---
title: "Project 3: R Package Development and Documentation"
author: "INSERT YOUR NAME(S) HERE"
date: "Due Date Here"
output: html_document
---


# Part 1. R Package Development and Documentation (25 points)

## Instructions

For this portion of Project 3, you are being asked to develop a well-documented, well-tested, and well-explained R package.

This R package should include the following  functions we've written throughout the class:

* `my_t.test`
* `my_lm`
* `my_knn_cv`

Follow the instruction on Lecture Slides 10 to set up the skeleton of your package (5 points for proper setup). Your package should include the following:

1. (5 points) Complete documentation for each of your functions and data. Each function must include `@examples` in the documentation.

2. (15 points) A detailed vignette with examples and the use of all of these functions with the penguins data from the `palmerpenguins` package. You must add and document the `penguins` data to your own package and export it as the object `my_penguins` (with proper credit in the documentation!). Specifically, the vignette should have the following parts:

    a. A tutorial for `my_t.test` testing the hypothesis that the mean `body_mass_g` of Adelie penguins is equal to 4000 (where the alternative is that the mean is less than 4000). Carefully interpret the results using a p-value cut-off of $\alpha = 0.05$.
    b. A tutorial for `my_lm` using `flipper_length_mm` as the independent variable and `body_mass_g` as the dependent variable. Carefully interpret the `flipper_length_mm` coefficient describe the hypothesis test associated with the `flipper_length_mm` coefficient, and carefully interpret the results the `flipper_length_mm` hypothesis test using a p-value cut-off of $\alpha = 0.05$.
    c. A tutorial for `my_knn_cv` using `my_penguins`. Predict output class `species` using covariates `bill_length_mm`, `bill_depth_mm`, `flipper_length_mm`, and `body_mass_g`. 
    
        * Use $5$-fold cross validation (`k_cv = 5`).
        
        * Iterate from `k_nn`$= 1,\ldots, 10$. For each value of `k_nn`, record the training misclassification rate and the CV misclassification rate (output from your function).
        
        * State which model you would choose based on the training misclassification rates and which model you would choose based on the CV misclassification rates. Discuss which model you would choose in practice and why.
        
### Notes

* Your package directory should be submitted as a .zip file to Canvas.
* All code and documentation should follow the style guidelines outlined in class.


# Part 2. Self-Assessment and Reflection (5 points)

Submit answers to the following questions as a .pdf file to Canvas. A brief paragraph for each will be enough.

1. What was the hardest assignment for you and why? Did you learn anything from the experience? Is there anything you would change about the assignment to help your learning?

2. What are two areas in which you think you did well this quarter? What are two areas in which you could have improved?

3. Is there anything you are glad we covered this quarter? Is there anything you wish had been covered in this course? What programming and computing skills would you like to learn in the future?