README.md

Wearable technology is all the rage during this time. Comapnies like Fitbit, Nike, and Jawbone Up have created wearable devices that collect user data such as heart rate, speed, or distance traveled. The data collected can be used in countless ways to perform analyses on subjects using the devices. In the UCI HAR Dataset, various different measurements were collected using a Samsung Galaxy S smartphone.

From the UCI HAR Dataset:

"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."

About this script:

The script "run_analysis.R" reads in the UCI HAR Dataset, manipulates the data and renames the variables according to tidy data principles (see CodeBook.md for more information), and outputs a second tidy dataset titled "tidydata.txt" that summarizes average values of variables from the original dataset by each subject performing a given activity.