title	author	date	output
README document for Coursera Getting and Cleaning Data Project	mjpalacios	October 21, 2015	html_document

##Purpose of this document This document describes the contents of my GitHub repository for Coursera's "Getting and Cleaning Data" course project and provides instructions on how to run the R script used to transform an input data set (University of California at Irvine's Human Activity Recognition) into a subset of averaged measurements grouped by subject and activity.

##References

1. Getting and Cleaning Data Course Project Assignment Page.

##Contents This section describes the contents of this repository.

Filename	Description
README.md	This file
Codebook.md	Describes the variables (columns) in the output dataset
run_analysis.R	R Script that implements the project's requirements
run_analysis.txt	Output file created by the R script, provided for validation purposes.

##Setup Before running the script, the following has to be setup:

• The R Environment is installed. It can be either "plain vanilla R" or RStudio.
• Package "dplyr" has been installed. It does not have to be loaded, the script does that.
• The original dataset has been downloaded as "UCI_HAR_Dataset.zip" and placed into the working directory of your R environment. Do not decompress the file.

##Steps To obtain the processed dataset, perform the following steps:

1. Source the analysis function into R and run it in the R Console.

source("<your default R working directory>/run_analysis.R")
run_analysis()

2. Once the function has completed running, you will see one output file ("run_analysis.txt") in your working directory.

# Verify that output file is present
list.files(pattern="run_analysis.txt")

#Output
[1] "run_analysis.txt"

3. To read the file into R, issue the following command in the R Console:

# Read text file
result <- read.table("run_analysis.txt", header=TRUE)