Course project for the Getting and Cleaning Data course

##FILES INCLUDED run_analysis.R - R script to fulfill the requirements of the course data project in the "Getting and Cleaning Data" course

meandat.txt - tidy dataset containing the mean values of the training and test data from the "Human Activity Recognition Using Smartphones Dataset Version 1.0"

##Data Input Source: [1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012 This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited. Jorge L. Reyes-Ortiz, Alessandro Ghio, Luca Oneto, Davide Anguita. November 2012. ##Run_analysis.R The script creates a tidy dataset using the files from the Human Ativity Recognition Using Smartphones Dataset Assumes that the data files are in the same directory as the directory from which this script is run. Also assumes that the files and subdirectories are organized the same way as the original, with its root in directory of the script. https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

##Source: Jorge L. Reyes-Ortiz(1,2), Davide Anguita(1), Alessandro Ghio(1), Luca Oneto(1) and Xavier Parra(2) 1 - Smartlab - Non-Linear Complex Systems Laboratory DITEN - Università degli Studi di Genova, Genoa (I-16145), Italy. 2 - CETpD - Technical Research Centre for Dependency Care and Autonomous Living Universitat Politècnica de Catalunya (BarcelonaTech). Vilanova i la Geltrú (08800), Spain activityrecognition '@' smartlab.ws

##Data Set Information: 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.

##output: meandata.txt - means of all of the variables grouped by Activity Name (actname) and Subject - the volunteer participant (subjID) Variable(colname) Value (sample) Description actname LAYING Any of six activities: WALKING,WALKING_UPSTAIRSWALKING_DOWNSTAIRS,SITTING,STANDING,LAYING subjID 1 The subject ID - identifies the volunteer who did the activity (1 - 30) tbodyacc.mean.x 0.221598244 Variables that start with t - refer to time tbodyacc.mean.y -0.040513953 Variables that start with f - refer to frequency tbodyacc.mean.z -0.113203554 tgravityacc.mean.x -0.248881798 tgravityacc.mean.y 0.705549773 tgravityacc.mean.z 0.44581772 tbodyaccjerk.mean.x 0.081086534 tbodyaccjerk.mean.y 0.003838204 tbodyaccjerk.mean.z 0.010834236 tbodygyro.mean.x -0.016553094 tbodygyro.mean.y -0.064486124 tbodygyro.mean.z 0.148689436 tbodygyrojerk.mean.x -0.107270949 tbodygyrojerk.mean.y -0.041517287 tbodygyrojerk.mean.z -0.074050121 tbodyaccmag.mean -0.841929153 tgravityaccmag.mean -0.841929153 tbodyaccjerkmag.mean -0.954396265 tbodygyromag.mean -0.874759548 tbodygyrojerkmag.mean -0.96346103 fbodyacc.mean.x -0.939099052 fbodyacc.mean.y -0.867065205 fbodyacc.mean.z -0.882666876 fbodyacc.meanfreq.x -0.158792671 fbodyacc.meanfreq.y 0.097534842 fbodyacc.meanfreq.z 0.089437655 fbodyaccjerk.mean.x -0.957073884 fbodyaccjerk.mean.y -0.92246261 fbodyaccjerk.mean.z -0.948060904 fbodyaccjerk.meanfreq.x 0.132419092 fbodyaccjerk.meanfreq.y 0.024513619 fbodyaccjerk.meanfreq.z 0.024387945 fbodygyro.mean.x -0.850249175 fbodygyro.mean.y -0.952191495 fbodygyro.mean.z -0.909302721 fbodygyro.meanfreq.x -0.003546796 fbodygyro.meanfreq.y -0.091529131 fbodygyro.meanfreq.z 0.010458126 fbodyaccmag.mean -0.861767648 fbodyaccmag.meanfreq 0.086408563 fbodybodyaccjerkmag.mean -0.933300361 fbodybodyaccjerkmag.meanfreq 0.266391154 fbodybodygyromag.mean -0.862190185 fbodybodygyromag.meanfreq -0.139775013 fbodybodygyrojerkmag.mean -0.942366947 fbodybodygyrojerkmag.meanfreq 0.176485907 angle.tbodyaccmean.gravity 0.021365966 angle.tbodyaccjerkmean.gravitymean 0.003060407 angle.tbodygyromean.gravitymean -0.001666985 angle.tbodygyrojerkmean.gravitymean 0.084437165 angle.x.gravitymean 0.426706226 angle.y.gravitymean -0.520343818 angle.z.gravitymean -0.352413109 ...Etc...