initial commit with index page and some of lesson 01

AUTHORS

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+FIXME: list authors' names and email addresses.

CITATION

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+FIXME: describe how to cite this lesson.

_extras/discuss.md

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+---
+layout: page
+title: Discussion
+---
+FIXME

_extras/guide.md

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+---
+layout: page
+title: "Instructor Notes"
+---
+FIXME

episodes/01-introduction.md

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+---
+title: "Introducing R and RStudio IDE"
+teaching: 30
+exercises: 15
+questions:
+- "What is RStudio and why should I use it?"
+- "What is the difference between R and RStudio?"
+- "How do I get help using R?"
+objectives:
+- "Discuss advantages of analyzing data in R"
+- "Discuss advantages of using RStudio"
+- "Create an RStudio project, and discuss the benefits of working within a
+   project"
+- "Customize RStudio layout"
+- "Able to locate and change the current working directory with `getwd()` and
+  `setwd()`"
+- "Compose an R script file with comments and saved commands"
+- "Be able to define what an R function is"
+- "Locate help for an R function using `?`, `??`, and `args()`"
+- "Check the version of R"
+- "Able to enter a command in the R console (at the terminal)"
+- "List several websites for obtaining R software/packages"
+- "Ask effective questions when searching for help on forums or using web
+  searches"
+- "Research an issue you are experiencing with a package installation on
+  Stackoverflow"
+
+keypoints:
+- "First key point."
+---
+
+## Getting ready to use R for the first time
+In this lesson we will take you through the very first things you need to get
+R working, and conclude by showing you the most effective ways to get get help
+when you are working with R on your own.
+
+>## Tip: This lesson works best on the cloud
+> Remember, these lessons assume we are using the pre-configured virtual machine
+> instances provided to you at a genomics workshop. Much of this work could be
+> done on your laptop, but we use instances to simplify workshop setup
+> requirements, and to get you familiar with using the cloud (a common
+> requirement for working with big data).
+> Visit the [Genomics Workshop setup page](http://www.datacarpentry.org/genomics-workshop/setup/)
+> for details on getting this instance running on your own, or for the info you
+> need to do this on your own computer.
+ {: .callout}
+
+
+## A Breif History of R
+[R](https://en.wikipedia.org/wiki/R_(programming_language)) has been around
+since 1995, and was created by Ross Ihaka and Robert Gentleman at the University
+of Auckland, New Zealand. It was based off the S programming language developed
+at Bell Labs, and was developed to teach intro statistics. See this [slide deck](https://www.stat.auckland.ac.nz/~ihaka/downloads/Massey.pdf)
+by Ross Ihaka for more info on the subject.
+
+## Advantages of using R
+At more than 20 years old, R is fairly mature and [growing in popularity](https://www.tiobe.com/tiobe-index/r/). However, programming isin't a poularity contest. Here are key advantages of
+analzying data in R:
+
+ - **R is [open source](https://en.wikipedia.org/wiki/Open-source_software)**. Of
+   course this means R is free - which is an advantage if you end up at a
+   institution where you would have to pay for your own MATLAB or SAS license.
+   Open source, is important to your colleagues in parts of the world where
+   expensive software in inaccessible. It also means that R is actively
+   developed by a community (See [r-project.org](https://www.r-project.org/)),
+   and there are regular updates.
+ - **R is widely used**. Ok, maybe programming is a popularity contest. Because,
+   R is used in many areas (not just bioinformatics), you are more likely to
+   find help online when you need it. Chances are, almost any error message you
+   run into, someone else has already experienced.
+- **R is powerful**. R runs on multiple platforms (Windows/MacOS/Linux). It can
+  work with much larger datasets than popular spreadsheet programs like
+  Microsoft Excel, and because of its scripting capabilities is far more
+  reproducible. Also there are thousads of available software packages for
+  science, including genomics and other areas of life science.

index.md

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+---
+layout: lesson
+root: .
+permalink: index.html  # Is the only page that don't follow the partner /:path/index.html
+---
+**Welcome to R!** Working with a programming language (especially if it’s your
+first time) often feels intimidating, but the rewards outweigh any frustrations.
+An important secret of coding is that even experienced programmers find it
+difficult and frustrating at times – so if even the best feel that way, why let
+intimidation stop you? Given time and practice* you will soon find it easier
+and easier to accomplish what you want.
+
+Why learn to code? Bioinformatics – like Biology – is messy. Different
+organisms, different systems, different conditions, all behave differently.
+Experiments at the bench require a variety of approaches – from tested protocols
+to trial-and-error. Bioinformatics is also an experimental science, otherwise we
+could use the same software and same parameters for every genome assembly.
+Learning to code opens up the full possibilities of computing, especially given
+that most bioinformatics tools exist only at the command line. Think of it this
+way: if you could only do molecular biology using a kit, you could probably
+accomplish a fair amount. However, if you don’t understand the biochemistry of
+the kit, how would you troubleshoot? How would you do experiments for which
+there are no kits?
+
+R is one of the most widely-used and powerful programming languages in
+bioinformatics. R especially shines where a variety of statistical tools are
+required (e.g. RNA-Seq, population genomics, etc.) and in the generation of
+publication-quality graphs and figures. Rather than get into an R vs. Python
+debate (both are useful), keep in mind that many of the concepts you will learn
+apply to Python and other programming languages.
+
+Finally, we won’t lie; R is not the easiest-to-learn programming language ever
+created. So, don’t get discouraged! The truth is that even with the modest
+amount of R we will cover today, you can start using some sophisticated R
+software packages, and have a general sense of how to interpret an R script.
+Get through these lessons, and you are on your way to being an accomplished R
+user!
+
+\* We very intentionally used the word practice. One of the other “secrets” of
+programming is that you can only learn so much by reading about it. Do the
+exercises in class, re-do them on your own, and then work on your own problems.
+
+
+> ## Prerequisites
+>
+> - **Experimenter's Mindset**: We define the "Experimenter's mindset" as an
+>    approach to bioinformatics that treats it like any other experiment. There
+>    are probably a variety of metaphors we could employ (data are our
+>    reagents, scripts are our protocols, etc.), but the most important idea of
+>    the mindset is to remind you that as a researcher, you need to employee all
+>    of your training in the bench or field to working with analyses. Evaluate
+>    results critically, and don't expect that things will always work the first
+>    time, or that they will always work in the same way.
+> - **Genomics Data Carpentry Instance**: This lesson assumes you are using a
+>   Genomics Data Carpentry instance as described on the
+>   [Genomics Workshop setup page](http://www.datacarpentry.org/genomics-workshop/setup/)
+{: .prereq}

reference.md

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+---
+layout: reference
+root: .
+---
+
+## Glossary
+
+FIXME

setup.md

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+---
+layout: page
+title: Setup
+root: .
+---
+FIXME