kielejocain / coursera-getdata006

My solution to the Course Project for Coursera's 'Getting and Cleaning Data' course.

"Getting and Cleaning Data" Course Project

Description

This project is my solution to the Course Project for Coursera's "Getting and Cleaning Data" course.

• This README.md file is a general description of the contents.
• The CODEBOOK.md file is a description of the data culled and collated into the tidy_data.txt file, which is the output of the run_analysis.R file if sourced into R.
• All remaining files are the data, as originally formatted and structured, from the following link.

Use

If one were to clone this repository, delete the tidy_data.txt file, open R Studio, set the working directory to the repo, and source run_analysis.R, they would find a fresh copy of tidy_data.txt in the working directory.

Method

1. In an attempt to follow the steps given in the assignment, the run_analysis.R file first loads the data from both the train and test folders and merges them into a single data frame.
2. It then gets the column labels from the features.txt file and sets the resultant vector to names(data). With column names sorted, it uses a combination of grep commands to subset the data to columns containing either mean or standard deviation information.
3. The code next pulls the activity types from the train/y_train/txt and test/y_test.txt files, combines them as the previous data, and makes the column a factor with labels given by the activity_lables.txt file.
4. Though column names were sorted while dealing with Step 2, we'll add here that the code also adds a Subject factor column using the train/subject_train.txt and test/subject_test.txt files, mergeing them as before.
5. Finally, we use an aggregate command on the non-factor columns by the factor columns, running the function mean to get the means in each Activity/Subject factor pair. This data is then written to the tidy_data.txt file as output after the columns the aggregate function adds are appropriately renamed.

Thanks

I hope it meets your approval. Best wishes, and good luck with the rest of the Data Science sequence!