###Before You Start

Please download the data from link below, and extract it to any directory. https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

Set working directory to the extratced data folder (i.e. C:/UCI HAR Dataset/) and execute every line in run_analysis.R. The comma seperated tidy data will be created in your working directory, and named: tidy_data.txt.

###Key Attributes

• Training and test measurements are extracted from ./train/X_train.txt and ./test/X_test.txt. And measurement names are extracted from ./features.txt.

• Training and test subject IDs are extracted from ./train/subject_train.txt and ./test/subject_test.txt.

• Training and test activity IDs are extracted from ./train/y_train.txt and ./test/y_test.txt.

• All test data sets are concatenated below training data sets to ensure consistency.

• The transformed data is constructed by extracting measurements with names containing mean() or std() only. Corresponding subject ID and activity label have been merged with the transformed data as well.

• Tidy data is constructed by taking the average of each measurement for each activity and each subject from the transformed data.

###The Scripts

The scripts in run_analysis.R is made up of 5 steps:

• Concatenate training and test data, through rbind(), to create a complete data set (named df_step1), where test data is concatenated below training data.

• Working on df_step1, name each variable based on features.txt. Then, extract variables that are the mean and standard deviation for each measurement, by searching and matching variable names that contain keywords mean() or std(). Name the transformed data set as df_step2.

• Use cbind() to bring the corresponding acitivity ID and subject ID to the observations from the transformed data set (df_step2). Where test IDs are concatenated below training IDs to match the data structure in df_step1. Name the transformed data set with corresponding IDs as df_step3.

• Use merge() and subset to replace activity ID from transformed data (df_step3) with activity label. Name the new transformed data set as df_step4.

• Use df_step4 from step 4 to create a tidy data set with the average of each measurement for each activity and each subject. Name the tidy data set as tidy_data, and save it as a comma seperated text file: tidy_data.txt.