Getting and Cleaning Data Course Project - Merging and Organizing Data
Project Description The purpose of this project is to demonstrate ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis.
The project requirements are the submittal of the following:
1) a tidy data set as described below,
2) a link to a Github repository with script for performing the analysis,
3) a code book that describes the variables, the data, and any transformations or work that you
performed to clean up the data called CodeBook.md.
4) a README.md in the repository with scripts. This repository explains how all of the scripts work and
how they are connected.
About the data 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 smart-phone.
A full description is available at the site where the data was obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
Project data https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
R script requirements The R script called run_analysis.R should do the following. 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.