R Programming Interview Questions and Answers

Intermediate R Programming Interview Questions

We discussed some of the simple R programming Interview Questions. Let's now discuss a few of the medium-level questions.

11. What does R's confusion matrix mean?

Ans: A confusion or error matrix handles the common classification problem. It is possible to assess the accuracy of the created model using a confusion matrix. A cross-tabulation of observed and anticipated classes is calculated.

You can use the "confusionmatrix()" method from the "caTools" package to accomplish this. It uses a specific table design that makes it easier for data analysts to see how an algorithm works.
 

12. How to create a custom function in R? 

Ans: The syntax for creating a custom function in R is as follows:

Name of the function = function(Arguments) {
    Statement 1
    Statement 2
    Statement 3
    …………….
}

Here is an example of how to write a custom function in R.

CodingNinjas = function (a) {
 ifelse(a > 15, 20, 0)
}
X = c(1, 2, 3, 4, 5, 6, 7, 8, 9)
CodingNinjas(X) = X
 

13. What do you mean by Data Imputation?

Ans: Most datasets may contain missing values due to a mistake or because they weren't input. Data imputation is the process of replacing these missing values with a different value. You can replace the column's missing value in R in various ways, including by setting the column's missing value to zero, the average, the median, and so on.
 

14. Which R function is used to combine datasets?

Ans: The rbind() function can combine two data frames by rows. The same variables must be present in both data frames, although they need to be in a different order.

The syntax is as follows:
rbind(object1, object2,.....)
 

15. What is the use of the cbind() function?

Ans: To combine a specified Vector, Matrix, or Data Frame by columns, use the cbind() function.
The syntax is as follows:
cbind(object1, object2,.....)
 

16. Which R packages are available for data imputation?

Ans: The following R packages can be used for data imputation.

1. Amelia
    
2. Hmisc
    
3. imputeR
    
4. Mi
    
5. MICE
    
6. missForest
    

17. List some of the functions in the "dplyr" package.

Ans: The following R functions are in the "dplyr" package.

1. Arrange
    
2. Count
    
3. Filter
    
4. Mutate
    
5. Select

18. What is the difference between lapply() and sapply() functions in R?

Both lapply() and sapply() are used to apply a function to elements of a list or vector, but they differ in their output:

• lapply(): Always returns a list, regardless of the function’s output.

result <- lapply(1:5, function(x) x^2)

• sapply(): Returns a simplified result if possible. If the result is a list, it simplifies it to a vector or matrix if applicable.

result <- sapply(1:5, function(x) x^2)

19. How can you merge two data frames in R?

In R, you can merge two data frames using the merge() function. You can specify the common column(s) for merging using the by, by.x, and by.y arguments. There are different types of joins available:

• Inner join (default): Retains only matching rows.
• Outer join: Retains all rows from both data frames.
• Left join: Retains all rows from the left data frame.
• Right join: Retains all rows from the right data frame.

20. What is the difference between a list and a data frame in R?

A list in R can contain elements of different types, such as vectors, strings, numbers, or even other lists. Each element of a list can have a different length. You can access list elements by indexing or by name.

my_list <- list(a = 1:5, b = "R Programming", c = TRUE)

A data frame, on the other hand, is a two-dimensional structure where each column contains data of the same type, but different columns can store different types. Each column is a vector, and all columns must have the same length.

my_data <- data.frame(Name = c("John", "Jane"), Age = c(25, 30))

Advanced R Programming Interview Questions

It is important to be able to answer simple and medium-level questions, but your ability to answer more challenging questions will give you an advantage over other applicants. Let's go over a few of the trickier R programming Interview Questions.

21. What is ShinyR?

Ans: An R package called Shiny makes it simple to create dynamic web applications directly from R. You can create dashboards, embed standalone apps in Rmarkdown pages, or host them on a website. Further, you may add CSS themes, HTML widgets, and JavaScript actions to your Shiny apps.
 

22. What is the Random Walk model?

Ans: The simplest instance of a non-stationary process is a random walk. A random walk has an undefined mean and variance, high time dependence, and changes or increments that are nothing more than white noise.
 

