Object Oriented Programming in R

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Answering the top questions on Object Oriented Programming in R: What is S4? What is a Reference Class? When should I use them? This post provides definitive answers on S4 class features, RC key characteristics, and how generics enable multiple dispatch. Level up your R programming skills today.

Object Oriented Programming in R

What is OOP in R?

OOP stands for Object Oriented Programming in R, and it is a popular programming language. OOP allows us to construct modular pieces of code that are used as building blocks for large systems. R is a functional language. It also supports exists for programming in an object-oriented style. OOP is a superb tool to manage complexity in larger programs. It is particularly suited to GUI development.

Object Oriented Programming in R is a paradigm for structuring your code around objects, which are data structures that have attributes (data) and methods (functions). However, unlike most other languages, R has three distinct object-oriented systems:

  1. S3: The simplest and most common system. Informal and flexible.
  2. S4: A more formal and rigorous version of S3.
  3. R6 (and others): A modern system that supports more familiar OOP features like reference semantics (objects that can be modified in place).

What is S4 Class in R?

S4 Class in R is a formal object-oriented programming (OOP) system in R. It is a more structured and rigorous evolution of the simpler S3 system. While S3 is informal and flexible, S4 introduces formal class definitions, validity checks, and a powerful feature called multiple dispatch.

One can think of it as providing a blueprint for your objects, ensuring they are constructed correctly and used properly.

When to use S4 Class in R?

Use S4 when you are building large, complex systems or packages where the integrity of your objects is critical. It’s heavily used in the Bioconductor project, which manages complex biological data, because its rigor helps prevent bugs and ensures interoperability between packages. For simpler, more interactive tasks, S3 or R6 is often preferable.

What is the Reference Class?

The Reference Class (often abbreviated RC) is another object-oriented system in R, introduced in the methods package around 2010. It was the precursor to the more modern and robust R6 system.

What are the key features of Reference Class?

  1. Encapsulation: Methods (functions) and fields (data) are defined together within the class. You use the $ operator to access both.
  2. Mutable State: Because of reference semantics, the object’s internal state can be changed by its methods.
  3. Inheritance: RC supports single inheritance, allowing a class to inherit fields and methods from a parent class.
  4. Built-in: They are part of the base methods package, so no additional installations are needed (unlike R6, which is a separate package, though also very popular).

When to use Reference Class?

  • When maintaining legacy code that already uses them.
  • When you need mutable state and reference semantics and cannot rely on an external package (though R6 is a lightweight, recommended package).
  • For modeling real-world entities that have a changing identity over time (e.g., a game character, a bank account, a connected device).

What is S4 Generic Function?

An S4 generic function is a fundamental concept in R’s S4 object-oriented system. It’s the mechanism that enables polymorphism, allowing the same function name to perform different actions depending on the class of its arguments.

What are the key features of S4 Class in R?

  1. Multiple Dispatch: This is the superpower of S4. While S3 generics only dispatch on the first argument, S4 generics can look at the class of multiple arguments to choose the right method.
  2. Formal Definition: S4 generics are formally defined, which makes the system more robust and less prone to error than the informal S3 system.
  3. Existing Generics: You can define new methods for existing generics (like show, plot) without creating a new generic function. This is very common.

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R Language MCQs Test 33

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Test your R programming expertise with this 20-question MCQ quiz! R Language MCQs Test designed for both learners and professionals, this quiz covers essential topics like data wrangling with dplyr (group_by, summarize, pipes), string manipulation, lubridate, tidymodels, and predictive modeling. Perfect for preparing for data scientist job interviews, brushing up on core R concepts, and mastering the tidyverse ecosystem. Let us start with the R Language MCQs Test now.

Online R Language MCQs Test

Online R Language Programming Quiz with Answers

1. You have a variable called “Status” that contains a status code in the format “error_type-severity_level”, for example “10-07”, and you want to reformat the column so that the “error_type” and “severity_level” are in different columns. What is the correct function to do this?

 
 
 
 

2. Assume you have a dataset called “new_dataset”, a predictor variable called X, and a target called Y, and you want to fit a simple linear regression model. Which command should you use?

 
 
 
 

3. What is the result of the following statement?

sub_airline %>% map(~sum(is.na(.)))

 
 
 
 

4. You are checking your data using the glimpse() function before beginning your analysis, and determine that the data type of a variable called TimeStamp is in a character format. What should you do next?

 
 
 
 

5. Let’s say you want to calculate how many days passed from 14 July, 1789 until 1 December 1941. How can you calculate that?

 
 
 
 

6. When grouping data and calculating the mean of each group as part of your exploratory data analysis, you typically use the group_by() function with which other function?

