title	runanalysis.R
author	Darren McDowell
date	September 21, 2014
intput	none
library	plyr
output	Tidy data set with the average of each variable for each activity and each subject

imputData()

Steps:

1: Setup our files for import 2: Import our column names 3: Import our test activity data 4: update our activity labels for our test data 5: Import our test subjects 6: Import our test data 7: bind our test activity and subjects 8: bind our subjects+activity and test data 9: import our train activity 10: update our activity label for our training set 11: import our train subjects 12: import our train data 13: bind our activity and subjects 14: bind our activity/subjects with data 15: rbind our test and train data 16: get mean names from columns 17: get std names from columns 18: concat activity,subject + mean + std columns into a single column list 19: subset data frame 20: aggrigate data group by activity,subject, and calculate mean for each column 21: write our final data frame to our file

Data Dictionary

Activity Type: Charector Description: Activity the subject was performing Subject Type: Charector Description: Subject performing the activity tBodyAcc.mean...X Type: numeric Description: Average of tBodyAcc.mean...X for a given subject performing the given activity tBodyAcc.mean...Y Type: numeric Description: Average of tBodyAcc.mean...Y for a given subject performing the given activity tBodyAcc.mean...Z Type: numeric Description: Average of tBodyAcc.mean...Z for a given subject performing the given activity tGravityAcc.mean...X Type: numeric Description: Average of tGravityAcc.mean...X for a given subject performing the given activity tGravityAcc.mean...Y Type: numeric Description: Average of tGravityAcc.mean...Y for a given subject performing the given activity tGravityAcc.mean...Z Type: numeric Description: Average of tGravityAcc.mean...Z for a given subject performing the given activity tBodyAccJerk.mean...X Type: numeric Description: Average of tBodyAccJerk.mean...X for a given subject performing the given activity tBodyAccJerk.mean...Y Type: numeric Description: Average of tBodyAccJerk.mean...Y for a given subject performing the given activity tBodyAccJerk.mean...Z Type: numeric Description: Average of tBodyAccJerk.mean...Z for a given subject performing the given activity tBodyGyro.mean...X Type: numeric Description: Average of tBodyGyro.mean...X for a given subject performing the given activity tBodyGyro.mean...Y Type: numeric Description: Average of tBodyGyro.mean...Y for a given subject performing the given activity tBodyGyro.mean...Z Type: numeric Description: Average of tBodyGyro.mean...Z for a given subject performing the given activity tBodyGyroJerk.mean...X Type: numeric Description: Average of tBodyGyro.mean...Z for a given subject performing the given activity tBodyGyroJerk.mean...Y Type: numeric Description: Average of tBodyGyroJerk.mean...Y for a given subject performing the given activity tBodyGyroJerk.mean...Z Type: numeric Description: Average of tBodyGyroJerk.mean...Z for a given subject performing the given activity tBodyAccMag.mean.. Type: numeric Description: Average of tBodyAccMag.mean.. for a given subject performing the given activity tGravityAccMag.mean.. Type: numeric Description: Average of tGravityAccMag.mean.. for a given subject performing the given activity tBodyAccJerkMag.mean.. Type: numeric Description: Average of tBodyAccJerkMag.mean.. for a given subject performing the given activity tBodyGyroMag.mean.. Type: numeric Description: Average of tBodyGyroMag.mean.. for a given subject performing the given activity tBodyGyroJerkMag.mean.. Type: numeric Description: Average of tBodyGyroJerkMag.mean.. for a given subject performing the given activity fBodyAcc.mean...X Type: numeric Description: Average of