23. What is the White Noise model?

Ans: It is a basic example of a stationary process and a primary time series model. A white noise model has no time correlation, a fixed constant mean, and a fixed constant variance.
 

24. How does the lattice package work?

Ans: The lattice package aims to enhance the base R graphics. To depict multivariate connections simply, it provides

1. Better Defaults.
    
2. Good Capability to depict.
    

25. What is the rattle package in R?

Ans: A widely used GUI for R data mining is called Rattle. Rattle performs the following actions:

1. It displays graphical and statistical summaries of the data.
    
2. Converts the data to model them easily.
    
3. Creates learning models from the data.
    
4. Scores new datasets for use in production.
   One of its primary features is that all your interactions with the GUI are recorded as an R script that can be quickly run in R without using the Rattle interface.
    

26. What is the class() function in R? Explain with some examples.

Ans: The class function in R allows us to identify the type of an object. See the examples with various kinds of objects.
# numeric data
n <- 1.5
class(n)

# integer data
i <- 100L
class(i)

# complex data
c <- 2i + 3
class(c)

# character/string data
s <- "CodingNinjas"
class(s)

# logical data
l <- TRUE
class(l)

Output:
[1] "numeric"
[1] "integer"
[1] "complex"
[1] "character"
[1] "logical"
 

27. What various R import functions are there?

Ans: R allows for the import of data in many forms and from various sources. Let's explore the various import functions provided by R:

1. For reading.csv files, use read.csv().
    
2. For reading.sas7bdat files, use read sas().
    
3. For XL sheets, use read excel().
    
4. For the spss data, use read sav().
    

28. What do you mean by scatter plot in R? How to compare two plots?

Ans: Numbers can be plotted against one another using the plot() method. The plot type, known as a "scatter plot", places one dot on the graph for each observation and shows the relationship between two numerical variables.

You can use the points() function to compare the plot to another plot. Below is an example of comparing two plots.

# Ninja Computer one, the age and speed of 12 computers:
x1 < -c(5, 7, 8, 7, 2, 2, 9, 4, 11, 12, 9, 7)
y1 < -c(99, 86, 87, 88, 111, 103, 88, 94, 78, 77, 85, 86)

# Ninja Computer two, the age and speed of 15 computers:
x2 < -c(2, 2, 8, 1, 15, 9, 12, 9, 7, 3, 11, 4, 7, 14, 12)
y2 < -c(100, 105, 84, 105, 90, 99, 90, 95, 94, 100, 79, 112, 92, 80, 85)
plot(x1, y1, main = "Observation of Ninja Computers", xlab = " Age of Ninja Computer", ylab = "Speed of Ninja Computer", col = "red", cex = 2)
points(x2, y2, col = "blue", cex = 2)

Output:
 

Conclusion: By comparing the two plots, we can conclude that the newer the Computer, the faster it performs.

29. What is the <<- operator and when would you use it?

The <<- operator in R is used for assigning values to variables in a parent environment (outside the current function scope). This can be useful when you want to modify a global variable inside a function.

Example:

counter <- 0
increment_counter <- function() {
    counter <<- counter + 1
}
increment_counter()  # counter is now 1

30. How does R handle missing values, and what are the different types of missing values in R?

R has several types of missing values:

1. NA: General purpose missing value
2. NA_integer_, NA_real_, NA_complex_, NA_character_: Type-specific NAs
3. NaN: Not a Number, for undefined mathematical operations
4. NULL: Represents the absence of an object

R handles missing values in various ways:

• Most functions have an na.rm argument to remove NAs
• Logical operations with NA typically result in NA
• Special functions like is.na(), na.omit(), complete.cases() help in handling NAs
• NULL is often used to represent missing elements in lists

It's important to check for and handle missing values appropriately in data analysis to avoid unexpected results.

31. Explain the concept of closures in R.

A closure in R is a function that retains access to its environment, even after the environment in which it was created has been destroyed. This allows the function to "remember" the values of variables that were in its scope at the time of creation.

