R Programming Telephonic Interview Questions &amp; Answers

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This is a classic sign of multicollinearity, where predictors are highly correlated, for example total spend and online spend. The model cannot separate their effects, so coefficients become unstable. I check correlations and the variance inflation factor with car::vif(); values above 5 or 10 are a concern. Solutions are removing or combining correlated variables, using principal components or ridge regression. Prediction accuracy may be fine, but interpretation is unreliable.

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 an array in R?

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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.

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

How does a random forest work and which R packages implement it?

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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 renv and why should you use it?

Reports, Shiny and Production REasy

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renv is a package that creates a project-specific library and records exact package versions in a renv.lock file. renv::init() sets up the project, renv::snapshot() updates the lockfile and renv::restore() installs the same versions on another machine or server. This prevents code from breaking when packages update, makes analysis reproducible months later and is essential before deploying scripts or Shiny apps to production.

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

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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$')

What are the principles of tidy data?

Data Wrangling with dplyr and tidyrEasy

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Tidy data follows three rules: each variable is a column, each observation is a row and each type of observational unit forms a table. For example, a sales sheet with separate columns for Jan, Feb and Mar is not tidy, because month is a variable spread across columns. Reshaping it into month and sales columns makes it tidy. Tidy data works directly with dplyr, ggplot2 and modelling functions, reducing reshaping effort later.

Dates in your file look like 25/12/2025 and as.Date() returns NA. What is wrong and how do you fix it?

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Indian files often store dates as dd/mm/yyyy, but as.Date() expects yyyy-mm-dd by default, so values become NA. I specify the format, as.Date(x, format = '%d/%m/%Y'), or use lubridate::dmy(x), which handles different separators automatically. Then I count NAs before and after conversion to confirm nothing failed silently. If the column came from Excel as serial numbers, I use as.Date(x, origin = '1899-12-30') instead.

When would you report the median instead of the mean?

Statistics and Hypothesis TestingEasy

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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.

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.

How is a matrix different from a data frame?

Functions and Functional ProgrammingEasy

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A matrix is a two-dimensional atomic vector, so all elements share one type, usually numeric. A data frame allows a different type per column. Matrices are faster for linear algebra, such as %*% multiplication, solve() and distance calculations, while data frames suit mixed real-world data. For example, the output of cor() is a matrix, while customer records with names, dates and amounts belong in a data frame. as.matrix() and as.data.frame() convert between them.

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.

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 overfitting and how do you prevent it?

Modelling and Machine Learning in REasy

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Overfitting happens when a model learns noise in the training data, so it performs very well on training data but poorly on new data. Signs are a large gap between training and validation scores. Prevention includes using simpler models, regularization such as lasso or ridge, cross-validation for tuning, limiting tree depth, getting more data and removing leaky or irrelevant features. Underfitting is the opposite: the model is too simple to capture the pattern.

How do you test whether two categorical variables are related in R?

Statistics and Hypothesis TestingMedium

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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)

What is the difference between mapping and setting an aesthetic in ggplot2?

Data Visualization with ggplot2Medium

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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.

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

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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 create sequences and repeated values in R?

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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

How do you interpret the summary output of an lm() model?

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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.

What is R and why is it popular for data analysis?

Functions and Functional ProgrammingEasy

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R is an open-source language and environment built for statistics, data analysis and graphics. It is popular because of CRAN, which hosts over 20,000 packages, strong visualization with ggplot2 and built-in statistical functions. Analysts, biostatisticians and data scientists use it for reporting, modelling and research. For example, a pharma analytics team may use R for clinical trial analysis because many statistical methods are first released as R packages.

What is the difference between %>% and |> in R?

Data Wrangling with dplyr and tidyrMedium

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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.

How does the switch() function work in R?

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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.

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.

How do you keep improving your statistics knowledge?

Statistics and Hypothesis TestingEasy

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I revise core concepts from a standard textbook and practise each method in R on public datasets, so I understand both the theory and the code. For example, after learning hypothesis testing, I reproduced published A/B test results from Kaggle datasets. I follow the R Weekly newsletter and read package vignettes. When I meet a method at work that I do not understand, I note it and study it that weekend.

Tell me about the messiest dataset you have cleaned.

Data Wrangling with dplyr and tidyrMedium

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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.

The finance team wants the daily output as a formatted Excel file, not a CSV. How do you produce it?

Reports, Shiny and Production REasy

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I use openxlsx2 or openxlsx to write a workbook with multiple sheets, formatted headers, number formats for INR, column widths, frozen panes and conditional formatting, all from R. writexl is a lightweight option for plain sheets without formatting. I create a template once with the team's preferred layout and generate the file in the scheduled script, then email or upload it to a shared drive. This removes manual formatting every day.

What is a data frame in R?

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A data frame is a list of equal-length vectors, where each vector is a column with its own type. It is R's main structure for tabular data, similar to a spreadsheet or SQL table. str(df) shows column types, nrow() and ncol() give dimensions and summary() gives quick statistics. Tibbles from the tidyverse are modern data frames that print neatly, never convert strings to factors and do not partially match column names.

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)

What are the atomic data types in R?