 
 
 
 

7. You have a character vector that looks like this:
my_dates<-c(
“05-28-1984”,
“07-15-1981”,
“9-12-1986”,
“1-15-1982”)
You want to extract the year values from this vector, using the tools in lubridate. Which is correct?

 
 
 
 

8. What is the purpose of the pipe (%>%) operator?

 
 
 
 

9. Assume you have a dataset called “new_dataset”, two predictor variables called X and Y, and a target variable called Z, and you want to fit a multiple linear regression model. Which command should you use?

 
 
 
 

10. Which functions do you use together to correct data types in all columns of your dataset?

 
 
 
 

11. Which function can you use to read a text file that uses the “%” character as a delimiter?

 
 
 
 

12. Which tidymodels function do you use to create the grid for a grid search?

 
 
 
 

13. How can the factor() function be used to map R onto a relational database management system (RDBMS)?

 
 
 
 

14. You’ve still got this same messy data:
my_strings<-c(
"xyztiger",
" i33tiger",
"898natiger "
)
You want to use a function to take this data and create a column of data that looks like this:

“tiger”
“tiger”
“tiger”
What is the correct function?

 
 
 
 

15. Which of the following can you accomplish using the spread() function?

 
 
 
 

16. When using the predict() function in R, what is the default confidence level?

 
 
 
 

17. What is the main similarity between the summarize() and group_by() functions?

 
 
 
 

18. You’ve got some messy data that looks like this:
my_strings<-c(
"xyztiger",
" i33tiger",
"898natiger "
)
You want to use a function to do a logical test for whether the character string “tiger” is present in any of the items in this vector. What is the correct function?

 
 
 
 

19. What’s the point of using group_by()?

 
 
 
 

20. Say you want to split a character vector and split the strings, so you have a matrix with two columns, splitting the string as indicated. Your character vector looks like this:
my_strings<-c(
"paper_store1",
"pens_store1",
"pencils_store1")
You want to split the strings at the underscore. What function do you use?

 
 
 
 

Question 1 of 20

Online R Language MCQs Test with Answers

  • What’s the point of using group_by()?
  • Which tidymodels function do you use to create the grid for a grid search?
  • What is the purpose of the pipe (%>%) operator?
  • You are checking your data using the glimpse() function before beginning your analysis, and determine that the data type of a variable called TimeStamp is in a character format. What should you do next?
  • How can the factor() function be used to map R onto a relational database management system (RDBMS)?
  • Which function can you use to read a text file that uses the “%” character as a delimiter?
  • What is the main similarity between the summarize() and group_by() functions?
  • What is the result of the following statement?
    sub_airline %>% map(~sum(is.na(.)))
  • Which functions do you use together to correct data types in all columns of your dataset?
  • You have a variable called “Status” that contains a status code in the format “error_type-severity_level”, for example “10-07”, and you want to reformat the column so that the “error_type” and “severity_level” are in different columns. What is the correct function to do this?
  • Which of the following can you accomplish using the spread() function?
  • When grouping data and calculating the mean of each group as part of your exploratory data analysis, you typically use the group_by() function with which other function?
  • Assume you have a dataset called “new_dataset”, a predictor variable called X, and a target called Y, and you want to fit a simple linear regression model. Which command should you use?
  • When using the predict() function in R, what is the default confidence level?
  • Assume you have a dataset called “new_dataset”, two predictor variables called X and Y, and a target variable called Z, and you want to fit a multiple linear regression model. Which command should you use?
  • You’ve got some messy data that looks like this:
    my_strings<-c( “xyztiger”, ” i33tiger”, “898natiger ” )
    You want to use a function to do a logical test for whether the character string “tiger” is present in any of the items in this vector. What is the correct function?
  • You’ve still got this same messy data:
    my_strings<-c( “xyztiger”, ” i33tiger”, “898natiger ” )
    You want to use a function to take this data and create a column of data that looks like this:
    “tiger”
    “tiger”
    “tiger”
    What is the correct function?
  • Say you want to split a character vector and split the strings, so you have a matrix with two columns, splitting the string as indicated. Your character vector looks like
    this: my_strings<-c( “paper_store1”, “pens_store1”, “pencils_store1”)
    You want to split the strings at the underscore. What function do you use?
  • You have a character vector that looks like this:
    my_dates<-c( “05-28-1984”, “07-15-1981”, “9-12-1986”, “1-15-1982”)
    You want to extract the year values from this vector, using the tools in lubridate. Which is correct?
  • Let’s say you want to calculate how many days passed from 14 July, 1789 until 1 December 1941. How can you calculate that?

