R Interview Questions from Codingcompiler – In this article we have prepared the most frequently asked R Interview Questions and Answers for beginners and experienced by covering all the core areas by professionals.
R Interview Questions
- What is R?
- Can you write and explain some of the most common syntax in R?
- What are some basic operations that can be used in R?
- Explain the data import in R language.
- What makes a valid variable name in R?
- What is the main difference between an Array and a matrix?
- Explain how you can start the R commander GUI?
- How missing values and impossible values are represented in R language?
- Which function in R language is used to find out whether the means of 2 groups are equal to each other or not?
- How many data structures does R language have?
R Interview Questions and Answers
1 What is R?
R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
2 Can you write and explain some of the most common syntax in R?Again, this is an easy—but crucial—one to nail. For the most part, this can be demonstrated through any other code you might write for other R interview questions, but sometimes this is asked as a standalone. Some of the basic syntax for R that’s used most often might include:
# — as in many other languages, # can be used to introduce a line of comments. This tells the compiler not to process the line, so it can be used to make code more readable by reminding future inspectors what blocks of code are intended to do.
“” — quotes operate as one might expect; they denote a string data type in R.
<- — one of the quirks of R, the assignment operator is <- rather than the relatively more familiar use of =. This is an essential thing for those using R to know, so it would be good to display your knowledge of it if the question comes up.
\ — the backslash, or reverse virgule, is the escape character in R. An escape character is used to “escape” (or ignore) the special meaning of certain characters in R and, instead, treat them literally.
3 What are some basic operations that can be used in R?
- R’s basic data types are character, numeric, integer, complex, and logical.
- R’s basic data structures include the vector, list, matrix, data frame, and factors.
- Objects may have attributes, such as name, dimension, and class.
# this is a comment in R
- Use x <- 3 to assign a value, 3, to a variable, x
- R counts from 1, unlike many other programming languages (e.g., Python)
- length(thing) returns the number of elements contained in the variable collection
- c(value1, value2, value3) creates a vector
- container[i] selects the i’th element from the variable container
List objects in current environment ls()
Remove objects in current environment rm(x)
Remove all objects from current environment rm(list = ls())
4 Explain the data import in R language.
R provides to import data in R language. To begin with the R commander GUI, user should type the commands in the command Rcmdr into the console. Data can be imported in R language in 3 ways such as:
- Select the data set in the dialog box or enter the name of the data set as required.
- Data is entered directly using the editor of R Commander via Data->New Data Set. This works good only when the data set is not too large.
- Data can also be imported from a URL or from plain text file (ASCII), or from any statistical package or from the clipboard.
5 What makes a valid variable name in R?
A valid variable name consists of letters, numbers and the dot or underline characters. The variable name starts with a letter or the dot not followed by a number.
6 What is the main difference between an Array and a matrix?
A matrix is always two dimensional as it has only rows and columns. But an array can be of any number of dimensions and each dimension is a matrix. For example a 3x3x2 array represents 2 matrices each of dimension 3×3.
7 Explain how you can start the R commander GUI?
Typing the command, (“Rcmdr”) into the R console starts the R commander GUI.
8 How missing values and impossible values are represented in R language?
NaN (Not a Number) is used to represent impossible values whereas NA (Not Available) is used to represent missing values. The best way to answer this question would be to mention that deleting missing values is not a good idea because the probable cause for missing value could be some problem with data collection or programming or the query. It is good to find the root cause of the missing values and then take necessary steps handle them.
9. Which function in R language is used to find out whether the means of 2 groups are equal to each other or not?
Frequently Asked R Interview Questions and Answers
10. How many data structures does R language have?
R language has Homogeneous and Heterogeneous data structures. Homogeneous data structures have same type of objects – Vector, Matrix ad Array. Heterogeneous data structures have different type of objects – Data frames and lists.
11. What is the value of equation1(3) for the following R code?
> num <- 4
> equation1 <- function (val)
+ num <- 3
+ num^3 + g (val)
> equation2 <- function (val)
For the above code snippet, we obtain the output as 39.
12. What arguments are required in the R6Class?
In order to create an R6 class in R, the following arguments are required –
- The first argument in the R6 class is the classname itself. It improves the error messages and allows us to make use of R6 objects with the given S3 generics.
- The second argument of the R6 argument is ‘public’. This argument provides several methods and fields which make use of snake_case.
