Best R Programming Training Institute In Coimbatore - Idmtechpark Coimbatore

R Programming Interview Questions and Answers

Top 100 R Programming Programming Interview Questions for Freshers

R Programming is an essential skill for data analysts, statisticians, and machine learning professionals. It enables users to analyze, visualize, and manipulate data efficiently, making it a preferred choice for statistical computing and data-driven applications. Mastering R Programming allows professionals to build robust data models, perform statistical analysis, and create interactive data visualizations.Candidates should be well-prepared to tackle both the R Programming Online Assessment and Technical Interview Round at IDM TechPark.
To help you succeed, we have compiled a comprehensive list of the Top 100 R Programming Interview Questions along with their answers. Mastering these concepts will give you a competitive edge in securing a role in data analytics, machine learning, and data science.

1. What is R programming?

Answer:
R is an open-source programming language primarily used for statistical computing, data analysis, and visualization. It provides extensive libraries for data science and machine learning.

2. How do you install a package in R?

Answer:
Use the install.packages() function:

install.packages("ggplot2")

3. How do you load a package in R?

Answer:
Use the library() function:

library(ggplot2)

4. How do you check the version of R installed on your system?

Answer:
Use the version or R.version.string command:

R.version.string

5. How do you create a vector in R?

Answer:
Using the c() function:

x <- c(1, 2, 3, 4, 5)

6. What are the data types in R?

Answer:

Numeric
Integer
Character
Logical
Factor
Complex

7. How do you check the data type of a variable in R?

Answer:
Use the class() function:

class(10) # Output: "numeric"

8. How do you create a matrix in R?

Answer:
Use the matrix() function:

mat <- matrix(1:6, nrow=2, ncol=3)

9. How do you create a data frame in R?

Answer:
Using the data.frame() function:

df <- data.frame(Name = c("Alice", "Bob"), Age = c(25, 30))

10. How do you access elements in a vector?

Answer:
Using indexing:

x[2] # Access the second element

11. What is the difference between == and = in R?

Answer:

== is used for comparison.
= is used for assigning values inside function arguments.

Example:

x <- 10 # Assignment x == 10 # Comparison

12. What is the difference between NA, NULL, and NaN in R?

Answer:

NA (Not Available): Missing values in data.
NULL: Represents an empty object.
NaN (Not a Number): Result of undefined mathematical operations (e.g., 0/0).

Example:

is.na(NA) # TRUE is.null(NULL) # TRUE is.nan(NaN) # TRUE

13. How do you generate a sequence of numbers in R?

Answer:
Using the seq() function:

seq(1, 10, by=2) # Output: 1, 3, 5, 7, 9

14. What function is used to apply an operation to each element of a vector?

Answer:
The sapply() or lapply() function:

sapply(c(1, 2, 3), sqrt) # Computes the square root

15. How do you read a CSV file in R?

Answer:
Using the read.csv() function:

df <- read.csv("data.csv")

16. How do you write a CSV file in R?

Answer:
Using the write.csv() function:

write.csv(df, "output.csv", row.names=FALSE)

17. What is the difference between paste() and paste0() in R?

Answer:

paste() adds a separator (default is space).
paste0() concatenates without spaces.

Example:

paste("Hello", "World") # "Hello World" paste0("Hello", "World") # "HelloWorld"

18. How do you remove missing values (NA) from a dataset?

Answer:
Using na.omit():

clean_data <- na.omit(df)

19. How do you check the structure of a dataset in R?

Answer:
Using the str() function:

str(df)

20. How do you find the number of rows and columns in a dataset?

Answer:
Using nrow() and ncol():

nrow(df) # Number of rows ncol(df) # Number of columns

21. How do you rename columns in R?

Answer:
Using colnames():

colnames(df) <- c("NewCol1", "NewCol2")

22. What is the difference between apply(), lapply(), and sapply()?

Answer:

FunctionWorks onReturns

apply()Matrices & Data FramesArray or vector

lapply()Lists & VectorsList

sapply()Lists & VectorsSimplified output

Example:

apply(matrix(1:6, nrow=2), 1, sum) # Sum by row lapply(1:3, sqrt) # Square root for each element sapply(1:3, sqrt) # Simplified output

23. How do you subset data in R?

Answer:
Using indexing:

df[df$Age > 25, ] # Select rows where Age > 25

24. What is the difference between a list and a vector in R?

Answer:

A vector contains elements of the same type.
A list can store elements of different types.

