Skip to content

Latest commit

 

History

History
52 lines (34 loc) · 3.09 KB

README.md

File metadata and controls

52 lines (34 loc) · 3.09 KB
Raw

README

Purpose

The goal is to prepare tidy data that can be used for later analysis.

  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.

Background

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

I have created one R script called run_analysis.R that does 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.

Citation

[1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012

http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

run_analysis.R

You must have already downloaded the dataset and unzipped it then setwd() to the directory containing the test and train files.

  1. Merges the training and the test sets to create one data set using rbind().
  2. Extracts only the measurements on the mean and standard deviation for each measurement using grep
  3. Uses descriptive activity names to name the activities in the data set using the activity names.
  4. Appropriately labels the data set with descriptive variable names, mostly from features.txt. The resulting dataset is saved under the variable joined2. You can look at it with View(joined2) provided you are using RStudio
  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 and exports the data into tidydata.csv

See the comments throughout the code or contact me if you have any confusion. -H