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#Getting and Cleaning Data Course Project

#Date Created: 9-17-2014

#Created by: Samia Askari

# Project Objective:

## 1. Merge the training and the test sets to create one data set.

## 2. Extract only the measurements on the mean and standard deviation for each measurement.

## 3. Use descriptive activity names to name the activities in the data set

## 4. Appropriately label the data set with descriptive activity names.

## 5. Creates a second, independent tidy data set with the average of each variable for each activity and each subject.

#run_analysis.R: This script performs steps to achieve objectives above

# Prerequisites to run the script:

## 1. Download data from site: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

## 2. Downloaded file has folder "UCI HAR Dataset"

## 3. Find the path to folder "UCI HAR Dataset" on your computer

## 4. Run function "run_analysis.R" by passing this path as input parameter to function

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run_analysis <- function(workingDirectory){

## Set working directory

setwd(workingDirectory)

## Pre-work to create one data set

# Clean up space

rm(list=ls())

# Reading train datasets

x_train <- read.table('./train/X_train.txt',header=FALSE) #read x_train.txt

y_train <- read.table('./train/y_train.txt',header=FALSE) #read y_train.txt

subject_train <- read.table('./train/subject_train.txt',header=FALSE) #read subject_train.txt

features <- read.table('./features.txt',header=FALSE) #read features.txt

activityType <- read.table('./activity_labels.txt',header=FALSE) #read activity_labels.txt

# Assigining column names to train data imported above

colnames(x_train) <- features[,2]

colnames(y_train) <- "activityId"

colnames(activityType) <- c('activityId','activityType')

colnames(subject_train) <- "subjectId"

# Merging x_train, y_train and subject_train to create final Train_Data

Train_Data <- cbind(y_train,subject_train,x_train)

# Reading test datasets

subject_test <- read.table('./test/subject_test.txt',header=FALSE) #read subject_test.txt

x_test <- read.table('./test/X_test.txt',header=FALSE) #read X_test.txt

y_test <- read.table('./test/y_test.txt',header=FALSE) #read y_test.txt

# Assign column names to the test data imported above

colnames(subject_test) <- "subjectId"

colnames(x_test) <- features[,2]

colnames(y_test) <- "activityId"

# Merging x_test, y_test and subject_test to create final Test_Data

Test_Data <- cbind(y_test,subject_test,x_test)

#############

## 1: Merges the training and the test sets to create one data set.

############

# Merging Train_Data and Test_Data

Final_Data <- rbind(Train_Data, Test_Data)

#############

## 2: Extract only the measurements on the mean and standard deviation for each measurement.

############

# Creating a vector for the column names from the Final_Data to compute mean() & stddev()

colNames <- colnames(Final_Data)

# Create a vector that contains TRUE values for the ID, mean() & stddev() columns

# and FALSE for the rest

meanANDstd <- (grepl("activity..",colNames) | grepl("subject..",colNames) | grepl("-mean..",colNames) & !grepl("-meanFreq..",colNames) & !grepl("mean..-",colNames) | grepl("-std..",colNames) & !grepl("-std()..-",colNames))

# Subset Final_Data table based on the meanANDstd to keep only desired columns

Final_Data <- Final_Data[meanANDstd==TRUE]

#############

## 3: Use descriptive activity names to name the activities in the data set

############

# Merge Final_Data table with acitivity_labels table to get descriptive activity names

Final_Data <- merge(Final_Data, activityType, by='activityId', all.x=TRUE)

# Updating the colNames vector to include the new column names after merge

colNames <- colnames(Final_Data)

#############

## 4: Appropriately label the data set with descriptive activity names.

#############

# Renaming variables

for (i in 1:length(colNames))

{

colNames[i] <- gsub("\\()","",colNames[i])

colNames[i] <- gsub("-std$","StdDev",colNames[i])

colNames[i] <- gsub("-mean","Mean",colNames[i])

colNames[i] <- gsub("^(t)","time",colNames[i])

colNames[i] <- gsub("^(f)","freq",colNames[i])

colNames[i] <- gsub("([Gg]ravity)","Gravity",colNames[i])

colNames[i] <- gsub("([Bb]ody[Bb]ody|[Bb]ody)","Body",colNames[i])

colNames[i] <- gsub("[Gg]yro","Gyro",colNames[i])

colNames[i] <- gsub("AccMag","AccMagnitude",colNames[i])

colNames[i] <- gsub("([Bb]odyaccjerkmag)","BodyAccJerkMagnitude",colNames[i])

colNames[i] <- gsub("JerkMag","JerkMagnitude",colNames[i])

colNames[i] <- gsub("GyroMag","GyroMagnitude",colNames[i])

};

# Assigning new descriptive column names to the Final_Data table

colnames(Final_Data) <- colNames

#############

## 5: Create a second, independent tidy data set with the average of each variable for each activity and each subject.

#############

# Create a new table without the activityType column

newData <- Final_Data[ ,names(Final_Data) != 'activityType']

# Summarizing newData table to include only mean of each variable for each activity and each subject

tidyData <- aggregate(newData[ ,names(newData) != c('activityId','subjectId')],by=list(activityId=newData$activityId,subjectId = newData$subjectId),mean)

# Merging tidyData table with activityType table to include descriptive acitvity names

tidyData <- merge(tidyData, activityType, by='activityId', all.x=TRUE)

# Export the tidyData set

write.table(tidyData, './TidyData.txt',row.names=FALSE,sep='\t')

} #function end