https://github.com/indaco/getdata-005

Github repo with the code for the Getting and Cleaning Data Course Project
https://github.com/indaco/getdata-005

Last synced: 2 days ago
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Github repo with the code for the Getting and Cleaning Data Course Project

• Host: GitHub
• URL: https://github.com/indaco/getdata-005
• Owner: indaco
• Created: 2014-07-19T20:04:37.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2014-07-23T07:42:31.000Z (over 10 years ago)
• Last Synced: 2024-10-11T12:59:01.131Z (26 days ago)
• Language: R
• Size: 152 KB
• Stars: 0
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Getting and Cleaning Data Course Project (getdata-005)

======================================================

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](http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphone) 

Here are the data for the project: 

[https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip](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 variable names. 

* Creates a second, independent tidy data set with the average of each variable for each activity and each subject. 

Good luck!

Steps to work on this course project

------------------------------------

**```run_analysis.R``` requires an internet connection to download the data source zip file.**

1. Create a folder and save the ```run_analysis.R``` into it.

2. Set the working directory to the folder previously created.

3. Run ```source("run_analysis.R")```, then it will download the dataset file and it will generate a new file ```tiny.txt``` in your working directory.

Dependencies

------------

```run_analysis.R``` file will help you to install the dependencies automatically. Anyway, it depends on ```data.table``` and ```reshape2```.