Get-n-Clean-Data

Get and Clean Data programming assignment for Coursera

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Original 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.

For each record (observation), the following is provided:

• Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.
• Triaxial Angular velocity from the gyroscope.
• A 561-feature vector with time and frequency domain variables.
• Its activity label.
• An identifier of the subject who carried out the experiment.

The dataset includes the following files:

• 'train/subject_train.txt' & 'test/subject_test.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.

• 'features_info.txt': Shows information about the variables used on the feature vector.

• 'features.txt': List of all features.

• 'activity_labels.txt': Links the class labels with their activity name.

• 'train/X_train.txt': Training set.

• 'train/y_train.txt': Training labels.

• 'test/X_test.txt': Test set.

• 'test/y_test.txt': Test labels.

Notes:

• The file run_analysis.R in the main directory can be run as long as the Samsung data is in your working directory.

• run_analysis collects, consolidates and cleans the data by performing the following:

• 1. reading the test and training subject files (subject_train.txt & /subject_test.txt) and combining them into one column with a header.

• 2a) reading the activity_labels.txt file, and cleaning up the activity description text.

• 2b) reading the test and training y files (y_train.txt & y.test.txt), combining them into one column, creating a vector of activity descriptions that correspond to the activity codes in the y files, and combining the train and test activity descriptions into 1 column with a header.

• 3a) reading the features.txt file, extracting only the measurement headings on the mean and standard deviation for each observation, and cleaning up the measurement headings.

• 3b) reading the test and training X files (X_train.txt & X_test.txt), extracting only the measurements on the mean and standard deviation for each observation, and combining the train and test measurement data into a data frame with the headings created in 3a.

• 4. combining the subject data, activity data and measurement data into one data frame.
• 5. summarizing the data, such that the averages of all mean and standard deviation measurements for each unique subject/activity pair is provided in a tidy data frame.
• 6. creating a text file, 'tidy_move_data.txt', from the tidy data frame.

• The output, 'tidy_move_data.txt', is a tidy data set giving the mean for all observations associated with a subject/activity pair. Here is a statement that will read the file: xfiles <- read.table("tidy_move_data.txt", sep="\t").

• For more information about this dataset contact: [email protected] Jorge L. Reyes-Ortiz, Alessandro Ghio, Luca Oneto, Davide Anguita. November 2012.