The course_project from vaibhav6215

course_project's Introduction

title	author	date	output
README	Vaibhav	Monday, August 25, 2014	html_document

y_test<-read.table("UCI HAR Dataset/test/y_test.txt")
subject_test<-read.table("UCI HAR Dataset/test/subject_test.txt")
subject_train<-read.table("UCI HAR Dataset/train/subject_train.txt")
y_train<-read.table("UCI HAR Dataset/train/y_train.txt")
X_train<-read.table("UCI HAR Dataset/train/X_train.txt")
features<-read.table("UCI HAR Dataset/features.txt")
activity_labels<-read.table("UCI HAR Dataset/activity_labels.txt")

The above code chunk reads all the relevant files from the home directory and assigns them to corresponding objects

Y<-rbind(y_train,y_test)

The training and testng datasets are merged together using the rbind() function

activity<-data.frame(activity$V2)

The activity labels are merged with the activity values

names(X)<-c(feature_name)
names(activity)<-"Activity"
Subject<-rbind(subject_train,subject_test)
names(Subject)<-"Subject"

The column names for Subject, Activity and Features are added here

Dat1<-cbind(X,Subject,activity)

The activity , subject and X,Y datasets are merged together to form a data frame Dat1

Dat2<-cbind(activity,Subject,Dat2)

The columns corresponding to mean() and std() values are extracted and stored in Dat2.

Dat3<-data.frame()
for(i in 1:6)
{
        
        for(j in 1:30)
        {
                Dat3<-Dat2[which(Dat2$Activity==activity_labels[i,2] & Dat2$Subject==j),]
                Final_Dat<-rbind(Final_Dat,colMeans(Dat3[,-1]))
        }
}
a<-rep(c("WALKING","WALKING_UPSTAIRS","WALKING_DOWNSTAIRS","SITTING","STANDING","LAYING"),each=30)
b<-rep(1:30,6)
Final_Dat<-cbind(a,b,Final_Dat)
Final_Dat<-Final_Dat[,-3]
names(Final_Dat)<-names(Dat2)
Final_Dat<-Final_Dat[complete.cases(Final_Dat),]

for loops are used for creating the final data frame that contains the average of each column for each actiivity and each subject value

Recommend Projects