R Interview Questions

R is a programming language and software environment for statistical computing and graphics.

It is widely used for data manipulation, calculation, and visualization.

It has a vast ecosystem of packages for data science, machine learning, and statistical analysis.

RStudio is a popular IDE for writing and running R code.

R is used in academia, finance, biostatistics, and social sciences for data analysis and visualization.

R packages are collections of functions, documentation, and data that extend R's capabilities.

They are installed using install.packages() and loaded with library() or require().

Popular packages include dplyr for data manipulation, ggplot2 for visualization, and caret for machine learning.

Packages are essential for extending R's capabilities and building production-ready analysis pipelines.

R is designed for statistical analysis and visualization, while Python is a general-purpose language with extensive ML and DL libraries.

R excels in statistical modeling and academic research, while Python is better for production ML and deep learning.

R has rich packages like tidyverse, while Python has pandas, NumPy, scikit-learn, and TensorFlow.

Choice depends on use case: R for stats and visualization, Python for ML and production systems.

lapply() applies a function to each element of a list and returns a list.

sapply() does the same but tries to simplify the output to a vector or matrix when possible.

They are part of R's apply family, which includes vapply, tapply, and mapply.

These functions help avoid explicit loops and improve code readability and performance.

They are used for applying functions across data structures like lists, vectors, and data frames.

Data frames are the traditional tabular data structure in R, while tibbles are modern, tidyverse-compatible replacements from the tibble package.

Tibbles print more cleanly, never convert strings to factors, and support delayed evaluation.

They are more user-friendly and integrate better with tidyverse packages like dplyr and ggplot2.

Tibbles are preferred in modern R workflows for improved usability and performance.

The pipe operator from magrittr allows chaining functions in a readable, intuitive way.

Instead of nesting functions, you pass the output of one function as the first argument to the next.

It is widely used with dplyr and tidyverse for data wrangling and analysis.

It improves code readability and maintainability in complex data pipelines.

R has strong visualization tools like base plotting, lattice, and ggplot2 for static and interactive visualizations.

ggplot2 follows the grammar of graphics and allows building complex visuals with layers.

Python uses matplotlib, seaborn, plotly, and Altair for data visualization.

While both are capable, R's ggplot2 is preferred for statistical and exploratory visualizations.

Python's libraries are better for integration into web apps and machine learning pipelines.

apply() is used for applying a function to margins of an array or data frame.

lapply() applies a function to each element of a list and returns a list.

sapply() is similar but tries to simplify the output to a vector or matrix when possible.

These functions are part of R's apply family and reduce the need for explicit loops.

They are essential for efficient data processing and functional programming in R.

Shiny is an R package for building interactive web applications directly from R.

It enables data scientists to expose R code as web dashboards without needing frontend development skills.

Shiny apps are built using server and UI functions and can be extended with JavaScript and HTML.

Shiny is used for interactive dashboards, data exploration, and internal tools in organizations.

It brings data science and visualization to the web with minimal effort.

R is purpose-built for statistical analysis and has hundreds of packages for statistics, econometrics, and machine learning.

Python has mature libraries like pandas, scikit-learn, and statsmodels for statistical and ML tasks.

R is more expressive for statisticians, while Python is preferred for production ML and integration with backend systems.

Both are powerful, and choice depends on use case, team skills, and deployment requirements.

In practice, both R and Python are used together in data science pipelines.