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Top 50 Most Asked R Programming Interview Questions

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A loop that adds rows to a data frame with rbind() takes very long. How do you speed it up?

Data Structures in RIn 9 company setsMedium

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Calling rbind() inside a loop copies the growing data frame each iteration, so time grows roughly quadratically. I instead store each piece in a preallocated list, results[[i]] <- piece, and combine once at the end with dplyr::bind_rows() or data.table::rbindlist(), which is the fastest. Better still is to vectorize the computation or use purrr::map() followed by list_rbind(). On 10,000 iterations this can cut runtime from minutes to seconds.

Reading a 2 GB CSV file with read.csv() is very slow. What would you do?

R Basics and Data TypesIn 8 company setsMedium

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read.csv() is single-threaded and guesses types slowly. I would switch to data.table::fread() or readr::read_csv(), which are many times faster and multi-threaded. I would read only the needed columns with the select argument and set column types explicitly. If the file is reused, I convert it once to a binary format like Parquet with the arrow package, which loads in seconds and supports reading just a subset of columns.

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

Data Structures in RIn 8 company setsMedium

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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

What do geom_smooth() and stat_summary() do?

Data Visualization with ggplot2In 8 company setsMedium

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geom_smooth() adds a fitted trend line with a confidence band, using loess for small data and a GAM for large data by default, or method = 'lm' for a straight line. stat_summary() computes summaries of y for each x, for example stat_summary(fun = mean, geom = 'point') to plot group means, or fun.data = mean_cl_normal for error bars. Both let you show patterns without precomputing summaries.

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

Modelling and Machine Learning in RIn 8 company setsMedium

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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)

What are facets in ggplot2?

Data Visualization with ggplot2In 7 company setsMedium

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Facets split a plot into small multiples, one panel per category, sharing the same axes and geoms. facet_wrap(~ region) wraps panels for one variable, and facet_grid(rows = vars(segment), cols = vars(year)) creates a grid for two variables. scales = 'free_y' lets each panel have its own y-axis. Facets are often clearer than one chart with many overlapping coloured lines, for example comparing sales trends across ten states.

How do you perform a t-test in R?

Statistics and Hypothesis TestingIn 7 company setsMedium

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t.test() handles one-sample, two-sample and paired tests. Welch's test, which does not assume equal variances, is the default. # two independent groups t.test(score ~ batch, data = students) # paired: before vs after training t.test(after, before, paired = TRUE) # one-sample against a target of 30 minutes t.test(delivery_time, mu = 30, alternative = 'greater') res <- t.test(score ~ batch, data = students) res$p.value; res$conf.int

You ran an A/B test on a checkout page: 4.1 percent conversion versus 4.6 percent. How do you decide if the difference is real?

Statistics and Hypothesis TestingIn 7 company setsMedium

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Conversion is a proportion, so I use a two-proportion test: prop.test(c(conv_a, conv_b), c(n_a, n_b)). I check the p-value and, more importantly, the confidence interval for the difference. Before trusting it, I confirm the sample size was planned in advance, the test ran for full weeks to cover weekday effects, users were randomized properly and nobody stopped the test early when results looked good. Then I judge business significance.

How do you validate inputs inside an R function?

Functions and Functional ProgrammingIn 7 company setsMedium

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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 }

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

Functions and Functional ProgrammingIn 7 company setsMedium

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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().

The same 40 lines of cleaning code are copied into ten scripts. What would you do?

Functions and Functional ProgrammingIn 7 company setsMedium

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I would extract the logic into well-named functions with arguments for the parts that differ, then move them into one shared file loaded with source() or, better, a small internal R package. A package gives documentation with roxygen2, tests with testthat and versioning, so every script uses the same tested logic. Fixing a bug then happens in one place. I would migrate the scripts gradually and compare outputs to confirm nothing changed.

How do you perform k-means clustering in R?

Modelling and Machine Learning in RIn 7 company setsMedium

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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)

Code reviews show inconsistent R code style across the team. How would you standardise it?

