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R Programming Interview Questions in Pune

36 practice questions with answers for R Programming interviews in Pune.

R Programming jobs in Pune

Pune combines IT-services campuses in Hinjewadi and Kharadi with product, automotive and BFSI technology centres. It has a large mid-level job market for data and QA roles, and interviews are often practical and project-based.

Below are 36 questions that match the mix of service, product and domain employers hiring r data analysts in Pune.

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What does drop = FALSE do when subsetting?

Data Structures in RMedium

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When you subset a single column or row from a matrix or base data frame with [ ], R simplifies the result to a vector by default. Writing m[, 1, drop = FALSE] keeps it as a matrix or data frame. This matters inside functions, because code expecting a data frame breaks when a selection leaves only one column. Tibbles never drop dimensions, which is one reason developers prefer them in reusable code and packages.

What does a 95 percent confidence interval mean?

Statistics and Hypothesis TestingMedium

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If we repeated the sampling many times and built an interval each time, about 95 percent of those intervals would contain the true population value. It does not mean there is a 95 percent probability that this specific interval contains it. A narrow interval shows precise estimation and a wide one shows uncertainty. For example, average order value of 850 INR with CI 820 to 880 is far more useful than CI 600 to 1100.

How is a matrix different from a data frame?

Data Structures in REasy

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A matrix is a two-dimensional atomic vector, so all elements share one type, usually numeric. A data frame allows a different type per column. Matrices are faster for linear algebra, such as %*% multiplication, solve() and distance calculations, while data frames suit mixed real-world data. For example, the output of cor() is a matrix, while customer records with names, dates and amounts belong in a data frame. as.matrix() and as.data.frame() convert between them.

What is the structure of an R package?

Reports, Shiny and Production RMedium

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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 $ when subsetting?

Data Structures in RMedium

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For lists, [ ] returns a sub-list, while [[ ]] and $ return the element itself. So lst['a'] is a list of length one and lst[['a']] is the value. $ allows partial name matching and cannot take a variable, while [[ ]] accepts a string variable like lst[[col]]. On data frames, df['x'] returns a one-column data frame, and df[['x']] or df$x returns a vector. Think of [ ] as the train car and [[ ]] as its contents.

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

Reports, 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.

What makes a chart misleading?

Data Visualization with ggplot2Medium

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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.

What is an array in R?

Data Structures in REasy

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An array is a multi-dimensional generalization of a matrix, created with array(data, dim = c(rows, cols, layers)). All elements share one type. For example, array(1:24, dim = c(2, 3, 4)) has four layers of 2 by 3 matrices, and you index with arr[1, 2, 3]. Arrays are used for image data, simulation outputs and time by region by product cubes, though for business analysis long-format data frames are usually easier to work with.

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

Functions and Functional ProgrammingMedium

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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 create sequences and repeated values in R?

R Basics and Data TypesEasy

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seq(), rep() and the colon operator build most vectors. 1:10 # integers 1 to 10 seq(0, 100, by = 10) # 0, 10, ..., 100 seq(1, 2, length.out = 5) # 5 evenly spaced values rep(c('A', 'B'), times = 3) # A B A B A B rep(c('A', 'B'), each = 2) # A A B B seq_along(c('x', 'y', 'z')) # 1 2 3, safe for empty vectors seq_len(0) # empty, unlike 1:0

A loop calls an external API 5,000 times and crashes at call 3,200. How do you make it robust?

Functions and Functional ProgrammingHard

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I wrap each call with purrr::safely() or tryCatch() so one failure does not stop the loop, and add retries with exponential backoff, which httr2 supports through req_retry(). I respect rate limits with Sys.sleep() or req_throttle(). I save results incrementally, for example every 100 calls to an RDS file or database, so a restart resumes from the last checkpoint. Finally I log failed IDs and rerun only those.

Write ggplot2 code for a monthly sales line chart with points.

Data Visualization with ggplot2Easy

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A basic time-series line chart: library(ggplot2) ggplot(monthly, aes(x = month, y = sales)) + geom_line(colour = 'steelblue', linewidth = 1) + geom_point(size = 2) + scale_y_continuous(labels = scales::label_comma()) + scale_x_date(date_labels = '%b %Y', date_breaks = '2 months') + labs(title = 'Monthly Sales', x = NULL, y = 'Sales (INR)') + theme_minimal()

You are building a fraud model where only 0.5 percent of transactions are fraud. How do you handle the imbalance?

Modelling and Machine Learning in RMedium

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Accuracy is useless here, since predicting no fraud gives 99.5 percent. I use stratified splits and evaluate with precision, recall, F1 and the precision-recall AUC. To help the model learn the minority class, I try class weights, downsampling the majority or SMOTE through the themis package in a recipe, applied only to training data. Then I tune the probability threshold based on the cost of missed fraud versus false alarms agreed with the business.

What is a data frame in R?

Data Structures in REasy

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A data frame is a list of equal-length vectors, where each vector is a column with its own type. It is R's main structure for tabular data, similar to a spreadsheet or SQL table. str(df) shows column types, nrow() and ncol() give dimensions and summary() gives quick statistics. Tibbles from the tidyverse are modern data frames that print neatly, never convert strings to factors and do not partially match column names.

