R Programming in Data Science Interview Questions and Answers (2025)

Interview Questions and Answers for R Programming in Data Science (2025)

1. What is R programming, and why is it used in Data Science?
Answer:
R is a powerful open-source programming language and environment primarily used for statistical computing and data analysis. In Data Science, R is extensively used for data manipulation, statistical analysis, visualization, and building predictive models. Its vast collection of libraries like ggplot2, dplyr, and tidyr make it an ideal tool for exploring, processing, and analyzing large datasets.

2. What are the different data types in R?
Answer:
In R, there are several core data types, including:
Numeric: Used for numbers, both integers and floating-point numbers.
Integer: Specifically used for whole numbers (e.g., 5L).
Character: Used for text strings (e.g., "Hello World").
Logical: Boolean values, either TRUE or FALSE.
Complex: Used for complex numbers (e.g., 1 + 2i).
Raw: Used for raw byte data.
Understanding these basic data types is crucial for manipulating and transforming data in R.

3. How does R handle missing values?
Answer:
R uses NA to represent missing or undefined values in a dataset. There are several ways to handle missing values in R:
Identifying missing values: Use is.na() to check for missing values.
Removing missing values: Functions like na.omit() or na.exclude() can remove rows with missing data.
Replacing missing values: The tidyr package provides fill() to replace missing values with the last or next valid entry, or you can use custom imputation techniques.

4. What is a data frame in R, and how do you create one?
Answer:
A data frame in R is a table-like structure used to store datasets. It is similar to a spreadsheet or SQL table, with rows and columns. Each column in a data frame can contain different types of data.
To create a data frame, you can use the data.frame() function:
df <- data.frame(
  Name = c("John", "Jane", "Doe"),
  Age = c(28, 34, 45),
  Salary = c(50000, 60000, 70000)
)
This creates a data frame with columns for Name, Age, and Salary.

5. What are some common data manipulation functions in R?
Answer:
R provides a variety of functions for data manipulation, particularly through libraries such as dplyr and tidyr:
·         filter(): To filter rows based on conditions.
·         select(): To select specific columns.
·         mutate(): To add new columns or modify existing ones.
·         arrange(): To sort data.
·         group_by(): To group data for summary statistics.
·         summarize(): To generate summary statistics.
These functions are part of the tidyverse, a popular collection of R packages for data manipulation and visualization.

6. Explain the difference between apply(), lapply(), and sapply() in R.
Answer:
apply(): Applies a function to the rows or columns of a matrix or array. Example: apply(matrix, 1, sum) sums each row of a matrix.
lapply(): Applies a function to each element of a list and returns a list of the same length.
sapply(): Similar to lapply(), but it simplifies the output (e.g., to a vector or matrix) if possible.
These functions are used for iteration over data structures, but the output format differs based on the function used.

7. What is the use of the ggplot2 library in R?
Answer:
ggplot2 is a popular data visualization library in R, known for its ability to create complex, multi-layered visualizations with ease. It uses a grammar of graphics to create plots, where you define the plot in layers:
Aesthetic mappings (e.g., which variables map to the x and y axes).
Geometries (e.g., scatter plots, bar charts).
Statistics (e.g., adding a regression line).
Coordinates (e.g., polar or Cartesian coordinates).
For example, to create a scatter plot:
library(ggplot2)
ggplot(data, aes(x = variable1, y = variable2)) +
  geom_point()

8. What are the key differences between R and Python in Data Science?
Answer:
Both R and Python are widely used in Data Science, but they have distinct strengths:
R: Best suited for statistical analysis and visualization. It has a rich ecosystem for data manipulation and statistical tests. Libraries like ggplot2, caret, and shiny make it a go-to for data scientists working in academia or research.
Python: Known for its flexibility and ease of use. It is more versatile for software development, and libraries like pandas, NumPy, matplotlib, and scikit-learn make it excellent for machine learning, web development, and data analysis.
Both are valuable, and the choice depends on the specific use case and familiarity with the language.

9. What is the purpose of the tidyr package in R?
Answer:
The tidyr package in R is used to tidy up datasets. It helps in reshaping, transforming, and organizing data for analysis. Some key functions in tidyr include:
·         gather(): To gather columns into key-value pairs.
·         spread(): To spread key-value pairs into columns.
·         separate(): To split a column into multiple columns.
·         unite(): To combine multiple columns into one.
These functions help clean and structure data, making it easier to analyze.

10. How do you perform linear regression in R?
Answer:
In R, linear regression can be performed using the lm() function. Here’s a basic example:
# Sample data
data <- data.frame(
  x = c(1, 2, 3, 4, 5),
  y = c(2, 4, 5, 4, 5)
)
 
# Fit the linear regression model
model <- lm(y ~ x, data = data)
 
# View model summary
summary(model)
The lm() function fits a linear model, and summary() provides statistics like coefficients, p-values, R-squared, etc.

11. What is the role of caret package in R?
Answer:
The caret (short for Classification And REgression Training) package in R provides a set of functions for building machine learning models, performing data pre-processing, and model evaluation. It supports a wide range of algorithms for classification, regression, and resampling.
Key functionalities of caret include:
Data pre-processing: Scaling, centering, imputation, and encoding.
Model training: Training machine learning models with various algorithms.
Cross-validation: For evaluating model performance through resampling techniques.

