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Table of contents
1.
Easy level
2.
Medium Level
3.
Hard Level
4.
Conclusion
Last Updated: Mar 27, 2024
Medium

R Programming Interview Questions

Author Nidhi Kumari
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Anubhav Sinha
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12 Jun, 2024 @ 01:30 PM

R initially appeared in 1993, which is more than 28 years ago! Right now, the fourteenth most used programming language is R.  it is trendy and is utilised by many firms for various projects. 

Let's go ahead and hit on 25 important R programming Interview questions. We will cover the most vital and likely R Programming interview questions in this blog.

Be interview ready by brushing your concepts and practising these R programming Interview questions.

R programming Interview questions

Also see,  Operating System Interview Questions

Easy level

This section will get the basic R programming interview questions to build a strong foundation. This part is essential because it creates a firm basis for your R programming interview questions.

  1. What are the different data structures present in R? 
    Ans: In general, R includes the following data structures:

    Vector: The most common data structure in R. A vector consists of a series of data elements(ordered) of the same data type. It is a one-D data structure. The data elements in the vector are known as components.

    List: The data structure that stores elements of various types, such as numbers, strings, vectors, or another list. It also consists ordered collection of elements. 

    Matrix: It is a two-D data structure. Vectors of the same length are bound together using matrices. A matrix's elements must all be the same type (numbers, strings, characters).

    Dataframe: Unlike a matrix, a data frame allows various columns to include various data types (numeric, character, logical, etc.). It combines aspects of rectangular lists and matrices.
     
  2. List any five features of R.
    Ans: The features of R:

    • Quick and simple programming language.
       
    • It is a programme for data analysis.
       
    • It provides efficient data handling and storage.
       
    • High visual approaches are provided.
       
    • It is an interpreted language.
       
  3. What functional differences exist between R and Python?
    Ans: R comes with built-in functionality for data analysis. However, Python does not have these features. They can be found in packages such as Pandas and Numpy.
     
  4. What distinguishes R's sample() and subset() functions?
    Ans: The subset( ) method chooses observations and variables, whereas the sample() method selects a random sample of size n from a dataset.
    Also see, Servicenow Interview Questions
     
  5. Why do we employ R's apply() function?
    Ans: The apply() function enables us to apply a function to a matrix's or data frame's rows or columns. This function accepts as arguments a matrix or data frame. It returns the result as a vector, array, or list of values obtained.
     
  6. What is the use of t-test() in R?
    Ans: One of the most used statistical tests is the t-test in R. To determine whether the means of the two groups are equal, use the t-test() function.
     
  7. Distinguish between the require() and library() functions.
    Ans: If the packages are not being loaded inside the function, there isn't any significant difference between the two. The require() function is utilised inside the function and throws a warning whenever a specific package is not found. The library() function displays an error message if the system cannot load the desired package.
     
  8.  How to create co-relations and covariances in R?
    Ans: The cor() function can be used to create co-relations, and You can use the cov() function to create covariances.
     
  9. What are the goals of R's with() and by() functions?
    Ans: The by() function assigns a function to each factor level.
    The with() function delivers an expression to a dataset.
     
  10. What are some disadvantages of R?
    Ans: An interviewer can also ask this kind of R Programming Interview Questions.
    Some of the cons of R:

    1. Data Handling.
       
    2. Difficult Language.
       
    3. Simple Security.
       
    4. Slower Speed.
       
    5. Weak Origin.

Medium Level

We discussed some of the simple R programming Interview Questions. Let's now discuss a few of the medium-level questions.

  1. What does R's confusion matrix mean?
    Ans: A confusion or error matrix handles the common classification problem. It is possible to assess the accuracy of the created model using a confusion matrix. A cross-tabulation of observed and anticipated classes is calculated.

    You can use the "confusionmatrix()" method from the "caTools" package to accomplish this. It uses a specific table design that makes it easier for data analysts to see how an algorithm works.
     
  2. How to create a custom function in R? 
    Ans: The syntax for creating a custom function in R is as follows:

    Name of the function = function(Arguments) {
        Statement 1
        Statement 2
        Statement 3
        …………….
    }

    Here is an example of how to write a custom function in R.

    CodingNinjas = function (a) {
     ifelse(a > 15, 20, 0)
    }
    X = c(1, 2, 3, 4, 5, 6, 7, 8, 9)
    CodingNinjas(X) = X
     
  3. What do you mean by Data Imputation?
    Ans: Most datasets may contain missing values due to a mistake or because they weren't input. Data imputation is the process of replacing these missing values with a different value. You can replace the column's missing value in R in various ways, including by setting the column's missing value to zero, the average, the median, and so on.
     
