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R Programming Interview Questions in Bangalore

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

R Programming jobs in Bangalore

Bangalore (Bengaluru) is India's largest technology hub, with global product companies, engineering centres, unicorn start-ups and big IT-services campuses. Product-company interviews here are usually deeper, with more scenario, design and hands-on rounds.

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

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01

What tools does R offer for time series forecasting?

Modelling and Machine Learning in RMedium
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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.
02

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.
03

Explain the different types of joins in dplyr.

Data Wrangling with dplyr and tidyrMedium
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left_join() keeps all rows of the left table and adds matching columns from the right, inner_join() keeps only matching rows, right_join() keeps all rows of the right table and full_join() keeps all rows from both, filling NA where there is no match. Keys are given with by = join_by(customer_id) or by = c('id' = 'cust_id') for different names. Left joins are most common when enriching a fact table with lookup details.
04

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.
05

What is the difference between mutate() and summarise()?

Data Wrangling with dplyr and tidyrEasy
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mutate() adds or modifies columns and keeps the same number of rows, so each row gets a value. summarise() collapses each group into one row of summary values. For example, group_by(city) %>% mutate(city_avg = mean(sales)) adds the city average to every row, useful for comparing each row with its group, while group_by(city) %>% summarise(city_avg = mean(sales)) returns one row per city.
06

Your data is heavily skewed and the sample is small. Can you still use a t-test?

Statistics and Hypothesis TestingMedium
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With small, heavily skewed samples the t-test's normality assumption is doubtful. I would check with a histogram, Q-Q plot or shapiro.test(). Options are a non-parametric test, wilcox.test() for two groups or kruskal.test() for more, a log transformation if the data is right-skewed and positive, or a bootstrap confidence interval for the difference in means. With large samples, the Central Limit Theorem usually makes the t-test acceptable.
07

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')
08

How do you perform k-means clustering in R?

Modelling 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)
09

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')
10

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

Data Wrangling with dplyr and tidyrMedium
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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.
11

Why did you choose to learn R?

R Basics and Data TypesEasy
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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.
12

Write code using map2() and pmap() to iterate over multiple inputs.

Functions and Functional ProgrammingMedium
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map2() pairs two vectors and pmap() takes a list or data frame of arguments. library(purrr) prices <- c(100, 250, 400) qty <- c(2, 1, 5) map2_dbl(prices, qty, ~ .x * .y) # 200 250 2000 params <- tibble::tibble( region = c('North', 'South'), year = c(2025, 2026) ) pmap(params, function(region, year) { build_report(region = region, year = year) }) walk2(plots, paths, ~ ggsave(.y, .x))
13

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

Functions and Functional ProgrammingMedium
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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.
14

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

Functions 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.
15

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
16

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

Functions and Functional ProgrammingEasy
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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
17

What is the difference between coord_cartesian() limits and scale limits in ggplot2?

Data Visualization with ggplot2Hard
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Setting limits with scale_y_continuous(limits = c(0, 100)) or ylim() removes data outside the range before statistics are calculated, which changes boxplots, smooth lines and bar totals and gives warnings about removed rows. coord_cartesian(ylim = c(0, 100)) only zooms the view, keeping all data in calculations. So for zooming into part of a chart without altering the summary, coord_cartesian() is the correct choice.
18

When would you report the median instead of the mean?

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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.
19

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

R Basics and Data TypesMedium
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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.
20

What are facets in ggplot2?

Data Visualization with ggplot2Medium
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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.
21

Your Shiny dashboard becomes very slow when 50 users open it at once. How do you improve it?

Reports, Shiny and Production RHard
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I profile with profvis and reactlog to find slow reactives. I move heavy data loading outside the server function so it runs once per process, pre-aggregate data instead of computing on raw rows, and cache results with bindCache(). I use bindEvent() so expensive work runs only on a button click. Long computations can go to background tasks with ExtendedTask and promises. Finally I scale out with multiple R processes on Posit Connect or Shiny Server Pro.
22

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.
23

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

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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.
24

How do you validate inputs inside an R function?

Functions 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 }
25

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

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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.
26

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.
27

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

Functions 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().
28

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.
29

What is Shiny and how is a Shiny app structured?

Reports, Shiny and Production REasy
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Shiny is an R package for building interactive web applications without writing HTML or JavaScript. An app has a ui that defines layout and inputs such as dropdowns and sliders, and a server function that contains R logic and creates outputs such as plots and tables. shinyApp(ui, server) runs the app. Analytics teams use Shiny for internal dashboards, what-if calculators and tools that let business users explore data.
30

You are analysing a national survey where some groups were deliberately oversampled. How do you compute correct averages?