Example:

v <- c(1, 2, 3) # Vector l <- list(1, "Hello", TRUE) # List

25. How do you plot a graph in R?

Answer:
Using the plot() function:

plot(1:10, 1:10, type="l", col="blue") # Line plot

1. What is the difference between data.frame() and tibble() in R?

Answer:

data.frame() is the base R function for creating tabular data.
tibble() (from the tidyverse package) is an improved version that prints better and doesn’t automatically convert strings to factors.

Example:

library(tibble) df <- data.frame(Name = c("Alice", "Bob"), Age = c(25, 30)) tb <- tibble(Name = c("Alice", "Bob"), Age = c(25, 30))

2. How do you filter rows in an R data frame?

Answer:
Using subset() or dplyr::filter():

subset(df, Age > 25) library(dplyr) df %>% filter(Age > 25)

3. How do you merge two data frames in R?

Answer:
Using merge():

df1 <- data.frame(ID = c(1, 2), Name = c("Alice", "Bob")) df2 <- data.frame(ID = c(1, 2), Score = c(90, 85)) merged_df <- merge(df1, df2, by = "ID")

4. What is the difference between rbind() and cbind()?

Answer:

rbind() binds rows (adds new rows).
cbind() binds columns (adds new columns).

Example:

rbind(df1, df2) # Adds rows cbind(df1, df2) # Adds columns

5. How do you replace missing values in R?

Answer:
Using is.na() and replace():

df$Age[is.na(df$Age)] <- mean(df$Age, na.rm = TRUE)

6. What is the difference between table() and table(df$column)?

Answer:

table() creates frequency tables from vectors.
table(df$column) gives frequencies for a specific column.

Example:

table(c("A", "B", "A", "C", "B"))

7. How do you sort a data frame by a column in R?

Answer:
Using order():

df[order(df$Age), ]

8. How do you use apply() to calculate column sums?

Answer:

apply(df[, -1], 2, sum) # Apply sum function to each column

9. How do you remove duplicate rows in R?

Answer:
Using distinct() from dplyr:

df <- distinct(df)

10. How do you sample data randomly in R?

Answer:
Using sample():

df_sample <- df[sample(1:nrow(df), 5), ]

11. What is a factor in R?

Answer:
A categorical variable with levels.

factor(c("Low", "Medium", "High"))

12. How do you create a bar plot in R?

Answer:

barplot(table(df$Age))

13. What is the difference between mutate() and transmute()?

Answer:

mutate() adds new variables but retains all columns.
transmute() keeps only the new variables.

Example:

df %>% mutate(NewAge = Age + 5) df %>% transmute(NewAge = Age + 5)

14. How do you group and summarize data in R?

Answer:
Using group_by() and summarize():

df %>% group_by(Age) %>% summarize(Count = n())

15. What does %in% do in R?

Answer:
Checks if values exist in a vector.

x <- c(1, 2, 3, 4) 2 %in% x # TRUE

16. How do you reshape data in R using pivot_longer()?

Answer:

library(tidyr) pivot_longer(df, cols = c("col1", "col2"), names_to = "Category", values_to = "Value")

17. What is the difference between sapply() and lapply()?

Answer:

lapply() returns a list.
sapply() returns a vector or matrix.

