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View Code? Open in Web Editor NEWGetting and Cleaning Data
Getting and Cleaning Data
# Getting and Cleaning Data Course 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. # Dataset 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: # Course Project 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 activity names. * Creates a second, independent tidy data set with the average of each variable for each activity and each subject. # Steps to work on this course project Download the data source and put into a folder on your local drive. You'll have a UCI HAR Dataset folder. Put run_analysis.R in the parent folder of UCI HAR Dataset, then set it as your working directory using setwd() function in RStudio. Run source("run_analysis.R"), then it will generate a new file tiny_data.txt in your working directory. # Here are the data for the project: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip You should create one R script called run_analysis.R that does the following.
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