R Programming Interview Questions and Answers (2026 Guide)

Quick answer: Core R programming interview questions and answers covering data structures, data manipulation with dplyr, statistical functions, and common interview questions for analytics roles.

Why R Still Comes Up in Interviews

R remains widely used in statistics heavy roles, academic research, biostatistics and some corporate analytics teams, particularly where statistical rigour and visualisation are central to the work. Interviews for these roles commonly test R's core data structures, the tidyverse style of data manipulation, and statistical fundamentals.

R Data Structures

What are the main data structures in R?

What is the difference between a list and a vector?

A vector holds elements of a single type, such as all numbers or all text. A list can hold elements of different types and even other lists, making it more flexible but less memory efficient for uniform data.

Data Manipulation Questions

What is the tidyverse and why is it widely used?

The tidyverse is a collection of R packages, including dplyr and ggplot2, built around a consistent, readable syntax for data manipulation and visualisation, which is why it has become the default approach for most modern R analytics work.

library(dplyr)

sales_summary <- sales_data %>%
  filter(amount > 0) %>%
  group_by(region) %>%
  summarise(total_sales = sum(amount),
            avg_sale = mean(amount))

What does the pipe operator do?

The pipe, written as %>% in the tidyverse or |> in base R, passes the result of one expression as the first argument to the next, making a sequence of data transformations far more readable than deeply nested function calls.

How do you handle missing values in R?

# Check for missing values
sum(is.na(df$income))

# Remove rows with missing values in a specific column
df <- df[!is.na(df$income), ]

# Replace missing values with the median
df$income[is.na(df$income)] <- median(df$income, na.rm = TRUE)

Statistical Functions in R

How would you run a t-test in R?

t.test(group_a$score, group_b$score)

The output includes the t-statistic, degrees of freedom and p-value, letting you assess whether the difference between the two groups is statistically significant.

How would you fit a linear regression model?

model <- lm(sales ~ advertising_spend + price, data = sales_data)
summary(model)

Common Interview Questions

What is the difference between R and Python for data analysis?

R has particularly strong native support for statistical modelling and visualisation, and remains dominant in academic statistics and biostatistics. Python has broader general purpose use and a larger ecosystem for Machine Learning and production deployment. Many analytics teams use both depending on the task.

What is a factor in R and why does it matter?

A factor represents categorical data with a fixed set of levels, which affects how functions like regression models treat that variable, so understanding factor levels and their reference category is important for interpreting model output correctly.

Final Thoughts

R interviews typically reward genuine comfort with the tidyverse workflow, solid statistical fundamentals, and the ability to interpret model output correctly, more than exhaustive knowledge of every package available.