R Programming Interview Questions in Chennai

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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)

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}'))

What is the structure of an R package?

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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 |> in R?

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%>% comes from the magrittr package and |> is the native pipe built into R since version 4.1. Both pass the left side as the first argument of the right side, so df |> filter(x > 1) equals filter(df, x > 1). The native pipe is faster and needs no package, but it uses _ as a named placeholder only, while %>% supports the dot placeholder anywhere. New code increasingly uses |>, and both work with dplyr.

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 does a 95 percent confidence interval mean?

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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.

What is R Markdown and why is it useful?

Reports, Shiny and Production REasy

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R Markdown is a file format that combines narrative text written in Markdown with R code chunks. When rendered with knitr, the code runs and its output, such as tables and charts, is inserted into an HTML, PDF or Word document. This makes reports reproducible: when data changes, you re-render instead of copying charts manually. It is used for monthly reports, research papers, client deliverables and teaching material.

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.

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)

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 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.

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 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)')

How do you check whether data is normally distributed in R?

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Combine visual checks with formal tests, because tests become oversensitive with large samples. hist(x, breaks = 30) qqnorm(x); qqline(x, col = 'red') shapiro.test(x) # works for n between 3 and 5000 library(ggplot2) ggplot(df, aes(sample = x)) + stat_qq() + stat_qq_line() # for model residuals plot(lm_fit, which = 2)

Write a minimal Shiny app that filters sales by region and plots them.

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A ui with an input and an output, and a server that reacts to the input: library(shiny); library(ggplot2); library(dplyr) ui <- fluidPage( selectInput('region', 'Region', choices = unique(sales$region)), plotOutput('trend') ) server <- function(input, output, session) { filtered <- reactive(filter(sales, region == input$region)) output$trend <- renderPlot({ ggplot(filtered(), aes(month, revenue)) + geom_line() }) } shinyApp(ui, server)

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

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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.

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.

How do you create, modify and combine lists in R?

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Lists are built with list(), modified by name and combined or flattened when needed. emp <- list(name = 'Asha', skills = c('R', 'SQL'), exp = 3) emp$city <- 'Pune' # add element emp[['exp']] <- 4 # update emp$skills <- NULL # remove names(emp) parts <- lapply(1:3, function(i) data.frame(id = i, sq = i^2)) do.call(rbind, parts) # combine data frames unlist(list(a = 1, b = list(c = 2))) # flatten

Explain the different types of joins in dplyr.

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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.

Explain the difference between NA, NULL and NaN in R.

R Basics and Data TypesMedium

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NA means a missing value and has a type, such as NA_integer_ or NA_character_. NULL means the absence of an object, has length 0 and disappears when combined with c(). NaN means not a number, the result of undefined maths like 0/0. is.na() returns TRUE for both NA and NaN, while is.nan() is TRUE only for NaN. For example, length(c(1, NULL, 3)) is 2 but length(c(1, NA, 3)) is 3.

How do you perform a t-test in R?

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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 is plumber in R?

Functions and Functional ProgrammingEasy

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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.

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.

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

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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 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.

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.

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.

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 difference between [ ], [[ ]] and $ when subsetting?

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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.

How do you do feature engineering with recipes in tidymodels?

Modelling and Machine Learning in RMedium

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A recipe defines preprocessing steps that are learned on training data and applied identically to new data. rec <- recipe(price ~ ., data = train) %>% update_role(listing_id, new_role = 'id') %>% step_date(listed_on, features = c('month', 'dow')) %>% step_rm(listed_on) %>% step_log(area_sqft) %>% step_other(locality, threshold = 0.01) %>% step_dummy(all_nominal_predictors()) %>% step_normalize(all_numeric_predictors()) prep(rec) %>% bake(new_data = NULL) %>% glimpse()

What is the difference between Pearson and Spearman correlation?

Functions and Functional ProgrammingHard

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Pearson correlation measures the strength of a linear relationship between two numeric variables and is sensitive to outliers. Spearman correlation is computed on ranks, so it measures monotonic relationships and is robust to outliers and non-normal data. In R, cor(x, y, method = 'spearman') and cor.test() compute them. For example, ad spend and sales with diminishing returns may show a moderate Pearson value but a high Spearman value.

What are closures and function factories in R?

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A closure is a function that remembers the environment in which it was created. A function factory is a function that returns such functions. For example, power <- function(exp) function(x) x^exp; square <- power(2) creates a function that remembers exp = 2. They are useful for creating families of related functions, counters and memoised functions. Because of lazy evaluation, call force(exp) inside the factory so the value is captured immediately.

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