Coursera's Getting & Cleaning Data

• Download the zip file in the current working directory
• Unzip the file in the "UCI HAR Dataset" subfolder in the current working directory
• Read the subject_train, X_train and y_train files in train subfolder within "UCI HAR Dataset" folder into separate data frames
• Read the subject_test, X_test and y_test files in test subfolder within "UCI HAR Dataset" folder into separate data frames
• Union these 6 data frames into 3 data frames named subjects.df, features.df and activtites.df
• Read the features.txt and activity_labels.txt into separate data frames named feature.labels and activity.labels
• Remove the special characters (open bracket, close bracket and hyphen) in the feature.labels data frame which represents mean or standard deviation (contains 'mean' or 'std' in it)
• Name the features.df with the clensed values of feature.labels data frame
• Name the subjects.df and activities.df properly
• Merge activities.df and activity.labels by the key column
• Combine features.df, subjects.df and activities.df data frames into a new data frame
• Select only the attributes which are measure of mean or standard deviation (contains 'mean' or 'std' in it)
• Convert the subject id and activity description as factors
• Create the tidy data frame by calculating the mean of all the columns grouped by subject id and activity description
• Write the data of this data frame into tidy.txt file