Getting & Cleaning Data - Course Project

This document describes the process of run_analysis.R to clean data. The step as the followings:

1. Merges the training and the test sets to create one data set.

• The training and testing dataset are first read into two variables, train and test, respectively
• The training & testing dataset are then merged using rbind into a single variable named all Here, in the first part of the code, I only merged X_train & X_test. y_train and y_test, subject_train, subject_test are merged into all later in part 3 for coding convenience.

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

• First the list of all feature names are read into variable features
• Then the features with mean and standard deviation are found using regular expression. The column indices are stored in variable good_columns
• Variable all is subsetted with good_columns Notes:

In here I extracted 66 columns. There are columns "meanFreq" that I purposely excluded. Only columns that has "mean()" and "std()" in its name are included.

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

4. Appropriately labels the data set with descriptive activity names.

• First the English names of the activites are read into variable activity_labels.
• After that, activity_labels are used to labels y_train and y_test, and then merged into all
• Next, subject are read and merged into subject_all
• Finally, subject_all is merged into all. After this step, all contains all the information that we need to merge

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

• The average are calculated using function aggregate. The result is stored in variable tidy which will be then stored into a file named "tidy_data.txt"
• Tidy data has 180 rows. This is because: There are 30 subject and 6 activities, thus there are 30*6 = 180 averages for each variable.