The Assignment for Coursera.org course for Getting and Cleaning Data
The script for run_analysis.r takes raw data downloaded from:
https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
Once downloaded, it was unzipped in R. From the new working directory of the unzipped folder, all training and test data, labels, and subject identifiers wer downloaded and combined into a single data frame.
Labels were added to columns using the featurs.txt file then modified to be more readable using several gsub() command lines. Activity codes were also replaced by the activity names.
The script then selected only columns that measured the mean or standard deviation.
Using the 'dplyr' package, the remaining measurements were grouped by subject and activity to create the output "tidydata.txt" to the working directory.
#Codebook for "tidydata.txt"
subject: a numeric code for the subject performing the activities
activity: the description of the activity being measured
Columns 3:81 are the mean of all recorded data for that particular measurement as described in the heading
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
Additional vectors obtained by averaging the signals in a signal window sample. These are used on the angle() variable:
gravityMean tBodyAccMean tBodyAccJerkMean tBodyGyroMean tBodyGyroJerkMean