Coursera Getting and Cleaning Data Course Project

Course taught by COURSERA

This repository contains the following files:

• README.md, provides an overview of the dataset and how it was created.
• CodeBook.md, describes the contents of the data set (data, variables and transformations used to generate the data).
• Run_Analysis.R, the R script that was used to create the data set
• Tidy_Data.txt, contains the data set.

Raw dataset information

The source dataset was based on the human activity recognition project using smartphones, which describes how the data was initially collected as follows:

The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.

Create the dataset

The R Run_Analysis.R script can be used to create the dataset. Retrieves the source dataset and transforms it to produce the final dataset by implementing the following steps:

1. Download and unzip the source data if it doesn't exist. Download here
2. Combine the training and test sets to create a data set.
3. Extract only the measurements in the mean and standard deviation for each measurement.
4. Use descriptive activity names to name the activities in the dataset.
5. Properly label the dataset with descriptive variable names.
6. Create a second independent ordered set with the average of each variable for each activity and each topic.

This script requires the package "plyr" and "data.table".