Example:

sapply(1:3, sqrt) lapply(1:3, sqrt)

18. How do you find correlation between two variables?

Answer:
Using cor():

cor(df$Var1, df$Var2)

19. How do you perform a t-test in R?

Answer:

t.test(df$Var1, df$Var2)

20. What is set.seed() used for?

Answer:
Ensures reproducibility in random operations.

set.seed(123) sample(1:10, 5)

21. How do you check memory usage in R?

Answer:
Using object.size():

object.size(df)

22. How do you standardize a column in R?

Answer:

df$Standardized <- scale(df$Var1)

23. What is the difference between left_join() and inner_join()?

Answer:

left_join() keeps all rows from the left dataset.
inner_join() keeps only matching rows.

Example:

left_join(df1, df2, by = "ID") inner_join(df1, df2, by = "ID")

24. How do you create a linear regression model in R?

Answer:

model <- lm(Salary ~ Experience, data = df) summary(model)

25. How do you save an R object to a file?

Answer:
Using saveRDS() and readRDS():

saveRDS(df, "data.rds") df <- readRDS("data.rds")

1. What is lazy evaluation in R?

Answer:
Lazy evaluation means that R does not evaluate function arguments until they are actually used within the function body.

Example:

f <- function(x, y) { return(x) } f(10, stop("Error!")) # No error because y is never used

2. How do you optimize code performance in R?

Answer:

Use vectorization instead of loops
Use data.table instead of data frames
Use parallel processing (foreach, parallel package)
Avoid unnecessary memory allocations

3. What is the difference between S3 and S4 classes in R?

Answer:

S3: Informal, uses class() and generic functions.
S4: Formal, uses setClass() and setMethod().

Example (S4 class):

setClass("Person", slots = list(name = "character", age = "numeric"))

4. What is an environment in R?

Answer:
An environment is a collection of symbol-value pairs.

Example:

e <- new.env() e$x <- 10 ls(e) # Lists objects in environment

5. How do you perform parallel processing in R?

Answer:
Using parallel package:

library(parallel) cl <- makeCluster(detectCores() - 1) result <- parLapply(cl, 1:5, sqrt) stopCluster(cl)

6. What is memoization in R?

Answer:
Caching results of function calls to improve efficiency.

Example using memoise:

library(memoise) slow_function <- function(x) { Sys.sleep(2); x^2 } fast_function <- memoise(slow_function)

7. How do you handle large datasets in R?

Answer:

Use data.table instead of data.frame.
Use database connections (DBI package).
Use bigmemory for large matrices.

8. How do you create a custom function with variable arguments in R?

Answer:
Using ... (dot-dot-dot):

my_func <- function(a, ...) { print(a) print(list(...)) } my_func(1, b = 2, c = 3)

9. What is the difference between sys.time() and proc.time()?

Answer:

Sys.time() measures real-world (clock) time.
proc.time() measures CPU usage.

Example:

Sys.time() # Current timestamp proc.time() # CPU processing time

10. How do you profile R code performance?

Answer:
Using Rprof():

Rprof("profile.out") # Run some code Rprof(NULL) summaryRprof("profile.out")

11. What is the purpose of the traceback() function?

Answer:
Shows the call stack after an error occurs.

Example:

f <- function() stop("Error") f() traceback()

12. How do you create a custom S3 generic function?

Answer:

print.myClass <- function(x) { cat("This is my class:", x, "\n") } x <- structure(10, class = "myClass") print(x)

13. How do you read a large CSV file efficiently?

Answer:
Using fread() from data.table:

library(data.table) df <- fread("large_file.csv")

14. What is an R closure?

Answer:
A function that captures variables from its environment.

Example:

counter <- function() { x <- 0 function() { x <<- x + 1; x } } c1 <- counter() c1() # 1 c1() # 2

15. How do you reshape data using dcast()?

Answer:

library(reshape2) dcast(melted_data, variable1 ~ variable2, sum)

16. What is the difference between quote() and substitute()?

Answer:

quote() captures an expression.
substitute() replaces variables in an expression.

