Author: Weichen Wang

This document aims to explain underlying assumption and logic behind run_analysis.R.

• 1. run_analysis.R file will read UCI HAR Dataset under the condition that current working directory has "UCI HAR Dataset" folder. Inside the folder, "train" and "test" folders are also present.
• 2. Mean and standard deviation extracted does not include those variables of meanFreq(). Only mean() and std() are included in the final tidy data set.

• 1. Read raw data files into memory (features.txt, activity_labels.txt, X_train.txt, y_train.txt, X_test.txt, y_test.txt, subject_train.txt, subject_test.txt)
• 2. Add subject and label columns into training and testing sets.
• 3. Merge training and testing sets.
• 4. Changing column names into descriptive feature names.
• 5. Extract mean and std columns from merged table.
• 6. Calculate average of each feature variable in merged table for each subject and each activity.
• 7. Export the final tidy data table into .txt file for future processing and analysis.