Reports, Shiny and Production RIn 7 company setsMedium

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I would adopt the tidyverse style guide as the team standard, then automate it rather than relying on reviewers. styler reformats code automatically, including an RStudio add-in, and lintr flags issues such as long lines, unused variables and inconsistent naming. I add lintr to a pre-commit hook or a GitHub Actions check so pull requests are checked automatically. A short template for new scripts and projects helps new joiners follow the same structure.

How do you add logging to a production R script?

Reports, Shiny and Production RIn 7 company setsMedium

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The logger package gives levelled, timestamped logs written to the console or a file. library(logger) log_appender(appender_file('logs/etl.log')) log_threshold(INFO) log_info('ETL started') res <- tryCatch({ df <- load_orders() log_info('Loaded {nrow(df)} rows') df }, error = function(e) { log_error('Load failed: {conditionMessage(e)}') quit(status = 1) }) log_info('ETL finished')

After merging two data frames you get more rows than expected. What happened?

Data Structures in RIn 6 company setsMedium

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Extra rows after a join usually mean the key is duplicated in one or both tables, so each match multiplies rows. I check uniqueness with df %>% count(key) %>% filter(n > 1) on both sides. Then I decide whether to deduplicate, aggregate before joining or join on a composite key such as customer_id plus month. In dplyr 1.1 and later, the relationship argument, for example 'many-to-one', makes the join fail loudly when this assumption breaks.

You need month-over-month sales growth for each product. How do you calculate it in R?

Data Wrangling with dplyr and tidyrIn 6 company setsMedium

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I first aggregate to one row per product and month with group_by() and summarise(). Then I group by product, arrange by month and use mutate(growth = (sales - lag(sales)) / lag(sales) * 100). The first month for each product becomes NA, which is correct. I make sure months with zero sales exist using tidyr::complete(), otherwise lag() compares non-consecutive months. Finally I round and format growth for reporting.

Salary data has a few extreme values that distort averages. How do you handle them?

Data Wrangling with dplyr and tidyrIn 6 company setsMedium

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First I investigate whether they are data errors, such as an extra zero, or genuine high earners. I flag outliers with the IQR rule, values beyond Q1 - 1.5 IQR or Q3 + 1.5 IQR, or with percentiles. Errors I fix or remove with documentation. Genuine values I keep, but I report the median alongside the mean, or winsorize at the 1st and 99th percentiles for modelling. The choice depends on the business question.

Tell me about the messiest dataset you have cleaned.

Data Wrangling with dplyr and tidyrIn 6 company setsMedium

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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.

How do you customise scales, labels and themes in ggplot2?

Data Visualization with ggplot2In 6 company setsMedium

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Scales control how data maps to visuals, and themes control non-data elements. p + scale_y_continuous(labels = scales::label_percent()) + scale_fill_brewer(palette = 'Set2') + labs(title = 'Conversion by Channel', subtitle = 'FY 2025-26', caption = 'Source: CRM') + theme_minimal(base_size = 12) + theme(legend.position = 'bottom', plot.title = element_text(face = 'bold'), panel.grid.minor = element_blank())

Your scatter plot of 200,000 points looks like a solid blob. How do you fix it?

Data Visualization with ggplot2In 6 company setsMedium

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This is overplotting. I reduce point size and add transparency with geom_point(alpha = 0.1, size = 0.5). Better options are geom_hex() or geom_bin2d(), which show density as colour, or geom_density_2d() contours. I could also plot a random sample for shape and a smoothed trend with geom_smooth() on the full data. If x values are discrete, geom_jitter() spreads points. The goal is to make density visible, not every point.

How do you build a logistic regression model for churn in R?

Modelling and Machine Learning in RIn 6 company setsMedium

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Use glm() with family = binomial; predictions with type = 'response' are probabilities. fit <- glm(churned ~ tenure + monthly_charges + contract, data = train, family = binomial) summary(fit) exp(coef(fit)) # odds ratios test$prob <- predict(fit, newdata = test, type = 'response') test$pred <- ifelse(test$prob > 0.5, 1, 0) table(actual = test$churned, predicted = test$pred)

How do you do feature engineering with recipes in tidymodels?

