GACD_Assignment_1

How Script Works

Download the file

fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"

if (!file.exists("Dataset.zip")) {
    download.file(fileUrl, destfile = "Dataset.zip", method = "wget")
}

Unzip the file.

if (!file.exists("UCI HAR Dataset")) {
    unzip("Dataset.zip", overwrite = TRUE)
}

read the file with white-space as delimiter

testDataset <- read.csv("./UCI HAR Dataset/test/X_test.txt", sep = "", colClasses = "numeric", 
    header = FALSE)
trainDataset <- read.csv("./UCI HAR Dataset/train/X_train.txt", sep = "", colClasses = "numeric", 
    header = FALSE)

Read the features from features.txt

featuresData <- read.csv("./UCI HAR Dataset/features.txt", sep = "", header = FALSE)

Extract the features vector from featuresData

featuresVector <- featuresData[, 2]

Add header to data.frame. Code referred from Stack-Overflow

colnames(testDataset) <- featuresVector
colnames(trainDataset) <- featuresVector

Check if the dataset is displayed correctly

# head(testDataset) head(trainDataset)

Results Look Good

Now lets add features

Features Vector

testY <- read.csv("./UCI HAR Dataset/test/y_test.txt", sep = "", header = FALSE)
trainY <- read.csv("./UCI HAR Dataset/train/y_train.txt", sep = "", header = FALSE)

Assign Header

colnames(testY) <- "activityLabel"
colnames(trainY) <- "activityLabel"

Activity Label Data

activityLabels <- read.csv("./UCI HAR Dataset//activity_labels.txt", sep = "", 
    header = FALSE)

colnames(activityLabels) <- c("activityLabel", "activity")

merge activity with labels

activityTest <- merge(testY, activityLabels)
# head(activityTest)
activityTrain <- merge(trainY, activityLabels)
# head(activityTrain)

Add activity data to test and train dataset

testDataset <- cbind(testDataset, activityTest)
trainDataset <- cbind(trainDataset, activityTrain)

Check Sample Data

# head(testDatasetwithactivity) head(trainDatasetwithactivity)

Merge test and train datasets

completeDataset <- rbind(testDataset, trainDataset)

Remove train and test datasets to save some space

# rm('testDataset', 'trainDataset')

Now, lets subset the dataset for mean and std measures only!

Use Grep for identifying the subset measures. Code referred from Stack-Overflow

completeDataset <- subset(completeDataset, select = grepl("mean|std", featuresVector))

# head(completeDataset)

write.table(completeDataset, file = "tidyDataset.txt", sep = "\t", row.names = FALSE)

subjectTest <- read.csv("./UCI HAR Dataset/test/subject_test.txt", sep = "", 
    header = FALSE)
subjectTrain <- read.csv("./UCI HAR Dataset/train/subject_train.txt", sep = "", 
    header = FALSE)
colnames(subjectTrain) <- "subject"
colnames(subjectTest) <- "subject"

testDataset <- cbind(testDataset, subjectTest)
trainDataset <- cbind(trainDataset, subjectTrain)

completeDataset1 <- rbind(testDataset, trainDataset)

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

tidyDataset2 <- aggregate(completeDataset1[, 1:562], completeDataset1[, 563:564], 
    FUN = mean)

# head(tidyDataset2)

Write this tidy dataset

write.table(tidyDataset2, file = "tidyDataset2.txt", sep = "\t", row.names = FALSE)