R Language Interview Questions and Answers
Experienced / Expert level questions & answers
Ques 1. What is vectorization in R, and why is it important?
Vectorization is the process of applying operations to entire vectors at once. It is important for efficiency and simplicity in R programming.
Example:
vector1 <- c(1, 2, 3)
vector2 <- c(4, 5, 6)
result <- vector1 + vector2
Ques 2. Explain the concept of lazy evaluation in R.
Lazy evaluation is a feature in R where expressions are not evaluated until their values are actually needed. It can improve performance by avoiding unnecessary computations.
Example:
lazy_function <- function() { print('Lazy function') }
# The function is not executed until called: lazy_function()
Ques 3. How do you generate random numbers in R?
You can use functions like runif() for uniform distribution, rnorm() for normal distribution, and sample() for random sampling.
Example:
random_numbers <- runif(5)
Ques 4. Explain the purpose of the 'shiny' package in R.
The 'shiny' package is used to create interactive web applications directly from R. It allows users to interact with R-based visualizations through a web browser.
Example:
library(shiny)
shinyApp(ui, server)
Ques 5. What is the 'Rcpp' package, and how is it used?
'Rcpp' is a package in R that provides facilities for seamless integration of C++ code in R. It allows for improved performance in computationally intensive tasks.
Example:
#include
// C++ code with Rcpp
// ...
Ques 6. Explain the purpose of the 'dtplyr' package in R.
'dtplyr' is an extension of the 'dplyr' package designed for large datasets. It uses the 'data.table' package to improve performance in data manipulation operations.
Example:
library(dtplyr)
large_data %>% filter(Age > 30) %>% summarise(mean(Salary))
Ques 7. What is memoization in R, and how can it be implemented?
Memoization is a technique to cache and reuse the results of expensive function calls. In R, it can be implemented using the 'memoise' package.
Example:
library(memoise)
my_function <- memoise(function(x) { # function body })
Ques 8. Explain the concept of closures in R.
Closures in R allow functions to capture and store the environment in which they were created. This is useful for creating functions with embedded data or behavior.
Example:
closure_function <- function() {
x <- 10
function() { x + 1 }
}
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