat, tz=\"GMT\", usetz=FALSE);\n",
    "  CFromPOSIX = as.POSIXct(CFrom, format=Intimeformat, tz=\"GMT\", usetz=FALSE);\n",
    "  CToPOSIX = as.POSIXct(CTo, format=Intimeformat, tz=\"GMT\", usetz=FALSE);\n",
    "  \n",
    "  alarmdf$TimeStampPOSIX <- as.POSIXct(alarmdf$TimeStamp, \n",
    "                          format=Datatimeformat, tz=\"GMT\", usetz=FALSE);\n",
    "\n",
    "  series1 = alarmdf[which(alarmdf$SensorID ==SensorID & \n",
    "                          alarmdf$TimeStampPOSIX >= PFromPOSIX & alarmdf$TimeStampPOSIX < PToPOSIX),];\n",
    "  series2 = alarmdf[which(alarmdf$SensorID ==SensorID & \n",
    "                          alarmdf$TimeStampPOSIX >= CFromPOSIX & alarmdf$TimeStampPOSIX < CToPOSIX),];\n",
    "  return(list(series1, series2));\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the 2 Data series to compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>TimeStamp</th><th scope=col>SensorID</th><th scope=col>SensorValue</th><th scope=col>TimeStampPOSIX</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>24-03-2016 00:00:00</td><td>3B1                </td><td>9.225              </td><td>2016-03-24 00:00:00</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>24-03-2016 00:10:00</td><td>3B1                </td><td>8.610              </td><td>2016-03-24 00:10:00</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>24-03-2016 00:20:00</td><td>3B1                </td><td>8.610              </td><td>2016-03-24 00:20:00</td></tr>\n",
       "\t<tr><th scope=row>39</th><td>24-03-2016 00:30:00</td><td>3B1                </td><td>8.610              </td><td>2016-03-24 00:30:00</td></tr>\n",
       "\t<tr><th scope=row>135</th><td>24-03-2016 00:40:00</td><td>3B1                </td><td>8.610              </td><td>2016-03-24 00:40:00</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>24-03-2016 00:50:00</td><td>3B1                </td><td>8.610              </td><td>2016-03-24 00:50:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & TimeStamp & SensorID & SensorValue & TimeStampPOSIX\\\\\n",
       "\\hline\n",
       "\t1 & 24-03-2016 00:00:00 & 3B1                 & 9.225               & 2016-03-24 00:00:00\\\\\n",
       "\t2 & 24-03-2016 00:10:00 & 3B1                 & 8.610               & 2016-03-24 00:10:00\\\\\n",
       "\t10 & 24-03-2016 00:20:00 & 3B1                 & 8.610               & 2016-03-24 00:20:00\\\\\n",
       "\t39 & 24-03-2016 00:30:00 & 3B1                 & 8.610               & 2016-03-24 00:30:00\\\\\n",
       "\t135 & 24-03-2016 00:40:00 & 3B1                 & 8.610               & 2016-03-24 00:40:00\\\\\n",
       "\t3 & 24-03-2016 00:50:00 & 3B1                 & 8.610               & 2016-03-24 00:50:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | TimeStamp | SensorID | SensorValue | TimeStampPOSIX | \n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 24-03-2016 00:00:00 | 3B1                 | 9.225               | 2016-03-24 00:00:00 | \n",
       "| 2 | 24-03-2016 00:10:00 | 3B1                 | 8.610               | 2016-03-24 00:10:00 | \n",
       "| 10 | 24-03-2016 00:20:00 | 3B1                 | 8.610               | 2016-03-24 00:20:00 | \n",
       "| 39 | 24-03-2016 00:30:00 | 3B1                 | 8.610               | 2016-03-24 00:30:00 | \n",
       "| 135 | 24-03-2016 00:40:00 | 3B1                 | 8.610               | 2016-03-24 00:40:00 | \n",
       "| 3 | 24-03-2016 00:50:00 | 3B1                 | 8.610               | 2016-03-24 00:50:00 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "    TimeStamp           SensorID SensorValue TimeStampPOSIX     \n",
       "1   24-03-2016 00:00:00 3B1      9.225       2016-03-24 00:00:00\n",
       "2   24-03-2016 00:10:00 3B1      8.610       2016-03-24 00:10:00\n",
       "10  24-03-2016 00:20:00 3B1      8.610       2016-03-24 00:20:00\n",
       "39  24-03-2016 00:30:00 3B1      8.610       2016-03-24 00:30:00\n",
       "135 24-03-2016 00:40:00 3B1      8.610       2016-03-24 00:40:00\n",
       "3   24-03-2016 00:50:00 3B1      8.610       2016-03-24 00:50:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>TimeStamp</th><th scope=col>SensorID</th><th scope=col>SensorValue</th><th scope=col>TimeStampPOSIX</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>151</th><td>25-03-2016 00:00:00</td><td>3B1                </td><td>18.450             </td><td>2016-03-25 00:00:00</td></tr>\n",
