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R Programming Online Assessment Questions & Answers

40 questions for the online assessment stage — 18 easy, 21 medium, 1 hard.

About the online assessment round

Most companies start with an online test: aptitude and reasoning, a few MCQs on core R Programming concepts, and one or two short hands-on tasks. It filters a large number of applicants, so speed and accuracy both count.

Tips for the online assessment round

Practise under a timer: about one minute per MCQ and 15-20 minutes per hands-on task.
Read every option before answering; tests often include two nearly correct choices.
Use a stable internet connection, keep your camera on and avoid switching tabs in proctored tests.
Attempt the questions you are sure about first, then return to the harder ones.

Solved — online assessmentStart practicing

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How does the switch() function work in R?

Functions and Functional ProgrammingEasy

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switch() selects one of several alternatives based on a value, which is cleaner than a long chain of if-else statements. With a character input, it matches names: switch(type, mean = mean(x), median = median(x), stop('Unknown type')). The final unnamed argument acts as the default. Empty arguments fall through to the next value, so switch(x, a = , b = 'AB') returns 'AB' for both. It is not vectorized, so use it for single values.

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

Data Visualization with ggplot2Medium

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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())

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') ) })

How do you add logging to a production R script?

Reports, Shiny and Production RMedium

Show answer

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')

Why does correlation not imply causation?

Statistics and Hypothesis TestingEasy

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Two variables can move together because of a third confounding variable, reverse causation or pure chance. For example, ice cream sales and drowning incidents are correlated because both rise in summer. To claim causation you need a randomized experiment such as an A/B test, or careful causal methods like difference-in-differences or regression with controls. In interviews, mention that observational correlations are useful for hypotheses but not proof of impact.

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.

What is vectorization in R and why does it matter?

R Basics and Data TypesMedium

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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.

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.

Explain reactivity in Shiny.

Reports, Shiny and Production RMedium

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Shiny uses reactive programming: outputs automatically update when the inputs they depend on change. Inputs are reactive sources, outputs such as renderPlot() are reactive endpoints, and reactive() expressions are cached intermediate results that recompute only when their dependencies change. For example, filtering data once in a reactive() and using it in three charts avoids filtering three times. Shiny tracks dependencies automatically, so you do not write explicit event handlers for most cases.

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.

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().

What is Rcpp and when would you use it?

Functions and Functional ProgrammingHard

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Rcpp is a package that lets you write C++ functions and call them from R with almost no boilerplate, using cppFunction() for small snippets or sourceCpp() for files. It is used when R code has loops that cannot be vectorized, such as recursive calculations, simulations with dependence between steps or custom algorithms. Many popular packages, including dplyr and data.table internals, rely on compiled code. Use it only after profiling shows a real bottleneck.

How do you handle missing values in calculations in R?

R Basics and Data TypesEasy

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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

Which chart types would you use for comparing categories, trends and distributions?

Data Visualization with ggplot2Easy

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For comparing categories I use a bar chart, sorted by value, or a dot plot when there are many categories. For trends over time I use a line chart with time on the x-axis. For distributions I use histograms or density plots for one variable and boxplots or violin plots to compare groups. For relationships between two numeric variables I use a scatter plot. I avoid pie charts beyond three or four slices.

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 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

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.

What are the assumptions of linear regression?

Modelling and Machine Learning in REasy

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The main assumptions are a linear relationship between predictors and the outcome, independent errors, constant variance of errors (homoscedasticity), normally distributed errors for valid inference and no severe multicollinearity. In R, plot(fit) shows residuals versus fitted values, a Q-Q plot and leverage, and car::vif() checks collinearity. Violations can be addressed with transformations like log, robust standard errors or different models such as GLMs.

What is the difference between a vector and a list in R?

Data Structures in REasy

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An atomic vector holds elements of a single type, such as all numbers or all strings, while a list can hold elements of any type, including other lists, data frames or functions. c(1, 'a') coerces to character, but list(1, 'a', TRUE) keeps each type. Lists are used for nested data such as JSON responses and for returning multiple results from a function, which is why model objects like lm() output are lists.

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.

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

Data Wrangling with dplyr and tidyrMedium

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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'))

Finance says 120 transactions are missing from your report compared with their system. How do you find them?

