Objective List out all the relevant data, documents, scripts to prepare tidy data set as directed by the course as:

1. Merges the training and the test sets to create one data set.
2. Extracts only the measurements on the mean and standard deviation for each measurement.
3. Uses descriptive activity names to name the activities in the data set
4. Appropriately labels the data set with descriptive variable names.
5. 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.

The dataset includes the following files:

'README.txt' 'features_info.txt': Shows information about the variables used on the feature vector. 'features.txt': List of all features. 'activity_labels.txt': Links the class labels with their activity name. 'train/X_train.txt': Training set. 'train/y_train.txt': Training labels. 'test/X_test.txt': Test set. 'test/y_test.txt': Test labels.

Data source https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

Supportive Document Codebook.md - Lists out the detailed data analysis process steps

Scripts run_analysis.R - R program data preparation scripts

How 'run_analysis.R' performs the prescribed cleansing and prepares the tidy data set?

Process 1 Download data files, merge the training and test sets to create one full data set Load the training and test data files, Merge as:

• x_train.txt, y_train.txt and subject_train.txt into ‘trainingdata’ data frame
• x_test.txt, y_test.txt and subject_test.txt into ‘testdata’ data frame
• Combine trainingdata’ and testdata’ data frames into single data frame - sensordata

Process 2: Extracts only the measurements on the mean and standard deviation for each measurement

• Label data frame columns
• Extract ‘mean’ and ‘std’(standard deviation) measurements

Process 3: Uses descriptive activity names to name the activities in the data set

• Join ‘sensordata’ data frame with ‘activities’ by common column name “ActivityIdentifier” , and associate descriptive names

Process 4 Appropriately labels the data set with descriptive variable names.

• By using gsub, substitute label acronyms with expanded descriptive names

Process 5 Creates a second, independent tidy data set with the average of each variable for each activity and each subject.

• By using ddply function, compute average value of each variable, each activity and subject

R Program environment

1. Require plyr package
2. Working directory is set to the unzipped data file directory
3. R version platform x86_64-w64-mingw32
   arch x86_64
   os mingw32
   system x86_64, mingw32
   status
   major 3
   minor 1.2
   year 2014
   month 10
   day 31
   svn rev 66913
   language R
   version.string R version 3.1.2 (2014-10-31) nickname Pumpkin Helmet