       "\t<tr><th scope=row>142</th><td>25-03-2016 00:10:00</td><td>3B1                </td><td>13.530             </td><td>2016-03-25 00:10:00</td></tr>\n",
       "\t<tr><th scope=row>143</th><td>25-03-2016 00:20:00</td><td>3B1                </td><td>12.300             </td><td>2016-03-25 00:20:00</td></tr>\n",
       "\t<tr><th scope=row>144</th><td>25-03-2016 00:30:00</td><td>3B1                </td><td>11.685             </td><td>2016-03-25 00:30:00</td></tr>\n",
       "\t<tr><th scope=row>145</th><td>25-03-2016 00:40:00</td><td>3B1                </td><td>11.685             </td><td>2016-03-25 00:40:00</td></tr>\n",
       "\t<tr><th scope=row>146</th><td>25-03-2016 00:50:00</td><td>3B1                </td><td>16.605             </td><td>2016-03-25 00:50:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & TimeStamp & SensorID & SensorValue & TimeStampPOSIX\\\\\n",
       "\\hline\n",
       "\t151 & 25-03-2016 00:00:00 & 3B1                 & 18.450              & 2016-03-25 00:00:00\\\\\n",
       "\t142 & 25-03-2016 00:10:00 & 3B1                 & 13.530              & 2016-03-25 00:10:00\\\\\n",
       "\t143 & 25-03-2016 00:20:00 & 3B1                 & 12.300              & 2016-03-25 00:20:00\\\\\n",
       "\t144 & 25-03-2016 00:30:00 & 3B1                 & 11.685              & 2016-03-25 00:30:00\\\\\n",
       "\t145 & 25-03-2016 00:40:00 & 3B1                 & 11.685              & 2016-03-25 00:40:00\\\\\n",
       "\t146 & 25-03-2016 00:50:00 & 3B1                 & 16.605              & 2016-03-25 00:50:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | TimeStamp | SensorID | SensorValue | TimeStampPOSIX | \n",
       "|---|---|---|---|---|---|\n",
       "| 151 | 25-03-2016 00:00:00 | 3B1                 | 18.450              | 2016-03-25 00:00:00 | \n",
       "| 142 | 25-03-2016 00:10:00 | 3B1                 | 13.530              | 2016-03-25 00:10:00 | \n",
       "| 143 | 25-03-2016 00:20:00 | 3B1                 | 12.300              | 2016-03-25 00:20:00 | \n",
       "| 144 | 25-03-2016 00:30:00 | 3B1                 | 11.685              | 2016-03-25 00:30:00 | \n",
       "| 145 | 25-03-2016 00:40:00 | 3B1                 | 11.685              | 2016-03-25 00:40:00 | \n",
       "| 146 | 25-03-2016 00:50:00 | 3B1                 | 16.605              | 2016-03-25 00:50:00 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "    TimeStamp           SensorID SensorValue TimeStampPOSIX     \n",
       "151 25-03-2016 00:00:00 3B1      18.450      2016-03-25 00:00:00\n",
       "142 25-03-2016 00:10:00 3B1      13.530      2016-03-25 00:10:00\n",
       "143 25-03-2016 00:20:00 3B1      12.300      2016-03-25 00:20:00\n",
       "144 25-03-2016 00:30:00 3B1      11.685      2016-03-25 00:30:00\n",
       "145 25-03-2016 00:40:00 3B1      11.685      2016-03-25 00:40:00\n",
       "146 25-03-2016 00:50:00 3B1      16.605      2016-03-25 00:50:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the 2 Data series to compare (sample values shown for help)\n",
    "#  s = f_splitdataseries (SensorID=\"3B1\",\n",
    "#                       Intimeformat=\"%Y%m%d %H:%M:%S\", Datatimeformat=\"%d-%m-%Y %H:%M:%S\",\n",
    "#                       PFrom=\"20160324 00:00:00\", PTo=\"20160325 00:00:00\",\n",
    "#                       CFrom=\"20160325 00:00:00\", CTo=\"20160326 00:00:00\")\n",
    "\n",
    "s = f_splitdataseries (SensorID=v_sensorid,\n",
    "                       Intimeformat=v_rangetimeformat, Datatimeformat=v_datatimeformat,\n",
    "                       PFrom=v_Pfrom, PTo=v_Pto,\n",
    "                       CFrom=v_Cfrom, CTo=v_Cto)\n",
    "\n",
    "\n",
    "# Unpack the 2 list of data frames\n",
    "series1 <- s[[1]]\n",
    "series2 <- s[[2]]\n",
    "\n",
    "head(series1)\n",
    "head(series2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Analyze the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Plot the line graphs for both the series <br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot the line graphs for both the series\n",
