Coursera

Getting and Cleaning Data Course Project

Script does the following:

• Extract only the measurements on the mean and standard deviation for each measurement

  means_and_std_colnames<-colnames(X_test)[indices]
  X_test_subset<-cbind(subject_test,y_test,subset(X_test,select=means_and_std_colnames))
  X_train_subset<-cbind(subject_train,y_train,subset(X_train,select=means_and_std_colnames))

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

  Xy<-rbind(X_test_subset, X_train_subset)

• Uses descriptive activity names to name the activities in the data set

  tidy$Activity<-activity_labels[tidy$Activity,]

• Appropriately labels the data set with descriptive activity names.

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

  tidy<-aggregate(Xy[,3:ncol(Xy)],list(Subject=Xy$subjects, Activity=Xy$labels), mean)
  tidy<-tidy[order(tidy$Subject),]