overview.Rmd

---
title: "R Bootcamp for Biolgists"
---

## Overview

Introduction to the R programming language with a focus on using it for biological data analysis.  The purpose of this course is to teach scientists (students, postdocs, PIs) in the biological and medical sciences to use R for typical data analysis tasks they might encounter routinely.  This includes sequence analysis and other bioinformatics tasks.  No prior knowledge of R is expected and workshop attendees can expect to come away with a skill set that is immediately translatable to their respective data tasks.


## Learning Objectives

At the end of the workshop you will able to:

* Install and update R
* Use the Rstudio IDE
* Understand what CRAN and Bioconductor are and what the differences are between them
* Install and update R packages from CRAN and Bioconductor
* Import a wide variety of data types into R
* Understand the basic data types: integer, numeric, logical, character
* Understand R's basic data structures: vector, matrix, list, data.frame
* Understand basic programming concepts: functions, objects, loops, vectorization, conditionals
* Manipulate data structures by subsetting and indexing
* Understand key base R functions: seq, apply (and friends)
* Manipulate data with dplyr and friends
* Make plots with ggplot
* Find help about any function
* Understand some common R errors and how to deal with them
* Find and evaluate R packages needed for a particular analysis
* Understand the difference between `<-` and `=` and make your own choice about which one to use


## Preparation

Attendees are expected to come with their own laptops and have already installed R and RStudio as well as completed at least one of the following online tutorials. 

 - https://www.datacamp.com/courses/free-introduction-to-r
 - http://tryr.codeschool.com/
 - http://swirlstats.com/
 - http://rforcats.net/

This small bit of preparation will allow us to move quickly through the basics and get to the good stuff.

### Course reference

Main text:  
[R for Data Science](http://r4ds.had.co.nz/) by Garret Grolemund and Hadley Wickham

Secondary reading:  
[Advanced R](http://adv-r.had.co.nz/) by Hadley Wickham

## Course Materials

### Understanding R 

* R History
* Packages
* Assignment
* Environments
* Other important key R functions including basic statistics
* Errors and getting help

### Data Structures

* Vectors
* Lists
* Factors
* Matrices 
* Data frames

### Subsetting

* Ways to subset
* Subsetting operators

### Programming Concepts

* Functions
* Conditionals
* Loops

### Practical data management

* Tidy data
* Pipes: Ceci n'est pas un pipe
* Intro to dplyr and tidyr
* Restructuring data and doing stuff to it
* Regular expressions and stringr


### Data Visualization

* ggplot
* heatmaps
* What makes effective visualizations
* Building up a complex visualization


### Introduction to Bioconductor

* Finding and installing biocondutor packages
* Learning what packages do and how to evaluate them
* Intro to some key data structures: XStringSet, IRanges, expressionset, etc.


### Reproducibility

* Rapid introduction to managing and reproducing your analysis: Rmarkdown, git and github, best practices
* Writing your own functions