Getting and Cleaning Data - Course Project

Course project submission for the "Getting and Cleaning Data" course from Coursera (see https://www.coursera.org/course/getdata).

The Assignment

From the assignment text:

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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 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.

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Downloading / Cloning the Repository

A git (http://git-scm.com/) repository containing the documentation and analysis script can be found on GitHub:

https://github.com/hpmcwill/GettingCleaningDataProject

Assuming that the git command-line tools are installed and are on the PATH cloning the repository can be down with the command:

git clone https://github.com/hpmcwill/GettingCleaningDataProject.git

MS Windows users may want to look at user interfaces to git, such as TortoiseGit (http://tortoisegit.org/).

Alternativly the project can be downloaded as a ZIP archive:

https://github.com/hpmcwill/GettingCleaningDataProject/archive/master.zip

And unpacked in the normal way (e.g. open and copy content in Windows Explorer, or unzip archive.zip).

Usage

Assuming that R (http://www.r-project.org/) has been installed and is on the PATH the analysis can be run by:

1. UNIX and Linux systems

To run the script directly:

Rscript run_analysis.R

or if you prefer using the R console:

R source('path/to/run_analysis.R')

Where "path/to/run_analysis.R" is replaced by the path to the script (e.g. '~/GettingCleaningDataProject/run_analysis.R').

2. MS Windows

Start R using the application in the "Start Menu".

Use 'getwd()' to ensure you are in the desired working directory.

In the R console type:

source('path/to/run_analysis.R')

Where "path/to/run_analysis.R" is replaced by the path to the script (e.g. '~/GettingCleaningDataProject/run_analysis.R').

This will download the raw data (if required) and perform the processing specifed the "The Assignment" section above. Finally generating the output data file 'tidy_data.txt' which has the format described in the code book.

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