fBodyAcc.mean...X for a given subject performing the given activity fBodyAcc.mean...Y Type: numeric Description: Average of fBodyAcc.mean...Y for a given subject performing the given activity fBodyAcc.mean...Z Type: numeric Description: Average of fBodyAcc.mean...Z for a given subject performing the given activity fBodyAcc.meanFreq...X Type: numeric Description: Average of fBodyAcc.meanFreq...X for a given subject performing the given activity fBodyAcc.meanFreq...Y Type: numeric Description: Average of fBodyAcc.meanFreq...Y for a given subject performing the given activity fBodyAcc.meanFreq...Z Type: numeric Description: Average of fBodyAcc.meanFreq...Z for a given subject performing the given activity fBodyAccJerk.mean...X Type: numeric Description: Average of fBodyAccJerk.mean...X for a given subject performing the given activity fBodyAccJerk.mean...Y Type: numeric Description: Average of fBodyAccJerk.mean...Y for a given subject performing the given activity fBodyAccJerk.mean...Z Type: numeric Description: Average of fBodyAccJerk.mean...Z for a given subject performing the given activity fBodyAccJerk.meanFreq...X Type: numeric Description: Average of fBodyAccJerk.meanFreq...X for a given subject performing the given activity fBodyAccJerk.meanFreq...Y Type: numeric Description: Average of fBodyAccJerk.meanFreq...Y for a given subject performing the given activity fBodyAccJerk.meanFreq...Z Type: numeric Description: Average of fBodyAccJerk.meanFreq...Z for a given subject performing the given activity fBodyGyro.mean...X Type: numeric Description: Average of fBodyGyro.mean...X for a given subject performing the given activity fBodyGyro.mean...Y Type: numeric Description: Average of fBodyGyro.mean...Y for a given subject performing the given activity fBodyGyro.mean...Z Type: numeric Description: Average of fBodyGyro.mean...Z for a given subject performing the given activity fBodyGyro.meanFreq...X Type: numeric Description: Average of fBodyGyro.meanFreq...X for a given subject performing the given activity fBodyGyro.meanFreq...Y Type: numeric Description: Average of fBodyGyro.meanFreq...Y for a given subject performing the given activity fBodyGyro.meanFreq...Z Type: numeric Description: Average of fBodyGyro.meanFreq...Z for a given subject performing the given activity fBodyAccMag.mean.. Type: numeric Description: Average of fBodyAccMag.mean.. for a given subject performing the given activity fBodyAccMag.meanFreq.. Type: numeric Description: Average of fBodyAccMag.meanFreq.. for a given subject performing the given activity fBodyBodyAccJerkMag.mean.. Type: numeric Description: Average of fBodyBodyAccJerkMag.mean.. for a given subject performing the given activity fBodyBodyAccJerkMag.meanFreq.. Type: numeric Description: Average of fBodyBodyAccJerkMag.meanFreq.. for a given subject performing the given activity fBodyBodyGyroMag.mean.. Type: numeric Description: Average of fBodyBodyGyroMag.mean.. for a given subject performing the given activity fBodyBodyGyroMag.meanFreq.. Type: numeric Description: Average of fBodyBodyGyroMag.meanFreq.. for a given subject performing the given activity fBodyBodyGyroJerkMag.mean.. Type: numeric Description: Average of fBodyBodyGyroJerkMag.mean.. for a given subject performing the given activity fBodyBodyGyroJerkMag.meanFreq.. Type: numeric Description: Average of fBodyBodyGyroJerkMag.meanFreq.. for a given subject performing the given activity tBodyAcc.std...X Type: numeric Description: Average of tBodyAcc.std...X for a given subject performing the given activity tBodyAcc.std...Y Type: numeric Description: Average of tBodyAcc.std...Y for a given subject performing the given activity tBodyAcc.std...Z Type: numeric Description: Average of tBodyAcc.std...Z for a given subject performing the given activity tGravityAcc.std...X