Biochemistry R training

22 February 2016

Overview - workshop

Monday Session 1 - 9:30am-12:30pm (Mik/James)
- R/RStudio overview
- Data organisation & import
Monday Session 2 - 1:30pm-4:30pm (Murray/Tom/Tanya)
- Introduction to R
Tuesday Session 3 - 9:30am-12:30pm (Murray/Elizabeth/Tanya)
- Data manipulation & visualisation
Tuesday Session 4 - 1:30pm-4:30pm (Mik/James)
- Reproducible research with Rmarkdown

Reason 1: we are offering a workshop
Reason 2: you think you need to know the things we are teaching
Motivation behind both:
- our disciplines are becoming much more "data-centric"
- relevant analytic tools are always evolving, so are not available in canned, easy-to-use software applications
- there is movement internationally towards increasing the "digital literacy" of early-career researchers through formal training workshops outside the current academic curriculum.

In order of appearance:

We will be teaching you very basic concepts in data organisation, manipulation and visualisation
We are using the R language, which we interact with through the RStudio interface
Some of the material we are using has come from the following sources:
- Data Carpentry: http://www.datacarpentry.org/
- Software Carpentry: http://software-carpentry.org/
Both of the above sites are excellent sources of information if you are looking to improve your ability to perform basic data analysis, or get more into programming.

Etherpad is a tool from Mozilla that allows real-time collaborative document editing within a browser.
- Workshop Etherpad link
We can use Etherpad during the workshop for lots of things:
- collecting attendance info
- taking notes
- asking questions
- sending information
You can set up your own Etherpad (note that anyone with the link can see it) via Mozilla:
- https://public.etherpad-mozilla.org/

R is a programming language for statistical analysis
- based on the S language written by John Chambers in the 1980s.
- developed under an open source (i.e., free) model at the University of Auckland by Ross Ihaka and Robert Gentleman
Rapidly grew to be the preferred tool for statisticians around the world
- international team of core developers
- thousands of contributing programmers from all over the world
Now used for data analysis in many fields.

R provides an attractive and (somewhat) simple platform for data manipulation and analysis:
- Free Open Source Software (FOSS) from an international developer community
- Hugely extendable through downloadable packages
- For a lot of people, R is all they will ever need for data manipulation, analysis and visualisation
R also provides a good environment for learning basic (and advanced) programming concepts.

The free RStudio application provides an "integrated development environment" (IDE) for working in R.

Open RStudio and spend some time getting familiar with the layout:
- Console: type commands here
- Source: save your code here
- Environment/History: session info
- Files/Plots/Packages/Help/Viewer: everything else :)
You can write commands/code in the source pane, and then "submit" it (i.e., run it) in the console by hitting Ctrl+Enter.
Comments start with # and are ignored by R

# This is a comment

# This adds 3 + 5
3 + 5

## [1] 8

# This adds 3 + 2 and assigns it to an object called "a"
a = 3 + 2

# This shows the value of "a"
a

## [1] 5

# This also adds 3 + 2 and assigns it to an object called "a"
a <- 3 + 5
a

## [1] 8

# This takes the object "a", and adds 10 to it
a + 10

## [1] 18

# This calculates the square root of "a"
# sqrt is called a "function" - we'll use functions a lot
sqrt(a)

## [1] 2.828427

While it is fun to use R as a giant calculator, that's not going to help (much) with your research.
We want to do some data analysis in R
- First, we need data
- Good, clean, well-organised data
Go James, go!