CodeBook.Rmd

---
title: "CodeBook"
author: "NML DesJardins"
date: "June 21, 2015"
output: html_document
---

This code book accompanies the run_analysis script generated for the 
Data Science Specialization - Getting and Cleaning Data - Course Project.

The script produces two datasets: data.txt and agg_data.txt Both datasets
contain data from all participants in the study.

**data.txt**
This dataset contains the mean and standard deviation of measurements from all 
participants. It is structured such that each observation contains the measurements
from one participant performing one activity at one point in time.

*Measured Variables*
The original data was obtained from 
http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

Per the original authors, 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

*Subject and Activity Variables*
Individual subjects are indexed with the variable subID.

Data were collected when participants were (1) walking, (2) walking upstairs, 
(3) walking downstairs, (4) sitting, (5) standing, and (6) laying. The factor variable 
"activity" provides a numeric index (1:6) of the activity being measured. The
factor variable "activityName" provides codes the activities with their 
descriptive names.

The full list of variables in data.txt are below:

 [1] "subID"                           "activity"                        "tBodyAcc-mean()-X"              
 [4] "tBodyAcc-mean()-Y"               "tBodyAcc-mean()-Z"               "tGravityAcc-mean()-X"           
 [7] "tGravityAcc-mean()-Y"            "tGravityAcc-mean()-Z"            "tBodyAccJerk-mean()-X"          
[10] "tBodyAccJerk-mean()-Y"           "tBodyAccJerk-mean()-Z"           "tBodyGyro-mean()-X"             
[13] "tBodyGyro-mean()-Y"              "tBodyGyro-mean()-Z"              "tBodyGyroJerk-mean()-X"         
[16] "tBodyGyroJerk-mean()-Y"          "tBodyGyroJerk-mean()-Z"          "tBodyAccMag-mean()"             
[19] "tGravityAccMag-mean()"           "tBodyAccJerkMag-mean()"          "tBodyGyroMag-mean()"            
[22] "tBodyGyroJerkMag-mean()"         "fBodyAcc-mean()-X"               "fBodyAcc-mean()-Y"              
[25] "fBodyAcc-mean()-Z"               "fBodyAcc-meanFreq()-X"           "fBodyAcc-meanFreq()-Y"          
[28] "fBodyAcc-meanFreq()-Z"           "fBodyAccJerk-mean()-X"           "fBodyAccJerk-mean()-Y"          
[31] "fBodyAccJerk-mean()-Z"           "fBodyAccJerk-meanFreq()-X"       "fBodyAccJerk-meanFreq()-Y"      
[34] "fBodyAccJerk-meanFreq()-Z"       "fBodyGyro-mean()-X"              "fBodyGyro-mean()-Y"             
[37] "fBodyGyro-mean()-Z"              "fBodyGyro-meanFreq()-X"          "fBodyGyro-meanFreq()-Y"         
[40] "fBodyGyro-meanFreq()-Z"          "fBodyAccMag-mean()"              "fBodyAccMag-meanFreq()"         
[43] "fBodyBodyAccJerkMag-mean()"      "fBodyBodyAccJerkMag-meanFreq()"  "fBodyBodyGyroMag-mean()"        
[46] "fBodyBodyGyroMag-meanFreq()"     "fBodyBodyGyroJerkMag-mean()"     "fBodyBodyGyroJerkMag-meanFreq()"
[49] "tBodyAcc-std()-X"                "tBodyAcc-std()-Y"                "tBodyAcc-std()-Z"               
[52] "tGravityAcc-std()-X"             "tGravityAcc-std()-Y"             "tGravityAcc-std()-Z"            
[55] "tBodyAccJerk-std()-X"            "tBodyAccJerk-std()-Y"            "tBodyAccJerk-std()-Z"           
[58] "tBodyGyro-std()-X"               "tBodyGyro-std()-Y"               "tBodyGyro-std()-Z"              
[61] "tBodyGyroJerk-std()-X"           "tBodyGyroJerk-std()-Y"           "tBodyGyroJerk-std()-Z"          
[64] "tBodyAccMag-std()"               "tGravityAccMag-std()"            "tBodyAccJerkMag-std()"          
[67] "tBodyGyroMag-std()"              "tBodyGyroJerkMag-std()"          "fBodyAcc-std()-X"               
[70] "fBodyAcc-std()-Y"                "fBodyAcc-std()-Z"                "fBodyAccJerk-std()-X"           
[73] "fBodyAccJerk-std()-Y"            "fBodyAccJerk-std()-Z"            "fBodyGyro-std()-X"              
[76] "fBodyGyro-std()-Y"               "fBodyGyro-std()-Z"               "fBodyAccMag-std()"              
[79] "fBodyBodyAccJerkMag-std()"       "fBodyBodyGyroMag-std()"          "fBodyBodyGyroJerkMag-std()"     
[82] "activityName" 


**agg_data.txt**
This dataset contains aggregated measures of the variables in data.txt. In this 
dataset, each observation contains the average measurements for one activity for
one subject. It contains the same index and measurement variables as data.txt. 
However, all measurement variables are averaged over all timepoints that a 
given participant completed a particular activity.