Getting and Cleaning Data - Peer Assessment/Course Project

Overview

In this repository you will find the R-source code for the course project in the coursera course "Getting and Cleaning Data". It contains: 1.README.md - this file 2.run_analysis.R - the R-source code 3. codeBook.md - describes the variables used in the data

Raw Data

Name: Human Activity Recognition Using Smartphones

Origin: UCI's Machine Learning Repository

Link: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

Source: Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto. Smartlab - Non Linear Complex Systems Laboratory DITEN - Università degli Studi di Genova, Genoa I-16145, Italy. activityrecognition '@' smartlab.ws www.smartlab.ws

Reference: 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

The data set was downloaded and unzipped to its default folder named UCI HAR Dataset.

The raw data archive needs to be downloaded and extracted into 'UCI HAR Dataset' inside the R-Studio working directory. (A script for doing so is provided in the source code).

Code description

The script run_analysis.R will do the following steps:

1. Merge the training set and the test set to create a single data set.

• check if directory "UCI HAR Dataset" exists
• convert files (for subjects, measurement sets and labels) into r-table objects
• merge the tables

2. Extract the measurement on the mean and the standard deviation for each measurement.

• find measurement types with "std()" or "mean()" in its name
• replace the default column names in measurement set
• extract only relevant measurements

3. Use descriptive activity names to name the activities in the data set.

• transform numeric activity labels into correct written names

4. Appropriately label the data set with descriptive variable names.

• apply capitalization for names easier to read
• fix typos
• add proper names for subject and activity column
• merge data of subjects, measurement sets and labels into one table

5. Create a second, independent tidy data set with the average of each variable for each activity and for each subject.

• subset data by subject and activity
• aggregate data by calculating the mean of all measurement types
• write the returned table to hard disk

Output

tidyData_average_activity_subject.txt in R-working directory.