Summary of source data
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.
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.
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'features.txt': List of all features.
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'activity_labels.txt': Links the class labels with their activity name.
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'train/X_train.txt': Training set.
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'train/y_train.txt': Training labels.
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'test/X_test.txt': Test set.
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'test/y_test.txt': Test labels.
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'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.
All of these files are into separate variables
- Combine training and test data for X ( x_train + x_test)
- Combine training and test data for Y ( x_train + x_test)
- Apply tidy principles to transform features values into lower cases, remove letters such as (, -) and replace them with empty spaces
- Add headings x.train.test
- Add headings to y.train.test
- Combine subject.train and subject.test
- Rename columns variables to subject
- Assign each y.train.test to its activity label in activity.label
- Only graps data with mean and std variables
- Column Combine subject + y.train.test, observations with mean and std
# tidy data set
df <- cbind(subject,y.train.test, df.mean, df.std)