title	author	date	output
readme	MNicola76	Saturday, June 21, 2014	html_document

Introduction

The script run_analysis.R using the data from UCI Machine Learning Repository

• merges the training and test sets to create one data set
• extracts only the measurements (features) on the mean and standard deviation for each measurement
• replaces activity values in the dataset with descriptive activity names
• appropriately labels the columns with descriptive names
• creates a second, independent tidy dataset with an average of each variable for each each activity and each subject. In other words, same type of measurements for a particular subject and activity are averaged into one value and the tidy data set contains these mean values only. The processed tidy data set is also exported as a text (txt) file.

run_analysis.R

The script is parititioned into 5 steps such that each section performs one of the steps described above (and aligned to the 5 steps outlined in the project instructions).

The original data set

The original data set is split into training and test sets (70% and 30%, respectively) where each partition is also split into three files that contain

• measurements from the accelerometer and gyroscope
• activity label
• identifier of the subject

Getting and cleaning data

The first step of the preprocessing is to merge the training and test sets. Two sets combined, there are 10,299 observations where each instance contains 561 features. After the merge operation on the resulting data, the table contains 563 columns (561 measurements, subject identifier and activity label).

After the merge operation, mean and standard deviation features are extracted for further processing. Out of 561 measurement features, 79 mean and standard deviations features are extracted, yielding a data frame with 81 features (additional two features are subject identifier and activity label).

Next, the activity labels are replaced with descriptive activity names, defined in activity_labels.txt in the original data folder.

The final step creates a tidy data set with the average of each variable for each activity and each subject. 10299 instances are split into 180 groups (30 subjects and 6 activities) and 79 mean and standard deviation features are averaged for each group. The resulting data table has 180 rows and 81 columns. The tidy data set is exported to tidy_dataset.txt where the first row is the header containing the names for each column.