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.
Download the dataset Dataset downloaded and extracted under the folder called UCI HAR Dataset
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
Merges the training and the test sets to create one data set x_full(10299 rows, 561 columns) is created by merging x_train and x_test using rbind() function y_full(10299 rows, 1 column) is created by merging y_train and y_test using rbind() function subject_full (10299 rows, 1 column) is created by merging subject_train and subject_test using rbind() function data_full (10299 rows, 563 column) is created by merging subject_full, y_full and x_full using cbind() function
Extracts only the measurements on the mean and standard deviation for each measurement mean_std_data (10299 rows, 88 columns) is created by subsetting data_full, selecting only columns: subject, code and the measurements on the mean and standard deviation (std) for each measurement
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
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.
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 Cleandata1 (180 rows, 88 columns) is created by sumarizing Cleandata taking the means of each variable for each activity and each subject, after groupped by subject and activity. Export Cleandata into Cleandata1.txt file.
Identifiers in the final created dataset subject - The ID of the test subject activity - The type of activity performed when the corresponding measurements were taken