R Programming Interview Questions and Answers Set 8

71. Write a custom function in R

Sometimes you’ll be asked to create a custom function on the fly. An example of a custom function below,

myFunction <- function(arg1, arg2, … ){

Functions can be simple or complex, but they should make your code more extensible, readable, and efficient. This is a chance to show your ingenuity and experience.

72What is the use of sink(), Library () and search() function?

These functions are used for the following purposes:

Sink(): It defines the output direction i.e. where the output will reach?

Library(): It can be used to show installed packages

Search(): This function can display the currently loaded packages

73. Write the full form MANOVA and why is it used?

Full form of MANOA is a multivariant analysis of variance. Through this function, we can test more than one dependent variable. We can check them simultaneously.

74. Why are the VCD and lattice packages used?

Through vcd package, we can visualize multivariate categorical data. Lattice package is used to improve R graphics and better defaults are given to the package. We can display the multivariate relationship.

75. How can we create a table using R language without using external files?

The following syntax can be used to create a table in R language:



Through the above code a new Excel Spreadsheet so that you can enter the values in NewTable. In this way, a new table can be created.

76. Enlist the addition function used in R language?

To add two datasets there is two functions rbind() function, through which we can add the column values of two data sets.

The syntax is as follows: rbind(x1,x2,—-) where x1, x2: vector, matrix, data frames

77. Which packages are used to store and restore R objects to and from a file in R language?

To store an object in a file we can use Save command and the syntax for this is as:

Syntax: > save(z,file=”z.Rdata”)

While to restore R object we can use the following command:

Syntax:> load(“z.Rdata”)

78. Why is R useful for data science?

R turns otherwise hours of graphically intensive jobs into minutes and keystrokes. In reality, you probably wouldn’t encounter the language of R outside the realm of data science or an adjacent field. It’s great for linear modeling, nonlinear modeling, time-series analysis, plotting, clustering, and so much more.

Simply put, R is designed for data manipulation and visualization, so it’s natural that it would be used for data science.

79. What is a factor variable, and why would you use one?

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.

80. Mention what does not ‘R’ language do?

  • Though R programming can easily connect to DBMS is not a database
  • R does not consist of any graphical user interface
  • Though it connects to Excel/Microsoft Office easily, R language does not provide any spreadsheet view of data