2.1 Mac

2.2 Windows

2.3 Linux

• R is part of many Linux distributions, you should check with your Linux package management system.
• If you don’t have it, have a look here https://cloud.r-project.org/ for your distribution.

2.4 Chromebook/Android Tablet/iPad (Actually any computer)

A chromebook doesn’t really allow you to download software. It wants you to work on the cloud. So lets do that.

• On a web browser open in a new window
• Log in or sign up
• Create a new project (Blue New Project button)
• On the cloud you’ll first have to upload (bottom right pane) the fish data before you import it (see below).

2.5 University PCs (for when you want to work in the computer rooms)

5.1 Using R as a ridculously overpowered calculator

In the console window, type some mathematical operations. I’ll get you started.

2 * 3

[1] 6

sin(2 * pi)

[1] -2.449294e-16

Try adding subtracting and dividing. What does log do? Hint: not what you think.

5.2 Creating variables

An important skill you need is creating variables. At its simplest, I want x to be equal to 2.

x <- 2

Whats with <-? <- is called the assignment operator. Why don’t we use =? Short answer, thats just the way R is. Long answer, if you’re interested.

Bit more complicated, make y equal 0,2,4,6,8,10

y <- seq(0, 10, 2)

You just used a function. Its called seq. Want to find out about it? Use your googlefu skills. More and more doing the practicals, we won’t give you the answers. Instead, we’ll expect you to find them yourself. Why? Because thats how everyone does analysis. Once you figure that out, you’ll realise that with the basics we are teaching you and the ability to look things up, no analysis or complicated data visualization is beyond your abilities.

Task: Calculate z which is the product of x and y. (Hint: In mathematics, a product is the result of multiplying)

Blackboard MCQ: z is a vector with six values. What is the highest value?

5.3 Comments

Comments are remarks in a program that is intended to help human readers understand what is going on, but are ignored by the computer. Comments in R start with a # character and run to the end of the line. Why do we use comments? To remind ourselves or to tell our collaborators what a line of code was meant to do. Remember the adage, we write comments for the next idiot to read the code, because it will probably be us.

demo(graphics)  # Note for students, run this comment its cool. 
# Also note the computer will completely ignore what comes after the #.  Let me
# demonstrate: It was the best of times, it was the worst of times, it was the
# age of wisdom, it was the age of foolishness, it was the epoch of belief, it
# was the epoch of incredulity, it was the season of Light, it was the season
# of Darkness, it was the spring of hope, it was the winter of despair, we had
# everything before us, we had nothing before us, we were all going direct to
# Heaven, we were all going direct the other way – in short, the period was so
# far like the present period, that some of its noisiest authorities insisted
# on its being received, for good or for evil, in the superlative degree of
# comparison only.

5.4 R packages

What we have been using so far is called base R. Its the stuff that comes working out of the box with R. You can do an amazing amount with this. But the capabilities of R have being extended hugely over the years with packages. The below is from Hadley Wickham’s R package book:

In R, the fundamental unit of shareable code is the package. A package bundles together code, data, documentation, and tests, and is easy to share with others. As of January 2015, there were over 6,000 packages available on the Comprehensive R Archive Network, or CRAN, the public clearing house for R packages. This huge variety of packages is one of the reasons that R is so successful: the chances are that someone has already solved a problem that you’re working on, and you can benefit from their work by downloading their package.

• You install them from CRAN with install.packages(“x”).
• You use them in R with library(“x”).
• You get help on them with package?x and help(package = “x”).

So lets use a package called skimr. The install.packages command only has to be used the first time you use a package (from then on its on your computer). After that the library command turns it on.

install.packages("skimr")  #You will only have to do this once on your computer.
library("skimr")  #You will only have to do this once per session. It 'turns on' the package
skim(iris)  #skim is a command in r. iris is a built-in dataset we are going to use.

Variable type: factor

Variable type: numeric

I haven’t run this package on all possible computers. If you are having problems with it, we could get similar information using base R with a command unsurprisingly called summary.

summary(iris)

  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
 Median :5.800   Median :3.000   Median :4.350   Median :1.300  
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
       Species  
 setosa    :50  
 versicolor:50  
 virginica :50  
                
                
                

Task: Perform the skim (or summary if there is a problem) command on your fish.data (the data you loaded from a csv file in Section 4)

Blackboard MCQ: Whats the mean value of Standard.Length? (clue:The mean value for Sepal.Length was 5.84 from skim and 5.843 from summary)

5.5 Getting your session info

Task: Finally, as a test that you have R up and running on your computer, I’d like you to type the following command

sessionInfo()

Blackboard MCQ: What version of R do you have? For example, if you got R version 1.4.3 (2010-04-26), select 1 as the answer.