Example:

expr <- quote(x + y) substitute(expr, list(x = 2, y = 3)) # Output: 2 + 3

17. How do you perform hierarchical clustering in R?

Answer:

dist_matrix <- dist(mtcars) hclust_model <- hclust(dist_matrix) plot(hclust_model)

18. What are non-standard evaluation (NSE) techniques in R?

Answer:
Techniques that delay evaluation, used in dplyr.

Example:

library(dplyr) filter(mtcars, mpg > 20) # mpg is evaluated later

19. How do you remove objects from memory in R?

Answer:
Using rm() and gc():

rm(my_variable) gc()

20. How do you perform text mining in R?

Answer:
Using tm package:

library(tm) corpus <- Corpus(VectorSource(c("Text data", "More text"))) tdm <- TermDocumentMatrix(corpus) inspect(tdm)

21. How do you work with APIs in R?

Answer:
Using httr package:

library(httr) response <- GET("https://api.example.com/data") content(response, "text")

22. What is the difference between eval() and parse()?

Answer:

parse() converts a string into an expression.
eval() evaluates an expression.

Example:

expr <- parse(text = "2 + 3") eval(expr) # Output: 5

23. How do you create a Shiny web app in R?

Answer:

library(shiny) ui <- fluidPage(textOutput("text")) server <- function(input, output) { output$text <- renderText("Hello!") } shinyApp(ui, server)

24. How do you use rvest for web scraping in R?

Answer:

library(rvest) page <- read_html("https://example.com") text <- page %>% html_nodes("p") %>% html_text()

25. How do you perform Principal Component Analysis (PCA) in R?

Answer:

pca <- prcomp(iris[, -5], scale = TRUE) summary(pca) biplot(pca)

These 25 Advanced R Programming Questions cover deep concepts in R, from functional programming to big data handling. Let me know if you need technical R interview questions next! 🚀

1. How does R handle memory management?

Answer:
R uses garbage collection (GC) to manage memory. The gc() function triggers manual garbage collection.

Example:

x <- rnorm(1e6) # Allocate memory rm(x) # Remove object gc() # Free memory

2. How do you create and use an R environment?

Answer:
An environment is a collection of objects.

e <- new.env() e$x <- 10 ls(e) # Lists objects in the environment

3. What are the differences between apply(), lapply(), sapply(), and tapply()?

FunctionDescriptionReturns

apply()Apply function over rows/columns of a matrixVector or matrix

lapply()Apply function over a listList

sapply()Like lapply(), but simplifies outputVector or matrix

tapply()Apply function over subsets of a vectorVector or list

Example:

apply(matrix(1:9, 3, 3), 2, sum) # Sum of each column

4. How do you debug an R function?

Answer:

Use debug(my_function).
Insert browser() inside the function.
Use traceback() after an error.

Example:

debug(my_function) my_function() # Step through function execution undebug(my_function)

5. How do you vectorize a function in R?

Answer:
Use Vectorize().

vectorized_func <- Vectorize(function(x) x^2) vectorized_func(1:5) # Outputs: 1, 4, 9, 16, 25

6. How do you check if an object is a list?

Answer:

is.list(my_object)

7. What is the difference between factor() and as.factor()?

Answer:

factor() creates a factor from a vector.
as.factor() coerces an object to a factor.

x <- c("A", "B", "A") factor(x) # Creates a factor as.factor(x) # Converts existing variable

8. What is the eval() function used for?

Answer:
Evaluates an expression.

expr <- expression(2 + 2) eval(expr) # Output: 4

9. How do you join two datasets efficiently in R?

Answer:
Using merge() or dplyr::left_join().

merge(df1, df2, by = "ID") # Base R df1 %>% left_join(df2, by = "ID") # dplyr

10. How do you implement a map function in R?

Answer:
Using purrr::map().

library(purrr) map(1:5, sqrt) # Applies sqrt to each element

11. What is the purpose of the sys.call() function?

Answer:
Returns the function call that generated the error.

