Step 1: Merges the training and the test sets to create one data set. Step 1a: Download and unzip the files Download the zip from the provided link (assigned in url variable) Unzip the files Step 1b: Create a table of features (column names) and activity labels Make 2 tables: features (column names) and activity labels with associated index. Assign to features, and activity_labels, respectively Step 1c: Load both training and test data sets Load training and test data sets. X_train table uses features as column names, y_train uses "Activity" as column name for modification later, subject for subject_train. Do the same for test sets Step 1d: Merge the tables together Row-bind the train and test-sets( X_train vs X_test, y_train vs y_test, and subject_train vs subject_test) Column-bind the subject, y and X in order to create tidy dataset Step 2: Extracts only the measurements on the mean and standard deviation for each measurement. Make a list of column names containing "std" or "mean" Filter columns with corresponding list (with subject_id and activity) Step 3: Uses descriptive activity names to name the activities in the data set Using the activity labels table for changing the activity from number to corresponding name Step 4: Appropriately labels the data set with descriptive variable names Change all the acronym to readable values, remove duplicate, replace bad symbols with appropriate symbols to avoid misreading codes Step 5: From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.