Author - Guillaume Le Mener
Requirements:
* The directoty "/UCI HAR Dataset/" must be present in your working directory with the Samsung data
* A working connection to Internet to download the "stringr" package if need be
* Install the script in your working directory
Notes:
* The Samsung data can downloaded here:
https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
* The new data set will be created in your working directory with the follwing name
"GLEMENER_test_results.txt"
How to use the script?
* The script should be started from your R Studio console with source("./run_analysis.R")
How the script is working?
* The script itself contains comments to help understand the work done.
* From high level, multiple steps are required:
* Checking if the stringr package is installed
* Setting the working directory
* Loading the various files (8 total)
* 2 cbind() calls are performed on the X train and test sets with the subject and activity
* 1 rbind() call is performed to merge the new train and test sets created above
* New column names are created from the feature files and 2 column names added (subject, activity)
* Then, a new vector is created that match column names with ?-mean() or ?-std()
* A subset of data is created by using this index of columns and create the data set
* Finally, a new data set is created by aggregating the columns subject and activiy using the aggreate() function
* The file is created using the write.table() function with row.names=FALSE to make it cleaner to use
The final new data set contains only 180 rows wich is equal to 30 subjects x 6 activity. This match the following description from the initial README - "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."
The first 2 columns are subject and activity.
Subject represents 30 volunteers from 1 to 30.
Activity represents the 6 possible values such as (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING)
The rest are 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
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 here:
"subject"
"activity"
"tBodyAcc-mean()-X"
"tBodyAcc-mean()-Y"
"tBodyAcc-mean()-Z"
"tBodyAcc-std()-X"
"tBodyAcc-std()-Y"
"tBodyAcc-std()-Z"
"tGravityAcc-mean()-X"
"tGravityAcc-mean()-Y"
"tGravityAcc-mean()-Z"
"tGravityAcc-std()-X"
"tGravityAcc-std()-Y"
"tGravityAcc-std()-Z"
"tBodyAccJerk-mean()-X"
"tBodyAccJerk-mean()-Y"
"tBodyAccJerk-mean()-Z"
"tBodyAccJerk-std()-X"
"tBodyAccJerk-std()-Y"
"tBodyAccJerk-std()-Z"
"tBodyGyro-mean()-X"
"tBodyGyro-mean()-Y"
"tBodyGyro-mean()-Z"
"tBodyGyro-std()-X"
"tBodyGyro-std()-Y"
"tBodyGyro-std()-Z"
"tBodyGyroJerk-mean()-X"
"tBodyGyroJerk-mean()-Y"
"tBodyGyroJerk-mean()-Z"
"tBodyGyroJerk-std()-X"
"tBodyGyroJerk-std()-Y"
"tBodyGyroJerk-std()-Z"
"tBodyAccMag-mean()"
"tBodyAccMag-std()"
"tGravityAccMag-mean()"
"tGravityAccMag-std()"
"tBodyAccJerkMag-mean()"
"tBodyAccJerkMag-std()"
"tBodyGyroMag-mean()"
"tBodyGyroMag-std()"
"tBodyGyroJerkMag-mean()"
"tBodyGyroJerkMag-std()"
"fBodyAcc-mean()-X"
"fBodyAcc-mean()-Y"
"fBodyAcc-mean()-Z"
"fBodyAcc-std()-X"
"fBodyAcc-std()-Y"
"fBodyAcc-std()-Z"
"fBodyAccJerk-mean()-X"
"fBodyAccJerk-mean()-Y"
"fBodyAccJerk-mean()-Z"
"fBodyAccJerk-std()-X"
"fBodyAccJerk-std()-Y"
"fBodyAccJerk-std()-Z"
"fBodyGyro-mean()-X"
"fBodyGyro-mean()-Y"
"fBodyGyro-mean()-Z"
"fBodyGyro-std()-X"
"fBodyGyro-std()-Y"
"fBodyGyro-std()-Z"
"fBodyAccMag-mean()"
"fBodyAccMag-std()"
"fBodyBodyAccJerkMag-mean()"
"fBodyBodyAccJerkMag-std()"
"fBodyBodyGyroMag-mean()"
"fBodyBodyGyroMag-std()"
"fBodyBodyGyroJerkMag-mean()"
"fBodyBodyGyroJerkMag-std()"