Getting and Cleaning Data - Peer Assessment

The purpose of this project is to demonstrate your ability to collect,
work with, and clean a data set. The goal is to prepare tidy data that
can be used for later analysis. You will be graded by your peers on a
series of yes/no questions related to the project. You will be required
to submit: 1) a tidy data set as described below, 2) a link to a Github
repository with your script for performing the analysis, and 3) a code
book that describes the variables, the data, and any transformations or
work that you performed to clean up the data called CodeBook.md. You
should also include a README.md in the repo with your scripts. This
repo explains how all of the scripts work and how they are connected.

One of the most exciting areas in all of data science right now is
wearable computing - see for example this article . Companies like
Fitbit, Nike, and Jawbone Up are racing to develop the most advanced
algorithms to attract new users. The data linked to from the course
website represent data collected from the accelerometers from the
Samsung Galaxy S smartphone. A full description is available at the
site where the data was obtained:

http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+
Smartphones

Here are the data for the project:

https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR
%20Dataset.zip

 You should create one R script called run_analysis.R 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