Introduction to Functions and Arguments in R Programming: Part 2

The Beginner’s Guide to Functions in R Programming:

Functions are an essential part of programming, and they play a critical role in R programming. In R, a function is a set of instructions that perform a specific task. Functions in R can have several arguments, and their evaluation can be lazy or eager. In this blog post, we will explore functions in R, including their "dot-dot-dot" or ellipsis argument, lazy evaluation, and more.

Ellipsis or "dot-dot-dot" Argument in R Functions

The "dot-dot-dot" or ellipsis argument in R programming is a special argument that can be used in functions to represent a variable number of additional arguments that are not explicitly defined in the function. The ellipsis argument is represented by three dots ... and is typically used at the end of the function's argument list.

When the function is called, any additional arguments provided by the user after the defined arguments are collected by the ellipsis argument and passed to the function as a list. The function can then use this list of additional arguments as needed.

The ellipsis argument is particularly useful when a function needs to be flexible enough to handle different types and numbers of inputs. It allows users to provide additional arguments to the function without having to explicitly define each one in the function's argument list.

Here is an example of a function that uses the ellipsis argument to calculate the sum of multiple numbers:

my_sum <- function(...){

  sum(...)

}

my_sum(1,2,3,4) # returns 10

my_sum(1:10) # returns 55

my_sum(2,3,5,7,11) # returns 28

In this example, the my_sum function accepts any number of arguments using the ellipsis argument ... and returns their sum using the sum function in R.

Lazy Evaluation

Lazy evaluation is a concept in programming where the evaluation of an expression is delayed until it is needed. In R, functions are evaluated lazily by default, which means that the arguments are only evaluated when they are needed by the function. Lazy evaluation is an important concept in R, as it allows for more efficient use of memory and computing resources.

Lazy evaluation in R is achieved using the substitute function, which creates an unevaluated expression from a function call. The unevaluated expression is then evaluated when it is needed by the function. For example, consider the following code:

my_function <- function(x, y) {

  z <- substitute(x + y)

  z

}

my_function(2+2, 3+3)

In this example, the arguments 2+2 and 3+3 are not evaluated when the function is called. Instead, the substitute function creates an unevaluated expression from the function call, and the expression is evaluated when it is needed by the function.

Practice Material:

Here are few tasks for you to practice with functions, arguments, and lazy evaluation in R programming:

• Write a function in R that calculates the sum of two numbers and returns the result. The function should have two arguments and use lazy evaluation.

• Write a function in R that calculates the factorial of a number. The function should have one argument, and it should use a loop to calculate the factorial.

• Write a function in R that takes a vector of numbers as an argument and returns the mean, median, and mode of the vector. The function should use the mean, median, and table functions in R.

• Write a function in R that takes a string as an argument and returns the reverse of the string. The function should use lazy evaluation to reverse the string.

• Write a function in R that takes a data frame as an argument and returns the number of rows and columns in the data frame. The function should use the nrow and ncol functions in R.

• For more practice you should start swirl's lessons in R Programming. Complete download process of swirl and R Programming is here, click on the link!

• You can look in to the practice and reading material that is provided in the text book, click here to download the textbook.

• Lecture slides can be downloaded from here. It would be great if you go through them too.

These tasks should provide you with a good foundation in R programming functions, arguments, and lazy evaluation. With practice, you can gain confidence in using R programming to perform data analysis and machine learning tasks.

Conclusion

Functions in R are a powerful tool for data analysts and data scientists. They allow for the efficient processing and analysis of large amounts of data, and their lazy evaluation can help to conserve memory and computing resources. By understanding the concepts of arguments, lazy evaluation, and ellipses argument in R functions, you can develop more efficient and effective code that can help you to achieve your data analysis and machine learning goals.