bvandgrift / coursera-gcd-final

final submission for the getting and cleaning data course

Getting and Cleaning Data (GCD): Course Project

This repo contains the final project implementation for Coursera's Getting and Cleaning Data course.

Assignment Specifics

The run_analysis.R file 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.

On Tidiness

To produce tidy data, I'm depending on Wickham's expression of Codd's 3rd Normal Form, as follows:

1. Each variable forms a column.
2. Each observation forms a row.
3. Each type of observational unit forms a table.

In particular, I'm looking for the five most common problems:

1. Column headers are values, not variable names.
2. Multiple variables are stored in one column.
3. Variables are stored in both rows and columns.
4. Multiple types of observational units are stored in the same table.
5. A single observational unit is stored in multiple tables.

In these data, there are two sets (test and train), which have been disassembled into three .txt files each. In the test set, we have:

1. subject_test.txt - identifies the subject who performed the activity for each window sample. Its range is from 1 to 30
2. test_X.txt - test set
3. test_y.txt - test labels

In the train set, we have:

1. subject_train.txt - identifies the subject who performed the activity for each window sample. Its range is from 1 to 30
2. train_X.txt - training set
3. train_y.txt - training labels

Code Book

The code book for this assignment can be found at the root of this repository, as CodeBook.md. link

Acknowledgement

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