wenjun0303 / gcdata_project Goto Github PK

The purpose of this project is to demonstrate students' ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for future analysis.

This repository contains the following files:

• readme.md, this file, which provides an overview of this project
• codebook.pdf, which contains the descriptions of the data set
• tidy.txt, which contains the tidy data set generated by this project
• project.md, which contains the details of executions
• run_analysis.R, the R script used to generate the tidy data set

One of the most exciting areas in all of data science right now is wearable computing. Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users. The data linked to from the course website represent data collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained here.

Data for the project is available here.

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.

The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain. See 'features_info.txt' for more details.

Training and test data were first merged together to create one data set, then the measurements on the mean and standard deviation were extracted for each measurement (79 variables extracted from the original 561), and then the measurements were averaged for each subject and activity, resulting in the final data set.

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 tidy.txt was created using R version 3.4.2 (2017-09-28) -- "Short Summer" on macOS High Sierra Version 10.13.1.