https://github.com/awjudd/coursera-getting-and-cleaning-data

For the "Getting and Cleaning Data" course project
https://github.com/awjudd/coursera-getting-and-cleaning-data

Last synced: 9 days ago
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For the "Getting and Cleaning Data" course project

• Host: GitHub
• URL: https://github.com/awjudd/coursera-getting-and-cleaning-data
• Owner: awjudd
• Created: 2015-11-20T02:48:51.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2015-11-21T14:04:02.000Z (almost 9 years ago)
• Last Synced: 2024-10-12T10:44:04.774Z (about 1 month ago)
• Language: R
• Size: 89.8 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Getting and Cleaning Data - Course Project

This repository was built for the course project of the "Getting and Cleaning Data" course at Coursera.  This is the third module in the Data Science specialization.

## Project Summary

The goal of this project is to create a tidy data data set with the help of R to analyze experimental results which were captured in the [Human Activity Recognition Using Smartphones] (http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones) study.

The `run_analysis.R` script should be run on the [data] (https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip) and it will complete the following steps to transform the data into something that we are able to glean information out of.

 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.

## Repository Information

This repository contains 3 main files:

 - `run_analysis.R` - This script is used to build up and perform the analysis on the required sample data.

 - `tidy.txt` - This is the final output from the `run_analysis.R` script.  It contains a cleansed version of the sample data.

 - `CodeBook.md` - Contains the definitions of each of the columns in our generated `tidy.txt` file.

## Please Note

 - If you do not have the `reshape2` plugin installed, the `run_analysis.R` file will automatically install it.

 - If you do not have the data downloaded then it will automatically download the files so it can work on them.