Example:

create_counter <- function() {
    counter <- 0
    function() {
        counter <<- counter + 1
        return(counter)
    }
}
counter_fn <- create_counter()
counter_fn()  # Output: 1
counter_fn()  # Output: 2

Here, the counter_fn retains the value of counter, even after the create_counter() function has exited.

32. What is lazy evaluation in R and how does it work?

Lazy evaluation in R refers to the delayed evaluation of function arguments. This means that arguments to a function are not evaluated until they are actually used inside the function body. This allows for more flexible and efficient coding.

Example:

lazy_eval_example <- function(x, y) {
    return(x^2)
}
lazy_eval_example(3, stop("Error"))  # The stop() function is never executed because `y` is not used.

Here, stop("Error") is never executed because y is not needed for the function to return a result.

33. Explain the difference between S3 and S4 object systems in R.

S3 and S4 are two object-oriented systems in R:

S3:

• Informal and flexible
• Uses generic functions for method dispatch
• Classes are implicit (no formal class definition)
• Easy to use and extend

S4:

• More formal and rigorous
• Uses formal class definitions
• Supports multiple dispatch
• Allows for slot access and validation
• Better suited for large, complex systems

R Programming MCQ

1. Which of the following data structures in R is used to store elements of different types?

a) Vector

b) Matrix

c) List

d) Data frame

Answer: c) List

2. Which function in R is used to combine data frames by rows or columns?

a) cbind()

b) rbind()

c) merge()

d) Both a and b

Answer: d) Both a and b

3. What is the correct way to check for missing values in a dataset in R?

a) is.null()

b) is.na()

c) na.rm()

d) missing()

Answer: b) is.na()

4. Which of the following functions is used to apply a function to each element of a list in R?

a) apply()

b) lapply()

c) tapply()

d) sapply()

Answer: b) lapply()

5. What is the purpose of the ggplot2 package in R?

a) Perform statistical tests

b) Data manipulation

c) Data visualization

d) Data transformation

Answer: c) Data visualization

6. How can you read a CSV file into R as a data frame?

a) read.csv()

b) read.csvfile()

c) readFile()

d) readData()

Answer: a) read.csv()

7. What does the function summary() do in R?

a) Summarizes character data

b) Summarizes numerical data

c) Provides statistical summaries of vectors or data frames

d) Visualizes data

Answer: c) Provides statistical summaries of vectors or data frames

8. In R, how do you remove duplicate rows from a data frame?

a) removeDuplicates()

b) unique()

c) distinct()

d) deleteDuplicates()

Answer: b) unique()

9. What does the function str() do in R?

a) Displays the structure of an object

b) Displays the string length

c) Splits a string

d) Joins two strings

Answer: a) Displays the structure of an object

10. Which package in R is used for data manipulation and transformation?

a) dplyr

b) ggplot2

c) caret

d) stringr

Answer: a) dplyr

Conclusion

In this blog, you learned about the R programming interview questions. R programming is a powerful language widely used in statistical analysis, data visualization, and machine learning. Mastering R opens the door to a variety of career opportunities, particularly in data science and analytics. Preparing for R programming interview questions can help you gain a deeper understanding of the language’s core concepts, from basic syntax to advanced techniques like parallel processing and memory management.

We hope this blog has helped you. We recommend you visit our articles on different topics of Data Science, such as

• Data Analyst Interview Questions.
• Html interview questions
• Data Science Interview Questions.
• Data Modelling Interview Questions.
• CSS Interview Questions
• MVC Interview Questions
• SQL Query Interview Questions
• MySQL Interview Questions
• MongoDB Interview Questions
• C++ Interview Questions
• Excel Interview Questions

If you liked our article, do upvote our article and help other ninjas grow.  You can refer to our Guided Path on Coding Ninjas Studio to upskill yourself in Data Structures and Algorithms, Competitive Programming, System Design, and many more!

Also check out - String Interview Questions In Java

Head over to our practice platform Coding Ninjas Studio to practice top problems, attempt mock tests, read interview experiences and interview bundles, follow guided paths for placement preparations, and much more!!

Happy Reading!!