/GettingCleaningDataCourseProject

Primary LanguageR

Getting & Cleaning Data Course Project

Purpose

Demonstrate ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis.

How it works

This repo contains a single R script, run_analysis.R that executes the following on the data described below:

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

The below dataset should be downloaded and extracted into the 'data' folder located in the working directory.

The Codebook.md contains more details.

###Deliverables

  1. A tidy data set as described below
  2. A link to a Github repository with script for performing the analysis
  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.

Description from Course Instructions

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. 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

References

[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

Jorge L. Reyes-Ortiz, Alessandro Ghio, Luca Oneto, Davide Anguita. November 2012.

Getting and Cleaning Data by Jeff Leek, PhD, Roger D. Peng, PhD, Brian Caffo, PhD