How this is going to go down

We are going to cover the material in a different way than you would get taught a statistics course, and a different way than you would get taught a programming course.

We’re going to start pretty slow, and slowly ramp the pace up. The idea is to get you to a point where you can continue learning yourself. It is not possible to learn to program in a day. It will take time and practice. Don’t wait until the assignment is due, or the conference is next week.

If you get lost in the material at the second half of the day, don’t despair. What we cover in the first half will actually account for most of what you need to do (apart from statistics and plotting). If you get lost in the first half make sure you speak up.

We’re going to focus on concepts, best practices and workflow almost as much on getting you going with the syntax and commands. R is simply too large to teach in just one day (or the three days that the full course runs for), and so my main hope is that you get enough of a flavour of it to continue the learning process yourself. I probably have more material here than we’ll get through today, and I feel that this really just scratches the surface.

Learning R can be frustrating

• Learning R is not necessarily hard, but not necessarily easy either. Different people will make the logical connections faster than others, and until it “clicks” it may seem like a battle.
• Ask questions as you are unclear and we’ll help make those connections faster.
• It is a programming language
  • don’t think of it a statistical program that you use from a command line
  • think of it as a programming language that happens to have a lot of statistical functions.
• The big challenge is going to be bridging between the nebuluous “what I think I want to do” and the precice “computer – this is what I need you to do”.
• It is easy to underestimate how precice instructions need to be. We’ll see examples, but things like (“take the mean of the leg lengths by species” are intuitively obvious to us, but can be hard precicely convey to a computer.
  • what if there are missing data?
  • does it matter if there are different numbers individual per species?
  • does it matter if there are different sized legs on each species (which leg?)
  • what happens if someone gave ranges for some of the leg measurements, rather than one number?
  • what about subspecies?
• There is a good Douglas Adams quote about this: “If you really want to understand something, the best way is to try and explain it to someone else. That forces you to sort it out in your mind. And the more slow and dim-witted your pupil, the more you have to break things down into more and more simple ideas. And that’s really the essence of programming. By the time you’ve sorted out a complicated idea into little steps that even a stupid machine can deal with, you’ve learned something about it yourself.” (from Dirk Gentley’s Holistic Detective Agency).

RStudio

We’re going to focus on using RStudio for two reasons:

1. It makes my life easier: you are all using the same tools and we’ll be able to help you with your problems faster.

2. It will make your life easier: it’s got lots of features that help people, especially beginners. It will help you organise your work, develop good workflows. On the other hand, it’s not very intrusive and if you use a different interface (such as the plain R interface that you installed) it will feel very similar.

Getting started with RStudio

Load R studio however you do that on your platform.

RStudio has four panes:

1. Bottom left – this is the R interpreter. If you type code here, it is “evaulated” so that you get an answer.
2. Top left – editor for writing longer pieces of code.
3. Top right will tell you things about objects in the workspace. We’ll get to this soon, but this will be things like data objects, or functions that will process them. It is completely unrelated to the file system. The “History” tab will keep an eye on what you’ve done.
4. Will display files, plots, packages, and help information, usually as needed.

We’ll do everything in a project, as that will help when we get some data.

• “Project”: “Create Project…”
• choose “New Project, (start a project in a new directory)”.
• Leave the “Type” as the default.
• In the “Directory name” type the name for the project (in our case g2g might be a good name).
• In the “Create project as a subdirectory of” field select (type or browse) for the parent directory of the project. By default this is probably your home directory, but you might prefer your Documents folder.

The RStudio window morphs around a bit, and the top left pane will disappear.

In the bottom right panel, make sure that the “Files” tab is selected and make a new folder called data. I strongly recommend keeping a directory like this in every project that contains your data. Treat it read only (that is, write once, then basically don’t change). This may be a large shift in thinking if you’ve come from doing data analysis and management in Excel.

In more complicated projects, I would generally have a folder called R that contains scripts and function definitions, another called figs that contained figures that I’ve generated, and one called doc that contains the manuscript, talks, etc.

We’re going to spend a bit of time using a data set. You can download this from here and put it into that directory. Download this file, open the g2g/data folder and move it there (if you click More: show folder in new window, you’ll get a file browser window opening in about the right place). Similarly, also grab this file and put it in the data directory too.

To make sure everything is working properly, in the console window type

1

file.exists("data/seed_root_herbivores.csv")

which should print

1

[1] TRUE

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