Top 40 R Programming Interview Questions 2026

Last Updated: June 2026 | Level: Freshers to 3 Years Experience | Read Time: ~18 min

R is the dominant language for statistical computing and data analysis in academia, pharma, and financial analytics. Candidates report that data manipulation with dplyr, visualization with ggplot2, and statistical modeling are the most tested areas in data analyst and data scientist interviews.

Pair with Python Interview Questions 2026 for comparison. Confirm current interview requirements on the official careers portal of your target company.

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Table of Contents

1. R Basics (Q1-Q12)
2. Data Frames and dplyr (Q13-Q22)
3. Visualization with ggplot2 (Q23-Q27)
4. Statistical Modeling (Q28-Q35)
5. Advanced R and Performance (Q36-Q40)
6. Mock Interview: 5 Questions
7. FAQ

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R Basics

Q1. What are the basic data types in R? Easy

# Atomic vector types
x_num   <- 3.14           # numeric (double)
x_int   <- 5L             # integer (note the L)
x_chr   <- "hello"        # character
x_bool  <- TRUE           # logical
x_cplx  <- 2 + 3i         # complex

# Check types
class(x_num)              # "numeric"
typeof(x_int)             # "integer"
is.numeric(x_num)         # TRUE
is.character(x_chr)       # TRUE

# Type coercion (implicit hierarchy: logical < integer < double < complex < character)
c(TRUE, 1L, 3.14, "a")  # "TRUE" "1" "3.14" "a"  (all to character)

---

Q2. What is a vector in R? Easy

# Create vector
v <- c(1, 2, 3, 4, 5)
w <- 1:10               # integer sequence
seq(0, 1, by = 0.2)    # 0.0 0.2 0.4 0.6 0.8 1.0
rep(c(1, 2), times = 3) # 1 2 1 2 1 2
rep(c(1, 2), each = 3)  # 1 1 1 2 2 2

# Indexing (1-based, NOT 0-based like Python/Java)
v[1]        # 1 (first element)
v[c(1, 3)]  # 1 3
v[-1]       # 2 3 4 5 (exclude first)
v[v > 3]    # 4 5 (logical indexing)

# Vectorized operations
v * 2       # 2 4 6 8 10
v + 10      # 11 12 13 14 15
sum(v)      # 15
mean(v)     # 3

---

Q3. What is the difference between = and <-? Easy

x <- 5      # preferred assignment operator in R
x = 5       # also works for assignment in most contexts
5 -> x      # right-assign (rare)

# Key difference: inside function arguments
f(x <- 1)   # assigns to x in parent env, passes x to f
f(x = 1)    # only passes as named argument, does NOT assign to x

# Style guide (tidyverse): use <- for assignment, = for function arguments
result <- mean(x = c(1, 2, 3))

---

Q4. What are R lists? Easy

# List: heterogeneous container (unlike vectors)
person <- list(
    name = "Aditya",
    age  = 25,
    scores = c(90, 85, 92)
)

# Access
person$name       # "Aditya" (dollar sign)
person[["name"]]  # "Aditya" (double bracket, returns element)
person["name"]    # list with one element (single bracket, returns list)
person[[3]]       # c(90, 85, 92)
person[[3]][2]    # 85

# Modify
person$city <- "Delhi"
person[["age"]] <- 26

---

Q5. What are R matrices and arrays? Medium

# Matrix: 2D array
m <- matrix(1:9, nrow = 3, ncol = 3)
# [,1] [,2] [,3]
# [1,]  1    4    7
# [2,]  2    5    8
# [3,]  3    6    9

m[2, 3]      # row 2, col 3 = 8
m[1, ]       # first row
m[, 2]       # second column
t(m)         # transpose
m %*% m      # matrix multiplication
det(m)       # determinant
solve(m)     # inverse (if invertible)

# Array: n-dimensional
arr <- array(1:24, dim = c(2, 3, 4))
dim(arr)  # 2 3 4

---

Q6. What are factors in R? Medium

# Factor: categorical variable with defined levels
grade <- factor(c("A", "B", "A", "C", "B", "A"))
levels(grade)          # "A" "B" "C"
nlevels(grade)         # 3
table(grade)           # frequency table: A=3, B=2, C=1

# Ordered factor
severity <- factor(c("Low", "High", "Med", "High"),
                   levels = c("Low", "Med", "High"),
                   ordered = TRUE)
severity[1] < severity[2]  # TRUE (Low < High)

