The Course project requires to create a tidy data set from the data collected from accelerometers of Samsung galaxy s smartphones. The process requires to load, extract and the data sets to create the tidy set asked.

The run_analysis.R script if run on the data given, will produce a tidy data with the average of each variable for each activity and each subject.

The steps taken in the run_analysis.R script to achieve this goal are as follows:

• Load the libraries reshape2 and dplyr as their functions would be used
• Load all the data sets relevant to the train and test data sets.
  • Xtrain, Xtest, Ytrain, Ytest, subjecttrain, subjecttest, features, activitylabels
• Bind the subjects and activities of test and train data sets to their respective main data sets
• Using features which contains the variable names, name both the train and the test data set
• Merge both the train and test sets using rbind
• Now, use the activity numbers in the merged set and use the match function to create a factor variable having the activity labels for all the measurements.
• Bind the new activity variable with the merged data set and remove the old activity numbers. Also, name the subject and activity columns
• Now, subset the mean and standard deviation measurements from the merged data set and create a new data set, including the activity and subject columns
• Use the melt function to melt the extracted set setting subject and activity as the ids
• Order the data set using rearrange by subject and activity
• Using the dcast function, create a wide tidy data set and also averaging the values of each variable for each subject and each activity