version_control_training.Rmd

---
title: "Version Control Training"
author: "Ghislain Nono Gueye, Ph.D."
date: ""
output: pdf_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## The Project

Develop a suite of functions (i.e. a package) that will help professors in their class-management tasks (e.g. computing grades, visualizing class performance, ...).


## Guidelines

- The **activities** below will make you produce new code and, in many cases, alter it.

- If you come up with ideas to improve the code in ways that are not specified in the instructions, feel free to do it and commit your changes.

- Push your local repo to Github at the end of each activity.


## Activity 1 (learning goals: local repository, remote repository, commit, push, diff)

- Create a function which assigns a letter grade based on a numeric grade (`assign_letter_grade()`).

	- the first version of the function should use `if()`
	
	- the second version of the function should use `cut()`
	
	- the final version of the function should use `dplyr::case_when()`

- Push your local repository to Github.

- Visualize the **diffs** between your commits.


## Activity 2 (same learning goals as activity 1)

- Create a function to impute `NA` values.

	- The first version of the function should be on a single vector such that it has to be used with several `mutate()` functions.
	
	- The second version of the function should used a scoped version of `mutate()` (i.e. `mutate_*()`).

	- Push your local repository to Github.

	- Visualize the **diffs** between your commits.


## Activity 3 (learning goals: branch, pull request, merge)

- Create a function which computes weight averages (`compute_weighted_average()`).

	- The first version of the function should use a pure base `R` approach (with (a1+a2+a3)/3).
	
	- A collaborator on the project must fork the project improve on it and send a **pull request**. Improvements must make use of `rowMeans()`.
	
	- The owner of the master branch must analyze the code and merge it.
	
	
## Activity 4 (learning goals: branch, pull request, merge)

- Create a function to drop the lowest n scores in a group of evaluations (`drop_n()`).

	- Version 1: Make use of `apply(..., 1, ....)
	
	- Version 2: Make use of `transpose()` and `pmap()`.
	
	- Version 2: