R Programming Interview Questions- Shikshaglobe

Content Creator: Satish kumar

 Make sense of what is R?

R is information investigation programming which is utilized by examiners, quants, analysts, information researchers and others.

 List out a portion of the capability that R gives?

The capability that R gives are







Blended Effects


GAM. and so on.

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Explain how you can begin the R officer GUI?

Composing the order, ("Rcmdr") into the R console begins the R commandant GUI.

Certainly! Here are some common R programming interview questions that you might encounter:

  1. What is R programming, and why is it used in data analysis?
    • R is a programming language and environment designed for statistical computing and data analysis. It is used for data manipulation, statistical modeling, data visualization, and more.
  2. Explain the difference between R and other programming languages like Python or SAS in the context of data analysis.
    • Compare R's strengths and weaknesses to other data analysis languages, such as Python or SAS. Highlight R's focus on statistics and data visualization.
  3. What are the basic data types in R?
    • R has several basic data types, including numeric, character, logical, integer, and complex. Explain these data types and their characteristics.
  4. How do you create a vector in R?
    • Vectors are fundamental in R. Explain how to create vectors using the c() function and other methods.
  5. What is data frame in R? How is it different from a matrix?
    • Data frames are commonly used for storing data in R. Describe what they are and highlight the differences between data frames and matrices.
  6. Explain the concept of factors in R. When and why are they used?
    • Factors are used to represent categorical data in R. Explain why they are important and how to work with them.
  7. What is the purpose of the ggplot2 package in R? How is it different from base R graphics?
    • ggplot2 is a popular package for data visualization. Describe its purpose and how it differs from the base R graphics system.
  8. How do you handle missing data in R?
    • Discuss strategies and functions for handling missing data, such as NA values.
  9. Explain the concept of packages in R. How do you install and load packages?
    • R packages extend its functionality. Explain how to install and load packages using functions like install.packages() and library().
  10. What is the dplyr package used for, and what are some common functions within it?
    • dplyr is used for data manipulation. Discuss its purpose and common functions like filter(), mutate(), group_by(), and summarize().
  11. How can you read data from a CSV file into R?
    • Explain how to import data from a CSV file using functions like read.csv().
  12. What is the purpose of the apply() function in R?
    • Describe the apply() function and how it can be used to apply a function to rows or columns of a matrix or data frame.
  13. Explain the concept of data reshaping in R using functions like reshape() or pivot_longer().
    • Discuss how to transform data from wide to long format or vice versa using these functions.
  14. What is the purpose of the lm() function in R, and how is it used for linear regression?
    • Explain how to perform linear regression using the lm() function and interpret the results.
  15. How do you export data or plots from R to external files (e.g., CSV, PDF, or image formats)?
    • Describe the functions or methods to export data or plots to various file formats.

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These questions cover a range of topics related to R programming and data analysis. Be prepared to demonstrate your knowledge and problem-solving skills during the interview by providing examples and explanations where appropriate.

 In R how you can import Data?

You use R authority to import Data in R, and there are three different ways through which you can enter information into it

You can enter information straightforwardly through Data  New Data Set

Import information from a plain text (ASCII) or different records (SPSS, Minitab, and so forth.)

Peruse an informational index either by composing the name of the informational index or choosing the informational collection in the exchange box

Mention how doesn't 'R' language respond?

However R programming can without much of a stretch interfaces with DBMS isn't a data set

R comprises of no graphical UI

However it interfaces with Excel/Microsoft Office effectively, R language gives no bookkeeping sheet perspective on information

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Explain how R orders are composed?

In R, anyplace in the program you need to introduce the line of code with a #sign, for instance

# deduction

# division

# note request of activities exists

How could you at any point save your information in R?

To save information in R, there are numerous ways, however the simplest approach to doing this isGo to Data > Active Data Set > Export Active Data Set and a discourse box will show up, when you click alright the exchange box let you save your information in the standard manner.

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Mention how you can deliver co-relations and covariances?

You can deliver co-relations by the cor () capability to create co-relations and cov () capability to create covariances.

Explain what is t-tests in R?

In R, the t.test () capability delivers an assortment of t-tests. T-test is the most well-known test in measurements and used to decide if the method for two gatherings are equivalent to one another.

Make sense of what is With () and By () capability in R is utilized for?

With() capability is like DATA in SAS, it apply an articulation to a dataset.

BY() capability applies a capability to each even out of variables. It is like BY handling in SAS.

What are the information structures in R that is utilized to perform measurable examinations and make diagrams?

R has information structures like




Information outlines

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In R how missing qualities are addressed ?

In R missing qualities are addressed by NA (Not Available), why unimaginable qualities are addressed by the image NaN (not a number).

Explain what is translate?

For re-molding information previously, examination R gives different strategy and translate are the least complex technique for reshaping a dataset. To render a grid or an information outline t () capability is utilized.

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Must Know!

While Loop in R 

apply(), lapply(), sapply(), tapply() Function in R 

Import Data into R 

na.omit & na.rm 

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