Data Wrangling with dplyr and tidyrEasy

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I load both datasets and standardise the key, for example trimming spaces and matching data types for transaction_id. Then anti_join(finance, report, by = 'transaction_id') lists transactions in finance but not in my report, and the reverse anti_join shows extras. I examine the missing rows for patterns such as a date range, status or filter in my code. Usually the cause is a timezone cut-off or an excluded status.

What are the main dplyr verbs?

Data Wrangling with dplyr and tidyrEasy

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dplyr has a small set of verbs for data transformation: filter() keeps rows that meet conditions, select() picks columns, mutate() creates or changes columns, arrange() sorts rows, summarise() reduces groups to summary rows and group_by() defines groups for the next operations. Others include distinct(), rename(), relocate(), count() and slice(). Each takes a data frame as the first argument and returns a data frame, so verbs chain naturally with the pipe.

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.

Write a recursive function in R to calculate factorial and Fibonacci numbers.

Functions and Functional ProgrammingEasy

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A recursive function calls itself with a smaller input and needs a base case. fact <- function(n) { if (n <= 1) return(1) n * fact(n - 1) } fact(5) # 120 fib <- function(n) { if (n <= 2) return(1) fib(n - 1) + fib(n - 2) } fib(10) # 55 # vectorized and efficient alternative factorial(5); cumprod(1:5)

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.

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)

What is the difference between Quarto and R Markdown?

Reports, Shiny and Production RMedium

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Quarto is the next-generation publishing system from Posit. It works with R, Python, Julia and Observable, and is a standalone command-line tool rather than an R package. It uses the same knitr engine for R and similar syntax, with chunk options written as #| comments. Quarto adds consistent support for websites, books, presentations and dashboards. R Markdown is still supported, but new projects increasingly use Quarto, especially in mixed R and Python teams.

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.

Your function gives correct results when you test it but wrong results in the main script. What could be the cause?

Functions and Functional ProgrammingMedium

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A common cause is that the function reads a variable from the global environment instead of taking it as an argument. During testing that global variable had one value, and in the main script it has another. Lexical scoping finds it silently. I would check the function body for names not defined as arguments or locally, for example using codetools::findGlobals(fn), and pass everything explicitly so the function is self-contained.

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 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.

What is the tidyverse?

Data Wrangling with dplyr and tidyrEasy

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The tidyverse is a collection of R packages that share a common design and grammar for data science. Core packages include readr for importing, dplyr for transformation, tidyr for reshaping, ggplot2 for visualization, purrr for functional programming, stringr for text, forcats for factors and lubridate for dates. library(tidyverse) loads the core set. Its consistent function names and the pipe make analysis code readable, which is why most R analytics teams use it.

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.

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

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

How do you create interactive charts in R?

Data Visualization with ggplot2Medium

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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.

How do you save a ggplot chart for a report or presentation?

Data Visualization with ggplot2Easy

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ggsave() saves the last plot or a named plot with exact size and resolution. p <- ggplot(df, aes(city, sales)) + geom_col() ggsave('output/sales_by_city.png', plot = p, width = 8, height = 5, dpi = 300) ggsave('output/sales_by_city.pdf', plot = p, width = 8, height = 5) # vector, sharp at any zoom # svg for web use ggsave('output/sales_by_city.svg', plot = p)

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

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)

Frequently asked questions

What happens in the R Programming online assessment round?

How do I clear the online assessment round?

Practise under a timer: about one minute per MCQ and 15-20 minutes per hands-on task. Read every option before answering; tests often include two nearly correct choices. Use a stable internet connection, keep your camera on and avoid switching tabs in proctored tests.

Which round comes next?

A typical order is online assessment, telephonic screening, one or two technical rounds, a managerial round and HR. Use the other round pages to practise each stage.

Practise every interview round

Telephonic Interview questions Technical Round questions Managerial Round questions Rapid-Fire questions HR & behavioural questions Full mock interview

More ways to practise

Company-wise R Programming questions Experience-wise R Programming questions Role-wise R Programming questions City-wise R Programming questions Most asked R Programming questions Scenario-based questions Practical / hands-on questions HR & behavioural questions R Programming mock interview Online assessment questions Technical round questions Managerial round questions R Programming topic sheets