michaelklos / gettingandcleaningdata-courseproject Goto Github PK

The purpose of this course project is defined on the Getting and Cleaning Data course assignment page as follows:

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.

As such, this project is composed of the following items: 1. AverageActivity.txt - A tidy data set resulting from the analysis 2. run_analysis.R - The R script leveraged to generate the tidy data set from the input data. 3. CodeBook.md - Describes the variables, the data, and transformations to create the resulting data set 4. README.md (this) file, describing the contents of the Getting and Cleaning Data Course Project.

All of the above files are hosted on GitHub, here.

• Download project files noted above to working directory.
• Downloadand/unzip the input data to data sub-directory in working directory as indicated in [Initial Conditions].
• To generate AverageActivity.txt in working directory, execute the following:

source("run_analysis.R")

• To load an already-generated AverageActivity.txt into R (assumes file in working directory):

library(plyr)
avgActivity <- tbl_df(read.table("AverageActivity.txt"))

The input data initially consisted of 561 attributes across 10299 multivariate, time-series instances. Note that the data-set-indicated attribute names can be found alongside the downloaded dataset in features.txt (part of the original data download, and not part of this analysis package - please see the README.txt file in the input data download for additional information).

For the purposes of this analysis, the compressed datafile containing the input data has been extracted to a data sub-directory so that the directory structure is as follows:

• /Working Directory
  • README.md (this file)
  • CodeBook.md
  • run_analysis.R
  • /data (containing the input data)
    • activity_labels.txt
    • features.txt
    • features_info.txt
    • README.txt
    • /test
      • subject_test.txt
      • X_test.txt
      • y_test.txt
    • /train
      • subject_train.txt
      • X_train.txt
      • y_train.txt

• The author has extensive experience with databases - this was the primary driving reason for leveraging the dplyr/tidyr packages so heavily.
• Much credit and thanks to David Hood for his excellent thread containing insight into this project.
• Please see the individual files for additional details on the data manipulation and resulting fields.

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.