13. How Do You Solve a Problem in R?
The solutions available are referred to as “packages” in R, so be sure to use this term in your answer. First, explain that the CRAN package ecosystem has an extensive amount of packages available (over 6,000) to solve potential issues. Each R user might have their own way of making their selection, but the best way to answer this question is to explain how reviews from others go a long way: Were other R users with similar issues able to solve their problems with a particular package? If so, were these issues similar to the problems you’re encountering? In your answer, explain that you’d be wary of packages that don’t encompass good software development principles, have poor reviews, or are lacking reviews altogether.
14. What Objects Do You Use Most Often in R?
Your interviewer wants to get a sense of how experienced you are in R programming with this question. Be prepared by knowing, in detail, some of your recent work and explain your most frequently used objects, while also explaining how and why you use them.
15. What Are Some of Your Favorite R Programming Functions?
If you’re interviewing for a job in R programming, it’s not just important that you understand and know R inside and out, but that you’re passionate about it, too. Your interviewer should expect that as an expert in R, you’ll not only be able to easily come up with functions on the spot but that you can easily name the ones you like the most (and why). Be sure to answer this question with confidence and enthusiasm.
16. Describe the R environment (Features).
- R is termed as an integrated suite which contains various software solutions for data calculation, manipulation, and illustration.
- An optimum data handling software with data storage facilities.
- R provides graphical solutions for analyzing data which is displayed either on screen or saved in a hard copy.
- Acts as a large, coherent repository containing intermediate tools for data analysis.
- A collection of operators for calculations on arrays, in particular, matrices.
17. Who and what are the uses of R software environment?
The statistical programming language is widely used amongst data miners and statistics analysts for the development of data analysis and statistical software respectively.
18. Explain the evolution of the language R.
R is an application of the S programming language with lexical scoping semantics encouraged by Scheme. This computing program was created by Mr. Ross Ihaka and Mr. Robert Gentleman at Auckland University, New Zealand. The language is named “R” after the first names of the authors. Although the project was initiated in the year 1992, its first version was released in 1995 and a stable version after half a decade (2000) respectively.
19. Two vectors X and Y are characterized as pursues – X <-c(3, 2, 4) and Y <-c(1, 2). What will be a yield of vector Z that is characterized as Z <-X*Y.
In R dialect when the vectors have distinctive lengths, the duplication starts with the littler vector and proceeds till every one of the components in the bigger vector have been increased.
The yield of the above code will be – Z <-(3, 4, 4)
20. Explain about information import in R dialect
R Commander is utilized to import information in R dialect. To begin the R officer GUI, the client must sort in the direction Rcmdr into the comfort. There are 3 diverse manners by which information can be transported in R dialect
- Users can choose the informational collection in the discourse box or enter the name of the informational index (in the event that they know).
- Data can likewise be entered specifically utilizing the supervisor of R Commander by means of Data->New Data Set. In any case, this functions admirably when the informational index isn’t excessively vast.
- Data can likewise be foreign made from a URL or from a plain content document (ASCII), from some other measurable bundle or from the clipboard.
Advanced R Interview Questions and Answers
21. Name the various components of the grammar of graphics?
The different components of the grammar of graphics are:
- Data layer
- Facet layer
- Themes layer
- Aesthetics layer
- Geometry layer
- Co-ordinate layer
22. How to install a package in R?
To install a package in R, you have to write this command:
23. What is Rmarkdown?
RMarkdown is R’s reporting tool. It allows you to create high-quality reports of R code.
There are three types of output format of Rmarkdown:
24. What are the R packages used for data imputation?
The R packages most commonly used for data imputation are:
25. What is a “confusion matrix” in R?
In R, a confusion matrix is used to assess the accuracy of a developed model. It offers a cross-tabulation calculation of observed and predicted classes by using the “confusionmatrix()” function contained within the “caTools” package.
26. What is the difference between a matrix and a dataframe?
A dataframe can contain heterogenous inputs and a matrix cannot. (You can have a dataframe of characters, integers, and even other dataframes, but you can’t do that with a matrix — a matrix must be all the same type.)
27. What is the difference between seq(4) and seq_along(4)?
seq(4) produces a vector from 1 to 4 (c(1, 2, 3, 4)), whereas seq_along(4) produces a vector of length(4), or 1 (c(1)).
28. What are the different data structures in R?
Data structure is a form of organizing and storing data. It is imperative to have a strong understanding of various data types and data structures in order to make the best use of R languages. R programming supports five types of data structures namely vector, matrix, list, data frame and factor.
