Introduction

This repository is hosting the R code for the assignment of the DataScience track's "Getting and Cleaning Data" course which will be peer assessed.

The purpose of this project is to demonstrate the collection, work with, and cleaning of this data set. Tidy data have been prepared so can be used for later analysis.

Data Set

The data set "Human Activity Recognition Using Smartphones" has been taken from UCI.

Execution and files

The Data Set has been stored in UCI HAR Dataset/ directory.

The CodeBook.md describes the variables, the data, and the work that has been performed to clean up the data.

The run_analysis.R is the script that has been used for this work. It can be loaded in R/Rstudio and executed without any parameters.

The result of the execution is that a tidy.csv file is being created, that stores the data (mean and standard deviation of each measurement per activity&subject) for later analysis.

Data Set Description

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.

run_analysis.R

The cleanup script (run_analysis.R) does the following:

Merges the training and the test sets to create one data set. Extracts only the measurements on the mean and standard deviation for each measurement. Uses descriptive activity names to name the activities in the data set Appropriately labels the data set with descriptive activity names. Creates a second, independent tidy data set with the average of each variable for each activity and each subject.