This is the part of the Peers Assessments of Getting and Cleaning Data Course on coursera.org

The raw data of this project can be found at DATASET

Description about the data set at http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

(As seen at coursera.org) 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.

README.md - This readme

CODEBOOK.md - Description of data, variables, functions and transformations used to clean up the data.

run_analysis.R - Script that contains the code to run the project.

tidy.txt - The result file (cleaned data) for the script above.