36 practice questions with answers for R Programming interviews in Indore.
R Programming jobs in Indore
Indore is one of central India's fastest-growing IT hubs, with IT-services campuses and product start-ups in its Super Corridor and IT parks. Companies here hire many freshers and value hands-on project work.
Below are 36 questions that match the mix of service, product and domain employers hiring r data analysts in Indore.
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01
Your function gives correct results when you test it but wrong results in the main script. What could be the cause?
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.
02
How do you generate the same report for each of 20 regions automatically?
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')
)
})
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.
04
Rendering a ggplot with 10 million points is extremely slow. What would you do?
I first question whether all points are needed. I aggregate before plotting, for example binning into hexagons with geom_hex() or summarising by time interval. If raw points are required, the scattermore package rasterizes millions of points quickly, and ggrastr rasterizes just the point layer while keeping text as vector. Saving as PNG instead of PDF also helps, because vector files with millions of shapes are huge and slow to open.
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.
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.
After an error, traceback() shows the call stack so I can see which function failed. I insert browser() inside a function to pause execution and inspect variables interactively, stepping with n and continuing with c. debug(fn) or debugonce(fn) enters the browser on the next call without editing code. options(error = recover) lets me choose a frame to inspect after an error. RStudio breakpoints offer the same features visually.
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.
10
Product wants to detect a lift in conversion from 5 to 5.5 percent. How many users do you need per group?
I use power analysis before the test starts. In R, power.prop.test(p1 = 0.05, p2 = 0.055, power = 0.8, sig.level = 0.05) gives roughly 31,000 users per group. Smaller lifts need far larger samples, since sample size scales with the inverse square of the effect. I would then convert the sample into test duration using daily traffic, round up to full weeks, and agree on the stopping rule in advance to avoid peeking bias.
11
Why should R projects use Git and how does RStudio support it?
Git tracks every change to code, lets you revert mistakes, work on branches and collaborate through pull requests and reviews. RStudio has a built-in Git pane to stage, commit, diff, pull and push, and usethis::use_git() and use_github() set up repositories. Keep data files, secrets and outputs out of Git with .gitignore, commit the renv.lock file and write clear commit messages. Version control is expected even for analysis work.
12
Explain the d, p, q and r functions for probability distributions in R.
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
13
How do you produce descriptive statistics for each group in R?
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.
15
How do you connect R to a SQL database and run queries?
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)
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.
17
Business users do not trust your gradient boosting model because it is a black box. What do you do?
I explain both global and individual behaviour. Globally, I show variable importance and partial dependence plots, using DALEX or the vip package, to show how key features affect predictions. For individual cases I use SHAP values, for example through the shapviz or fastshap packages, so a manager can see why a customer was flagged. I compare against a simple logistic model and walk users through real examples they know.
18
The same 40 lines of cleaning code are copied into ten scripts. What would you do?
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.
19
How would you impute missing values differently for each group?
I group by the relevant category, such as product category, and replace NAs with that group's median: group_by(category) %>% mutate(price = coalesce(price, median(price, na.rm = TRUE))) %>% ungroup(). Group medians are more realistic than a global value. I also add a flag column marking imputed rows so models and reviewers can see them. If a whole group is missing, I fall back to the overall median, and for important features I consider model-based imputation.
20
How do you manage working directories and file paths in an R project?
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$')
21
Tell me about a time your analysis did not support what a stakeholder hoped to see.
Situation: a marketing head expected a new campaign to have doubled sign-ups. Task: evaluate its impact. Action: my analysis in R using a pre-post comparison with a control region showed only a 6 percent lift, much of it seasonal. I presented the method and confidence interval openly, acknowledged what went well and suggested a cleaner randomized test. Result: the stakeholder accepted the finding, and the next campaign was run as a proper experiment.
22
Tell me about a chart or visualization that changed a business decision.
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.
23
Code reviews show inconsistent R code style across the team. How would you standardise it?
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.
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.
25
Tell me about feedback you received in a code review and what you changed.
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.
26
Tell me about the messiest dataset you have cleaned.
Situation: I received three years of vendor invoices from different branches in Excel files with inconsistent headers, merged cells and dates in four formats. Task: build one clean table for spend analysis. Action: I used readxl to read all sheets, janitor::clean_names() to standardise headers, lubridate to parse dates and a mapping table for vendor name variants. I wrote validation checks for totals. Result: a clean dataset of 180,000 rows that finance now refreshes monthly.
27
How do you reshape data between wide and long formats in R?
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.
29
Write a recursive function in R to calculate factorial and Fibonacci numbers.
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)
When you subset a single column or row from a matrix or base data frame with [ ], R simplifies the result to a vector by default. Writing m[, 1, drop = FALSE] keeps it as a matrix or data frame. This matters inside functions, because code expecting a data frame breaks when a selection leaves only one column. Tibbles never drop dimensions, which is one reason developers prefer them in reusable code and packages.
31
Write a tidymodels workflow to train and evaluate a random forest.
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)
33
How do you do feature engineering with recipes in tidymodels?
An environment is a collection of name-value bindings with a pointer to a parent environment. R uses lexical scoping: when a function looks up a variable, it searches its own environment, then the environment where it was defined, up through the global environment and attached packages. So a function defined inside another can read the outer function's variables, which enables closures. The <<- operator modifies a binding in a parent environment and should be used sparingly.
35
What is the difference between base R graphics and ggplot2?
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.
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.
Few big employers listed in Indore?
Many Indore candidates also apply to teams in Pune and to remote or hybrid roles. Practise the Pune set as well.
Which companies hire R Programming professionals in Indore?
Companies with large offices in Indore include many IT services and product firms. Use their company pages below to practise employer-specific sets.
Are R Programming interviews in Indore different from other cities?
The questions depend more on the company than the city. Indore's mix of employers decides the style: Companies here hire many freshers and value hands-on project work
How do I find R Programming jobs in Indore?
Shortlist companies with Indore offices, keep your CV focused on R Programming projects, apply on company career pages and job portals with the Indore filter, and practise the questions on this page before each interview.