    "# IN parameters: (x,y values for both the Time series in sorted order) x1, y1, x2, y2\n",
    "f_plot2lines <- function(x1, y1, x2, y2)\n",
    "{\n",
    "  # Draw Line plot\n",
    "  # dev.new()\n",
    "  plot(y1,type=\"l\",col=\"green\",xlab=\" \", ylab=\" \",pch=21,xaxt=\"n\",yaxt=\"n\")\n",
    "  par(new=T)\n",
    "  plot(y2,type=\"l\",col=\"red\",xlab=\"Time\", ylab=\"Sensor Values\",pch=21,xaxt=\"n\",yaxt=\"n\")\n",
    "  axis(2,las=3,cex.axis=0.8)\n",
    "  axis(1,at=c(1:length(x1)),c(x1),las=2,cex.axis=0.4)\n",
    "  title(\"Sensor readings by Time overlay\")\n",
    "  par(new=F)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nglAR/g99ElPytArCWgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Sensor readings by Time overlay”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Line plot of the 2 Data series\n",
    "f_plot2lines(x1 <- series1$TimeStamp, y1 <- series1$SensorValue,\n",
    "             x2 <- series2$TimeStamp, y2 <- series2$SensorValue)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Plot the box plots for both the distributions <br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot the box plots for both the distributions <br/>\n",
    "# Time series order does not matter for this <br/>\n",
    "#     IN parameters: (Sensorvalues-series 1, Sensorvalues-series 2)\n",
    "    \n",
    "f_plot2boxes <- function(s1sensorvalue, s2sensorvalue)\n",
    "{\n",
    "  data_list = NULL\n",
    "  col_list = c(\"green\", \"blue\")\n",
    "  names_list = c(\"Previous\", \"Current\")\n",
    "  \n",
    "  data_list = list()\n",
    "  data_list[[1]] = s1sensorvalue\n",
    "  data_list[[2]] = s2sensorvalue\n",
    "  \n",
    "  # dev.new() # Works in PC only\n",
    "  boxstats <- boxplot.stats(data_list[[1]], coef=1.57, do.conf = TRUE, do.out = TRUE)\n",
    "  #par(new=T)\n",
    "  boxplot(data_list, las = 2, col = col_list, ylim=c(-2.0,70),\n",
    "          names= names_list,\n",
    "          mar = c(12, 5, 4, 2) + 0.1,\n",
    "          main=\"Change point detection\",\n",
    "          sub=paste(\"Spread of Sensor reading distributions\", \":\", sep=\"\"),\n",
    "          ylab=\"Sensor Readings\", \n",
    "          coef=1.57, do.conf = TRUE, do.out = TRUE)\n",
    "  abline(h=boxstats$stats, col=\"green\", las=2)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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",
      "text/plain": [
       "Plot with title “Change point detection”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 2 box plots for the distribution\n",
    "f_plot2boxes(s1sensorvalue = series1$SensorValue,\n",
    "             s2sensorvalue = series2$SensorValue)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6 Statistical Change point detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Calculate the stats for both the series <br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate the stats for both the series <br/>\n",
    "# Avg, Median, p1sd, p2sd, p3sd, n1sd, n2sd, n3sd, q0, q1, q2, q3, q4, f1range, <br/>\n",
    "#      iqrange, f2range, sku, kurt, outliers\n",
    "\n",
    "f_seriesstats <- function(series)\n",
    "{\n",
    "  boxstats <- boxplot.stats(series, coef=1.57, do.conf = TRUE, do.out = TRUE)\n",
    "  smin <- min(series)\n",
    "  smax <- max(series)\n",
    "  smean <- mean(series)\n",
    "  #Spread measures\n",
    "  sq0 <- boxstats$stats[1]\n",
    "  sq1 <- boxstats$stats[2]\n",
    "  sq2 <- boxstats$stats[3]\n",
    "  sq3 <- boxstats$stats[4]\n",
    "  sq4 <- boxstats$stats[5]\n",
    "  siqr <- (sq3 - sq1)\n",
    "  # Normal distribution\n",
    "  s1sd <- sd(series)\n",
    "  s1sdp <- smean + s1sd\n",
    "  s1sdn <- smean - s1sd\n",
    "  s2sdp <- smean + (2*s1sd)\n",
    "  s2sdn <- smean - (2*s1sd)\n",
    "  s3sdp <- smean + (3*s1sd)\n",
    "  s3sdn <- smean - (3*s1sd)\n",
    "  # Outlier counts @ 2sd\n",
    "  s2sdout <- sum(series > s2sdp) + sum(series < s2sdn)\n",
    "  # return(list(smin, smax, smean, sq0, sq1, sq2, sq3, sq4, siqr, s1sd, s1sdp,\n",
    "                                        # s1sdn, s2sdp, s2sdn, s3sdp, s3sdn))\n",
    "  return(list(smin=smin, smax=smax, smean=smean,\n",
