title	author	date	output
ReadMe.md	Giovanni Pavolini	21 de septiembre de 2015	html_document

This is the companion documentatio for the run_analysis.R file.

Data source and Meaning

The script works with data from http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones with a number of measurements of Samsung motion sensors. Data was collected and initially analyzed by Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.

Two sets exist in the data: training and test sets. Please refer to the README.txt file in the root folder to find more detailed explanations.

How the Script Works

First, it's necessary to read all variables into space. Taking into account that variable names, subjects and activities are separated from measured data, those values need to be loaded too.

# Reading the data sets into space
test <- read.table("test/X_test.txt",header = F)
train<- read.table("train/X_train.txt",header = F)

# Reading feature names into space
nm.test<- read.table("features.txt",header = F,stringsAsFactors = F)
nm.test<-nm.test[,2]

# Reading subjects into space
subsubj.test <- read.table("test/subject_test.txt",header = F)
subj.training<-read.table("train/subject_train.txt")


#Reading activities into space
y.test <- read.table("test/y_test.txt")
y.train<-read.table("train/y_train.txt")
# Reading human-readable activities descriptions
act.lab <- read.table("activity_labels.txt")
#Joining activities with their relevant names
y.train2<-merge(y.train,act.lab,by.y = "V1")[,2]
y.test2<-merge(y.test,act.lab,by.y = "V1")[,2]

Then, it's time to start merging the data. First, each data set needs to be binded (column-wise) together:

#Column-binding each data set
test<- cbind(subj.test,test,y.test2)
train<-cbind(subj.training,train,y.train2)

With both sets properly assembled, it's time to bind them together (row-wise), this is what's called "merging" in the assignment description:

#Row-binding (merging) both data sets
full.dataset<-rbind(test,train)

# Appropriately naming columns
colnames(full.dataset)<-c("subject",nm.test,"Activity")

Next, it's time to determine wich columns we need to keep, per the assignment instruction to keep only those columns with either mean or standard deviation.

#Finding which columns to "keep", based in being either "mean" or "standard deviation (std)" of the measurement
colsToKeep<-grep("mean|std",names(full.dataset),perl=T)

The use of grep here helps us to find the columns whose names have the substrings "std" or "mean".

At this point, we have a fully merged data set and have defined which columns we're interested in, so we can proceed to calculcate the average value of all variables, by both subject and activity:

library(data.table)
tidy.dataset <- full.dataset[,lapply(.SD,mean),by=.(Activity,subject),.SDcols=colsToKeep][order(Activity,subject),]

which we accomplish with the help of the data.table package.

Variable Code Book

(Taken from features_info.txt)

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 (variables in bold are those kept in the tidy data set):

• 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'