###Coursera : Getting and Cleaning Data Project

• Peer assessed project for the analysis of Samsung Galaxy II movement measurements
• Dataset was downloaded from (https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip) on 21st April 2014
• Further description of the data can be found at http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

###Aim of the script

• Using the training and test datasets to read the measurements taken (X)
• Subset the measurements taken to those only reporting Mean and Standard Deviation values (those including mean() and std())
• Assign columns referring to the IDs of Activity and Subject
• Merge training and test datasets into a single resource
• Rename the Activity IDs to more descriptive values found in activity_labels.txt
• Calculate the mean values for each Subject and Activity by melting the dataset and recasting
• Write the final tidy dataset to files

###Steps for usage of run_analysis.R script

• Begin by cloning this repository
• Download the dataset and extract the files maintaining the folder structure. This will result in a /UCI HAR Dataset/ folder that has all the necessary data files.
• Place the run_analysis.R script in this base folder.
• Set the working directory to the /UCI HAR Dataset/ folder.
• Run source("run_analysis.R")
• The tidy dataset will be created in the working directory as tidy_data.txt and tidy_data.csv files

###Archive Files

• The Dataset.zip archive contains folders named /test and /train
• Each of these folders contain data in the form of:
  • X_(test/train).txt - Measurements of data
  • y_(test/train).txt - Activity ID for each row of the data
  • subject_(test/train).txt - Corresponding Subject ID for each row of data
• As well as the raw data:
  • Names of the measurements taken in X_(test/train).txt are reported in features.txt
  • Descriptive Activity labels for the Activity IDs are reported in activity_labels.txt