Coursera/JHU project: Getting and Cleaning Data Course

by: Steven Balzer
date: 2021-02-13

This is the peer-graded assignment for week 4 of the 'Getting and Cleaning Data' Course.

The purpose of this assignment is to demonstrate the ability to collect, work with, and clean a data set.

This approach uses the wide form as mentioned in the rubric as either long or wide form is acceptable.

The following files are included in this repository.

README.md

Lists and defines the different components of this project. (The file your are currently reading.)

CodeBook.md

Defines all of the variables of the resulting data set, the original data source, and outlines the transformations made to the original data sets.

import_tidy_data.R

R script commands used to import and view the submitted file, 'measurement_means.txt', for this assignment.

run_analysis.R

R script file that contains all of the transformations needed to recreate the tidy data set in the wide form as mentioned in the rubric. You can either run it as a function with no arguments or as individual commands. A data frame is returned and a text file, measurement_means.txt, is written to the working directory. In order to successfully run, the following prerequisites must be met:

1. Pre-installation of 'reshape2' library.
2. This script must run in the same folder as the unpacked source zip file (link). Once the source data is unzipped, the working directory needs to contain the following folder structure/file listing before running the script:

• [working directory]
  • run_analysis.R
  • UCI HAR Dataset
    • features.txt
    • activity_labels.txt
    • test
      • subject_test.txt
      • X_test.txt
      • y_test.txt
    • train
      • subject_train.txt
      • X_train.txt
      • y_train.txt

Citation

• 'Part 6: Getting and Cleaning the Assignment', by David Hood, as posted online (link).