Getting and Cleaning Data - Course Project

This repository contains the submission for the "Getting and Cleaning Data" Coursera course, part of the Data Science specialization.

Overview

The purpose of this project is to demonstrate the ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis.

Raw data

The source data for this project can be downloaded here. A full description of these data used can be found at The UCI Machine Learning Repository.

Submission files

CodeBook.md is a code book that describes the variables, the data, and any transformations or work performed to clean up the data. run_analysis.R is an R script that does the following:

• merges the training and the test sets to create one data set;
• extracts only the measurements on the mean and standard deviation for each measurement;
• uses descriptive activity names to name the activities in the data set;
• appropriately labels the data set with descriptive activity names;
• creates a second, independent tidy data set with the average of each variable for each activity and each subject.

Prerequisites to run the code

The code takes for granted that:

• the data must be unzipped and the "UCI HAR Dataset" directory must be placed in the R home directory;
• data directories and data files names must not be changed.