lpaoliespin / GettingAndCleannigData

Coursera Peer-graded Assignment: Getting and Cleaning Data Course Project

Getting and Cleaning Data Course Project

Author: Lucrecia Paolini @lpaoliespin

This repository contains the code for my solution to the assignment of the course "Getting and Cleaning Data Course Project".

Description of the 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 http://www.insideactivitytracking.com/data-science-activity-tracking-and-the-battle-for-the-worlds-top-sports-brand/. 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.

1. Merges the training and the test sets to create one data set.
2. Extracts only the measurements on the mean and standard deviation for each measurement.
3. Uses descriptive activity names to name the activities in the data set.
4. Appropriately labels the data set with descriptive variable names.
5. From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

My solution

Steps to run the analysis

1. Place the script run_analysis.R in your working directory.
2. It will Download and Unzip the file getdata-projectfiles-UCI HAR Dataset.zip in the folder UCI HAR Dataset.
3. Load the source run_analysis.R at your R session with source("run_analysis.R").
4. It will generate a new file tiny_data.txt in your working directory.

Dependencies

The script run_analysis.R will install automatically the packages dplyr, data.table and reshape2.