GettingCleaningData

Coursera Getting and Cleaning Data course repository

This repository only contains the script run_analysis.R which take as input the following text files:

• '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.

• 'X_train.txt': Training set.

• 'y_train.txt': Training labels.

• 'X_test.txt': Test set.

• 'y_test.txt': Test labels.

• 'subject_train.txt': Each row identifies the subject who performed the activity for each window sample.

and get a data set containing the average of all columns relative to all mean and std movement variables for each activity and each subject

Study Design

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.

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.

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 target for this analysis is get all mean() and std() variables and get the average of this magnitudes by activiy and subject

Code Book

• id_activity (integer):
• activity_name (string):
• subject (integer):
• tBodyAcc.mean.X (dbl):
• tBodyAcc.mean.Y (dbl):
• tBodyAcc.mean.Z (dbl):
• tGravityAcc.mean.X (dbl):
• tGravityAcc.mean.Y (dbl):
• tGravityAcc.mean.Z (dbl):
• tBodyAccJerk.mean.X (dbl):
• tBodyAccJerk.mean.Y (dbl):
• tBodyAccJerk.mean.Z (dbl):
• tBodyGyro.mean.X (dbl):
• tBodyGyro.mean.Y (dbl):
• tBodyGyro.mean.Z (dbl):
• tBodyGyroJerk.mean.X (dbl):
• tBodyGyroJerk.mean.Y (dbl):
• tBodyGyroJerk.mean.Z (dbl):
• tBodyAccMag.mean (dbl):
• tGravityAccMag.mean (dbl):
• tBodyAccJerkMag.mean (dbl):
• tBodyGyroMag.mean (dbl):
• tBodyGyroJerkMag.mean (dbl):
• fBodyAcc.mean.X (dbl):
• fBodyAcc.mean.Y (dbl):
• fBodyAcc.mean.Z (dbl):
• fBodyAcc.meanFreq.X (dbl):
• fBodyAcc.meanFreq.Y (dbl):
• fBodyAcc.meanFreq.Z (dbl):
• fBodyAccJerk.mean.X (dbl):
• fBodyAccJerk.mean.Y (dbl):
• fBodyAccJerk.mean.Z (dbl):
• fBodyAccJerk.meanFreq.X (dbl):
• fBodyAccJerk.meanFreq.Y (dbl):
• fBodyAccJerk.meanFreq.Z (dbl):
• fBodyGyro.mean.X (dbl):
• fBodyGyro.mean.Y (dbl):
• fBodyGyro.mean.Z (dbl):
• fBodyGyro.meanFreq.X (dbl):
• fBodyGyro.meanFreq.Y (dbl):
• fBodyGyro.meanFreq.Z (dbl):
• fBodyAccMag.mean (dbl):
• fBodyAccMag.meanFreq (dbl):
• fBodyBodyAccJerkMag.mean (dbl):
• fBodyBodyAccJerkMag.meanFreq (dbl):
• fBodyBodyGyroMag.mean (dbl):
• fBodyBodyGyroMag.meanFreq (dbl):
• fBodyBodyGyroJerkMag.mean (dbl):
• fBodyBodyGyroJerkMag.meanFreq (dbl):
• angletBodyAccMean,gravity (dbl):
• angletBodyAccJerkMean,gravityMean (dbl):
• angletBodyGyroMean,gravityMean (dbl):
• angletBodyGyroJerkMean,gravityMean (dbl):
• angleX,gravityMean (dbl):
• angleY,gravityMean (dbl):
• angleZ,gravityMean (dbl):
• tBodyAcc.std.X (dbl):
• tBodyAcc.std.Y (dbl):
• tBodyAcc.std.Z (dbl):
• tGravityAcc.std.X (dbl):
• tGravityAcc.std.Y (dbl):
• tGravityAcc.std.Z (dbl):
• tBodyAccJerk.std.X (dbl):
• tBodyAccJerk.std.Y (dbl):
• tBodyAccJerk.std.Z (dbl):
• tBodyGyro.std.X (dbl):
• tBodyGyro.std.Y (dbl):
• tBodyGyro.std.Z (dbl):
• tBodyGyroJerk.std.X (dbl):
• tBodyGyroJerk.std.Y (dbl):
• tBodyGyroJerk.std.Z (dbl):
• tBodyAccMag.std (dbl):
• tGravityAccMag.std (dbl):
• tBodyAccJerkMag.std (dbl):
• tBodyGyroMag.std (dbl):
• tBodyGyroJerkMag.std (dbl):
• fBodyAcc.std.X (dbl):
• fBodyAcc.std.Y (dbl):
• fBodyAcc.std.Z (dbl):
• fBodyAccJerk.std.X (dbl):
• fBodyAccJerk.std.Y (dbl):
• fBodyAccJerk.std.Z (dbl):
• fBodyGyro.std.X (dbl):
• fBodyGyro.std.Y (dbl):
• fBodyGyro.std.Z (dbl):
• fBodyAccMag.std (dbl):
• fBodyBodyAccJerkMag.std (dbl):
• fBodyBodyGyroMag.std (dbl):
• fBodyBodyGyroJerkMag.std (dbl)