Getting and Cleaning Data Programming Assignment

Files for Week 4 Programming Assignment

Human Activity Recognition Using Smartphones Dataset - Tidy Version

This version includes the following additional files:

• 'README.md' - updated to describe additional files to the tidy version of this dataset
• 'run_analysis.R' - script to produce the tidy data
• 'codebook.md' - describes the data cleaning script to produce the tidy data
• 'tidydata.txt' - the cleaned data

This describes how to use the script run_analysis.R to combine, streamline, label, and summarize the original data set into tidydata.txt.

Pre-cleaning Steps

1. Create and set the project directory
2. Download the raw data set to a temporary file, then extract files to project directory
3. Read in R the following data:

• subject_train.txt - identifies the subjects who performed the activity for each record for the training set
• y_train.txt - identifies the activity for each record of the training set
• X_train.txt - data for all 561-feature measurements for each record in the training set
• subject_test.txt - identifies the subjects who performed the activity for each record for the test set
• y_test.txt - identifies the activity for each record of the test set
• X_test.txt - data for all 561-feature measurements for each record in the test set

Cleaning Steps:

1. Merge the training and test sets to create one data set

• Merge the training and test records of subject, y, and X data sets using rbind
• Match the subject to the activity to the data by using cbind on the merged subject, y, and X data sets

2. Extract only the measurements on the mean and standard deviation for each measurement

• Read in R features.txt which describes the measurements in X
• Determine the indices of features that contains mean and standard deviations of measurements (a total of 66 indices will be returned)
• Streamline the merged X data in step 1a using the indices; this will be the new X data

3. Use descriptive activity names to name the activities in the data set

• Read in R activity_labels.txt which describes the activities represented as integers in y; label its variables as activityID for the integers and activity for the actual activity being done
• Label the merged y data in step 1b with activityID
• Use merge() by activityID to match the appropriate activity label to the integers in y

4. Appropriately label the data set with descriptive variable names

• Label the merged subject data in step 1b with subjectID
• Create xnames, a character vector which will be used as variable names for the merged and streamlined X data in step 2c. This vector will be produced by streamlining features using the same indices used to streamline the X data in step 2
• Make the variables in xnames be more readable by using gsub - eliminate "()" at the end of each line; rewrite std, mean, and mag as StdDev, Mean, and Magnitude; expand t and f to time and freq, respectively; eliminate duplicating words; add space in between each relevant word
• Use xnames as label to the merged and streamlined X data in step 2c
• Combine the streamlined data by using cbind on the merged subject data in step 1b, the activity variable in the merged y data in step 3c, and the streamlined and labeled X data in step 4d

5. From the data set in step 4, create a second, independent tidy data set with the average of each variable for each activity and each subject.

• Load the dplyr package
• Convert the combined data frame in step 4c into a data frame tbl
• Group the data into activity and subject ID using group_by ()
• Summarize the grouped data by taking the each of the streamlined measurements' average per unique subjectID-activity combination (35 unique combinations in total)
• Export the summarized data frame tbl into tidydata.txt
• The summarized data will have 35 records of unique subjectID-activity combinations and 68 variables - one for subjectID, one for activity, and 66 for the mean and standard deviation measurements

Original Data from:

Human Activity Recognition Using Smartphones Dataset (Version 1.0)

Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto
Smartlab - Non Linear Complex Systems Laboratory
DITEN - Università degli Studi di Genova
Via Opera Pia 11A, I-16145, Genoa, Italy
[email protected]
www.smartlab.ws

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 it 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:

• 'README.txt'
• '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.

The following files are available for the train and test data. Their descriptions are equivalent.

• 'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.
• 'train/Inertial Signals/total_acc_x_train.txt': The acceleration signal from the smartphone accelerometer X axis in standard gravity units 'g'. Every row shows a 128 element vector. The same description applies for the 'total_acc_x_train.txt' and 'total_acc_z_train.txt' files for the Y and Z axis.
• 'train/Inertial Signals/body_acc_x_train.txt': The body acceleration signal obtained by subtracting the gravity from the total acceleration.
• 'train/Inertial Signals/body_gyro_x_train.txt': The angular velocity vector measured by the gyroscope for each window sample. The units are radians/second.

Notes:

• Features are normalized and bounded within [-1,1].
• Each feature vector is a row on the text file.

For more information about this dataset contact: [email protected]

License:

Use of this dataset in publications must be acknowledged by referencing the following publication [1] [1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012 This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited. Jorge L. Reyes-Ortiz, Alessandro Ghio, Luca Oneto, Davide Anguita. November 2012.