# Relevance: factors affect regression (dummy variables) and plot ordering

---

Q7. What are control structures in R? Easy

# if/else
x <- 10
if (x > 5) {
    print("big")
} else if (x > 0) {
    print("small")
} else {
    print("non-positive")
}

# for loop
for (i in 1:5) { cat(i, " ") }  # 1 2 3 4 5

# while loop
n <- 1
while (n <= 3) { cat(n, " "); n <- n + 1 }

# ifelse: vectorized conditional
x <- c(-2, -1, 0, 1, 2)
ifelse(x > 0, "pos", "non-pos")  # "non-pos" "non-pos" "non-pos" "pos" "pos"

---

Q8. What are R functions? Easy

# Define function
greet <- function(name, greeting = "Hello") {
    paste(greeting, name)
}
greet("Aditya")             # "Hello Aditya"
greet("Aditya", "Hi")       # "Hi Aditya"
greet(greeting = "Namaste", name = "Aditya")  # named args

# Return multiple values as list
stats <- function(x) {
    list(mean = mean(x), sd = sd(x), n = length(x))
}
result <- stats(c(10, 20, 30))
result$mean  # 20

# Anonymous function
sapply(1:5, function(n) n^2)  # 1 4 9 16 25

---

Q9. What are apply functions? Medium

m <- matrix(1:9, 3, 3)

apply(m, 1, sum)   # row sums: 12 15 18
apply(m, 2, sum)   # col sums: 6 15 24
apply(m, 1, mean)  # row means

# sapply: simplifies result to vector/matrix
sapply(1:5, function(x) x^2)  # 1 4 9 16 25

# lapply: always returns list
lapply(1:3, function(x) x * 10)  # list(10, 20, 30)

# vapply: type-safe sapply (specify return type)
vapply(1:5, function(x) x^2, numeric(1))

# tapply: apply by group
scores <- c(85, 90, 78, 92, 88)
group  <- c("A", "B", "A", "B", "A")
tapply(scores, group, mean)  # A: 83.67, B: 91

---

Q10. What is which, any, all? Easy

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

which(x > 0)        # 1 3 5 (indices of positive values)
which.min(x)        # 4 (index of minimum: -4)
which.max(x)        # 5 (index of maximum: 5)

any(x > 0)          # TRUE (at least one positive)
all(x > 0)          # FALSE (not all positive)
any(is.na(x))       # FALSE (no NAs)

---

Q11. What are environments in R? Advanced

# Every R session has a global environment
ls()               # list objects in global env
exists("x")        # check if variable exists
rm(x)              # remove variable
rm(list = ls())    # clear all (use carefully!)

# Environment chain (lexical scoping)
x <- 1
f <- function() {
    x <- 2       # local x, shadows global
    g <- function() {
        x        # finds x=2 in f's environment
    }
    g()
}
f()   # 2

---

Q12. Predict the output: Easy

x <- c(1, 2, 3, 4, 5)
result <- x[x > 2 & x < 5]
cat(result, "\n")
cat(sum(x > 3), "\n")

Output:

3 4
2

Explanation: x > 2 & x < 5 is TRUE for indices 3,4 (values 3,4). x > 3 gives TRUE for 4,5 (count = 2).

---

Data Frames and dplyr

Q13. What is a data frame? Easy

# Create data frame
df <- data.frame(
    name   = c("Aditya", "Priya", "Rahul"),
    age    = c(25, 23, 27),
    score  = c(88, 92, 75),
    stringsAsFactors = FALSE  # keep strings as character
)

# Access
df$name          # character vector
df[, "age"]      # age column
df[1, ]          # first row
df[df$age > 24, ] # filter rows

# Dimensions
nrow(df)    # 3
ncol(df)    # 3
dim(df)     # 3 3
str(df)     # structure: column names, types, sample values
summary(df) # descriptive statistics per column

---

Q14. What is the tidyverse? Easy

library(tidyverse)
# Loads all core tidyverse packages at once

---

Q15. What are dplyr core verbs? Medium

library(dplyr)
df <- tibble(name = c("Aditya","Priya","Rahul"), age = c(25,23,27), score = c(88,92,75), dept = c("IT","IT","HR"))

# filter: rows
df |> filter(age > 24)

# select: columns
df |> select(name, score)
df |> select(-dept)        # drop dept
df |> select(starts_with("s"))  # columns starting with 's'

