#cleandatacoursera

Lee Tibbert's Repository for Coursera Getting & Cleaning Data course project, January 2016

The files in this repository document a transformation of data described in the course projects requirements description as:

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

A new data frame is constructed from the original data. First data from training files (_train.txt) is consolidated with data from the corresponding test (_text.txt) file. The fields corresponding to means and standard deviations are extracted.

An intermediate data frame is constructed which comprises activity names, subject identifiers, and the fields extracted above. This data frame is then grouped by activity name and subject identifier. Column names are changed, as described in CodeBook.md, to be more descriptive.

The final output data frame is constructed by calculating for each group and each ungrouped column the mean of the observations for that group on that column.

Files

1. CodeBook.md describes the fields and units of the output file and their correspondence to the fields in the input files.

2. README.md is this file.

3. run_analysis.R is an R script which does the transformation.

   Input

   The run_analysis.R script described below assumes and requires that the original data has been manually downloaded and unzipped (with the -j junk paths option) exactly into the current working directory, as described in the course project requirements.

   Nota Bene: I would normally put the input data into a ./data directory, as illustrated many times in the course videos. The course project requirement used the explicit term "current working directory", hence the mess of input files in the current directory after following the requirements.

   Having a ./data directory also allows preserving the directory hierarchy in the orginal .zip file. I find that directory structure aids my understanding.

   Output

   The newly constructed data frame is written to the file "./output/courseProjectOutput.txt". The sub-directory is created if it does not exist.

   To run

   There are a number of ways to run the R script. Use what ever works for you. This is one simple way.

# Requires that the current working directory contains the
# R script and unzipped data files.
# Also requires write permission to the current working directory.
    
R
> source("run_analysis.R")
Starting course project at: Fri Jan 29 14:27:21 2016

Current working directory:
  /Coursera/2016/getclean_data_2016/CourseProject/Assignments

|===========================================| 100%   62 MB

Output data frame written to file: ./output/courseProjectOutput.txt

Ending course project at: Fri Jan 29 14:27:27 2016

> quit(save = "no")