Modelling and Machine Learning in RIn 6 company setsMedium

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A recipe defines preprocessing steps that are learned on training data and applied identically to new data. rec <- recipe(price ~ ., data = train) %>% update_role(listing_id, new_role = 'id') %>% step_date(listed_on, features = c('month', 'dow')) %>% step_rm(listed_on) %>% step_log(area_sqft) %>% step_other(locality, threshold = 0.01) %>% step_dummy(all_nominal_predictors()) %>% step_normalize(all_numeric_predictors()) prep(rec) %>% bake(new_data = NULL) %>% glimpse()

How do you write unit tests for an R function?

Reports, Shiny and Production RIn 6 company setsMedium

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testthat organises tests into files under tests/testthat/ using expectations. # tests/testthat/test-gst.R test_that('gst_price adds 18 percent by default', { expect_equal(gst_price(100), 118) expect_equal(gst_price(c(100, 200)), c(118, 236)) }) test_that('gst_price rejects text input', { expect_error(gst_price('abc'), 'numeric') }) # run all tests devtools::test()

Describe a disagreement with a teammate about a technical approach.

Reports, Shiny and Production RIn 6 company setsMedium

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Situation: a teammate wanted to build our reporting pipeline in Excel macros, while I preferred R scripts. Task: agree on an approach quickly. Action: instead of arguing, we listed requirements together: refresh frequency, data size and who would maintain it. I built a small R prototype in a day to compare. Result: we chose R for the pipeline and kept an Excel output for users, and my teammate later helped improve the formatting.

Dates in your file look like 25/12/2025 and as.Date() returns NA. What is wrong and how do you fix it?

Data Structures in RIn 5 company setsEasy

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Indian files often store dates as dd/mm/yyyy, but as.Date() expects yyyy-mm-dd by default, so values become NA. I specify the format, as.Date(x, format = '%d/%m/%Y'), or use lubridate::dmy(x), which handles different separators automatically. Then I count NAs before and after conversion to confirm nothing failed silently. If the column came from Excel as serial numbers, I use as.Date(x, origin = '1899-12-30') instead.

What is the difference between %>% and |> in R?

Data Wrangling with dplyr and tidyrIn 5 company setsMedium

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%>% comes from the magrittr package and |> is the native pipe built into R since version 4.1. Both pass the left side as the first argument of the right side, so df |> filter(x > 1) equals filter(df, x > 1). The native pipe is faster and needs no package, but it uses _ as a named placeholder only, while %>% supports the dot placeholder anywhere. New code increasingly uses |>, and both work with dplyr.

How do you create a column with multiple conditions in dplyr?

Data Wrangling with dplyr and tidyrIn 5 company setsMedium

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case_when() evaluates conditions in order and returns the value of the first one that is TRUE; .default handles the rest. customers <- customers %>% mutate(segment = case_when( spend >= 50000 ~ 'Platinum', spend >= 20000 ~ 'Gold', spend >= 5000 & visits > 5 ~ 'Silver', .default = 'Regular' )) # simple two-way split mutate(df, high_value = if_else(spend > 20000, 'Yes', 'No'))

How do you apply the same transformation to many columns in dplyr?

Data Wrangling with dplyr and tidyrIn 5 company setsMedium

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across() applies one or more functions to a set of columns selected with tidyselect helpers. df %>% mutate(across(where(is.character), str_trim)) %>% mutate(across(starts_with('amt_'), ~ round(.x, 2))) df %>% group_by(region) %>% summarise(across(c(sales, profit), list(mean = mean, max = max), .names = '{.col}_{.fn}'))

Some stakeholders are colour-blind. How do you design charts for them?

Data Visualization with ggplot2In 5 company setsMedium

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I use colour-blind-safe palettes such as viridis with scale_colour_viridis_d() or the Okabe-Ito palette, and avoid red-green contrasts. I never rely on colour alone: I add direct labels, different shapes or line types, and facets. I limit categories to around six colours and highlight one key series while greying the rest. I also check charts with a colour-blindness simulator, such as the colorblindr package, before sharing.

What makes a chart misleading?

Data Visualization with ggplot2In 5 company setsMedium

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Common problems are a truncated y-axis on bar charts, which exaggerates differences because bar length should start at zero, inconsistent scales across panels, 3D effects, cherry-picked time ranges, dual axes with arbitrary ranges and too many colours. Area or bubble charts can mislead when size is mapped to radius instead of area. Good practice is to start bars at zero, label units, cite the data source and show context such as targets or prior periods.