# mutate: add/modify columns
df |> mutate(
    age_next_year = age + 1,
    grade = if_else(score >= 90, "A", "B")
)

# arrange: sort
df |> arrange(desc(score))

# summarise + group_by
df |>
    group_by(dept) |>
    summarise(avg_score = mean(score), n = n())

# The pipe |> chains operations

---

Q16. What are dplyr join operations? Medium

employees <- tibble(id = 1:3, name = c("A","B","C"), dept_id = c(1,1,2))
departments <- tibble(id = 1:2, dept_name = c("IT","HR"))

# Types match SQL joins
inner_join(employees, departments, by = c("dept_id" = "id"))
left_join(employees, departments, by = c("dept_id" = "id"))   # keep all employees
right_join(employees, departments, by = c("dept_id" = "id"))  # keep all departments
full_join(employees, departments, by = c("dept_id" = "id"))   # keep all
anti_join(employees, departments, by = c("dept_id" = "id"))   # employees without dept
semi_join(employees, departments, by = c("dept_id" = "id"))   # employees that match

---

Q17. What is pivot_longer and pivot_wider (tidyr)? Medium

library(tidyr)

# Wide to long (common for time series data)
wide <- tibble(
    name  = c("Aditya", "Priya"),
    jan   = c(90, 85),
    feb   = c(88, 92),
    mar   = c(95, 87)
)

long <- wide |> pivot_longer(
    cols = c(jan, feb, mar),
    names_to = "month",
    values_to = "score"
)

# Long to wide
long |> pivot_wider(names_from = month, values_from = score)

---

Q18. What is map from purrr? Advanced

library(purrr)

# map: apply function to each element, return list
map(1:5, ~ .x^2)  # list(1, 4, 9, 16, 25)

# map_dbl, map_int, map_chr: type-safe
map_dbl(1:5, ~ .x^2)  # numeric vector: 1 4 9 16 25
map_chr(1:3, ~ paste("item", .x))  # "item 1" "item 2" "item 3"

# map2: two-argument map
map2_dbl(1:3, 4:6, ~ .x + .y)  # 5 7 9

# walk: for side effects (like sapply with invisible return)
walk(c("a", "b", "c"), ~ cat(.x, "\n"))

---

Q19. What is across in dplyr? Advanced

# Apply the same operation to multiple columns
df |>
    mutate(across(where(is.numeric), ~ round(.x, 2)))

df |>
    summarise(across(c(age, score), list(
        mean = mean,
        sd   = sd,
        n    = length
    )))
# Creates: age_mean, age_sd, age_n, score_mean, score_sd, score_n

---

Q20. What are missing values (NA) in R? Medium

x <- c(1, NA, 3, NA, 5)

is.na(x)           # FALSE TRUE FALSE TRUE FALSE
sum(is.na(x))      # 2 (count of NAs)
mean(x)            # NA (NAs propagate by default)
mean(x, na.rm = TRUE)  # 3 (ignore NAs)

# Remove NAs
x[!is.na(x)]       # 1 3 5
na.omit(x)         # 1 3 5

# In data frames
df |> filter(!is.na(score))        # remove NA rows
df |> mutate(score = replace_na(score, 0))  # fill NAs with 0

---

Q21. Predict the output: Medium

df <- data.frame(x = c(1, 2, NA, 4, 5), y = c(2, NA, 4, 5, 6))
cat(nrow(na.omit(df)), "\n")
cat(sum(complete.cases(df)), "\n")

Output:

3
3

Explanation: Rows with any NA: row 2 (y is NA) and row 3 (x is NA). So 5 - 2 = 3 complete cases. na.omit and complete.cases both identify rows where ALL values are non-NA.

---

Q22. What is readr for reading data? Easy

library(readr)

# Read CSV
df <- read_csv("data.csv")           # auto-detects types, fast
df <- read_csv("data.csv", 
    col_types = cols(age = col_integer(), name = col_character()),
    na = c("", "NA", "N/A", "null"))

# Write
write_csv(df, "output.csv")
write_rds(df, "data.rds")   # R binary format (faster, preserves types)

# Excel
library(readxl)
df <- read_excel("data.xlsx", sheet = "Sheet1")

---

Visualization with ggplot2

Q23. What is the grammar of graphics (ggplot2)? Medium

library(ggplot2)

# ggplot2 layers: data + aesthetics + geometry + scales + facets + theme
ggplot(data = df, aes(x = age, y = score, color = dept)) +
    geom_point(size = 3, alpha = 0.7) +
    geom_smooth(method = "lm", se = FALSE) +
    scale_color_brewer(palette = "Set1") +
    labs(title = "Score vs Age", x = "Age (years)", y = "Score") +
    theme_minimal()

