R Language Interview Questions and Answers
Freshers / Beginner level questions & answers
Ques 1. What is R?
R is a programming language and free software environment for statistical computing and graphics.
Example:
print('Hello, R!')
Ques 2. How do you assign a value to a variable in R?
You can use the assignment operator <- or =.
Example:
x <- 10
Ques 3. Explain what a data frame is in R.
A data frame is a two-dimensional, heterogeneous tabular data structure with rows and columns.
Example:
df <- data.frame(Name=c('John', 'Jane'), Age=c(25, 30))
Ques 4. What is the use of the 'attach()' function in R?
The attach() function is used to attach a data frame to the search path, making it easier to refer to variables in the data frame.
Example:
attach(df)
Ques 5. How can you install a package in R?
You can use the install.packages() function.
Example:
install.packages('packageName')
Ques 6. What is the purpose of the 'str()' function in R?
The 'str()' function is used to display the structure of an R object. It provides a compact way to see the internal structure of data objects in R.
Example:
str(my_data)
Ques 7. How can you check if a variable is defined in R?
You can use the 'exists()' function to check if a variable exists in the specified environment.
Example:
exists('my_variable')
Ques 8. Explain the purpose of the 'ggplot2' package in R.
'ggplot2' is a popular data visualization package in R that allows users to create complex and customized plots with a grammar of graphics approach.
Example:
library(ggplot2)
ggplot(data = my_data, aes(x = variable1, y = variable2)) + geom_point()
Ques 9. What is the difference between 'ls()' and 'objects()' functions in R?
'ls()' and 'objects()' both list objects in the current environment, but 'ls()' is a shorthand form of 'objects()' and has additional options.
Example:
ls()
# or
objects()
Ques 10. How can you read data from a CSV file in R?
You can use the 'read.csv()' function to read data from a CSV file in R.
Example:
my_data <- read.csv('file.csv')
Ques 11. What is the purpose of the 'setwd()' function in R?
The 'setwd()' function is used to set the working directory in R. It changes the current working directory to the specified path.
Example:
setwd('/path/to/directory')
Ques 12. How can you install and load a package in R using a single command?
You can use the 'install.packages()' and 'library()' functions in a single line to install and load a package.
Example:
install.packages('my_package'); library('my_package')
Ques 13. Explain the purpose of the 'summary()' function in R.
The 'summary()' function is used to obtain a summary of the central tendency, dispersion, and shape of a distribution.
Example:
summary(my_data)
Intermediate / 1 to 5 years experienced level questions & answers
Ques 14. Explain the use of the 'apply()' function in R.
The apply() function is used to apply a function to the rows or columns of a matrix or array.
Example:
apply(matrix, 1, sum)
Ques 15. What is the purpose of the 'merge()' function in R?
The merge() function is used to merge two or more data frames based on a common column.
Example:
merged_data <- merge(df1, df2, by='common_column')
Ques 16. Explain the concept of factor variables in R.
Factor variables are used to represent categorical data in R. They can have levels, which represent the categories.
Example:
gender <- factor(c('Male', 'Female', 'Male'), levels=c('Male', 'Female'))
Ques 17. How do you handle missing values in a data frame in R?
You can use the na.omit() function to remove rows with missing values, or use functions like is.na() to identify missing values.
Example:
cleaned_data <- na.omit(df)
Ques 18. Explain the purpose of the 'dplyr' package in R.
The 'dplyr' package provides a grammar of data manipulation, with functions like filter(), select(), and mutate(), making data manipulation tasks more intuitive.
Example:
library(dplyr)
filtered_data <- filter(df, Age > 25)
Ques 19. Explain the purpose of the 'purrr' package in R.
'purrr' is a package in R that enhances functional programming with a consistent and concise syntax, making it easier to work with lists and vectors.
Example:
library(purrr)
map(my_list, my_function)
Ques 20. What is the purpose of the 'reshape2' package in R?
'reshape2' is a package used for reshaping data frames. It provides functions like melt() and cast() for converting between wide and long formats.
Example:
library(reshape2)
melted_data <- melt(my_data, id.vars=c('id', 'name'))
Ques 21. How do you handle exceptions in R?
You can use the 'tryCatch()' function to handle exceptions in R. It allows you to define code to be executed in case an error occurs.
Example:
tryCatch({
# code that might cause an error
}, error = function(e) {
# code to handle the error
})
Ques 22. Explain the purpose of the 'caret' package in R.
'caret' is a package in R used for streamlining the process of applying machine learning models. It provides a unified interface for various modeling techniques.
Example:
library(caret)
model <- train(y ~ ., data = my_data, method = 'lm')
Experienced / Expert level questions & answers
Ques 23. 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 24. 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 25. 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 26. 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 27. 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 28. 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 29. 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 30. 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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