How do you handle errors in R code?

Functions and Functional ProgrammingIn 5 company setsMedium

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tryCatch() catches errors and warnings so a script can recover or log them; finally runs cleanup. read_safe <- function(path) { tryCatch({ readr::read_csv(path, show_col_types = FALSE) }, warning = function(w) { message('Warning in ', path, ': ', conditionMessage(w)) NULL }, error = function(e) { message('Failed: ', path, ' - ', conditionMessage(e)) NULL }, finally = message('Done: ', path)) }

What is the purrr map family?

Functions and Functional ProgrammingIn 5 company setsMedium

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purrr's map() applies a function to each element of a list or vector and always returns a list. Typed variants return specific types: map_dbl(), map_chr(), map_lgl() and map_int(), and they error if a result has the wrong type, which is safer than sapply(). map2() iterates over two inputs, pmap() over many, and walk() is used for side effects like saving files. Formula shortcuts such as ~ .x * 2 keep code short.

What is the difference between errors, warnings and messages in R?

Functions and Functional ProgrammingIn 5 company setsMedium

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An error, raised with stop(), halts execution because something cannot continue. A warning, raised with warning(), lets code continue but signals a possible problem, such as NAs introduced by coercion. A message, from message(), is informational, such as progress updates. Messages go to stderr, so they can be suppressed with suppressMessages() without hiding results. Use print() or cat() for output the user requested, not for diagnostics.

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

Functions and Functional ProgrammingIn 5 company setsMedium

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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.

How do you interpret the summary output of an lm() model?

Modelling and Machine Learning in RIn 5 company setsMedium

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Each coefficient is the expected change in the outcome for a one-unit increase in that predictor, holding others constant. The p-value tests whether the coefficient differs from zero. R-squared is the share of variance explained, and adjusted R-squared penalizes extra predictors. Residual standard error shows typical prediction error in outcome units. The F-statistic tests whether the model beats an intercept-only model. For factors, coefficients are differences from the reference level.

In your regression, coefficient signs look wrong and standard errors are huge. What might be the problem?

Modelling and Machine Learning in RIn 5 company setsMedium

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This is a classic sign of multicollinearity, where predictors are highly correlated, for example total spend and online spend. The model cannot separate their effects, so coefficients become unstable. I check correlations and the variance inflation factor with car::vif(); values above 5 or 10 are a concern. Solutions are removing or combining correlated variables, using principal components or ridge regression. Prediction accuracy may be fine, but interpretation is unreliable.

What tools does R offer for time series forecasting?

Modelling and Machine Learning in RIn 5 company setsMedium

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The forecast package provides auto.arima(), ets() for exponential smoothing and easy plotting of forecasts with intervals. The newer tidyverts family, with tsibble for time-series data frames, fable for models and feasts for features and decomposition, works with tidy data and fits many series at once. prophet handles strong seasonality and holidays. Typical steps are visualizing, decomposing trend and seasonality, fitting candidates and comparing accuracy on a holdout period.

Walk me through an end-to-end machine learning project you have done in R.

Modelling and Machine Learning in RIn 5 company setsMedium

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Situation: a lending team wanted to reduce early loan defaults. Task: build a default risk score. Action: I cleaned two years of loan data with dplyr, engineered features like EMI to income ratio with recipes, compared logistic regression and ranger in tidymodels using 5-fold cross-validation and chose logistic regression for interpretability with AUC 0.78. Result: the score was used in a pilot, flagging 60 percent of defaulters in the riskiest 20 percent of applicants.

How do you generate the same report for each of 20 regions automatically?

Reports, Shiny and Production RIn 5 company setsMedium

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Use a parameterized Quarto or R Markdown report and render it in a loop. # report.qmd header: # params: # region: 'North' # then use params$region in the code regions <- unique(sales$region) purrr::walk(regions, function(r) { quarto::quarto_render( 'report.qmd', execute_params = list(region = r), output_file = paste0('report_', r, '.html') ) })

Your team wants to share a Shiny app with 200 internal users. What deployment options exist?

