{"comment":{"url":"https://api.github.com/repos/eathomas824/Getting-and-Cleaning-Data-Project-Week-4/comments/19893135","html_url":"https://github.com/eathomas824/Getting-and-Cleaning-Data-Project-Week-4/commit/c263a264e556e951cfc8c86903bd7920f1157cc0#commitcomment-19893135","id":19893135,"user":{"login":"eathomas824","id":22123950,"avatar_url":"https://avatars.githubusercontent.com/u/22123950?v=3","gravatar_id":"","url":"https://api.github.com/users/eathomas824","html_url":"https://github.com/eathomas824","followers_url":"https://api.github.com/users/eathomas824/followers","following_url":"https://api.github.com/users/eathomas824/following{/other_user}","gists_url":"https://api.github.com/users/eathomas824/gists{/gist_id}","starred_url":"https://api.github.com/users/eathomas824/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/eathomas824/subscriptions","organizations_url":"https://api.github.com/users/eathomas824/orgs","repos_url":"https://api.github.com/users/eathomas824/repos","events_url":"https://api.github.com/users/eathomas824/events{/privacy}","received_events_url":"https://api.github.com/users/eathomas824/received_events","type":"User","site_admin":false},"position":1,"line":1,"path":"README.md","commit_id":"c263a264e556e951cfc8c86903bd7920f1157cc0","created_at":"2016-11-20T03:01:42Z","updated_at":"2016-11-20T03:01:42Z","body":"Project Description\r\nThe 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.\r\nYou will be required to submit:\r\na tidy data set as described below\r\na link to a Github repository with your script for performing the analysis, and\r\na 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 file explains how all of the scripts work and how they are connected.\r\nOne of the most exciting areas in all of data science right now is wearable computing. 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\r\nHere are the data for the project: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip\r\nYou should create one R script called run_analysis.R that does the following.\r\nMerges the training and the test sets to create one data set.\r\nExtracts only the measurements on the mean and standard deviation for each measurement.\r\nUses descriptive activity names to name the activities in the data set\r\nAppropriately labels the data set with descriptive activity names.\r\nCreates a second, independent tidy data set with the average of each variable for each activity and each subject.\r\nWhat you find in this repository\r\nCodeBook.md: information about raw and tidy data set and elaboration made to transform them\r\nLICENSE: license terms for text and code\r\nREADME.md: this file\r\nrun_analysis.R: R script to transform raw data set in a tidy one\r\nHow to create the tidy data set\r\nclone this repository: git clone git@github.com:maurotrb/getting-cleaning-data-2014-project.git\r\ndownload compressed raw data\r\nunzip raw data and copy the directory UCI HAR Dataset to the cloned repository root directory\r\nopen a R console and set the working directory to the repository root (use setwd())\r\nsource run_analisys.R script (it requires the plyr package): source('run_analysis.R')\r\nIn the repository root directory you find the file sensor_avg_by_act_sub.txt with the tidy data set."}}
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