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R Programming Mock Interview: 20 Questions in 50 Minutes

Four rounds that copy a real interview. Answer aloud, then check the model answers.

Mock interview format

  1. Warm-up · 5 minutes — 4 questions
  2. Technical · 20 minutes — 7 questions
  3. Scenario · 15 minutes — 5 questions
  4. HR & behavioural · 10 minutes — 4 questions

Tip: record yourself and listen back. Most people talk too fast and skip the key idea.

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01

What are the principles of tidy data?

Warm-upData Wrangling with dplyr and tidyrEasy
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Tidy data follows three rules: each variable is a column, each observation is a row and each type of observational unit forms a table. For example, a sales sheet with separate columns for Jan, Feb and Mar is not tidy, because month is a variable spread across columns. Reshaping it into month and sales columns makes it tidy. Tidy data works directly with dplyr, ggplot2 and modelling functions, reducing reshaping effort later.
02

What is the difference between <- and = for assignment in R?

Warm-upR Basics and Data TypesEasy
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Both assign values at the top level, but <- is the conventional assignment operator in R style guides, while = is mainly used to pass named arguments inside function calls. For example, mean(x = 1:10) passes an argument and does not create x in the workspace, but mean(x <- 1:10) creates x as a side effect. Using <- for assignment keeps code readable and avoids confusion between assignment and argument matching.
03

What is the difference between variance and standard deviation?

Warm-upStatistics and Hypothesis TestingEasy
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Variance is the average squared deviation from the mean, and standard deviation is its square root. Because variance is in squared units, standard deviation is easier to interpret since it uses the same units as the data. In R, var() and sd() both use the sample formula with n - 1 in the denominator. For example, delivery times with mean 30 minutes and SD 5 minutes are far more consistent than with SD 15.
04

Why should R projects use Git and how does RStudio support it?

Warm-upReports, Shiny and Production REasy
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Git tracks every change to code, lets you revert mistakes, work on branches and collaborate through pull requests and reviews. RStudio has a built-in Git pane to stage, commit, diff, pull and push, and usethis::use_git() and use_github() set up repositories. Keep data files, secrets and outputs out of Git with .gitignore, commit the renv.lock file and write clear commit messages. Version control is expected even for analysis work.
05

How do you calculate ROC AUC and plot an ROC curve in R?

TechnicalModelling and Machine Learning in RMedium
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yardstick in tidymodels or pROC compute AUC from predicted probabilities. library(yardstick) preds <- tibble(truth = factor(test$churned, levels = c('1', '0')), .pred_1 = test$prob) roc_auc(preds, truth, .pred_1) roc_curve(preds, truth, .pred_1) %>% autoplot() # with pROC library(pROC) r <- roc(test$churned, test$prob) auc(r); plot(r)
06

How do you validate inputs inside an R function?

TechnicalFunctions and Functional ProgrammingMedium
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Fail early with clear messages using stopifnot(), stop() and match.arg(). summarise_kpi <- function(df, metric = c('revenue', 'orders', 'aov'), min_date = NULL) { stopifnot(is.data.frame(df), nrow(df) > 0) metric <- match.arg(metric) if (!is.null(min_date) && !inherits(min_date, 'Date')) { stop('min_date must be a Date', call. = FALSE) } required <- c('date', metric) missing <- setdiff(required, names(df)) if (length(missing)) stop('Missing columns: ', toString(missing)) # ... compute }
07

How do you add labels and annotations to a ggplot chart?

TechnicalData Visualization with ggplot2Medium
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Use geom_text() or geom_label() for data labels and annotate() for one-off notes. ggplot(city_sales, aes(reorder(city, sales), sales)) + geom_col(fill = 'steelblue') + geom_text(aes(label = scales::comma(sales)), hjust = -0.1, size = 3.5) + annotate('text', x = 2, y = 9e5, label = 'Festive season push') + coord_flip() + expand_limits(y = 1.1e6) + labs(x = NULL, y = 'Sales (INR)')
08

How do you debug an R function?

TechnicalFunctions and Functional ProgrammingMedium
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After an error, traceback() shows the call stack so I can see which function failed. I insert browser() inside a function to pause execution and inspect variables interactively, stepping with n and continuing with c. debug(fn) or debugonce(fn) enters the browser on the next call without editing code. options(error = recover) lets me choose a frame to inspect after an error. RStudio breakpoints offer the same features visually.
09

What is a p-value?

TechnicalStatistics and Hypothesis TestingMedium
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A p-value is the probability of seeing results at least as extreme as the observed ones if the null hypothesis were true. A small p-value, typically below 0.05, means the data would be unusual under the null, so we reject it. It is not the probability that the null hypothesis is true and does not measure effect size. A tiny, unimportant difference can be significant with a huge sample, so always report effect sizes and confidence intervals too.
10

How do you perform k-means clustering in R?

TechnicalModelling and Machine Learning in RMedium
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Scale features first, choose k using the elbow or silhouette method, then fit. library(factoextra) X <- scale(customers[, c('recency', 'frequency', 'monetary')]) fviz_nbclust(X, kmeans, method = 'wss') # elbow fviz_nbclust(X, kmeans, method = 'silhouette') set.seed(42) km <- kmeans(X, centers = 4, nstart = 25) customers$segment <- km$cluster aggregate(cbind(recency, frequency, monetary) ~ segment, data = customers, FUN = mean)
11

How do you create, modify and combine lists in R?