---

Q24. What are common geoms in ggplot2? Easy

---

Q25. What are facets? Medium

# facet_wrap: grid by one variable
ggplot(df, aes(x = age, y = score)) +
    geom_point() +
    facet_wrap(~ dept)

# facet_grid: rows x columns by two variables
ggplot(df, aes(x = score)) +
    geom_histogram() +
    facet_grid(gender ~ dept)

---

Q26. What is aes vs fixed aesthetics? Medium

# aes(): mapped to data (varies by observation)
ggplot(df, aes(x = age, y = score, color = dept, size = grade)) +
    geom_point()

# Fixed (outside aes): same for all points
ggplot(df, aes(x = age, y = score)) +
    geom_point(color = "steelblue", size = 3, alpha = 0.6)

---

Q27. How do you save a ggplot? Easy

p <- ggplot(df, aes(x = age, y = score)) + geom_point()

ggsave("plot.png", plot = p, width = 8, height = 6, dpi = 300)
ggsave("plot.pdf", plot = p, width = 8, height = 6)
ggsave("plot.svg", plot = p)

---

Statistical Modeling

Q28. How do you do linear regression in R? Medium

# Simple linear regression
model <- lm(score ~ age, data = df)
summary(model)  # coefficients, R^2, p-values, residuals

# Multiple regression
model2 <- lm(score ~ age + experience + education, data = df)

# Key output from summary():
# Coefficients: estimate, std error, t value, p value
# R-squared: proportion of variance explained
# F-statistic: overall model significance

# Predictions
predict(model, newdata = data.frame(age = c(25, 30)))

# Plot residuals
plot(model)  # 4 diagnostic plots

---

Q29. What is logistic regression in R? Medium

# Binary outcome (0/1)
model <- glm(passed ~ score + study_hours,
             data = df,
             family = binomial(link = "logit"))

summary(model)
# Coefficients are log-odds

# Probabilities
predict(model, type = "response")  # probabilities 0-1

# Model evaluation
library(pROC)
roc_curve <- roc(df$passed, predict(model, type = "response"))
auc(roc_curve)  # Area under ROC curve

# glm covers: gaussian (linear), binomial (logistic), poisson (count)

---

Q30. What are common statistical tests in R? Medium

# t-test: compare means
t.test(group1, group2, alternative = "two.sided")
t.test(score ~ gender, data = df)

# Chi-square: categorical association
chisq.test(table(df$dept, df$grade))

# ANOVA: compare means across groups
anova_result <- aov(score ~ dept, data = df)
summary(anova_result)
TukeyHSD(anova_result)  # post-hoc pairwise comparison

# Wilcoxon (non-parametric t-test)
wilcox.test(group1, group2)

# Shapiro-Wilk: test normality
shapiro.test(x)  # H0: x is normally distributed

---

Q31. What is cross-validation in R? Advanced

library(caret)

# 10-fold cross-validation
ctrl <- trainControl(method = "cv", number = 10)
model <- train(score ~ age + experience,
               data = df,
               method = "lm",
               trControl = ctrl)
print(model)  # RMSE, R^2, MAE averaged across folds

# For classification
ctrl_class <- trainControl(
    method = "cv",
    number = 10,
    classProbs = TRUE,
    summaryFunction = twoClassSummary
)

---

Q32. What are random forests in R? Advanced

library(randomForest)

# Classification
rf_model <- randomForest(
    Species ~ .,
    data = iris,
    ntree = 500,       # number of trees
    mtry = 2,          # features per split
    importance = TRUE
)

print(rf_model)        # OOB error, confusion matrix
varImpPlot(rf_model)   # feature importance plot

# Regression
rf_reg <- randomForest(
    score ~ age + experience,
    data = df,
    ntree = 200
)
predict(rf_reg, newdata = test_df)

---

Q33. What is the difference between cor and cor.test? Easy

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

cor(x, y)  # 0.832 (Pearson correlation coefficient)

cor.test(x, y)  # full test with p-value, CI
# t = 2.98, df = 3, p-value = 0.0587
# cor = 0.832 (not significant at alpha=0.05)

# Spearman (rank-based, robust to outliers)
cor(x, y, method = "spearman")

---

Q34. What is time series analysis in R? Advanced

# Create time series
ts_data <- ts(c(100, 105, 98, 110, 120, 115, 125), 
              frequency = 12, start = c(2024, 1))

