This is the Course Project for Coursera's "Getting and Cleaning Data" course.

• Create a directory for this project, called samsung

• Download the script run_analysis.R to samsung

• Download the raw data from https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip to samsung and unzip it. You can delete the zip file after this step. Your directory structure should look like this now (only showing 2 levels deep here):

  ├── UCI HAR Dataset
    │   ├── README.txt
    │   ├── activity_labels.txt
    │   ├── features.txt
    │   ├── features_info.txt
    │   ├── test
    │   └── train
    └── run_analysis.R 
  

The script run_analysis.R depends on the libraries plyr and reshape2.

• Change the working directory in R to samsung (the installation directory)
• Source the script run_analysis.R in R: source("run_analysis.R")
• Execute the function DeriveAndWriteDataSets with no arguments:DeriveAndWriteDataSets(). Two datasets will be written to your working directory now: tidy1.csv and tidy2.csv
• If you want to retrieve the datasets in R, without writing them to CSV-files, you can run tidy1 <- DeriveTidy1() tidy2 <- DeriveTidy2(tidy1)

Note that DeriveTidy2 uses the result from DeriveTidy1 as input. The objects tidy1 and tidy2 are of class data.frame.

Information about the datasets is provided in CodeBook.md.

The code contains detailed commments explaining the steps in which the original data was transformed to tidy1.csv and tidy2.csv