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The run_analysis.R script performs the data preparation and then followed by the 5 steps required as described in the course project's definition.

  1. Download the dataset

    • Dataset downloaded and extracted under the folder called UCI HAR Dataset

  2. Assign each data to variables

    • features <- features.txt : 561 rows, 2 columns
      The features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ.
    • activities <- activity_labels.txt : 6 rows, 2 columns
      List of activities performed when the corresponding measurements were taken and its codes (labels)
    • subject_test <- test/subject_test.txt : 2947 rows, 1 column
      contains test data of 9/30 volunteer test subjects being observed
    • x_test <- test/X_test.txt : 2947 rows, 561 columns
      contains recorded features test data
    • y_test <- test/y_test.txt : 2947 rows, 1 columns
      contains test data of activities'code labels
    • subject_train <- test/subject_train.txt : 7352 rows, 1 column
      contains train data of 21/30 volunteer subjects being observed
    • x_train <- test/X_train.txt : 7352 rows, 561 columns
      contains recorded features train data
    • y_train <- test/y_train.txt : 7352 rows, 1 columns
      contains train data of activities'code labels

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

    • X (10299 rows, 561 columns) is created by merging x_train and x_test using rbind() function
    • Y (10299 rows, 1 column) is created by merging y_train and y_test using rbind() function
    • Subject (10299 rows, 1 column) is created by merging subject_train and subject_test using rbind() function
    • Merged_Data (10299 rows, 563 column) is created by merging Subject, Y and X using cbind() function

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

    • TidyData (10299 rows, 88 columns) is created by subsetting Merged_Data, selecting only columns: subject, code and the measurements on the mean and standard deviation (std) for each measurement

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

    • Entire numbers in code column of the TidyData replaced with corresponding activity taken from second column of the activities variable

  6. Appropriately labels the data set with descriptive variable names

    • code column in TidyData renamed into activities
    • All Acc in column's name replaced by Accelerometer
    • All Gyro in column's name replaced by Gyroscope
    • All BodyBody in column's name replaced by Body
    • All Mag in column's name replaced by Magnitude
    • All start with character f in column's name replaced by Frequency
    • All start with character t in column's name replaced by Time

  7. 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

    • FinalData (180 rows, 88 columns) is created by sumarizing TidyData taking the means of each variable for each activity and each subject, after groupped by subject and activity.
    • Export FinalData into FinalData.txt file.