Project for Getting and Cleaning Data

Author: Sriram (https://github.com/sri12ram/GettingAndCleaningData)

Overview and Objective of the project

The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis. You will be graded by your peers on a series of yes/no questions related to the project. You will be required to submit: 1) a tidy data set as described below, 2) a link to a Github repository with your script for performing the analysis, and 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. You should also include a README.md in the repo with your scripts. This repo explains how all of the scripts work and how they are connected.

One of the most exciting areas in all of data science right now is wearable computing - see for example this article . 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 smartphone. 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

Here are the data for the project:

https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

You should create one R script called run_analysis.R that does the following
Merges the training and the test sets to create one data set
Extracts only the measurements on the mean and standard deviation for each measurement
Uses descriptive activity names to name the activities in the data set
Appropriately labels the data set with descriptive variable names
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.

Scripts achieves above in code chunks overall following below logic

Reads the test datasets
Reads the train datasets
Merges the test and train datasets
Merge the key variables (subject & activity) into the dataset
Read the activity and feature labels (from the source datasets)
Extracts only the measurements on the mean and standard deviation for each measure
Names descriptive variable names based on the feature & activity labels read earlier
Groups by on the subject and activity and calculate averages
Write into an output file

Steps to run this project

Place the UCI HAR Dataset folder in your working directory. This folder should contain the features datasets as well as the test and train directories and datasets
The script uses dplyr package. Please ensure this is installed
Run the R script run_analysis.R

Outputs produced

Tidy dataset file body_activity_measures.txt
Output file created with below code

write.table(data.frame(bodyActMeasures), file = "./body_activity_measures.txt", 
            row.name = FALSE, 
            quote = FALSE)

Associated files in the project

This README file README.md
R script run_analysis.R
Output file body_activity_measures.txt
Codebook codebook.md describing the above output dataset

Points to note

Mean and Std variables each number 33 (total of 66)
2 variables ot denote the keys - subject and activity
Output has total of 68 columns
Descriptive variables names provided to some extent based on the limited understanding of the source variables

sri12ram / GettingAndCleaningData