You should create one R script called run_analysis.R that does the following.
- 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.
- Download and unpack the content of the UCI HAR Dataset into your work directory.
- Load the label codes key from activity_labels.txt
- Load the feature key from features.txt
- Determine the indices of desired features (those containing -mean() or -std())
- Load the training and test data sets and only retain data columns determined by indices from step #3
- Merge the training and test data sets
- Replace label codes in the dataset with text labels determined by step #1
- Reshape data to use label and subject as identifiers
- Produce a tidy data set (tidy_means.txt) with the average of each variable for each activity/subject combination
- Produce CodeBook.md with a list of column names (which were taken from features.txt)
- For descriptions of data types and how data was collected, check these files included with the original data: README.txt, features_info.txt