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R Programming Interview Questions in Ahmedabad

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

R Programming jobs in Ahmedabad

Ahmedabad and GIFT City have a growing market for IT services, fintech and product start-ups. Companies here value hands-on skills and the ability to work across tools, especially in smaller teams.

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

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01

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

R Basics and Data TypesEasy
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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) }
02

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.
03

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)
04

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)
05

Why is ungroup() important in dplyr?

Data Wrangling with dplyr and tidyrMedium
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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.
06

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') ) })
07

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)
08

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()
09

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.
10

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.
11

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

Data Visualization with ggplot2Easy
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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.
12

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

Reports, Shiny and Production RMedium
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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.
13

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.
14

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

Data Structures in RMedium
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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')
15

What does copy-on-modify mean in R and how does it affect performance?

R Basics and Data TypesHard
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When you assign y <- x, both names point to the same object, and R makes a copy only when one of them is modified. tracemem() shows when copies happen. This is why growing a vector in a loop with x <- c(x, value) is slow, because each step can copy the whole vector. Preallocating with vector('numeric', n), using vectorized functions or data.table's modify-by-reference avoids repeated copies and memory spikes.
16

Tell me about a chart or visualization that changed a business decision.

Data Visualization with ggplot2Medium
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Situation: the operations team believed late deliveries were spread evenly across the week. Task: I was asked to review delivery data. Action: in R I built a heatmap of late deliveries by weekday and hour with ggplot2, which showed a sharp cluster on Monday mornings linked to weekend order backlog. Result: management added a Sunday evening shift, and late deliveries dropped by about 18 percent the next quarter.
17

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.
18

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.
19

How do you create maps in R?

Data Visualization with ggplot2Hard
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The sf package stores spatial data as data frames with a geometry column, and ggplot2's geom_sf() draws them. You read shapefiles or GeoJSON with st_read(), join your data, for example sales by state, and fill polygons by value to make a choropleth. tmap is a dedicated thematic mapping package and leaflet builds interactive web maps. Coordinate reference systems must match, and st_transform() reprojects layers when they do not.
20

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.
21

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.
22

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

Data Structures in RMedium
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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.
23

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

Data Wrangling with dplyr and tidyrMedium
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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}'))
24

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
25

How do you handle errors in R code?

Functions and Functional ProgrammingMedium
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tryCatch() catches errors and warnings so a script can recover or log them; finally runs cleanup. read_safe <- function(path) { tryCatch({ readr::read_csv(path, show_col_types = FALSE) }, warning = function(w) { message('Warning in ', path, ': ', conditionMessage(w)) NULL }, error = function(e) { message('Failed: ', path, ' - ', conditionMessage(e)) NULL }, finally = message('Done: ', path)) }
26

What are the atomic data types in R?

R Basics and Data TypesEasy
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R has six atomic types: logical (TRUE/FALSE), integer (5L), double or numeric (3.14), character ('text'), complex (2+3i) and raw (bytes). typeof() returns the internal type while class() returns the class attribute used for method dispatch. For example, typeof(5) is 'double' but typeof(5L) is 'integer'. Knowing types matters because mixing them in one vector triggers coercion to the most flexible type.
27

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

Data Wrangling with dplyr and tidyrMedium
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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.
28

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.
29

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

Data Wrangling with dplyr and tidyrMedium
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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)
30

Bars in your chart appear in alphabetical order instead of by value. How do you fix it?

Data Visualization with ggplot2Easy
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ggplot2 orders discrete axes by factor levels, which default to alphabetical. I reorder the factor by the value with forcats: aes(x = fct_reorder(city, sales), y = sales), and add coord_flip() or put the category on the y-axis for long labels. For a fixed custom order, such as months or ratings, I set levels explicitly with factor(x, levels = c(...)). fct_infreq() orders by frequency for count bars.
31

Describe a mistake you made in an analysis and how you handled it.

Data Wrangling with dplyr and tidyrHard
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Situation: I shared a retention analysis showing improvement, but a week later I noticed I had excluded customers with missing signup dates, who were mostly churned users. Task: correct it before decisions were made. Action: I informed my manager immediately, reran the analysis with proper handling, explained the impact clearly and added a missing-value check to my template. Result: the corrected numbers were used, and my manager appreciated the transparency.
32

How do you write unit tests for an R function?

Reports, Shiny and Production RMedium
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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()
33

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.
34

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.
35

How do you manage working directories and file paths in an R project?

R Basics and Data TypesEasy
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getwd() shows and setwd() changes the working directory, but a hardcoded setwd() breaks on other machines. RStudio projects with the here package build paths relative to the project root. getwd() library(here) sales <- read.csv(here('data', 'sales.csv')) write.csv(summary_df, here('output', 'summary.csv'), row.names = FALSE) file.exists(here('data', 'sales.csv')) list.files(here('data'), pattern = 'csv$')
36

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.

Frequently asked questions

Which companies hire R Programming professionals in Ahmedabad?

Companies with large offices in Ahmedabad include many IT services and product firms. Use their company pages below to practise employer-specific sets.

Are R Programming interviews in Ahmedabad different from other cities?

The questions depend more on the company than the city. Ahmedabad's mix of employers decides the style: Companies here value hands-on skills and the ability to work across tools, especially in smaller teams

How do I find R Programming jobs in Ahmedabad?

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

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