| title | README |
|---|---|
| author | Vaibhav |
| date | Monday, August 25, 2014 |
| output | 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