This code produces tidy datasets based on the UCI Human Activity Recognition Using Smarphones Dataset.
Instructions for use:
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Download zipped data from https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
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The entire UCI - HAR folder should be unzipped to your local R working directory (not the individual files). The R script contains several functions; however, these are all called internally by the main run_analysis() function. Calling this function will create the two output datafiles within the working directory.
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The function requires the stringi and reshape2 packages, which will need to be installed if not already installed on your machine. These can be installed by calling: install.packages("stringi"), install.packages("reshape2")
Source data, including more detailed Readme file and variable definitions, is avaialble at https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
ABOUT THE DATASET (From original ReadMe file) Human Activity Recognition Using Smartphones Dataset Version 1.0 Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto. Smartlab - Non Linear Complex Systems Laboratory DITEN - Università degli Studi di Genova. Via Opera Pia 11A, I-16145, Genoa, Italy. activityrecognition@smartlab.ws www.smartlab.ws
The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.
The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.
Peer Assessment - Getting & Cleaning Data