---
title: Course Introduction
layout: default_with_disqus
author: Eric C. Anderson
output: bookdown::html_chapter
---

# Course Introduction {#intro-lecture}

## Welcome and Introductions {#intro-welcome}

### Who is Eric and Why is he teaching this course?

* Statistician that specializes in genetic data
* Has used R (or S) since 1998, but didn't really understand it until recently
    + Was more of a C programmer
* Disliked R for most of the last 16 years
* Taught an R course two years ago
    + When I finally learned R, I learned to love it
* Seeing R incorporated into Reproducible Research excites me


### What the heck is reproducible research?

* In computational sciences and data analysis, let's go with __this definition__:
    + _the data and code used to make a finding are available and they
    are presented in such a way that it is (relatively) straightforward
    for an independent researcher to recreate the finding._
* This actually seldom happens.  Consider two interesting articles by Tim Vines:
    + [The Availability of Research Data Declines Rapidly with Article Age](http://www.sciencedirect.com/science/article/pii/S0960982213014000)
    "_of 516 articles published between 2 and 22 years ago...the odds of a data set being extant fell by 17% per year._"
    + [Recommendations for utilizing and reporting population genetic analyses: the reproducibility of genetic clustering using the program structure](http://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2012.05754.x/full)
    "_we reanalysed data sets gathered from papers using the software package 'structure'... 30% of analyses were unable to reproduce the same number of population clusters._"
* Scientific articles have fairly detailed methods sections, but those are typically insufficient to actually reproduce an analysis.
* Scientists owe it to themselves and their community to have an explicit record of all the steps in an analysis done at a computer.


### How should we do reproducible research?

* How do you efficiently record what it is you have done at your computer?
* There would be lots of ways, but we will look at using 4 tools:
    1. R -- open source, free, industry-standard analysis software
    1. RStudio -- open source, free, environment for working with R
    1. git -- open source, free version control software (because research is never really linear when it is happening)
    1. GitHub -- website with tools making it very easy to collaborate with people using git.


## Course Organization {#course-org}

* Weekly format
    + Tuesdays:  R and RStudio
    + Thursdays: How to write reports/articles using Rmarkdown.  Using git and GitHub
* However the course is integrated!
    + Would be hard to just do Tu or just Th.
    + We will use git to obtain and submit R homeworks, etc.
    + This is how it works in real life too...


### Course Website

* Main Page: [http://eriqande.github.io/rep-res-web/](http://eriqande.github.io/rep-res-web/)
* Syllabus:  [http://eriqande.github.io/rep-res-web/syllabus.html](http://eriqande.github.io/rep-res-web/syllabus.html)

Note that you can see the raw rmarkdown source used to write each page.


## Course Philosophy {#course-philos}

### Focus on the practical

* Two years ago my course was focused on R as a programming language
* Having satisfied my curiosity about that, this year I mostly want to teach people how to use R, practically, as a useful tool

### We have a big group!

* Quick show of hands for NMFS Fed, NMFS Contractor, grad student, PCS, MBARI, others?
* How about the range of experience with R and git?
* Great that we have that much interest
* But, it will dictate a few things about the course:
    1. I won't be able to answer everyone's every question
        + "It takes a village"
    1. My vision: we are a dedicated community of researchers that will work together,
    helping one another become fluent in these tools. So:
        + If a practical in-class exercise, or a homework assignment seems easy to you because
        you are already well-versed in R, then take a moment to help someone that is being
        challenged by it!
        + If you are new to all this, find someone in the class that can help you!
    1. Please add comments to the [DISQUS](https://disqus.com/) comment boxes on each page
        + Sign up with a reasonable identifier
        + Let's crowd source solutions to issues that come up for people in the course!
        + Particularly for PC users (which I am not...)
        
### Your contributions are invited!

* Every page of this course is editable
* If you see something that needs changing, you can do it and send me a pull request
* This will make much more sense after a few more weeks.


## Homework/Assignments {#hw-and-assign}

* There will be homework! (As long as I have time to write more...)
* You can't learn these methods without _doing_ and _using_ them
* Amounts of HW should not be overwhelming.
* I won't be grading everything, but:
    + I will establish "peer-review" system of homework
    + Everyone will do homework and "referee" homework
    + Doing this will let us get intimately familiar with how to use git and GitHub.
* Familiar and advanced R users:
    + Please contribute assignments and homework problems
    + Once we have gotten the rudiments of GitHub down I will make a place for you to submit new homework problems
    
### Any Questions?