# Decompose into trend, seasonal, remainder
decomposed <- decompose(ts_data)
plot(decomposed)

# ARIMA forecasting
library(forecast)
model <- auto.arima(ts_data)  # auto-selects p,d,q
forecast(model, h = 12)       # forecast 12 periods ahead
autoplot(forecast(model, h = 12))

---

Q35. What is broom for model output? Medium

library(broom)

model <- lm(score ~ age + dept, data = df)

# tidy(): coefficients as data frame
tidy(model)
# term, estimate, std.error, statistic, p.value

# glance(): model-level statistics
glance(model)
# r.squared, adj.r.squared, AIC, BIC, p.value, df

# augment(): row-level predictions and residuals
augment(model, data = df)
# .fitted, .resid, .hat, .sigma, .cooksd

---

Advanced R and Performance

Q36. What are R packages and CRAN? Easy

install.packages("dplyr")          # from CRAN
install.packages(c("dplyr","ggplot2"))  # multiple

library(dplyr)                     # load into session
require(dplyr)                     # like library() but returns FALSE if missing

# From GitHub
devtools::install_github("tidyverse/dplyr")

# Available packages
available.packages()               # all CRAN packages
installed.packages()               # what you have
update.packages()                  # update all

---

Q37. What is Rmarkdown? Medium

# R Markdown: reproducible reports combining R code + text
# File extension: .Rmd

# YAML header
---
title: "Analysis Report"
output: html_document
---

# Code chunks
` ` `{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
` ` `

# Render
rmarkdown::render("report.Rmd", output_format = "html_document")
rmarkdown::render("report.Rmd", output_format = "pdf_document")

---

Q38. What is Shiny? Medium

library(shiny)

ui <- fluidPage(
    titlePanel("Score Explorer"),
    sidebarLayout(
        sidebarPanel(
            sliderInput("min_age", "Min Age", min = 18, max = 40, value = 20)
        ),
        mainPanel(
            plotOutput("scorePlot")
        )
    )
)

server <- function(input, output) {
    output$scorePlot <- renderPlot({
        df |>
            filter(age >= input$min_age) |>
            ggplot(aes(x = age, y = score)) +
            geom_point()
    })
}

shinyApp(ui = ui, server = server)

---

Q39. What is data.table for performance? Advanced

library(data.table)

dt <- fread("large_file.csv")  # fast read (5-10x faster than read.csv)

# data.table syntax: dt[i, j, by]
# i = row filter, j = column ops, by = group by
dt[age > 25, .(name, score)]                    # filter + select
dt[, .(avg_score = mean(score)), by = dept]     # group by
dt[order(-score)][1:10]                         # top 10 by score
dt[, score_sq := score^2]                       # add column in-place (no copy!)

# Join
dt1[dt2, on = "id"]               # inner join
dt1[dt2, on = "id", nomatch = 0]  # inner join explicit

---

Q40. Predict the output: Medium

x <- list(a = 1:3, b = 4:6, c = 7:9)
result <- sapply(x, function(v) sum(v))
cat(result, "\n")
cat(class(result), "\n")

Output:

6 15 24
integer

Explanation: sapply applies sum to each vector: sum(1:3)=6, sum(4:6)=15, sum(7:9)=24. Since all returns are single integers, sapply simplifies to a named integer vector.

---

Mock Interview: 5 Questions

1. Read a CSV file, filter rows where salary > 50000, group by department, and compute mean salary per department using dplyr.
2. What is the difference between apply, sapply, and lapply? When would you use each?
3. Write a ggplot that shows a boxplot of salaries by department, colored by gender.
4. Explain what set.seed() does and why it matters in statistical computing.
5. Given a numeric vector with NAs, write a one-liner to compute the mean ignoring NAs and replacing NAs with the mean.

---

FAQ

Q: Is base R or tidyverse better?

A: For modern data analysis, tidyverse (especially dplyr and ggplot2) is more readable and maintainable. For performance on large datasets, data.table is often faster. Base R is important to know for understanding R fundamentals and writing portable code.

Q: What salary can I expect for an R analyst role in India?

A: Confirm current salary data on official job portals. Glassdoor and LinkedIn show current compensation ranges for analytics roles. R-heavy roles in pharma, finance, and consulting tend to pay well for specialists.

Q: Should I learn Quarto instead of R Markdown?

A: Quarto is the successor to R Markdown and supports Python, Julia, and Observable JS alongside R. New projects should prefer Quarto; existing R Markdown workflows are still fully supported.

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