Tell me about a time you had two urgent analysis requests at the same time.

Data Structures in RMedium

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Situation: two managers needed different analyses on the same Friday. Task: deliver both without compromising quality. Action: I asked each what decision the analysis would support and the minimum needed by Friday, reused one cleaned dataset in R for both, and shared a draft of the simpler one on Wednesday for early feedback. Result: both received usable outputs on time, and the fuller version of the second analysis followed on Monday as agreed.

What is overfitting and how do you prevent it?

Modelling and Machine Learning in REasy

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Overfitting happens when a model learns noise in the training data, so it performs very well on training data but poorly on new data. Signs are a large gap between training and validation scores. Prevention includes using simpler models, regularization such as lasso or ridge, cross-validation for tuning, limiting tree depth, getting more data and removing leaky or irrelevant features. Underfitting is the opposite: the model is too simple to capture the pattern.

How do you debug an R function?

Functions 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.

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

Data 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.

Write a tidymodels workflow to train and evaluate a random forest.

Modelling and Machine Learning in RMedium

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Split, preprocess, specify, fit and evaluate: library(tidymodels) set.seed(42) split <- initial_split(df, prop = 0.8, strata = churned) train <- training(split); test <- testing(split) rec <- recipe(churned ~ ., data = train) %>% step_impute_median(all_numeric_predictors()) %>% step_dummy(all_nominal_predictors()) rf <- rand_forest(trees = 500) %>% set_engine('ranger') %>% set_mode('classification') wf_fit <- workflow() %>% add_recipe(rec) %>% add_model(rf) %>% last_fit(split) collect_metrics(wf_fit)

What is the recycling rule in R?

R Basics and Data TypesMedium

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When two vectors of different lengths are used in an operation, R repeats the shorter one to match the longer one. For example, c(1, 2, 3, 4) + c(10, 20) gives 11, 22, 13, 24. If the longer length is not a multiple of the shorter, R gives a warning but still computes. Recycling is convenient for scalars, but silent recycling of mismatched vectors is a frequent source of wrong results in analysis code.

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

Reports, 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.

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 TestingMedium

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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.

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

Reports, Shiny and Production RMedium

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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.

Your manager says your charts are confusing. How do you respond?

Data Visualization with ggplot2Easy

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I would thank them and ask which part was unclear, such as too many colours, missing labels or an unclear message. In my internship this happened with a crowded chart of twelve product lines. I simplified it by highlighting the top three, greying the rest, adding a clear title stating the finding and labelling values directly. My manager found the new version easy to present, and I now review every chart for one clear message.

What are the principles of tidy data?

Data 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.

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

Functions and Functional ProgrammingMedium

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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.

How does a random forest work and which R packages implement it?

Modelling and Machine Learning in RMedium

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A random forest builds many decision trees, each on a bootstrap sample of rows and considering a random subset of features at each split, then averages their predictions for regression or takes a majority vote for classification. This reduces the variance of single trees and handles non-linear relationships well. In R, randomForest is the classic package and ranger is a much faster implementation. Key parameters are the number of trees, mtry and minimum node size.

How do you perform a t-test in R?

Statistics and Hypothesis TestingMedium

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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

How do you write unit tests for an R function?

Reports, Shiny and Production RMedium

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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()

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

Data Structures in RMedium

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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.

Tell me about the messiest dataset you have cleaned.

Data 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.

When would you report the median instead of the mean?

Statistics and Hypothesis TestingEasy

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The mean uses every value and is sensitive to outliers, while the median is the middle value and is robust to them. I report the median for skewed data such as salaries, house prices, order values or delivery times, where a few extreme values pull the mean up. For example, if most employees earn 6 LPA and a few earn 80 LPA, the median describes a typical employee better. The mode suits categorical data.

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

Reports, Shiny and Production RMedium

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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') ) })

Write dplyr code to find total and average sales by city, sorted by total.

Data Wrangling with dplyr and tidyrEasy

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Group, summarise and sort: library(dplyr) city_summary <- sales %>% filter(!is.na(amount)) %>% group_by(city) %>% summarise( orders = n(), total = sum(amount), avg_order = mean(amount), .groups = 'drop' ) %>% arrange(desc(total)) head(city_summary, 10)

What is the difference between mapping and setting an aesthetic in ggplot2?

Data Visualization with ggplot2Medium

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Mapping links an aesthetic to a variable inside aes(), so values change with the data and a legend is created, for example aes(colour = region). Setting fixes an aesthetic to a constant outside aes(), for example geom_point(colour = 'steelblue'). A common mistake is writing aes(colour = 'blue'), which treats 'blue' as a data value, creates a legend called blue and uses the default red colour instead.

Explain the apply family of functions in R.

Data Structures in RMedium

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apply() works over rows or columns of a matrix, lapply() applies a function to each element of a list and returns a list, sapply() simplifies the result to a vector or matrix when possible, vapply() is like sapply() but requires a declared output type, making it safer, and mapply() iterates over several vectors in parallel. tapply() applies a function by groups. For example, sapply(df, class) returns the class of each column.