Scenario based R programming Interview Questions and Answers (2025)

Scenario based R programming Interview Questions and Answers (2025)

1. Scenario: Handling Missing Data in a Large Dataset

Question:

You have a large dataset of customer transaction records, which includes missing values in columns like age, salary, and last_purchase_date. How would you handle missing data in R for accurate analysis and modeling?

Answer:

In R, you can handle missing data using several techniques:

Remove Missing Values: If the missing values are minimal and you can afford to drop rows or columns.

data_clean <- na.omit(data)

Imputation: You can impute missing values with the mean, median, or mode for numerical columns.

data$age[is.na(data$age)] <- mean(data$age, na.rm = TRUE)

Use Imputation Packages: You can use the mice or Amelia package for more advanced imputation strategies.

library(mice)

imputed_data <- mice(data, method = 'pmm', m = 5)

data_imputed <- complete(imputed_data, 1)

Predictive Modeling: You could also predict missing values based on other variables (e.g., using randomForest or regression).

2. Scenario: Time Series Forecasting for Sales Prediction

Question:

You are tasked with forecasting monthly sales of a retail store based on past sales data. The dataset includes historical sales data with timestamps. How would you approach this problem using R?

Answer:

For time series forecasting in R, you can use the following approach:

Load Data & Convert to Time Series:

library(tidyverse)

sales_data <- read.csv("sales_data.csv")

sales_ts <- ts(sales_data$sales, start = c(2015, 1), frequency = 12)

Exploratory Data Analysis (EDA):

Plot the data to check for seasonality, trend, and outliers.

plot(sales_ts)

Decompose the Time Series:

Decompose the series into trend, seasonal, and residual components.

decomposed <- decompose(sales_ts)

plot(decomposed)

Modeling:

Fit ARIMA or Exponential Smoothing model (e.g., auto.arima from the forecast package).

library(forecast)

fit <- auto.arima(sales_ts)

forecast_sales <- forecast(fit, h = 12) # Forecast next 12 months

plot(forecast_sales)

Evaluate the Model:

Evaluate forecast accuracy using RMSE or MAE.

accuracy(forecast_sales)

3. Scenario: Data Visualization for Business Insights

Question:

You are given a dataset containing customer demographic information and purchasing behavior. How would you visualize this data to extract key insights for a marketing team using R?

Answer:

To visualize demographic and purchasing behavior data in R, you can use the ggplot2 package for advanced visualizations:

Install and Load ggplot2:

install.packages("ggplot2")

library(ggplot2)

Histograms for Demographics:

For age distribution, you can use a histogram.

ggplot(data, aes(x = age)) +

geom_histogram(binwidth = 5, fill = "blue", color = "black") +

labs(title = "Age Distribution", x = "Age", y = "Frequency")

Boxplot for Purchasing Behavior by Demographics:

You can use boxplots to visualize purchasing behavior by customer demographics (e.g., income).

ggplot(data, aes(x = income, y = purchase_amount)) +

geom_boxplot() +

labs(title = "Purchase Amount by Income", x = "Income", y = "Purchase Amount")

Scatter Plot for Relationships:

To visualize relationships between two continuous variables like age and purchase amount.

ggplot(data, aes(x = age, y = purchase_amount)) +

geom_point(aes(color = gender), size = 2) +

labs(title = "Age vs Purchase Amount", x = "Age", y = "Purchase Amount")

Heatmaps for Correlation:

Use heatmaps to explore correlations between numeric variables.

library(reshape2)

correlation_matrix <- cor(data[, c("age", "income", "purchase_amount")])

melt_correlation <- melt(correlation_matrix)

ggplot(melt_correlation, aes(Var1, Var2, fill = value)) +

geom_tile() +

labs(title = "Correlation Heatmap")

4. Scenario: Building a Predictive Model for Churn Prediction

Question:

You are given customer data and need to build a predictive model to identify customers at risk of churn. How would you approach this task in R?

Answer:

To build a churn prediction model in R, follow these steps:

Load the Data:

customer_data <- read.csv("customer_data.csv")

Preprocessing:

Clean the data, handle missing values, and encode categorical variables (e.g., using factor or dummyVars).

customer_data$Churn <- as.factor(customer_data$Churn) # Churn is a binary outcome

Exploratory Data Analysis (EDA):

Check for class imbalance in the target variable (Churn).

table(customer_data$Churn)

Feature Engineering:

Create new features like tenure, average monthly spend, etc.

customer_data$avg_monthly_spend <- customer_data$total_spent / customer_data$tenure

Splitting the Data:

Split the data into training and test sets.

library(caret)

set.seed(123)

trainIndex <- createDataPartition(customer_data$Churn, p = .8, list = FALSE)

train_data <- customer_data[trainIndex, ]

test_data <- customer_data[-trainIndex, ]

Model Building:

Build a logistic regression model or a random forest model.

model <- randomForest(Churn ~ ., data = train_data, ntree = 100)

Model Evaluation:

Predict on the test data and evaluate using confusion matrix, ROC curve, etc.

predictions <- predict(model, test_data)

confusionMatrix(predictions, test_data$Churn)

5. Scenario: Optimizing an R Script for Large Datasets

Question:

You are working with a massive dataset, and your R script is running too slowly. What steps would you take to optimize your R code?

Answer:

To optimize R code for large datasets, you can follow these steps:

Use data.table Instead of data.frame:

The data.table package is more memory efficient and faster than base R data frames.

library(data.table)

data <- fread("large_dataset.csv")

Avoid Loops Where Possible:

Use vectorized operations and apply functions instead of loops.

result <- sapply(data$column, function(x) x^2) # vectorized operation

Use Parallel Processing:

Leverage multiple cores with the parallel package or foreach to parallelize computations.

library(parallel)

num_cores <- detectCores() - 1

result <- mclapply(1:num_cores, function(i) some_function(i), mc.cores = num_cores)

Memory Management:

Use gc() to trigger garbage collection and free up memory.

gc() # garbage collection to optimize memory usage

Efficient Data Import:

Use readr::read_csv() or data.table::fread() for faster data import.

library(readr)

data <- read_csv("large_dataset.csv")

Profiling:

Profile the script to identify bottlenecks using Rprof or microbenchmark.

library(microbenchmark)

microbenchmark(some_function(), times = 100)

These questions and answers cover a range of common scenarios, addressing data preprocessing, visualization, time series analysis, predictive modeling, and optimization techniques in R. They not only assess the candidate's R skills but also test their ability to handle real-world business problems.

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