    "              sq0=sq0, sq1=sq1, sq2=sq2, sq3=sq3, sq4=sq4, siqr=siqr,\n",
    "              s1sd=s1sd, s1sdp=s1sdp, s1sdn=s1sdn,\n",
    "              s2sdp=s2sdp, s2sdn=s2sdn, s3sdp=s3sdp, s3sdn=s3sdn))\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the statistics for both series and check results\n",
    "s1stats <- f_seriesstats(series1$SensorValue)\n",
    "s2stats <- f_seriesstats(series2$SensorValue)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Calculate change point deviations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Function to calculate change point deviatrion percentages\n",
    "f_changepercent <- function(val1, val2)\n",
    "{\n",
    "  return(((val2-val1)/val1)*100)\n",
    "}\n",
    "\n",
    "# Calculate percentage deviation for individual stats\n",
    "f_serieschangepercent <- function(series1stats, series2stats)\n",
    "{\n",
    "  n <- length(series1stats)\n",
    "  cols=names(series2stats)\n",
    "  cpdf <- data.frame(statname=character(), series1val = numeric(), \n",
    "                     series2val=numeric(), changeper=numeric());\n",
    "  for (i in 1:length(series2stats))\n",
    "  {\n",
    "    newrow = data.frame(statname=cols[i], \n",
    "                        series1val=series1stats[[i]], \n",
    "                        series2val=series2stats[[i]], \n",
    "                        changeper=f_changepercent(series1stats[[i]], series2stats[[i]]))\n",
    "    cpdf <- rbind(cpdf, newrow)\n",
    "  }\n",
    "  return(cpdf)\n",
    "}\n",
    "\n",
    "# Calculate overall percentage deviation and detect change point\n",
    "f_detectchangepoint <- function(dfcp, threshold)\n",
    "{\n",
    "  # Overall percentage deviation\n",
    "  newrow = data.frame(statname='overall',\n",
    "                      series1val=NA,\n",
    "                      series2val=NA,\n",
    "                      changeper=mean(abs(dfcp$changeper)))\n",
    "  dfcp <- rbind(dfcp, newrow)\n",
    "  # Overall change point percentage\n",
    "  changepointper <- dfcp[which(dfcp$statname==\"overall\"),c(\"changeper\")]\n",
    "  # Mark change point at threshold %\n",
    "  if(changepointper > threshold)\n",
    "    {return(paste(\"Change Point DETECTED exceeding threshold: \",threshold,\"% \", sep=\"\"))}\n",
    "  else\n",
    "    {return(paste(\"Change Point NOT DETECTED at threshold: \",threshold,\"% \", sep=\"\"))}\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall change percentage and individual key statistics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   statname series1val series2val changeper\n",
      "1      smin   8.610000   9.225000  7.142857\n",
      "2      smax  52.890000  55.965000  5.813953\n",
      "3     smean  13.073021  18.168125 38.974191\n",
      "4       sq0   8.610000   9.225000  7.142857\n",
      "5       sq1   8.610000  11.685000 35.714286\n",
      "6       sq2   9.840000  13.530000 37.500000\n",
      "7       sq3  12.915000  18.142500 40.476190\n",
      "8       sq4  19.065000  27.060000 41.935484\n",
      "9      siqr   4.305000   6.457500 50.000000\n",
      "10     s1sd   8.861824  11.444024 29.138478\n",
      "11    s1sdp  21.934845  29.612149 35.000497\n",
      "12    s1sdn   4.211197   6.724101 59.671956\n",
      "13    s2sdp  30.796669  41.056174 33.313685\n",
      "14    s2sdn  -4.650627  -4.719924  1.490057\n",
      "15    s3sdp  39.658493  52.500198 32.380721\n",
      "16    s3sdn -13.512451 -16.163948 19.622625\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "'Change Point DETECTED exceeding threshold: 25% '"
      ],
      "text/latex": [
       "'Change Point DETECTED exceeding threshold: 25\\% '"
      ],
      "text/markdown": [
       "'Change Point DETECTED exceeding threshold: 25% '"
      ],
      "text/plain": [
       "[1] \"Change Point DETECTED exceeding threshold: 25% \""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Overall change percentage in key statistics\n",
    "dfallstats <- f_serieschangepercent(s1stats, s2stats)\n",
    "print(dfallstats)\n",
    "# Detect changepoint\n",
    "f_detectchangepoint(dfallstats, v_thresholdpercent)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "r"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}