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CodeBook.md

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title CodeBook.md
author Erik Hemdal
date Tuesday, September 16, 2014
output html_document

Introduction

This is a data cleanup project for the course "Getting and Cleaning Data" at Johns Hopkins University.

I acknowledge use of this original data:

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

I also acknowledge use of R v.3.1.1:

R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

Study Design

My design choices were set by the requirements of the assignment, which asked to determine the averages of each of the means and standard deviations found in the original dataset.

For reference, and because my cleanup script downloads the original dataset, I am including the original codebook here.

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'

Code Book

The R script run_analysis.R performs the following actions:

    1. Downloads the raw data to the current working directory, unless the dataset is already downloaded.
    1. Unzips the resulting .ZIP file, which populates the UCI HAR Dataset subdirectory, unless the subdirectory already exists.
    1. Uses the provided activity labels and subject ID's to reconstruct these items for both the test and training datasets. It then combines the two datasets, using rbind(), because the two sets were originally created by randomly extracting records from the starting data.
    1. Extracts the variables related to mean and standard deviation by dropping columns that do not include "std()" and "mean()" in the column names.
    1. Replaces the activity numbers with the Activity names.
    1. Calculates the means of the subsets of each variable found by filtering on Subject_ID and Activity.
    1. Writes the resulting dataset to a file tidy_avg.txt in the working directory using data.table().

The Variables:

I have added the following columns in the resulting dataset:

"Subject_ID": The identification number for each test subject, an integer value from 1 to 30.

"Activity": Activity names from the following list: "WALKING", "WALKING_UPSTAIRS", "WALKING_DOWNSTAIRS" "SITTING", "STANDING", "LAYING"

The remaining values in the dataset are average values of the corresponding data items in the original dataset, grouped in subsets by the Subject_ID and Activity. Since the original values were normalized, these variables are dimensionless.

I have not changed the names of these variables from the originals to avoid confusion about the source of the columns in the dataset. For example, the parentheses in the original variable names indicate that these have been post-processed from the raw sensor data as described in the original codebook. For the physical interpretation of the original variables, please refer to the original codebook information above.

Here are the names of the remaining variables in the dataset:

"tBodyAcc-mean()-X" "tBodyAcc-mean()-Y" "tBodyAcc-mean()-Z" "tGravityAcc-mean()-X" "tGravityAcc-mean()-Y" "tGravityAcc-mean()-Z" "tBodyAccJerk-mean()-X" "tBodyAccJerk-mean()-Y" "tBodyAccJerk-mean()-Z" "tBodyGyro-mean()-X" "tBodyGyro-mean()-Y" "tBodyGyro-mean()-Z" "tBodyGyroJerk-mean()-X" "tBodyGyroJerk-mean()-Y" "tBodyGyroJerk-mean()-Z" "tBodyAccMag-mean()" "tGravityAccMag-mean()" "tBodyAccJerkMag-mean()" "tBodyGyroMag-mean()" "tBodyGyroJerkMag-mean()" "fBodyAcc-mean()-X" "fBodyAcc-mean()-Y" "fBodyAcc-mean()-Z" "fBodyAcc-meanFreq()-X" "fBodyAcc-meanFreq()-Y" "fBodyAcc-meanFreq()-Z" "fBodyAccJerk-mean()-X" "fBodyAccJerk-mean()-Y" "fBodyAccJerk-mean()-Z" "fBodyAccJerk-meanFreq()-X" "fBodyAccJerk-meanFreq()-Y" "fBodyAccJerk-meanFreq()-Z" "fBodyGyro-mean()-X" "fBodyGyro-mean()-Y" "fBodyGyro-mean()-Z" "fBodyGyro-meanFreq()-X" "fBodyGyro-meanFreq()-Y" "fBodyGyro-meanFreq()-Z" "fBodyAccMag-mean()" "fBodyAccMag-meanFreq()" "fBodyBodyAccJerkMag-mean()" "fBodyBodyAccJerkMag-meanFreq()" "fBodyBodyGyroMag-mean()" "fBodyBodyGyroMag-meanFreq()" "fBodyBodyGyroJerkMag-mean()" "fBodyBodyGyroJerkMag-meanFreq()"

"tBodyAcc-std()-X" "tBodyAcc-std()-Y" "tBodyAcc-std()-Z" "tGravityAcc-std()-X" "tGravityAcc-std()-Y" "tGravityAcc-std()-Z" "tBodyAccJerk-std()-X" "tBodyAccJerk-std()-Y" "tBodyAccJerk-std()-Z" "tBodyGyro-std()-X" "tBodyGyro-std()-Y" "tBodyGyro-std()-Z" "tBodyGyroJerk-std()-X" "tBodyGyroJerk-std()-Y" "tBodyGyroJerk-std()-Z" "tBodyAccMag-std()" "tGravityAccMag-std()" "tBodyAccJerkMag-std()" "tBodyGyroMag-std()" "tBodyGyroJerkMag-std()" "fBodyAcc-std()-X" "fBodyAcc-std()-Y" "fBodyAcc-std()-Z" "fBodyAccJerk-std()-X" "fBodyAccJerk-std()-Y" "fBodyAccJerk-std()-Z" "fBodyGyro-std()-X" "fBodyGyro-std()-Y" "fBodyGyro-std()-Z" "fBodyAccMag-std()" "fBodyBodyAccJerkMag-std()" "fBodyBodyGyroMag-std()" "fBodyBodyGyroJerkMag-std()"

Files:

  • CodeBook.md: The codebook file (this file).
  • README.md: The instruction list.
  • run_analysis.R: The script file.