TechnicalData Structures in RMedium
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Lists are built with list(), modified by name and combined or flattened when needed. emp <- list(name = 'Asha', skills = c('R', 'SQL'), exp = 3) emp$city <- 'Pune' # add element emp[['exp']] <- 4 # update emp$skills <- NULL # remove names(emp) parts <- lapply(1:3, function(i) data.frame(id = i, sq = i^2)) do.call(rbind, parts) # combine data frames unlist(list(a = 1, b = list(c = 2))) # flatten
12

An R script must run every morning at 7 AM and email a report. How do you schedule it?

ScenarioReports, Shiny and Production RMedium
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On a Linux server I schedule Rscript with cron, for example 0 7 * * * Rscript /path/report.R, and on Windows with Task Scheduler or the taskscheduleR package. Posit Connect can schedule Quarto reports and email them directly. GitHub Actions can run scheduled workflows for non-sensitive jobs. The script itself renders the report and sends it with blastula or emayili. I add logging and an alert on failure so silent misses are caught.
13

Your function fails with 'the condition has length > 1' when given a vector. What is wrong?

ScenarioFunctions and Functional ProgrammingMedium
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An if statement can only check a single TRUE or FALSE value, and since R 4.2 a longer condition is an error. The function was written for one value but received a vector. I would replace if with a vectorized alternative such as ifelse(), dplyr::if_else() or case_when(). If the function is truly meant for single values, I validate the input length with stopifnot(length(x) == 1) or apply it over elements with purrr::map().
14

You receive 60 monthly CSV files with the same structure. How do you combine them into one data frame?

ScenarioData Structures in RMedium
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I list the files with list.files(pattern = 'csv$', full.names = TRUE), then read them all at once with readr::read_csv(files, id = 'file'), which also records the source file. Alternatively I use purrr::map() with read_csv and dplyr::bind_rows(). Before combining, I check that column names and types match, because one file with text in a numeric column will fail or coerce the whole column. I then derive the month from the file name for traceability.
15

Your R session runs out of memory while processing a large dataset. What steps do you take?

ScenarioR Basics and Data TypesHard
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I check object sizes with lobstr::obj_size() and remove large intermediates with rm() followed by gc(). I load only the needed columns and use data.table, which modifies by reference without copies. For data larger than RAM, I push computation into a database through dbplyr, or use arrow with Parquet files so filters and aggregations run before data enters R memory. Chunked reading with readr::read_csv_chunked() is another option for one-pass jobs.
16

Your model has R-squared 0.92 on training data but 0.55 on test data. What do you do?

ScenarioModelling and Machine Learning in RMedium
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The gap indicates overfitting or data leakage. I first check for leakage, such as a feature computed using future information or the target itself, or duplicate records across train and test. Then I simplify the model, add regularization, reduce features or tune hyperparameters with cross-validation instead of a single split. If data is time-based, I make sure the split is chronological. I compare against a simple baseline to judge real improvement.
17

How do you explain technical results to non-technical managers?

HR & behaviouralData Structures in REasy
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I start with the business question and the answer in one sentence, then show one or two clear charts instead of tables of coefficients. For example, in a college project on customer churn, I said that customers with more than three complaints were twice as likely to leave, showed a simple bar chart and suggested a follow-up call process. I keep technical details in an appendix and invite questions so managers can decide confidently.
18

Tell me about the messiest dataset you have cleaned.

HR & behaviouralData Wrangling with dplyr and tidyrMedium
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Situation: I received three years of vendor invoices from different branches in Excel files with inconsistent headers, merged cells and dates in four formats. Task: build one clean table for spend analysis. Action: I used readxl to read all sheets, janitor::clean_names() to standardise headers, lubridate to parse dates and a mapping table for vendor name variants. I wrote validation checks for totals. Result: a clean dataset of 180,000 rows that finance now refreshes monthly.
19

Tell me about a dashboard or app you built in R.

HR & behaviouralReports, Shiny and Production RMedium
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Situation: regional sales managers waited for a weekly emailed PDF and could not drill into their own data. Task: give them self-service access. Action: I built a Shiny app with filters by region, product and period, used pre-aggregated tables for speed and deployed it on the internal server with login. I ran a short demo and gathered feedback. Result: over 40 managers used it weekly, and ad hoc data requests to our team fell by about half.
20

Tell me about feedback you received in a code review and what you changed.

HR & behaviouralFunctions and Functional ProgrammingMedium
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Situation: in my first quarter, a senior reviewed my R reporting script. Task: address the review comments. Action: the main feedback was that my 300-line script mixed data loading, cleaning and plotting with repeated code. I split it into small functions, added input checks and moved constants to a config file. Result: the next report took half the time to build, and I now structure every new script this way from the start.

Frequently asked questions

How do I use this mock interview?

Set a 50-minute timer, answer every question aloud (or ask a friend to read them), and open answers only after each round. Note every question where your answer missed the key idea.

How often should I take a mock?

Once a week while preparing and once two or three days before a real interview. The question order is fixed, so for fresh practice use the company and experience pages too.

More ways to practise

Company-wise R Programming questionsExperience-wise R Programming questionsRole-wise R Programming questionsCity-wise R Programming questionsMost asked R Programming questionsScenario-based questionsPractical / hands-on questionsHR & behavioural questionsOnline assessment questionsTechnical round questionsManagerial round questionsR Programming topic sheets