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<h1>Title: Code Book </h1>
<p>Author: Mohamed Hassan</p>
<h1>Course project</h1>
<p>Getting and Cleaning Data
Date: 5/25/2014</p>
<h1>John Hopkins/coursera Data Science</h1>
<p>Summary of source data</p>
<p>Human Activity Recognition Using Smartphones Dataset</p>
<h2>Version 1.0</h2>
<p>The experiments have been 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. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. </p>
<p>The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain. See 'features_info.txt' for more details.</p>
<h1></h1>
<h2>The dataset includes the following files:</h2>
<ul>
<li><p>'features.txt': List of all features.</p></li>
<li><p>'activity_labels.txt': Links the class labels with their activity name.</p></li>
<li><p>'train/X_train.txt': Training set.</p></li>
<li><p>'train/y_train.txt': Training labels.</p></li>
<li><p>'test/X_test.txt': Test set.</p></li>
<li><p>'test/y_test.txt': Test labels. </p></li>
<li><p>'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30. </p></li>
</ul>
<p>All of these files are into separate variables</p>
<h2>Transformations</h2>
<ol>
<li>Combine training and test data for X ( x_train + x_test) </li>
<li>Combine training and test data for Y ( x_train + x_test) </li>
<li>Apply tidy principles to transform features values into lower cases, remove letters such as (, -) and replace them with empty spaces </li>
<li>Add headings x.train.test</li>
<li>Add headings to y.train.test</li>
<li>Combine subject.train and subject.test </li>
<li>Rename columns variables to subject</li>
<li>Assign each y.train.test to its activity label in activity.label </li>
<li>Only graps data with mean and std variables</li>
<li>Column Combine subject + y.train.test, observations with mean and std</li>
</ol>
<pre><code class="r"># tidy data set
df <- cbind(subject,y.train.test, df.mean, df.std)
</code></pre>
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