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R Functions Explained

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Learn key R functions Explained: like sort(), search(), subset(), sample(), all(), and any() with practical examples. Discover how to check if an element exists in a vector and understand the differences between all() and any(). Perfect for R beginners!” learn Q&A guide on sort(), search(), subset(), sample(), all(), any(), and element checks in vectors. Boost your R skills today!”

Which function is used for sorting in the R Language?

Several functions in R can be used for sorting data. The most commonly used R functions for sorting are:

  • sort(): Sorts a vector in ascending or descending order. The general syntax is sort(x, decreasing = FALSE, na.last = NA)
  • order(): Returns the indices that would sort a vector (it is useful for sorting data frames). The general syntax of order() is order(x, decreasing = FALSE, na.last = TRUE)
  • arrange(): It sorts a data frame (however, it requires dplyr package). The general syntax is: arrange(.data, …, .by_group = FALSE)
# sort() Function
vec <- c(3, 1, 4, 1, 5)
sort(vec)                		# Ascending (default): 1 1 3 4 5
sort(vec, decreasing = TRUE)  	# Descending: 5 4 3 1 1

# order() Function
df <- data.frame(name = c("Ali", "Usman", "Umar"), age = c(25, 20, 30))
df[order(df$age), ]  # Sort data frame by age (ascending)

# arrange() Function from dplyr package
library(dplyr)
df %>% arrange(age)               # Ascending
df %>% arrange(desc(age))         # Descending
R functions explained sort arrange order

Why search() function used?

In R language, the search() function is used to display the current search path of R objects (such as functions, datasets, variables, etc.). This shows the order in which R looks for objects when you reference them.

What Does search() function do?

  • Lists all attached packages and environments in the order R searches them.
  • Helps diagnose issues when multiple packages have functions with the same name (name conflicts).
  • Shows where R will look when you call a function or variable.

What is the use of subset() and sample() functions in R?

In R language, subset() and sample() are two useful functions for data manipulation and sampling:

  • subset(): is used to extract subsets of data frames or vectors based on some condition. The general syntax is subset(x, subset, select, …)
  • sample(): is used for random sampling from a dataset with or without replacement. The general system is: sample(x, size, replace = FALSE, prob = NULL).

The examples of subset() and sample() are describe below

# Example data frame
df <- data.frame(
  name = c("Ali", "Usman", "Aziz", "Daood"),
  age = c(25, 30, 22, 28),
  salary = c(50000, 60000, 45000, 70000)
)

# Filter rows where age > 25
subset(df, age > 25)

# Filter rows and select specific columns
subset(df, salary > 50000, select = c(name, salary))
R functions explained
# Randomly sample 3 numbers from 1 to 10 without replacement
sample(1:10, 3)

# Sample with replacement (possible duplicates)
sample(1:5, 10, replace = TRUE)

# Sample rows from a data frame
df[sample(nrow(df), 2), ]  # Picks 2 random rows
R functions explained

What is the use of all() and any()?

In R language, the all() and any() functions are logical functions used to evaluate conditions across vectors or arrays.

  • all() function: checks if all elements of a logical vector are TRUE. It returns TRUE only if every element in the input is TRUE, otherwise, it returns FALSE. The general syntax is all(..., na.rm=FALSE)
  • any() Function: checks if at least one element of a logical vector is TRUE. It returns TRUE if any element is TRUE and FALSE only if all are FALSE. The general syntax is any(..., na.rm = FALSE)

The examples of all() and any() functions are:

x <- c(TRUE, TRUE, FALSE)
all(x)  # FALSE (not all elements are TRUE)

y <- c(5, 10, 15)
all(y > 3)  # TRUE (all elements are greater than 3)
x <- c(TRUE, FALSE, FALSE)
any(x)  # TRUE (at least one element is TRUE)

y <- c(2, 4, 6)
any(y > 5)  # TRUE (6 is greater than 5)

Note that if NA is present and na.rm = FALSE, any() returns NA unless a TRUE value exists.

What are the key differences between all() and any()?

The key differences between all() and any() are:

FunctionReturns TRUE WhenReturns FALSE When
all()All elements are TRUEAt least one is FALSE
any()At least one element is TRUEAll are FALSE

What is the R command to check if element 15 is present in a vector $x$?

One can check if the element (say) 15 is present in a vector x using either

  • %in% Operator
  • any() with logical comparison
  • which() to find the position of 15
# %in%
x <- c(10, 15, 20, 25)
15 %in% x  # Returns TRUE
30 %in% x  # Returns FALSE

# any()
x <- c(5, 10, 15)
any(x == 15)  # TRUE
any(x == 99)  # FALSE

# Which()
x <- c(10, 15, 20, 15)
which(x == 15)  # Returns c(2, 4)

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