This repository consists of the files:
tidy_data.txt(output data set).CodeBook.md(contents of the data set).run_analysis.R(the R script that was used to create the data set)README.md(overview of the data and its creation).
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 used in this project was collected from the accelerometers from the Samsung Galaxy S smartphone.
The goal is to prepare tidy data that can be used for later analysis.
The source data set was obtained from the Human Activity Recognition Using Smartphones Data Set:
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.
File tidy_data.txt was created by running the script run_analysis.R on R version 3.3.3 (2017-03-06) on Windows 10 (64-bit).
The R script in the repository transforms the source data set by applying the steps:
- Merging the training and the test sets to create one data set.
- Extracting only the measurements on the mean and standard deviation for each measurement.
- Using descriptive activity names to name the activities in the data set
- Appropriate labeling the data set with descriptive activity names.
- Creating a second, independent tidy data set with the average of each variable for each activity and each subject.
Description of variables, the data, and any transformations or work that was performed to clean up the data are presented in CodeBook.md.