nirski/GCD

GCD

The script run_analysis.R performs the following steps, the scope being a tidy data set, according to the rules suggested in Hadley Wickham's tidy data paper:

1. Reads the data sets, along with the dictionaries:
   • subject variable: UCI HAR Dataset/train/subject_train.txt and UCI HAR Dataset/test/subject_test.txt
   • activity variable: UCI HAR Dataset/train/y_train.txt and UCI HAR Dataset/test/y_test.txt
   • feature measurements: UCI HAR Dataset/train/X_train.txt and UCI HAR Dataset/test/X_test.txt
   • dictionaries: activities and features: UCI HAR Dataset/activity_labels.txt and UCI HAR Dataset/features.txt
2. Merges the training and the test sets to create one data set.
   • Merging colwise and rowwise.
   • The resulting data frame bound.all has a wide format: 10299 observations od 563 variables (1 subject, 1 activity and 561 feature variables).
   • The data is then converted to a long format tidy.all with 4 columns: subject, activity, feature id, and value.
3. Extracts only the measurements on the mean and standard deviation for each measurement.
   • Out of 561 features, only 66 are selected. The filtered tidy.all data frame has 679734 rows and 4 columns.
4. Uses descriptive activity names to name the activities in the data set.
   • The activities dictionary is used.
5. Appropriately labels the data set with descriptive variable names.
   • Each feature gets a descriptive label from the features dictionary.
6. From the data set in previous step, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
   • The resulting data frame tidy.mean is eventually written to the file tidy_mean.txt.