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CodeBook

This document describes the data, transformations and definition of variable used within run_analysis.R script to generate Tidy.csv.

Dataset Used

The experiments were carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. A full description is available at the site http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones.

The data set used can be downloaded from https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip.

Input Dataset Used

The input data containts the following data files:

  • X_train.txt contains variable features that are intended for training.
  • y_train.txt contains the activities corresponding to X_train.txt.
  • subject_train.txt contains information on the subjects from whom data is collected.
  • X_test.txt contains variable features that are intended for testing.
  • y_test.txt contains the activities corresponding to X_test.txt.
  • subject_test.txt contains information on the subjects from whom data is collected.
  • activity_labels.txt contains metadata on the different types of activities.
  • features.txt contains the name of the features in the data sets.

Transformations Performed

Following are the transformations that were performed on the input dataset:

  • X_train.txt is read into featuresTrain.
  • y_train.txt is read into activityTrain.
  • subject_train.txt is read into subjectTrain.
  • X_test.txt is read into featuresTest.
  • y_test.txt is read into activityTest.
  • subject_test.txt is read into subjectTest.
  • features.txt is read into featureNames.
  • activity_labels.txt is read into activityLabels.
  • The subjects in training and test set data are merged to form subject.
  • The activities in training and test set data are merged to form activity.
  • The features of test and training are merged to form features.
  • The name of the features are set in features from featureNames.
  • features, activity and subject are merged to form completeData.
  • Indices of columns that contain std or mean, activity and subject are extracted into requiredColumns.
  • extractedData is created with data from columns in requiredColumns.
  • Activity column in extractedData is updated with descriptive names of activities taken from activityLabels.
  • Activity column is expressed as a factor variable.
  • Acronyms such as 'Acc', 'Gyro', 'Mag', 't' and 'f' used as part of variable names in extractedData are replaced with descriptive labels such as 'Accelerometer', 'Gyroscpoe', 'Magnitude', 'Time' and 'Frequency'.
  • tidyData is created as a data set with average for each activity and subject of extractedData. Entries in tidyData are ordered based on activity and subject.
  • Finally, the data in tidyData is written into Tidy.csv.

Output Data Set

The output data Tidy.csv is a a comma-delimited file.