Peer Assessment Project for the Coursera Getting & Cleaning Data course

This repo consists of a single script, run_analysis.R. This script reads in data from the UCI HAR Dataset, and outputs a new data set of averages of the means and standards deviations of each of the variables contained in the dataset, per subject per activity.

Note: this script relies on the "reshape2" package.

1. Summary of Input data set
2. Overview of data transformation process

The data for this project is taken from the UCI HAR Dataset, available at http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

As the abstract says, the data is a "Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors." It takes various measures of six different activities: Laying, Sitting, Standing, Walking, Walking Upstairs, and Walking Downstairs.

This project produces arithmetic means for the means and standard deviations contained in the dataset, for each activity, for each subject.

Basic steps are as follows:

• The HAR data files for the training and testing datasets are read in.
• The measurements data are pared down from the original 561, to 66--those with titles which contain the string "mean()" or "std()".
• Data are combined into a single dataframe, consisting of subject ID, activity name, and the subset of the various measurements (as originally provided).
• The resulting data frame is then melted on subject and activity, then dcast using mean as the aggregation function.
• The data are then output to a csv file, tidy_data.csv