#An explanation of the run_analysis.R script

##Part 1 ###Here the training and test sets are extracted into separate data frames:

• The test and trainining activity data sets are then read into the test_data_activities and train_data_activities data frames
• The column within each of the test_data_activities and train_data_activities data frames are renamed to 'activity_id'
• The test and training subject data are then extracted into the test_data_subject and train_data_subject data frames
• The column within each test_data_subject and train_data_subject data frames are renamed to 'subject_id'
• The activity column labels are extracted and stored within the activity_labels data frame
• The features column labels are extracted and stored within the features data frame
• The columns in the activity_labels data frame are renamed to something more meaningful (activity_id and activity)
• The columns in the features data frame are renamed to something more meaningful (feature_id and feature)

##Part 2 ###Here, descriptive activity names are used to name the activities in the data set

• A column that contains the test activity description based on the activity_id is added to the test activity data
• Then the id column is removed
• A column that contains the training activity description based on the activity_id is added to the training activity data
• Then the id column is removed
• The test_data_subject data frame is merged with the test_data frame
• The train_data_subject data frame is merged with the train_data frame
• The activities data frame is then merged with the test_data frame
• The activities data frame is also merged with the train_data frame
• The test and train data frames are then concatenated

##Part 3 ###The data set is appropriately labelled with descriptive variable names.

• The columns in the merged data set are renamed based on the feature data frame

##Part 4 ###Extract only the measurements on the mean and standard deviation for each measurement.

• Data frames to hold the means columns and standard deviation columns are separately set up
• The mean measure names only are derived from the existing features list and set as the rows for the mean_col_names data. This is based on mean-based measure containing 'mean()' in the name.
• The standard deviation measure names only are derived from the existing features list and set as the rows for the stdev_col_names data. This is based on standard deviation-based measure containing 'sd()' in the name.
• The mean and standard deviation measures are stored within separate data frames
• Blank data frames are set up for each of the mean and standard deviation measures with the correct number of rows
• The mean data are bound columns together
• The sd data columns are bound together
• The row_num column which was just an initial placeholder to establish the correct number of rows in the data frame is then removed from the column lists in each variable

##Part 5 ###A second, independent tidy data set with the average of each variable for each activity and each subject is then created.

• The data.table package is included in the library
• The resultant data set from part 3 is converted into a data.table so that we can perform some grouping calculations on the data
• A variable is set up to include only the names of the columns for which the means are required
• The means are calculated across all numeric columns and grouped by activity and subject
• The data is then output to file as ordered data