The purpose of this project to show results from collecting and cleaning data pertaining to human activity recognition using smartphones, a typical example of the trendy and exciting area of wearable computing.
- The raw data =============== The data used were collected from the accelerometers from the Samsung Galaxy S smartphone. These were made available on the UCI Machine Learning Repositorw web site and through Coursera's platform.
For each record in the dataset, the following attributes were provided:
- Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.
- Triaxial Angular velocity from the gyroscope.
- A 561-feature vector with time and frequency domain variables.
- Its activity label.
- An identifier of the subject who carried out the experiment.
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The tidy data =============== This data was generated as a result of cleaning and transforming the raw data, through R code processing, into a format suitable for further analysis. The resulting data was stored in the "TidyData_Wearable.csv" file attached in the present repository. Two additional tidy data for the average measurement values across activities and subjects, are also included in long and wide formats.
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The code book =============== A code book describing the structure of the tidy data file has been attached in the repository. This mentions the names of various fields in the file and their data types.
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The run_analysis R script =============== This R script was used to clean and transform the raw data in order to generate the tidy data. Details of the code appear in the attached run_analysis.R script file.
Leopold Hillah - Wrote the initial version.