R Interview Questions For Freshers
  1. What is the difference between a vector and a matrix in R?
  2. How do you create a scatter plot in R?
  3. What is the difference between == and = operators in R?
  4. How do you remove missing values from a data frame in R?
  5. What is the difference between a factor and a character variable in R?
  6. How do you find the mean of a vector in R?
  7. How do you install and load packages in R?
  8. What is a for loop in R and how is it used?
  9. How do you use the apply() function in R to apply a function to a data frame?
  10. How do you select specific columns from a data frame in R?
  11. What is the difference between a function and a method in R?
  12. How do you use the readLines() function in R to read in a text file?
  13. How do you use the sample() function in R to randomly sample from a data frame?
  14. How do you use the ifelse() function in R to perform conditional operations?
  15. How do you use the subset() function in R to filter a data frame?
  16. How do you use the merge() function in R to combine data frames?
  17. How do you use the which() function in R to find the index of a specific value in a vector?
  18. How do you use the seq() function in R to generate a sequence of numbers?
  19. How do you use the grep() function in R to search for a pattern in a string?
  20. How do you write a for loop to perform a repetitive task in R?
  21. How do you write a conditional statement using if-else statements in R?
  22. How do you write a function to perform a specific task in R?
  23. How do you create a vector in R using the c() function?
  24. How do you perform arithmetic operations on vectors in R?
  25. How do you generate random numbers in R using the rnorm() function?
  26. How do you use the strsplit() function to split a string into substrings in R?
  27. How do you use the paste() function to concatenate two or more strings in R?
  28. How do you use the cat() function to print output to the console in R?
  29. How do you use the write.csv() function to write data to a CSV file in R?
  30. How do you use the read.csv() function to read data from a CSV file in R?
  31. How do you use the apply() family of functions to apply a function to a data frame in R?
R Intermediate Interview Questions
  1. What is a list in R and how is it different from a data frame?
  2. How do you create a boxplot in R and what information does it convey?
  3. What is the difference between a t-test and a chi-squared test in R?
  4. How do you create a function in R?
  5. What is a formula in R and how is it used in linear regression?
  6. How do you use ggplot2 to create customized plots in R?
  7. How do you perform a principal component analysis (PCA) in R?
  8. How do you use the dplyr package to filter, mutate and summarize data in R?
  9. What is the difference between correlation and covariance in R?
  10. How do you use the tidyr package to reshape data in R?
  11. How do you use the lapply() function in R to apply a function to a list of data frames?
  12. What is the difference between a linear regression and a logistic regression in R?
  13. How do you use the mapply() function to apply a function to multiple vectors in R?
  14. How do you use the switch() function to select a statement based on a condition in R?
  15. How do you use the repeat and break statements to create a loop in R?
  16. How do you use the tryCatch() function to handle errors in R?
  17. How do you use the debug() and browser() functions to debug R code?
  18. How do you use closures and lexical scoping to create functions with persistent state in R?
  19. How do you use environments in R to manage state and isolate code from the global environment?
  20. How do you use lazy evaluation and promises in R to improve performance?
  21. How do you use R's object-oriented programming system to define and work with classes and methods?
R Interview Questions For Experienced
  1. How do you create a Shiny app in R and what are its components?
  2. How do you use caret to perform machine learning tasks in R?
  3. How do you use random forests to perform feature selection in R?
  4. How do you use the nlme package to fit mixed effects models in R?
  5. How do you use Bayesian statistics in R using the Stan package?
  6. How do you use the mgcv package to fit generalized additive models in R?
  7. How do you use the brms package to fit Bayesian regression models in R?
  8. How do you use the igraph package to visualize and analyze network data in R?
  9. How do you use the shinydashboard package to create interactive dashboards in R?
  10. How do you use the caretEnsemble package to combine multiple machine learning models in R?
  11. How do you use the bigmemory package to handle large data sets in R?
  12. How do you use R's S4 system for object-oriented programming to define more complex classes and methods?
  13. How do you use R's reference classes to create mutable objects with shared state?
  14. How do you use R's function factory mechanism to create and return customized functions?
  15. How do you use R's higher-order functions to define functions that operate on other functions?
  16. How do you use R's closures and currying to define functions that take partial arguments?
  17. How do you use R's apply family of functions to parallelize operations across multiple cores?
  18. How do you use R's Rcpp package to write high-performance C++ code that can be called from R?
  19. How do you use R's foreign function interface (FFI) to call code written in other languages from R?
  20. How do you use R's JIT (Just-In-Time) compiler to improve performance of R code?
  21. How do you use R's garbage collector to manage memory usage and prevent memory leaks?
  22. How do you use R's low-level memory management functions to optimize memory usage in R?
  23. How do you use R's profiler to analyze and optimize R code performance?
  24. How do you use R's byte compiler to improve performance of R code?
  25. How do you use R's dynamic loading capabilities to load shared libraries at runtime?
  26. How do you use R's serialization capabilities to save and load R objects from disk?
  27. How do you use R's message passing interface (MPI) to enable distributed computing in R?