Statistics and Hypothesis TestingHard
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Simple averages would be biased toward oversampled groups, so I use the survey weights provided with the data. In R, the survey package defines the design with svydesign(ids = ~psu, strata = ~stratum, weights = ~wt, data = df), then svymean(), svytotal() and svyglm() give weighted estimates with correct standard errors that account for clustering and stratification. The srvyr package offers the same with dplyr-style syntax.
31

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

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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)
32

A customer table has multiple records per customer. How do you keep only the latest record for each?

Data Wrangling with dplyr and tidyrMedium
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I group by customer_id and keep the row with the latest updated_at using slice_max(updated_at, n = 1, with_ties = FALSE), then ungroup. An alternative is arrange(desc(updated_at)) followed by distinct(customer_id, .keep_all = TRUE). I check that updated_at is a proper date-time and has no NAs, since missing timestamps would be dropped or mis-sorted. Finally I confirm that n_distinct(customer_id) equals the number of rows.
33

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

Data 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)')
34

What are skewness and kurtosis?

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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.
35

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())
36

Why should we hire you as a fresher for an R analyst role?

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I have practical R skills, not just theory. In my projects I cleaned real datasets with dplyr, built ggplot2 dashboards and ran regression and hypothesis tests, and I can explain results simply. I also know SQL, which most analytics work needs. I learn quickly, accept feedback well and care about accuracy, so I double-check numbers before sharing them. I am ready to start contributing to reporting tasks from the first month.

Companies with offices in Bangalore (19)

Analytics

Mu Sigma

R Programming interview questions

  • 30 questions
  • 4 rounds

Bengaluru

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Analytics

Fractal Analytics

R Programming interview questions

  • 30 questions
  • 5 rounds

Mumbai, Bengaluru, Gurugram +1

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Analytics

LatentView Analytics

R Programming interview questions

  • 30 questions
  • 4 rounds

Chennai, Bengaluru

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Analytics

Tiger Analytics

R Programming interview questions

  • 30 questions
  • 5 rounds

Chennai, Bengaluru, Hyderabad

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Consulting

ZS Associates

R Programming interview questions

  • 30 questions
  • 5 rounds

Pune, Gurugram, Bengaluru

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Analytics

EXL

R Programming interview questions

  • 30 questions
  • 4 rounds

Noida, Gurugram, Bengaluru +1

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IT services

Genpact

R Programming interview questions

  • 30 questions
  • 4 rounds

Gurugram, Hyderabad, Bengaluru +2

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Analytics

Tredence

R Programming interview questions

  • 30 questions
  • 4 rounds

Bengaluru, Chennai, Gurugram

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Analytics

Course5 Intelligence

R Programming interview questions

  • 30 questions
  • 4 rounds

Mumbai, Bengaluru

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Consulting

Deloitte

R Programming interview questions

  • 30 questions
  • 4 rounds

Hyderabad, Bengaluru, Mumbai +2

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Consulting

Accenture

R Programming interview questions

  • 30 questions
  • 4 rounds

Bengaluru, Hyderabad, Pune +2

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IT services

TCS

R Programming interview questions

  • 30 questions
  • 4 rounds

Mumbai, Chennai, Pune +2

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IT services

Cognizant

R Programming interview questions

  • 30 questions
  • 4 rounds

Chennai, Bengaluru, Pune +2

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Consulting

EY

R Programming interview questions

  • 30 questions
  • 4 rounds

Bengaluru, Kolkata, Gurugram +2

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Consulting

KPMG

R Programming interview questions

  • 30 questions
  • 4 rounds

Gurugram, Bengaluru, Mumbai +2

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Banking & finance

American Express

R Programming interview questions

  • 30 questions
  • 5 rounds

Gurugram, Bengaluru

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Banking & finance

HSBC

R Programming interview questions

  • 30 questions
  • 4 rounds

Hyderabad, Pune, Bengaluru

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Analytics

Affine

R Programming interview questions

  • 30 questions
  • 4 rounds

Bengaluru

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Product

Amazon

R Programming interview questions

  • 30 questions
  • 5 rounds

Bengaluru, Hyderabad, Chennai +2

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Frequently asked questions

Which companies hire R Programming professionals in Bangalore?

Companies with large offices in Bangalore include Mu Sigma, Fractal Analytics, LatentView Analytics, Tiger Analytics, ZS Associates, EXL, Genpact, Tredence, Course5 Intelligence, Deloitte. Use their company pages below to practise employer-specific sets.

Are R Programming interviews in Bangalore different from other cities?

The questions depend more on the company than the city. Bangalore's mix of employers decides the style: Product-company interviews here are usually deeper, with more scenario, design and hands-on rounds

How do I find R Programming jobs in Bangalore?

Shortlist companies with Bangalore offices, keep your CV focused on R Programming projects, apply on company career pages and job portals with the Bangalore filter, and practise the questions on this page before each interview.

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