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Human Activity Recognition Using Smartphones Dataset, Version 1.0

The run_analysis.R script is used to prepare the data as defined in the course project.

Download and extract the dataset

The file downloaded represent data collected from the accelerometers from the Samsung Galaxy S smartphone, the data was saved in a file called projectfiles_UCI_HAR_Dataset, and extracted under the folder called UCI HAR Dataset.

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.

Read the dataset

The features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ.

  • features: List of all features.
  • activity_labels: Links the class labels with their activity name.

The following data are available for the train and test data (subject_test, X_test and y_test). Their descriptions are equivalent.

  • subject_train: Each row identifies the subject who performed the activity for each window sample.
  • X_train: Training set, with features names included as a column names.
  • y_train: Contains training labels of activities code labels.

Merges the training and the test sets to create one data set

  • X_data: Is created by merging X_train and X_test.
  • y_data: Is created by merging y_train and y_test.
  • subject_data: Is created by merging subject_train and subject_test.

Note: Merging is done by row binding.

Extracts only the measurements on the mean and standard deviation for each measurement

The means and stds calculated for each measure are selected.

  • X_mean_std: Is created by selecting the measurements on the mean and std (standard deviation) for each measurement.

Uses descriptive activity names to name the activities in the data set

In y_data the activity code labels were replaced by their respective activity name, labels taken from the activity_labels variable.

Appropriately labels the data set with descriptive variable names.

In the data set X_mean_std measures were renamed by more descriptive and complete names, in the code using regular expressions, the abbreviations for the measure names were found and replaced by their full names (e.g. Acc to Accelerometer).

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

  • all_data: contains the entire data set and is created by merging X_mean_std, y_data and subject_data using bind_cols() function
  • tidy_data: is created by grouping the data set all_data by activity and subject, then summarizing all_data taking the means of each variable for each activity and each subject.

The final dataset (tidy_data) is exported into tidy_data.csv file