- Vector– This data structure contains an integer, double, complex, etc. It is a sequence of same data elements and c() function is used to create a vector in R programming.
- Matrix- it is a two-dimensional data structure and is used to bind vectors from the same length. All the elements in the matrix have to be of the same type and it is created using a matrix() function. The value of row can be defined using nrow and the value of the column can be defined using ncol.
- List- list includes data of different types like numbers, strings, vectors, etc. It is somewhat like a vector but it contains mixed elements. A list is created using ().
- Data frame- it is a special list where each element is of the same length. A data frame has the features of both, matrices and lists. It is more generic than the matrix as different columns have different data types. It is crated using frame() function.
- Factors-it is created using factor() function and is used to store predefined and categorical data.
29. When to use the following functions: apply(), lapply(), sapply(), tapply() in R? Explain.
The differences are the following:
apply(): Use as an alternative to for() loop
lapply(): Applies function to every item and returns the result as a list
sapply(): function will be executed column wise
tapply(): Similar to aggregate() function
30. Are the following code snippets same or different? Explain why to support your response.
flights_mutate1 <- flights %>% mutate(speed=distance/air_time*60)
flights_mutate2 <- flights %>% select(carrier,arr_delay,speed)
These are NOT same. Flights_mutate1 will perform appropriately. Where as
flights_mutate2 will throw an error. We can not use select because the derived variables “speed” does not exist. It has to be created first using mutate() function and then select() function can be used to extract specific variables from the data frame.
R Interview Questions and Answers for Experienced
31. How to determine data type of an object?
class() is used to determine data type of an object. See the example below –
x <- factor(1:5)
It returns the factor
> x <-factor(1:5)
To determine structure of an object, use str() function :
str(x) returns “Factor w/ 5 level”
Example 2 :
xx <- data.frame(var1=c(1:5))
It returns “data.frame”.
str(xx) returns ‘data.frame’ : 5 obs. of 1 variable: $ var1: int
32. Which data structure is used to store categorical variables?
R has a special data structure called “factor” to store categorical variables. It tells R that a variable is nominal or ordinal by making it a factor.
gender = c(1,2,1,2,1,2)
gender = factor(gender)
33. Give an example usage of tapply() method
Consider two ordered vectors
1) students distributed across various schools (s1 is the school of the first student, s2 is the school of the second student, etc)
> students <- c(“s1″,”s2″,”s1″,”s3″,”s3″,”s2”)
2) Percentage of each student’s marks
> marks <- c(80,90,75,67,96,67)
> means <- tapply(marks,students,mean)
s1 s2 s3
77.5 78.5 81.5
The function tapply() applies a function ‘mean()’ to first argument ‘marks’, which is grouped by second argument ‘students’
34. How to modify and construct lists? Show with an example.
> Lst <- list(name=”Jack”, age=23, no.cars=3, cars.names = c(“Wagon”, “Bumper”, “Jazz”))
> Lst$cars.names <- “WagonR” OR > Lst[] <- “WagonR”
35. How to deal with missing values in sum(), prod(), min(), max() functions?
Consider a vector:
> x <- c(3, 6, 2, NA, 1)
Its sum will result in:
However, we can set the na.rm argument as True to ignore the missing values
> sum(x, na.rm=TRUE)
36. How to write your own functions?
A function in R can be written as follows:
> function_name <- function(arg1, arg2, . . . ) expression_in_R
expression_in_R is usually a set of different expressions clubbed together.
37. How can you load a .csv file in R?
Loading a .csv file in R is quite easy. All you need to do is use the “read.csv()” function and specify the path of the file.
38. Write code to accomplish a task?
Answer) In just about an interview for a position that involves coding, companies will ask you to accomplish a specific task by actually writing code. Facebook and Google both do as much. Because it’s difficult to predict what task an interviewer will set you to, just be prepared to write “whiteboard code” on the fly
39. What are some of your favorite functions in R?
Answer) As a user of R, you should be able to come up with some functions on the spot and describe them. Functions that save time and, as a result, the money will always be something an interviewer likes to hear about.
40. What is a factor variable, and why would you use one?
Answer) A factor variable is a form of the categorical variable that accepts either numeric or character string values. The most salient reason to use a factor variable is that it can be used in statistical modeling with great accuracy. Another reason is that they are more memory efficient.Simply use the factor() function to create a factor variable.
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