1. Why programming and computing?

We care about teaching you programming and computing skills because they are important skills in the current workforce and are not to be taken for granted. Programming is not just code, it’s a way of thinking and requires problem solving skills that are applicable to other scenarios. You can apply these skills to a range of problems or examples beyond what we will cover in this module.

C: (the hard drive)
  -> my documents (within drive C)
      -> file A (within my documents)
      -> folder 1 (within my documents)
          -> folder 2 (within folder 1)
              -> file B (within folder 2)

3. Opening RStudio for the first time

If you open RStudio you’ll see several windows that organise how information is passed to R and how output from R is presented:

The default RStudio layout can be customised.

• Left: The big window is the Console. This is the interface with R and is the same as using R on its own. (don’t worry about “Terminal”)
• Top right: This window has three tabs: “Environment” - shows you what information is stored in R’s memory, “History” - shows your code history & “Connections” - don’t worry about this one, it’s for connection to database servers.
• Bottom right: This has several tabs. The most important being “Files” - showing you where RStudio is looking at on your computer & “Plots” - shows you any plots you make in R.

When you open a Script, it will appear as a panel in the top left.

You can customise the layout, colour scheme, font and font size of RStudio in Options.
Shortcuts and hotkeys will make your life easier. You can see a list of them under Help. There are only minor differences between Windows and Mac shortcuts (e.g. using Cmd instead of Ctrl).

4. R structure and terminology

There are a number of terms you’ll come across when using R. Here are some basic ones:

R is a statistical environment that consist of packages. Packages are sets of functions that do something to input depending on the instructions described in the function. All your packages are stored in your library (a folder on your computer). When you download R it comes with a basic set of packages as default (base packages). Some of these base packages are loaded every time you open R.

R is CaSe SenSitiVe. Meaning that library() is different to Library().

install.packages("<insert name of package>") # installs a package

5. My first script

The greatest advantage of RStudio is that it allows you to write scripts. These are files ending in .R that are created and opened by RStudio. R itself cannot open, read or create scripts.

Scripts are text documents of code that you can save on your computer and open later. Scripts are instructions to give to R, but can also serve as a record of what you’ve done (transparency & reproducibility).

Before RStudio, we had to save our code in notepad or similar then copy and paste it into R (believe me it was a pain). Now we can do the same but in one click.

You can create a new script under File -> New file -> Script (Ctrl+Shift+N) or click the white square with a green and white plus sign in the top left corner.

Run your code from scripts, rather than the console. It is easier to spot and fix mistakes!

Loading packages

One of the first things you may need in an R script is to load the packages you will use. Some base R packages are automatically loaded.

To use a package in R, you need to call it from R’s library. The function to load a package is:

library(<insert name of package>) # loads a package

A package we will see later in the module is MASS which contains datasets you can use at home to practice the statistical tests covered in the lectures. MASS is a base R package but is not automatically loaded.

Running data() or data(package = .packages(all.available = TRUE)) will show the list of available built-in datasets. Some of these will be relevant to biological sciences, others are more general.

Once a package is loaded, entering the name of a dataset will display the entire dataset.

Activity

Type library(MASS) into your script.

• Put your cursor on the line you want to run then press Ctrl+Enter or click Run
• You can run multiple lines by highlighting the relevant lines
• You do not need to highlight a single line to run it
• You can run the entire script from beginning to end using the shortcut Ctrl+Alt+R

Shortcuts will make your life easier.

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Run the library(MASS) code. You have just run your first line of code.

R will load the package MASS in the background. You can check this in RStudio under the Packages tab where there will now be a tick in the box next to MASS.

Try calling one of the MASS datasets, like Rabbit, to view the entire dataset. To call a dataset, type in the name and run the code.

You can see the descriptions of each dataset by calling help(<dataset name>), e.g. help(Rabbit) will tell you it describes the blood pressure of rabbits before and after a drug treatment.

Activity

We will use the package learnr later in the module.

This code will install the package but there is one mistake - fix the mistake and install the package: install.packages(learnr)

Install any other packages you are asked to. If it worked you should see something under the Tutorial tab (next to Files, Plots etc.) in RStudio.

When you’ve installed the package, modify your code to install the package remotes. You now should have installed two packages.

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6. Working directories in R

Remember directories? Typing out whole addresses starting from the hard drive is annoying. There is a short cut if we use working directories.

The working directory in R is the default directory R will look in first. We can then use directory addresses that are relative to this default address to call files.

For example, following this directory structure:

C:
  -> documents
      -> file A 
      -> folder 1 
          -> folder 2 
              -> file B

If the default directory is C:/, then the address for file B is C:/documents/folder1/folder2/fileB.
But if we set folder 1 as the working directory C:/documents/folder1/, then we can use the relative address for the file: folder2/fileB. This way we don’t have to type C:/documents/folder1/ every time.

Navigating through directories using addresses can be confusing.
Another useful command is .. which tells the address to go up a directory. For example, if folder 2 was the working directory C:/documents/folder1/folder2 and we wanted to access file A, then we need to tell the computer to go up two directories. The relative address for the file is: ../../fileA which means that the computer is now looking in the documents folder. In contrast, the relative address for file B is even shorter, fileB, since folder 2 is already the working directory.

RStudio has a default working directory.
You can see which working directory is the default directory in RStudio on the “Files” tab. The code to see the working directory in R is:

getwd() # GET Working Directory. There is nothing in the brackets

Changing working directories

You can change the default RStudio directory under Tools -> Global Options but on a daily basis, changing the directory temporarily under Session -> Set working directory is more useful.

Setting the working directory using the drop down menu.

The R function to change your working directory is:

setwd("<insert directory address here>") # SET Working Directory

For example, setwd("~/") will change your working directory to your default directory (called Root in computing terms). It is good practice to keep similar files in the same folder. Otherwise you will need to specify the full address when you call a file that exists in another folder and it can get confusing if your files are all over the place.

Setting the working directory using R code is better for writing scripts.

Expectations

We expect you to know how to change working directories and tell R where to find files through relative addresses because that is how we import and export data to and from R.
Think of directories and addresses like postal addresses - if your address is incorrect the postman (R) wouldn’t know where to go to pick up your parcel (file).
If R cannot find a file, check the address or the working directory is correct.

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Rubber ducks

Problem solving is an important skill. If your code is not working then it’s likely that you’ve made an error somewhere - and that’s OK! It’s all part of learning how to program and there’s no shame in not getting it right the first time. The first thing is to retrace your steps and identify whether you’ve missed a step or misspelled something.

Learning to problem solve independently is not something you learn by reading or something you can be taught. It is a skill you have to learn by doing, which means having a go yourself before seeking help from others. Make a habit of asking for help as your last option because for the assessment you will need to use R and troubleshoot independently.

If you can’t identify the problem, then you need to be able to describe your problem to another person. The other person needs enough information and context to understand what you hope to achieve and what you’ve done so far to suggest a solution. Sometimes the solution isn’t immediately obvious so it may require some trial and error - and that’s all part of the process too! It’s really hard to fix someone’s coding problem without context.

Talking through your problem is called rubber ducking in computer science - talking through your thought process to a rubber duck may help you realise the solution.

Final checklist

You should now have R and RStudio installed on your computer, installed some packages and updated your R settings if needed.

You are now ready for the rest of the module.

• Do you know how to navigate computer directories and addresses to find files or folders?
• Do you know how to set up scripts and directories in RStudio?

We’ve introduced some basic computing concepts: scripts, directories and commenting.

We’ve also introduced some fundamental R functions. You will need these in the future:

• install.packages
• library
• getwd
• setwd