12. How do you visualize a correlation matrix in R?
Answer:
To visualize a correlation matrix in R, you can use the corrplot package or the ggplot2 package.
Here’s how to use corrplot:
library(corrplot)
 
# Sample correlation matrix
cor_matrix <- cor(mtcars)
 
# Plot correlation matrix
corrplot(cor_matrix, method = "circle")
This generates a plot with circles representing the correlation values, where the color intensity indicates the strength of the correlation.

Tidyverse Interview Questions and Answers

1. What is the Tidyverse in R?
Answer:

The Tidyverse is a collection of R packages designed for data science. It includes ggplot2, dplyr, tidyr, readr, purrr, and tibble, among others. These packages share a common philosophy of tidy data and grammar-based syntax, which makes data manipulation, visualization, and analysis easier and more consistent.

2. What does %>% do in Tidyverse?

Answer: %>% is the pipe operator from the magrittr package,commonly used in the Tidyverse. It allows chaining of multiple functions by passing the result of one function as the first argument to the next. This makes the code more readable and expressive.

Example:

mtcars %>% 

  filter(mpg > 20) %>%

  select(mpg, cyl)

3. Explain the difference between mutate() and transmute() in dplyr.

Answer:        

mutate()

adds new variables or modifies existing ones while keeping all other variables.·        

transmute()

only keeps the new or modified variables.

Example:

df %>% mutate(new_col = col1 + col2)   # Keeps all columns  

df %>% transmute(new_col = col1 + col2) # Keeps only `new_col`

4. How does group_by() work with summarize()in dplyr?

Answer: group_by() splits the data into groups based on one or more variables. summarize()then performs aggregate operations on each group.

Example:

df %>%

  group_by(category) %>%

  summarize(mean_value = mean(value))

5. What is the purpose of pivot_longer() and pivot_wider() in tidyr?

Answer:        

pivot_longer()
converts wide-format data into long format.        
pivot_wider()
does the opposite — converts long-format data to wide format.
They replace the older gather() and spread() functions.

6. How do you perform joins in Tidyverse?

Answer:

Using dplyr’s join

functions:        

left_join():
keeps all rows from the left table        
inner_join():
keeps only matching rows        
right_join():
keeps all rows from the right table        
full_join():
keeps all rows from both tables

7. How do you use ggplot2 to plot a bar chart?Answer:

ggplot(data, aes(x = category)) +

  geom_bar()

To plot frequencies of categories. For pre-counted data, use:

ggplot(data, aes(x = category, y = count)) +

  geom_col()

8. What does arrange() do in dplyr?

Answer:

arrange() reorders rows
based on one or more variables, ascending by default. Use desc() for descending.

Example:

df %>% arrange(desc(score))

9. How is filter() different from select() in dplyr?

Answer:        

filter()
subsets rows based on conditions.     

   
select()
subsets columns.

10. What are some common data wrangling workflows in Tidyverse?

Answer:

1.      Read data: read_csv()

2.      Clean/reshape: pivot_longer(), separate()

3.      Filter/select: filter(), select()

4.      Mutate/summarize: mutate(), summarize()

5.      Group and aggregate: group_by()

6.      Join data: left_join(), inner_join()

7.      Visualize: ggplot2

Top 10 Tidyverse Interview Questions and Answers

1. What is the Tidyverse in R? Why is it important?

Answer:

The Tidyverse is a collection of R packages designed for data science. It includes:    

dplyr

(data manipulation)

ggplot2

(visualization)

  

tidyr

(reshaping)

readr

(data import)

purrr

(functional programming)

tibble

(modern data frames)

These packages follow a consistent syntax and support “tidy” data principles, making them essential for reproducible workflows.

2. What does %>% do in the Tidyverse?

Answer: The pipe operator (%>%) passes the result of one function to the next, improving code readability. It's from the magrittr package,
widely used in Tidyverse pipelines.

Example:

mtcars %>%

  filter(mpg > 20) %>%

  arrange(desc(mpg))

3. What is the difference between mutate() and transmute() in dplyr?

Answer:        

mutate() adds or modifies variables while keeping all existing ones.        

transmute() returns only the newly created variables.

4. How do group_by() and summarize()work together in dplyr?

Answer:        

group_by()
creates groups based on one or more variables.        
summarize()
calculates summary statistics for each group.

Example:

df %>%

  group_by(department) %>%

  summarize(avg_salary = mean(salary))

5. What are joins in dplyr? List a few with examples.

Answer:        

left_join(): Keeps all rows from the left dataset        

inner_join(): Keeps only matching rows         

full_join(): Keeps all rows from both datasets

Example:

left_join(df1, df2, by = "id")

6. How do you reshape data with pivot_longer() and pivot_wider()?

Answer:        

pivot_longer() turns wide data into long format        

pivot_wider() does the reverse

pivot_longer(df, cols = starts_with("year"), names_to = "year", values_to = "value")

7. How would you plot a bar chart using ggplot2?

Answer:

ggplot(data, aes(x = category)) +

  geom_bar()

For pre-counted data, use geom_col() with y values.

8. What’s the purpose of select() and filter() in dplyr?

Answer:        

select()
chooses columns        
filter()
selects rows that meet a condition

df %>% select(name, age) %>% filter(age > 30)

9. Explain the use of across() in dplyr.

Answer:

across() allows operations
across multiple columns inside mutate()
or summarize().

df %>%

  summarize(across(starts_with("score"), mean))

10. What are some common Tidyverse best practices for interviews?

Answer:        

Use %>% for chaining operations        

Avoid writing intermediate variables unnecessarily         

Use glimpse() and summary() for initial exploration        

Stick to tidy data principles: one observation per row, one variable per column