This is done as part of the Getting and Cleaning Data course on Coursera, conducted by the Johns Hopkins University.
UCI Machine Learning Repository - Human Activity Recognition Using Smartphones Data Set
Data processing is done by run_analysis.R. The script assumes that the data set is in the dataset directory relative to itself.
The script is divided to 5 main parts (step1() to step5()), according to the steps listed as guidelines for the project:
- Merges the training and the test sets to create one data set.
- Extracts only the measurements on the mean and standard deviation for each measurement.
- Uses descriptive activity names to name the activities in the data set
- Appropriately labels the data set with descriptive variable names.
- 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.
However, in my implementation, I did not strictly stick to the above order. For example, the step4() function is actually a no-op because it has been performed before.
More detailed explanation on steps involved in data processing and the variables can be found in the code book.