R Programming Interview Questions in Noida

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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.

How do you handle missing values in calculations in R?

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Most summary functions return NA if any value is missing, so pass na.rm = TRUE or remove NAs first. x <- c(10, NA, 30, 40) mean(x) # NA mean(x, na.rm = TRUE) # 26.67 sum(is.na(x)) # count missing: 1 x[!is.na(x)] # drop NAs x[is.na(x)] <- median(x, na.rm = TRUE) # impute df[complete.cases(df), ] # rows with no NA

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

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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.

How do you test whether two categorical variables are related in R?

Statistics and Hypothesis TestingMedium

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Use a chi-square test of independence on a contingency table; use Fisher's exact test when expected counts are small. tab <- table(survey$city_tier, survey$preferred_plan) tab res <- chisq.test(tab) res$p.value res$expected # check expected counts >= 5 fisher.test(tab) # for small samples # effect size library(rcompanion) cramerV(tab)

What are skewness and kurtosis?

Statistics and Hypothesis TestingEasy

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Skewness measures asymmetry of a distribution. Positive or right skew has a long right tail, typical of income and order values, so the mean exceeds the median. Negative skew has a long left tail. Kurtosis measures how heavy the tails are compared with a normal distribution; high kurtosis means more extreme values, common in financial returns. In R, the moments or e1071 packages provide skewness() and kurtosis() functions.

How do you reshape data between wide and long formats in R?

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tidyr's pivot_longer() turns columns into rows and pivot_wider() does the reverse. library(tidyr) # wide: product, Jan, Feb, Mar long <- sales_wide %>% pivot_longer(cols = Jan:Mar, names_to = 'month', values_to = 'sales') wide <- long %>% pivot_wider(names_from = month, values_from = sales, values_fill = 0)

Write common data.table operations for filtering, aggregating and joining.

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Read, filter, aggregate, add columns by reference and join: library(data.table) DT <- fread('orders.csv') DT[status == 'delivered', .N, by = city] DT[, revenue := qty * price] DT[, .(total = sum(revenue)), by = .(city, month)][order(-total)] customers <- fread('customers.csv') merged <- customers[DT, on = 'customer_id'] fwrite(merged, 'merged.csv')

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

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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.

What is the purrr map family?

Functions and Functional ProgrammingMedium

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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 multiple testing problem and how do you correct for it?

Statistics and Hypothesis TestingHard

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When you run many hypothesis tests, the chance of at least one false positive rises sharply. With 20 independent tests at alpha 0.05, it is about 64 percent. Corrections adjust p-values: Bonferroni multiplies by the number of tests and is conservative, Holm is a less strict step-down version, and Benjamini-Hochberg controls the false discovery rate. In R, p.adjust(pvals, method = 'BH') applies them, which is common when testing many metrics or segments.

How do you build a linear regression model in R?

Modelling and Machine Learning in REasy

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lm() fits linear models using formula syntax; summary() shows coefficients and fit. fit <- lm(sales ~ ad_spend + price + region, data = stores) summary(fit) coef(fit) confint(fit) # 95% CIs for coefficients predict(fit, newdata = new_stores, interval = 'prediction') par(mfrow = c(2, 2)); plot(fit) # diagnostic plots broom::tidy(fit) # coefficients as a tibble

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.

Describe a disagreement with a teammate about a technical approach.

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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.

Why did you choose to learn R?

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I chose R because I enjoy working with data and statistics, and R is designed exactly for that. During my coursework I used it for a project analysing student performance, where dplyr and ggplot2 helped me move from raw data to clear insights quickly. I like that results are reproducible with R Markdown. I am also learning SQL and basic Python, so I can work comfortably in a mixed team and pick the right tool for each task.

You notice database passwords hardcoded in a shared R script. What do you do?

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I remove them immediately, inform the owner and ask for the password to be rotated, since it may exist in version history. Then I store credentials outside the code, in a user-level .Renviron file read with Sys.getenv(), the keyring package backed by the operating system's credential store, or the server's secret management, such as environment variables in Posit Connect. I make sure .Renviron is listed in .gitignore and scan the repository history.

What is plumber in R?

