Coursera / Johns Hopkins Getting and Cleaning Data Course Project Assignment
The goal of this assignment is to prepare tidy data that can be used for later analysis based on 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.
Source: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
This project depends on the following R packages:
The original data set used for this project was downloaded from the following URL:
https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
In order to get the tidy dataset you should download the files and put it inside current working directory (features.txt, activity_labels.txt, test and train... should be in working directory)
The main file of this project is run_analysis.R. There are two main functions on this file:
- tidyDataSet: parse test and training data to a tidy dataset containing only mean and standard deviation for each measurement. It also writes a file to the current workspace called tidy.txt with cleaned data
- averages: returns a new data set based on tidyDataSet, with the average of each variable for each activity and each subject.
You can take a look at which steps were done to get this clean data on CodeBook.md