Comments

Note that anything preceded by # is ignored by \({\bf\textsf{R}}\). We call it a comment operator, and it is useful for adding explanation to the script.

At a very basic level \({\bf\textsf{R}}\) is a fancy calculator. It will chug arithmetic operations:

  2+2  

## [1] 4

  2*2 

## [1] 4

  2*2+6 

## [1] 10

\({\bf\textsf{R}}\) follows proper order of operations, including parentheses:

  6+2*2 

## [1] 10

  6+(2*2) 

## [1] 10

Object-oriented

\({\bf\textsf{R}}\) is “object-oriented”, which means that character strings can be used to represent values. We have two options when writing script to define objects:

• <- assigns the operation on the right to the named object on the left.
• = will do the same, and is shorter.

For programming purposes I like the idea of moving one side to the other, especially when the right side has many entries or is large. I tend to reserve = for defining variables or long file paths in my script, and use <- when creating data objects or storing statistical results.

Note that \({\bf\textsf{R}}\) creates objects on the fly and does not need them to be defined at the beginning of the script or session as in C++.

answer <- 2+(2*20)

Note that you should now see answer in the global environment pane of R studio.

Calling the object will print its content in the console:

answer

## [1] 42

This object can now be used for additional operations…

answer*2

## [1] 84

…and the creation of new objects:

new.answer <- answer*2
new.answer

## [1] 84

Functions

Functions are a special type of \({\bf\textsf{R}}\) object that instead of containing data, contain a series of operations. Functions are essentially shortcuts for common sets of operations.

For example, researchers often want to find the mean of data. Say we have the following five observations:

24, 13, 12, 22, and 15

The arithmetic mean is defined as the sum of the observations divided by the number of observations, which in \({\bf\textsf{R}}\) looks like:

(24 + 13 + 12 + 22 + 15) / 5 

## [1] 17.2

Alternatively, we can assign the data to an object using the c function, which stands for concatenate. It joins everything between the parentheses, separated by commas, into a vector that we’ll call data:

data <- c(24, 13, 12, 22, 15)
data 

## [1] 24 13 12 22 15

To find the mean of data, one might first think we can simply divide the object by 5…

data / 5

## [1] 4.8 2.6 2.4 4.4 3.0

…but this is obviously incorrect. Here, \({\bf\textsf{R}}\) has applied the “divide by five” operation to each value in the vector. This is an example of how \({\bf\textsf{R}}\) is vectorized: it is designed to perform its operations along vectors. Although it will be awhile before you feed \({\bf\textsf{R}}\) large enough datasets to notice the difference, vectorization optimizes performance and makes \({\bf\textsf{R}}\) computations quick.

Calculating the mean is a two-step process, and we need to define both. Thus, we must first find the sum of the data, for which we can use the shortcut function sum:

sum(data)

## [1] 86

Then we divide the sum by 5 to calculate the mean:

sum(data) / 5 

## [1] 17.2

This is an example of hard-coding: we’ve specified the divisor in this operation as a fixed value (5). But what if the value varies – say your technician (definitely not you!) inadvertently lost or failed to enter some data, and a given set of replicates do not have the number of observations you expect? Hard-coding your count creates problems:

data2 <- c(24, 13, 12, 22)
sum(data2) / 5

## [1] 14.2

The calculated mean is too low, because our hard-coded operation divided the sum of four observations by five.

It is preferable to have \({\bf\textsf{R}}\) determine the count for each operation, so if counts differ, \({\bf\textsf{R}}\) can automatically account for it.

We can use the length function to determine how many observations are in the set:

length(data)

## [1] 5

If length sounds odd, remember data is a vector comprised of individual values. The number of entries determines how long the vector is, and so length is a convenient way to count the number of observations. This is a core concept in \({\bf\textsf{R}}\) that we will return to frequently.

Let’s see how this combination of functions performs:

sum(data) / length(data)

## [1] 17.2

length(data2)

## [1] 4

sum(data2) / length(data2)

## [1] 17.75

Of course, calculating the mean of a vector is a very common operation, and \({\bf\textsf{R}}\) has a built-in function that combines the sum, length, and / operations into one shortcut:

mean(data)

## [1] 17.2

Meaner <- function(x) { sum(x) / length(x) }

Meaner 

## function(x) { sum(x) / length(x) }

Meaner(data)

## [1] 17.2

Meaner <- function(x) { 
            m = sum(x) / length(x)
            m1 = paste(m, "!", sep="") 
            return(m1)
                        }
Meaner(data)

## [1] "17.2!"