This repository contains the files for the peer-reviewed project of the Getting and Cleaning Data class on Coursera.

###Data files (Original/raw data set)

• README.txt

  The readme file accompanying the original data set downloaded from https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip.
  Among others, it describes how the data was obtained and how the data is organized in the files included in the data set.

• features_info.txt

  Contains information about the variables used on the feature vector

• features.txt

  Contains the list of all features

• activity_labels.txt

  Links the activity name with their integer value equivalents

• train/X_train.txt

  Contains the training set feature values

• train/y_train.txt

  Contains the training labels (activity integer values corresponding to each observation in the training set)

• train/subject_train.txt

  Contains the data identifying the subject corresponding to each observation in the training set

• test/X_test.txt

  Contains the test set feature values

• test/y_test.txt

  Contains the test labels (activity integer values corresponding to each observation in the test set)

• test/subject_test.txt

  Contains the data identifying the subject corresponding to each observation in the test set

Note:
The data files contained in the Inertial Signals directory of the training set and test set were not included in the data preprocessing done in this project.

###R script file

• run_analysis.R

  This R script is provided for reproducibility of the preprocessing steps done on the original data set. It assumes that the data files described in the Data files section above have already been extracted from the .zip file and placed in the R working directory.

  The code in the script performs the following processing steps:

  1. Reading the data from the .txt files into R

  2. Merging the feature values, labels, and subject data to create the training set data frame

  3. Merging the feature values, labels, and subject data to create the test set data frame

  4. Merging the training set and test set into one data frame

  5. Replacing the activity integer values with the activity labels

  6. Extracting a subset of the data containing only the variables representing the mean and standard deviation measurements

  7. Fixing the variable names to remove dashes, parentheses, and duplicated substrings.

  8. Creating a tidy data set containing the average of each variable for each activity and subject

###CodeBook file

• CodeBook.md

  This code book markdown file describes where and when the original data set was downloaded, what preprocessing steps were done to produce the final tidy data set, and what variables are included in that final data set.