  4. Which R function is used to combine datasets?
    Ans: The rbind() function can combine two data frames by rows. The same variables must be present in both data frames, although they need to be in a different order.

    The syntax is as follows:
    rbind(object1, object2,.....)
     
  5. What is the use of the cbind() function?
    Ans: To combine a specified Vector, Matrix, or Data Frame by columns, use the cbind() function.
    The syntax is as follows:
    cbind(object1, object2,.....)
     
  6. Which R packages are available for data imputation?
    Ans: The following R packages can be used for data imputation.

    1. Amelia
       
    2. Hmisc
       
    3. imputeR
       
    4. Mi
       
    5. MICE
       
    6. missForest
       
  7. List some of the functions in the "dplyr" package.
    Ans: The following R functions are in the "dplyr" package.

    1. Arrange
       
    2. Count
       
    3. Filter
       
    4. Mutate
       
    5. Select
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Hard Level

It is important to be able to answer simple and medium-level questions, but your ability to answer more challenging questions will give you an advantage over other applicants. Let's go over a few of the trickier R programming Interview Questions.

  1. What is ShinyR?
    Ans: An R package called Shiny makes it simple to create dynamic web applications directly from R. You can create dashboards, embed standalone apps in Rmarkdown pages, or host them on a website. Further, you may add CSS themes, HTML widgets, and JavaScript actions to your Shiny apps.
     
  2. What is the Random Walk model?
    Ans: The simplest instance of a non-stationary process is a random walk. A random walk has an undefined mean and variance, high time dependence, and changes or increments that are nothing more than white noise.
     
  3. What is the White Noise model?
    Ans: It is a basic example of a stationary process and a primary time series model. A white noise model has no time correlation, a fixed constant mean, and a fixed constant variance.
     
  4. How does the lattice package work?
    Ans: The lattice package aims to enhance the base R graphics. To depict multivariate connections simply, it provides

    1. Better Defaults.
       
    2. Good Capability to depict.
       
  5. What is the rattle package in R?
    Ans: A widely used GUI for R data mining is called Rattle. Rattle performs the following actions:

    1. It displays graphical and statistical summaries of the data.
       
    2. Converts the data to model them easily.
       
    3. Creates learning models from the data.
       
    4. Scores new datasets for use in production.
      One of its primary features is that all your interactions with the GUI are recorded as an R script that can be quickly run in R without using the Rattle interface.
       
  6.  What is the class() function in R? Explain with some examples.
    Ans: The class function in R allows us to identify the type of an object. See the examples with various kinds of objects.
    # numeric data
    n <- 1.5
    class(n)

    # integer data
    i <- 100L
    class(i)

    # complex data
    c <- 2i + 3
    class(c)

    # character/string data
    s <- "CodingNinjas"
    class(s)

    # logical data
    l <- TRUE
    class(l)

    Output:
    [1] "numeric"
    [1] "integer"
    [1] "complex"
    [1] "character"
    [1] "logical"
     
  7. What various R import functions are there?
    Ans: R allows for the import of data in many forms and from various sources. Let's explore the various import functions provided by R:

    1. For reading.csv files, use read.csv().
       
    2. For reading.sas7bdat files, use read sas().
       
    3. For XL sheets, use read excel().
       
    4. For the spss data, use read sav().
       
  8. What do you mean by scatter plot in R? How to compare two plots?
    Ans: Numbers can be plotted against one another using the plot() method. The plot type, known as a "scatter plot", places one dot on the graph for each observation and shows the relationship between two numerical variables.

    You can use the points() function to compare the plot to another plot. Below is an example of comparing two plots.

    # Ninja Computer one, the age and speed of 12 computers:
    x1 < -c(5, 7, 8, 7, 2, 2, 9, 4, 11, 12, 9, 7)
    y1 < -c(99, 86, 87, 88, 111, 103, 88, 94, 78, 77, 85, 86)

    # Ninja Computer two, the age and speed of 15 computers:
    x2 < -c(2, 2, 8, 1, 15, 9, 12, 9, 7, 3, 11, 4, 7, 14, 12)
    y2 < -c(100, 105, 84, 105, 90, 99, 90, 95, 94, 100, 79, 112, 92, 80, 85)
    plot(x1, y1, main = "Observation of Ninja Computers", xlab = " Age of Ninja Computer", ylab = "Speed of Ninja Computer", col = "red", cex = 2)
    points(x2, y2, col = "blue", cex = 2)

    Output:
     
Output

Conclusion: By comparing the two plots, we can conclude that the newer the Computer, the faster it performs.

Conclusion

In this blog, you learned about the Top 25 R programming interview questions. In detail, we covered all the important topics, such as functions, datatypes, graphics, and GUI related to R programming Interview questions.

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