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###CodeBook

###DataSource

The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. This dataset is derived from Human Activity Recognition Using Smartphones Dataset.

##Source Files:

  • 'README.txt'
  • 'features_info.txt': Shows information about the variables used on the feature vector.
  • 'features.txt': List of all features like mean ,std,coeff of accelerometers.
  • 'activity_labels.txt': Links the class labels with their activity name,contains the list of activities.
  • 'train/X_train.txt': Training set,Records for all the trained test variables in Features.
  • 'train/y_train.txt': Training labels.
  • 'test/X_test.txt': Test set., Records for all the variables in Features.
  • 'test/y_test.txt': Test labels.
  • subject_train.txt: will give you the list with subjectID from 1to 30 , these ID belong to the group of people to whom the survey conducted with respect to training. *subject_test.txt: will give you the list with subjectID from 1to 30 , these ID belong to the group of people to whom the survey conducted with respect to test.

###Transformations

  • Training and test data set rows were merged and then a unified data set created from the source files.
  • Measurements were extracted for mean, standard deviation for each measurement.
  • variable/column names were labeled with descriptive cleaner names.
  • Results were output as an indepenent tidy data set at TidyData.txt

###run_analysis.R implements the above steps:

  • Require reshape2 librareis.
  • Load both test and train data which contains 2947 and 7352 respectively.
  • Load the features and activity labels.
  • Extract the mean and standard deviation column names and data.
  • Merge data set.
  • Melt the dataset and cast to get the average of each variable for each activity and each subject.

New Variables created as part of this analysis

  • activity_id : Id for all the activities
  • activity_name: name of the activities
  • subject_id: id for all the subjects from 1:30
  • mergeddata: contains merged data of Test and training dataset.