manishpatwal / getting_cleaning_data

Getting and cleaning data week4 assignment

Getting_Cleaning_Data

Getting and cleaning data week4 assignment 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 This is the course project for the Getting and Cleaning Data Coursera course. The R script, run_analysis.R, does the following:

Steps to Get things done:- Download the dataset if it does not already exist in the working directory Load the activity and feature info Loads both the training and test datasets, keeping only those columns which reflect a mean or standard deviation Loads the activity and subject data for each dataset, and merges those columns with the dataset Merges the two datasets Converts the activity and subject columns into factors Creates a tidy dataset that consists of the average (mean) value of each variable for each subject and activity pair. The end result is shown in the file Secondary_Tidy.Dataset.txt.