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plumber turns R functions into REST API endpoints using special comments. You annotate a function with #* @get /predict and its arguments become query parameters, then run the API with pr_run(). Other systems, such as a Java web app or a Python service, can call the R model over HTTP and receive JSON. It is commonly used to serve model predictions, and it can be deployed in Docker or on Posit Connect.

Explain the d, p, q and r functions for probability distributions in R.

Statistics and Hypothesis TestingMedium

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Each distribution has four functions: d for density, p for cumulative probability, q for quantiles and r for random draws. dnorm(0) # density at 0 pnorm(1.96) # P(Z <= 1.96) = 0.975 qnorm(0.975) # 1.96 rnorm(5, mean = 50, sd = 10) dbinom(3, size = 10, prob = 0.2) # exactly 3 successes ppois(2, lambda = 4) # P(X <= 2) qt(0.975, df = 20) # t critical value

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

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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 ggplot2Medium

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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.

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

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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'))

Where do you see yourself in three years in an R or analytics career?

Reports, Shiny and Production REasy

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In three years I see myself as a strong data analyst or junior data scientist who can own analyses end to end, from understanding the business question to building reproducible reports and models in R. I want to deepen my statistics and machine learning skills and learn cloud tools used by the team. I would also like to mentor new joiners, as seniors helped me, and contribute to reusable internal tools.

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

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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.

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.

How do you install and load a package, and what is the difference between library() and require()?

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install.packages() downloads from CRAN once; library() loads it in every session. library() throws an error if the package is missing, while require() returns FALSE with a warning, so require() suits conditional checks. install.packages('dplyr') library(dplyr) if (!require('data.table')) { install.packages('data.table') library(data.table) }

How do you connect R to a SQL database and run queries?

Reports, Shiny and Production REasy

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DBI provides a common interface, with drivers such as RPostgres, RMariaDB or odbc. library(DBI) con <- dbConnect(RPostgres::Postgres(), host = Sys.getenv('DB_HOST'), dbname = 'sales', user = Sys.getenv('DB_USER'), password = Sys.getenv('DB_PASS')) orders <- dbGetQuery(con, 'SELECT * FROM orders WHERE order_date >= $1', params = list('2026-01-01')) dbWriteTable(con, 'daily_summary', summary_df, append = TRUE) dbDisconnect(con)

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

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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 tidymodels and how does it compare with caret?

Modelling and Machine Learning in RMedium

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tidymodels is a collection of packages for modelling with tidyverse principles: rsample for splitting and resampling, recipes for preprocessing, parsnip for a unified model interface, workflows to bundle them, tune for hyperparameter tuning and yardstick for metrics. caret, by the same original author, is an older single package that does similar work with one train() function. caret is stable and still widely used, but new development happens in tidymodels.

How do you write unit tests for an R function?

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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()

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

Modelling 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)

How do you find out why your R code is slow?

R Basics and Data TypesMedium

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Measure before optimizing. system.time() gives total time, bench::mark() compares alternatives, and profvis shows which lines consume time and memory. system.time(result <- slow_function(df)) bench::mark( loop = sum_loop(x), vec = sum(x) ) library(profvis) profvis({ out <- clean_data(raw) model <- fit_model(out) })

How do you create sequences and repeated values in R?

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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

What is the difference between base R graphics and ggplot2?

Data Visualization with ggplot2Easy

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Base graphics use functions like plot(), hist() and barplot() that draw directly on a device, and you add elements with lines() or legend() step by step. They are quick for exploration. ggplot2 builds plots as objects from layers using the grammar of graphics, handles legends and facets automatically and is easier to style consistently. Base plots are fine for quick checks, while ggplot2 is preferred for reports and publications.

What does the ... (dots) argument do in an R function?

Functions and Functional ProgrammingMedium

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The dots let a function accept any number of additional arguments and pass them on to another function. For example, my_plot <- function(df, ...) ggplot(df, aes(x, y)) + geom_point(...) lets callers pass colour or size without declaring them. You can capture dots with list(...) or count them with ...length(). The downside is that misspelled argument names are silently ignored, so document them and validate with rlang::check_dots_used() in packages.

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