##Goal
Companies like FitBit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users. The data linked are 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
The data is available at:
https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
The aim of the project is to clean and extract usable data from the above zip file. 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.
run_analysis.R : the R-code run on the data set
Tidy.txt : the clean data extracted from the original data using run_analysis.R
CodeBook.md : the CodeBook reference to the variables in Tidy.txt
README.md : the analysis of the code in run_analysis.R