#Getting and Cleaning Data Course Project

This repo is set up for the course project of Getting and Cleaning Data Course Project.

##Script The script goes as followed:

• Read the train/test data sets , and merge them
• Extract the measurements on the mean and standard deviation for each measurement
• Use descriptive activity names to name the activities in the data set
• Label the data set with descriptive variable names
• Create a tidy data set and write it in tidy_data.txt

The specific details are commented in the code, and it could be easily understood.

##Raw Data Set Code Book

The features in raw data set 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

##Tidy Data Set Code Book Tidy data set extracts only the measurements on the mean and standard deviation for each measurement from the raw data set, and in order to be more descriptive, the extracted variables were renamed:

• Replace -mean() to Mean
• Replace -std() to Std
• Remove all punctuations, such as -()

Variables in raw and tidy data set

Raw data set	Tidy data set
subject	subject
label	label
tBodyAcc-mean()-X	tBodyAccMeanX
tBodyAcc-mean()-Y	tBodyAccMeanY
tBodyAcc-mean()-Z	tBodyAccMeanZ
tBodyAcc-std()-X	tBodyAccStdX
tBodyAcc-std()-Y	tBodyAccStdY
tBodyAcc-std()-Z	tBodyAccStdZ
tGravityAcc-mean()-X	tGravityAccMeanX
tGravityAcc-mean()-Y	tGravityAccMeanY
tGravityAcc-mean()-Z	tGravityAccMeanZ
tGravityAcc-std()-X	tGravityAccStdX
tGravityAcc-std()-Y	tGravityAccStdY
tGravityAcc-std()-Z	tGravityAccStdZ
tBodyAccJerk-mean()-X	tBodyAccJerkMeanX
tBodyAccJerk-mean()-Y	tBodyAccJerkMeanY
tBodyAccJerk-mean()-Z	tBodyAccJerkMeanZ
tBodyAccJerk-std()-X	tBodyAccJerkStdX
tBodyAccJerk-std()-Y	tBodyAccJerkStdY
tBodyAccJerk-std()-Z	tBodyAccJerkStdZ
tBodyGyro-mean()-X	tBodyGyroMeanX
tBodyGyro-mean()-Y	tBodyGyroMeanY
tBodyGyro-mean()-Z	tBodyGyroMeanZ
tBodyGyro-std()-X	tBodyGyroStdX
tBodyGyro-std()-Y	tBodyGyroStdY
tBodyGyro-std()-Z	tBodyGyroStdZ
tBodyGyroJerk-mean()-X	tBodyGyroJerkMeanX
tBodyGyroJerk-mean()-Y	tBodyGyroJerkMeanY
tBodyGyroJerk-mean()-Z	tBodyGyroJerkMeanZ
tBodyGyroJerk-std()-X	tBodyGyroJerkStdX
tBodyGyroJerk-std()-Y	tBodyGyroJerkStdY
tBodyGyroJerk-std()-Z	tBodyGyroJerkStdZ
tBodyAccMag-mean()	tBodyAccMagMean
tBodyAccMag-std()	tBodyAccMagStd
tGravityAccMag-mean()	tGravityAccMagMean
tGravityAccMag-std()	tGravityAccMagStd
tBodyAccJerkMag-mean()	tBodyAccJerkMagMean
tBodyAccJerkMag-std()	tBodyAccJerkMagStd
tBodyGyroMag-mean()	tBodyGyroMagMean
tBodyGyroMag-std()	tBodyGyroMagStd
tBodyGyroJerkMag-mean()	tBodyGyroJerkMagMean
tBodyGyroJerkMag-std()	tBodyGyroJerkMagStd
fBodyAcc-mean()-X	fBodyAccMeanX
fBodyAcc-mean()-Y	fBodyAccMeanY
fBodyAcc-mean()-Z	fBodyAccMeanZ
fBodyAcc-std()-X	fBodyAccStdX
fBodyAcc-std()-Y	fBodyAccStdY
fBodyAcc-std()-Z	fBodyAccStdZ
fBodyAccJerk-mean()-X	fBodyAccJerkMeanX
fBodyAccJerk-mean()-Y	fBodyAccJerkMeanY
fBodyAccJerk-mean()-Z	fBodyAccJerkMeanZ
fBodyAccJerk-std()-X	fBodyAccJerkStdX
fBodyAccJerk-std()-Y	fBodyAccJerkStdY
fBodyAccJerk-std()-Z	fBodyAccJerkStdZ
fBodyGyro-mean()-X	fBodyGyroMeanX
fBodyGyro-mean()-Y	fBodyGyroMeanY
fBodyGyro-mean()-Z	fBodyGyroMeanZ
fBodyGyro-std()-X	fBodyGyroStdX
fBodyGyro-std()-Y	fBodyGyroStdY
fBodyGyro-std()-Z	fBodyGyroStdZ
fBodyAccMag-mean()	fBodyAccMagMean
fBodyAccMag-std()	fBodyAccMagStd
fBodyBodyAccJerkMag-mean()	fBodyBodyAccJerkMagMean
fBodyBodyAccJerkMag-std()	fBodyBodyAccJerkMagStd
fBodyBodyGyroMag-mean()	fBodyBodyGyroMagMean
fBodyBodyGyroMag-std()	fBodyBodyGyroMagStd
fBodyBodyGyroJerkMag-mean()	fBodyBodyGyroJerkMagMean
fBodyBodyGyroJerkMag-std()	fBodyBodyGyroJerkMagStd