f <- function() { sys.call() } f() # Output: f()

12. How do you remove duplicate rows from a data frame?

Answer:
Using distinct() from dplyr.

library(dplyr) df <- distinct(df)

13. What is a closure in R?

Answer:
A function that captures variables from its environment.

counter <- function() { x <- 0 function() { x <<- x + 1; x } } c1 <- counter() c1() # Output: 1 c1() # Output: 2

14. How do you perform logistic regression in R?

Answer:

model <- glm(Y ~ X1 + X2, data = df, family = binomial) summary(model)

15. How do you create a correlation matrix in R?

Answer:

cor(df[, c("Var1", "Var2", "Var3")])

16. How do you measure execution time in R?

Answer:
Using Sys.time() or system.time().

start <- Sys.time() Sys.sleep(2) end <- Sys.time() end - start # Output: Time difference of 2 secs

17. What is the difference between filter() and subset()?

Answer:

filter() is from dplyr and more readable.
subset() is base R.

df %>% filter(Age > 25) subset(df, Age > 25)

18. How do you create an interactive plot in R?

Answer:
Using plotly.

library(plotly) plot_ly(data = df, x = ~Age, y = ~Salary, type = 'scatter', mode = 'markers')

19. How do you apply a function to a grouped dataset?

Answer:
Using group_by() and summarize().

df %>% group_by(Age) %>% summarize(AvgSalary = mean(Salary))

20. How do you reshape data in R using tidyverse?

Answer:
Using pivot_longer() and pivot_wider().

library(tidyr) pivot_longer(df, cols = c("Q1", "Q2"), names_to = "Quarter", values_to = "Sales")

21. How do you write an R script output to a file?

Answer:
Using sink().

sink("output.txt") print("This will be written to a file.") sink()

22. How do you visualize missing values in R?

Answer:
Using naniar or VIM package.

library(naniar) gg_miss_var(df)

23. What is bootstrapping in R?

Answer:
Resampling technique using boot package.

library(boot) boot_func <- function(data, indices) mean(data[indices]) boot(data = mtcars$mpg, statistic = boot_func, R = 1000)

24. How do you create a pipeline in R?

Answer:
Using %>% operator from magrittr or dplyr.

df %>% filter(Age > 25) %>% mutate(NewCol = Age * 2)

25. How do you execute shell commands from R?

Answer:
Using system() or shell().

system("ls") # Lists files in directory (Linux/macOS) shell("dir") # Lists files (Windows)

Best Software Training Institute In Coimbatore -Idm Techpark Coimbatore

"Deep Concepts to Elevate Your Career"

This guide provides 100+ R Programming Interview questions along with in-depth concepts to strengthen your expertise.

Download

R Programming Interview Questions and Answers

Top 100 R Programming Programming Interview Questions for Freshers

2. How do you install a package in R?

3. How do you load a package in R?

4. How do you check the version of R installed on your system?

5. How do you create a vector in R?

6. What are the data types in R?

7. How do you check the data type of a variable in R?

8. How do you create a matrix in R?

9. How do you create a data frame in R?

10. How do you access elements in a vector?

11. What is the difference between == and = in R?

12. What is the difference between NA, NULL, and NaN in R?

13. How do you generate a sequence of numbers in R?

14. What function is used to apply an operation to each element of a vector?

15. How do you read a CSV file in R?

16. How do you write a CSV file in R?

17. What is the difference between paste() and paste0() in R?

18. How do you remove missing values (NA) from a dataset?

19. How do you check the structure of a dataset in R?

20. How do you find the number of rows and columns in a dataset?

21. How do you rename columns in R?

22. What is the difference between apply(), lapply(), and sapply()?

23. How do you subset data in R?

24. What is the difference between a list and a vector in R?

25. How do you plot a graph in R?

"Deep Concepts to Elevate Your Career"

This guide provides 100+ R Programming Interview questions along with in-depth concepts to strengthen your expertise.

Courses

Contact Us