/coursera-getting-and-cleaning-data-project

Peer-graded Assignment Project - Getting and Cleaning Data Course (MOOC) - Coursera

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README - Peer-graded Assignment Project - Getting and Cleaning Data Course (MOOC) - Coursera

(author: Gilles Lasnier)

About

This repository hosts the R script and documentation files for the peer-graded assignment project of the Data Science's track course "Getting and Cleaning data". This course is provided by Johns Hopkins University as a MOOC and is available on Coursera.

Files description

  • CodeBook.md: describes variables, data, and transformation or work performed to clean up the data
  • run_analysis.R: R script to transform raw data set in a tidy one
  • README.md: this README file
  • tidy_dataset.txt: (carbon) output produced by the 5th step of the R script

Project description (from coursera)

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:

  • a tidy data set as described below
  • a link to a Github repository with your script for performing the analysis, and
  • 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.
  • a README.md in the repo with your scripts. This file 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. 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: Human Activity Recognition Using Smartphones. The source data for this project can be found here.

Project instructions (summary)

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.

How to create the tidy data set

  1. Clone this repository: git clone https://github.com/glasnier/coursera-getting-and-cleaning-data-project.git
  2. Open a R console and set the working directory to the repository root (use setwd())
  3. Source run_analysis.R R script: source('run_analysis.R')

The script doesn't need any data to be prepared prior to running it. It downloads and unzips the data set in a the root repository folder prior to cleaning up the data. As a result, you will find in the repository root directory the tidy_dataset.txt file which is the the tidy data set.

Additional information

This project used the dataset from: Human Activity Recognition Using Smartphones

For more information about this study and this dataset one may consult the following publication:

[1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012.