Type: numeric Description: Average of tGravityAcc.std...X for a given subject performing the given activity tGravityAcc.std...Y Type: numeric Description: Average of tGravityAcc.std...Y for a given subject performing the given activity tGravityAcc.std...Z Type: numeric Description: Average of tGravityAcc.std...Z for a given subject performing the given activity tBodyAccJerk.std...X Type: numeric Description: Average of tBodyAccJerk.std...X for a given subject performing the given activity tBodyAccJerk.std...Y Type: numeric Description: Average of tBodyAccJerk.std...Y for a given subject performing the given activity tBodyAccJerk.std...Z Type: numeric Description: Average of tBodyAccJerk.std...Z for a given subject performing the given activity tBodyGyro.std...X Type: numeric Description: Average of tBodyGyro.std...X for a given subject performing the given activity tBodyGyro.std...Y Type: numeric Description: Average of tBodyGyro.std...Y for a given subject performing the given activity tBodyGyro.std...Z Type: numeric Description: Average of tBodyGyro.std...Z for a given subject performing the given activity tBodyGyroJerk.std...X Type: numeric Description: Average of tBodyGyroJerk.std...X for a given subject performing the given activity tBodyGyroJerk.std...Y Type: numeric Description: Average of tBodyGyroJerk.std...Y for a given subject performing the given activity tBodyGyroJerk.std...Z Type: numeric Description: Average of tBodyGyroJerk.std...Z for a given subject performing the given activity tBodyAccMag.std.. Type: numeric Description: Average of tBodyAccMag.std.. for a given subject performing the given activity tGravityAccMag.std.. Type: numeric Description: Average of tGravityAccMag.std.. for a given subject performing the given activity tBodyAccJerkMag.std.. Type: numeric Description: Average of tBodyAccJerkMag.std.. for a given subject performing the given activity tBodyGyroMag.std.. Type: numeric Description: Average of tBodyGyroMag.std.. for a given subject performing the given activity tBodyGyroJerkMag.std.. Type: numeric Description: Average of tBodyGyroJerkMag.std.. for a given subject performing the given activity fBodyAcc.std...X Type: numeric Description: Average of fBodyAcc.std...Y for a given subject performing the given activity fBodyAcc.std...Y Type: numeric Description: Average of for a given subject performing the given activity fBodyAcc.std...Z Type: numeric Description: Average of fBodyAcc.std...Z for a given subject performing the given activity fBodyAccJerk.std...X Type: numeric Description: Average of fBodyAccJerk.std...X for a given subject performing the given activity fBodyAccJerk.std...Y Type: numeric Description: Average of fBodyAccJerk.std...Y for a given subject performing the given activity fBodyAccJerk.std...Z Type: numeric Description: Average of fBodyAccJerk.std...Z for a given subject performing the given activity fBodyGyro.std...X Type: numeric Description: Average of fBodyGyro.std...X for a given subject performing the given activity fBodyGyro.std...Y Type: numeric Description: Average of fBodyGyro.std...Y for a given subject performing the given activity fBodyGyro.std...Z Type: numeric Description: Average of fBodyGyro.std...Z for a given subject performing the given activity fBodyAccMag.std.. Type: numeric Description: Average of fBodyAccMag.std.. for a given subject performing the given activity fBodyBodyAccJerkMag.std.. Type: numeric Description: Average of fBodyBodyAccJerkMag.std.. for a given subject performing the given activity fBodyBodyGyroMag.std.. Type: numeric Description: Average of fBodyBodyGyroMag.std.. for a given subject performing the given activity fBodyBodyGyroJerkMag.std.. Type: numeric Description: Average of fBodyBodyGyroJerkMag.std for a given subject performing the given activity