Reports, Shiny and Production RIn 5 company setsMedium

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Options include shinyapps.io, a hosted service that is quickest to start but may not suit sensitive internal data, Posit Connect, a commercial on-premise or cloud server with authentication, scheduling and scaling, the free open-source Shiny Server on a Linux machine, and a Docker container behind a reverse proxy like Nginx, often with ShinyProxy for authentication. For internal data, I would choose Connect or containers inside the company network with single sign-on.

What is the structure of an R package?

Reports, Shiny and Production RIn 5 company setsMedium

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A minimal package has a DESCRIPTION file with metadata and dependencies, a NAMESPACE file listing exported functions, an R/ folder with function code, a man/ folder for documentation and usually tests/ for testthat tests. The usethis and devtools packages create and manage this: usethis::create_package(), use_r(), use_test(), and devtools::document(), test() and check(). Documentation is written as roxygen2 comments above each function. Internal packages are ideal for sharing team utilities.

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

R Basics and Data TypesIn 4 company setsEasy

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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.

What is vectorization in R and why does it matter?

R Basics and Data TypesIn 4 company setsMedium

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Vectorization means applying an operation to a whole vector at once instead of looping over elements. Most R functions, like +, sqrt() and ifelse(), are vectorized and implemented in C, so they are much faster than R-level for loops. For example, prices * 1.18 adds 18 percent GST to every price in one step. Writing vectorized code is the first optimization an R developer should try before parallel processing or Rcpp.

Your R script works on your laptop but fails on a colleague's machine with 'could not find function'. What do you check?

R Basics and Data TypesIn 4 company setsMedium

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First I check that the package providing the function is installed and loaded with library() at the top of the script, not only in my interactive session. Then I compare R and package versions using sessionInfo(), because functions get renamed or deprecated. To prevent this, I use renv to snapshot exact package versions into a lockfile, so the colleague runs renv::restore() and gets the same environment. Namespacing calls like dplyr::filter() also avoids masking conflicts.

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

R Basics and Data TypesIn 4 company setsHard

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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.

How would you compare R and Python for a data role?

R Basics and Data TypesIn 4 company setsMedium

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R is strongest for statistics, exploratory analysis, visualization and reproducible reporting, with mature packages for biostatistics, econometrics and survey analysis. Python is a general-purpose language that is stronger for production software, deep learning and web integration. Many teams use both: R for analysis and reporting, Python for pipelines and deployment. In an interview, avoid language wars and explain that the choice depends on the team's stack, the problem and who maintains the code.

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

Data Structures in RIn 4 company setsMedium

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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.

What is a tibble and how does it differ from a base data frame?

Data Structures in RIn 4 company setsMedium

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A tibble is the tidyverse version of a data frame. It prints only the rows and columns that fit the screen along with column types, never converts strings to factors, never changes column names, does not partially match with $ and always returns a tibble when subsetting with [ ]. It also supports list-columns easily. You convert with as_tibble(). Tibbles prevent many silent bugs, though some older packages expect base data frames.

What is data.table and when would you use it?

Data Structures in RIn 4 company setsMedium

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data.table is a high-performance package for large tabular data. Its syntax DT[i, j, by] means filter rows with i, compute j, grouped by by. It updates columns by reference with :=, avoiding copies, and offers fast keyed joins and fread()/fwrite() for file IO. For example, DT[city == 'Delhi', .(avg_sal = mean(salary)), by = dept] filters and aggregates in one step. It is popular for datasets with tens of millions of rows.

How are dates and times handled in R?

Data Structures in RIn 4 company setsMedium

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Base R stores dates as the Date class, counting days since 1970-01-01, and date-times as POSIXct, counting seconds. as.Date('2026-03-15') parses ISO strings and format() controls output. The lubridate package simplifies parsing with ymd(), dmy() and mdy(), extracting parts with month() and wday(), and arithmetic like date + months(1). Time zones matter: Indian data usually uses 'Asia/Kolkata', and mixing UTC with IST can shift records across days.

Frequently asked questions

How is this list made?

We count how often each question appears across the 20 company-wise R Programming practice sets on this site and show the 50 most common. They are the safest questions to master first.

Is this enough to clear an interview?

It is the best starting point, not the finish line. After these, practise the company pages for your target employers and the sheets for your weaker topics.

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