Getting and Cleaning Data - Course Project

The script is divided into five functionalities:

1.) Merges the training and the test sets to create one data set & Appropriately labels the data set with descriptive variable names:

First we download the compressed data set

temp<-tempfile()
fileUrl<-"https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fileUrl,temp,mode="wb")

Then we extract the zip file into current working directory and afterwards delete it

unzip(temp,exdir=".")
unlink(temp)

Then we read "features.txt"

features<-read.table("UCI HAR Dataset/features.txt",sep=" ")

Then we read X_test.txt (providing column names from the vector features), y_test.txt and subject_test.txt

testValues<-read_table("UCI HAR Dataset/test/X_test.txt",col_names=as.character(features$V2))
testLabels<-read_table("UCI HAR Dataset/test/y_test.txt",col_names="Activity")
testSubjects<-read_table("UCI HAR Dataset/test/subject_test.txt",col_names="Subject")

Similarly we read X_train.txt, y_train.txt and subject_train.txt

trainValues<-read_table("UCI HAR Dataset/train/X_train.txt",col_names=as.character(features$V2))
trainLabels<-read_table("UCI HAR Dataset/train/y_train.txt",col_names="Activity")
trainSubjects<-read_table("UCI HAR Dataset/train/subject_train.txt",col_names="Subject")

Then we combine testLabels, testSubjects & testValues by columns respectively.

testData<-cbind(testLabels,testSubjects,testValues)

Then we combine trainLabels, trainSubjects & trainValues by columns respectively.

trainData<-cbind(trainLabels,trainSubjects,trainValues)

Then we combine testData & trainData by rows respectively.

data<-rbind(testData,trainData)

The resulting variable data contains the single data set which merges the test and train data sets.

2.) Extracts only the measurements on the mean and standard deviation for each measurement:

Using Regular Expressions we extract all column names from variable data which contain the strings "Mean(", "mean(", "Std(" or "std(". We do not extract columns with names having "meanFreq()" in them.

index<-grep("[M,m]ean\\(|[S,s]td\\(",names(data))
data<-data[,c(1,2,index)]

3.) Uses descriptive activity names to name the activities in the data set

We read activity_labels.txt and then merge data and activities

activities<-read_table("UCI HAR Dataset/activity_labels.txt",col_names=FALSE)
data<-merge(data,activities,by.x="Activity",by.y="X1",all.x=TRUE)

Then we replace the first column of data with the newly created 69th column

data[,1]<-data[,69]

Then we remove the redundant 69th column

data<-data[,1:68]

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

We group data by columns Activity & Subject, and then summarize the new table with the mean function

tidyData<-group_by(data,Activity,Subject)
tidyData<-summarize_each(tidyData,funs(mean))

Lastly we convert the type of 'Activity' column to factor

tidyData$Activity<-as.factor(tidyData$Activity)

The resulting variable tidyData contains the tidy data set which contains the average of each variable for each activity and each subject.