R Programming Interview Questions in Kolkata

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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)')

How do you add logging to a production R script?

Reports, Shiny and Production RMedium

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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')

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

Modelling and Machine Learning in RMedium

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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.

What is the difference between geom_bar() and geom_col()?

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geom_bar() counts rows for each x value by default using stat = 'count', so you give only x, for example the number of orders per city from raw data. geom_col() uses y values as they are, so you supply already summarised data, for example total sales per city. geom_bar(stat = 'identity') is equivalent to geom_col(). Using geom_bar() with a y aesthetic gives an error, which is a common beginner issue.

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

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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.

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

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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.

How do you create interactive charts in R?

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plotly's ggplotly() converts an existing ggplot into an interactive chart with hover tooltips, zoom and legend toggling. ggiraph adds interactive tooltips and selection to ggplot geoms with fine control. highcharter and echarts4r wrap JavaScript charting libraries, and leaflet builds interactive maps. These outputs work in R Markdown or Quarto HTML reports and Shiny apps. For static PDF reports, standard ggplot2 is still the better choice.

Show different ways to index vectors and data frames in R.

Data Structures in REasy

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R indexes from 1 and supports positive, negative, logical and name-based indexing. x <- c(a = 10, b = 20, c = 30, d = 40) x[2] # 20 x[-1] # drop first x[c('a', 'd')] # by name x[x > 15] # logical filter which(x > 15) # positions df[df$age > 30, c('name', 'city')] df[order(-df$salary), ][1:5, ] # top 5 salaries

What is a pure function and why prefer them in data pipelines?

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A pure function always returns the same output for the same inputs and has no side effects, such as modifying global variables, writing files or printing. Pure functions are easy to test, reuse, cache and run in parallel, because they do not depend on hidden state. In data pipelines, I keep transformations pure, taking a data frame and returning a new one, and isolate side effects such as reading and writing in a few clearly named functions.

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.

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

Modelling and Machine Learning in RMedium

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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)

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.

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

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.

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'))

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.

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.

A predictor such as pincode has 500 levels. How do you use it in a model?

Modelling and Machine Learning in RHard

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Dummy-encoding 500 levels creates sparse, noisy features and overfits rare levels. I first lump rare levels into 'other' with step_other(). Then I consider target or effect encoding with the embed package, applied inside cross-validation to avoid leakage, or mapping pincodes to meaningful higher-level features such as city tier, region or distance to a warehouse. Tree models in some engines handle many levels better, but grouping by business meaning usually works best.

What window functions does dplyr provide?

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Window functions return one value per row while looking at other rows in the group. Common ones are lag() and lead() for previous or next values, row_number(), min_rank() and dense_rank() for ranking, cumsum() and cummean() for running totals, and ntile() for buckets. For example, group_by(product) %>% arrange(month) %>% mutate(prev = lag(sales)) gives the previous month's sales per product, which is the basis for growth calculations.

Tell me about a time a model you built did not perform as expected after deployment.

Modelling and Machine Learning in RHard

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Situation: a demand model I built performed well in testing but its errors doubled two months after launch. Task: diagnose and fix it. Action: I compared input distributions and found a new product category and a pricing change that the training data never saw. I added drift monitoring, retrained with recent data and set up monthly retraining. Result: accuracy returned to target, and I learned to plan monitoring before deployment, not after.

Why is ungroup() important in dplyr?

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A grouped data frame stays grouped after mutate() and some other verbs, so later operations silently run per group. For example, a mutate(share = sales / sum(sales)) meant to compute share of grand total would compute share within each group instead. Calling ungroup() after grouped work, or using .groups = 'drop' in summarise() or the .by argument for one-off grouping, prevents these subtle bugs in long pipelines.

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

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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())

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

R Basics and Data TypesMedium

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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.

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

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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 are semi_join() and anti_join() used for?

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They are filtering joins: they keep rows of the left table based on matches in the right table but add no columns. semi_join() keeps rows that have a match, and anti_join() keeps rows that have no match. For example, anti_join(customers, orders, by = 'customer_id') lists customers who never ordered. They are also safer than inner joins for filtering, since they never duplicate rows when the right table has repeated keys.

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

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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 validate inputs inside an R function?

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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 }

What is the purrr map family?

Functions and Functional ProgrammingHard

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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.

How do you run R code in parallel?

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The parallel package ships with R: mclapply() forks processes on Linux and macOS, while parLapply() uses a socket cluster that also works on Windows. The future framework gives a cleaner interface where you choose a plan, such as plan(multisession), and then use future_map() from furrr or future_lapply(). Parallelism helps when each task is independent and heavy, such as model fitting per group, but adds overhead for small tasks and multiplies memory use.

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

Reports, Shiny and Production RMedium

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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.

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

Functions and Functional ProgrammingEasy

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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().

Tell me about a time you automated a manual report.

R Basics and Data TypesMedium

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Situation: my team spent two days every month building a sales report in Excel. Task: I was asked to reduce the effort. Action: I wrote an R script that pulled data from the database with DBI, cleaned it with dplyr, created charts with ggplot2 and rendered a Quarto report scheduled through cron. Result: the report ran in ten minutes, copy-paste errors disappeared and the team used the saved time for deeper analysis.

How do you write a function in R with default arguments?

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A function is created with the function keyword and returns the value of its last expression, or an explicit return(). gst_price <- function(price, rate = 0.18, round_to = 2) { if (!is.numeric(price)) stop('price must be numeric') round(price * (1 + rate), round_to) } gst_price(1000) # 1180 gst_price(1000, rate = 0.05) gst_price(c(100, 250)) # vectorized

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