Other Notes
Readme of data before analysis

================================================================== 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 - Universit‡ 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. See 'features_info.txt' for more details.

For each record it is provided:

• Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.
• Triaxial Angular velocity from the gyroscope.
• A 561-feature vector with time and frequency domain variables.
• Its activity label.
• An identifier of the subject who carried out the experiment.

The dataset includes the following files:

• 'README.txt'

• 'features_info.txt': Shows information about the variables used on the feature vector.

• 'features.txt': List of all features.

• 'activity_labels.txt': Links the class labels with their activity name.

• 'train/X_train.txt': Training set.

• 'train/y_train.txt': Training labels.

• 'test/X_test.txt': Test set.

• 'test/y_test.txt': Test labels.

The following files are available for the train and test data. Their descriptions are equivalent.

• 'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.

• 'train/Inertial Signals/total_acc_x_train.txt': The acceleration signal from the smartphone accelerometer X axis in standard gravity units 'g'. Every row shows a 128 element vector. The same description applies for the 'total_acc_x_train.txt' and 'total_acc_z_train.txt' files for the Y and Z axis.

• 'train/Inertial Signals/body_acc_x_train.txt': The body acceleration signal obtained by subtracting the gravity from the total acceleration.

• 'train/Inertial Signals/body_gyro_x_train.txt': The angular velocity vector measured by the gyroscope for each window sample. The units are radians/second.

Notes:

• Features are normalized and bounded within [-1,1].
• Each feature vector is a row on the text file.

For more information about this dataset contact: activityrecognition@smartlab.ws

License:

Use of this dataset in publications must be acknowledged by referencing the following publication [1]

[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.

Other Notes
features_info.txt of data before analysis

Feature Selection

The features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ. These time domain signals (prefix 't' to denote time) were captured at a constant rate of 50 Hz. Then they were filtered using a median filter and a 3rd order low pass Butterworth filter with a corner frequency of 20 Hz to remove noise. Similarly, the acceleration signal was then separated into body and gravity acceleration signals (tBodyAcc-XYZ and tGravityAcc-XYZ) using another low pass Butterworth filter with a corner frequency of 0.3 Hz.

Subsequently, the body linear acceleration and angular velocity were derived in time to obtain Jerk signals (tBodyAccJerk-XYZ and tBodyGyroJerk-XYZ). Also the magnitude of these three-dimensional signals were calculated using the Euclidean norm (tBodyAccMag, tGravityAccMag, tBodyAccJerkMag, tBodyGyroMag, tBodyGyroJerkMag).

Finally a Fast Fourier Transform (FFT) was applied to some of these signals producing fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag. (Note the 'f' to indicate frequency domain signals).

These signals were used to estimate variables of the feature vector for each pattern:
'-XYZ' is used to denote 3-axial signals in the X, Y and Z directions.

tBodyAcc-XYZ tGravityAcc-XYZ tBodyAccJerk-XYZ tBodyGyro-XYZ tBodyGyroJerk-XYZ tBodyAccMag tGravityAccMag tBodyAccJerkMag tBodyGyroMag tBodyGyroJerkMag fBodyAcc-XYZ fBodyAccJerk-XYZ fBodyGyro-XYZ fBodyAccMag fBodyAccJerkMag fBodyGyroMag fBodyGyroJerkMag

The set of variables that were estimated from these signals are:

mean(): Mean value std(): Standard deviation mad(): Median absolute deviation max(): Largest value in array min(): Smallest value in array sma(): Signal magnitude area energy(): Energy measure. Sum of the squares divided by the number of values. iqr(): Interquartile range entropy(): Signal entropy arCoeff(): Autorregresion coefficients with Burg order equal to 4 correlation(): correlation coefficient between two signals maxInds(): index of the frequency component with largest magnitude meanFreq(): Weighted average of the frequency components to obtain a mean frequency skewness(): skewness of the frequency domain signal kurtosis(): kurtosis of the frequency domain signal bandsEnergy(): Energy of a frequency interval within the 64 bins of the FFT of each window. angle(): Angle between to vectors.

Additional vectors obtained by averaging the signals in a signal window sample. These are used on the angle() variable:

gravityMean tBodyAccMean tBodyAccJerkMean tBodyGyroMean tBodyGyroJerkMean

The complete list of variables of each feature vector is available in 'features.txt'