Important MCQs R Language History & Basics 4

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The post is about MCQs R Language. The quiz about MCQS R Language covers some basics of R language, its functionality, concepts of packages, and history of R Language.

MCQs about R Language

1. Which of the following is used for Statistical analysis in the R language?

 
 
 
 

2. The following packages are not contained in the “base” R system.

 
 
 
 

3. which of the following is a “base” package for R language?

 
 
 
 

4. In which year the R-Core Team was formed?

 
 
 
 

5. The wrong statement from the following is:

 
 
 
 

6. Advanced users of R can write _______ code to manipulate R objects directly

 
 
 
 

7. Which of the following are examples of variable names that can be used in R?

 
 
 
 

8. R Runs on the _________ operating system

 
 
 
 

9. Which of the following is the wrong statement:

 
 
 
 

10. R is published under the ______ General Public License version.

 
 
 
 

11. The primary source code copyright for R is held by the

 
 
 
 

12. Which of the following are best practices for creating data frames?

 
 
 
 

13. One limitation of R is that its functionality is based on _________

 
 
 
 

14. Which package contains most fundamental functions to run R?

 
 
 
 

15. Which of the following is a recommended package in R

 
 
 
 

16. The “base” R system can be downloaded from

 
 
 
 

17. The public version of R released in 2000 was

 
 
 
 

18. What is the output of getOption(“defaultPackages”) in R Studio?

 
 
 
 

19. The primary R system is available from the ______

 
 
 
 

20. R functionality is divided into a number of _______

 
 
 
 

The R language is a free and open-source language developed by Ross Ihaka and Robert Gentleman in 1991 at the University of Auckland, New Zealand. The R Language is used for statistical computing and graphics to clean, analyze, and graph your data.

MCQs R Language History and Basics

MCQs R Language History and Basics Online Quiz

  • In which year the R-Core Team was formed?
  • The public version of R released in 2000 was
  • R Runs on the operating system
  • The primary source code copyright for R is held by the
  • R is published under the General Public License version.
  • The “base” R system can be downloaded from
  • The following packages are not contained in the “base” R system.
  • One limitation of R is that its functionality is based on __________.
  • The wrong statement from the following is:
  • R functionality is divided into a number of
  • The primary R system is available from the ________.
  • Which package contains the most fundamental functions to run R?
  • Which of the following is the wrong statement:
  • Which of the following is a “base” package for the R language?
  • Which of the following is a recommended package in R
  • What is the output of getOption(“defaultPackages”) in R Studio?
  • Advanced users of R can write ___________ code to manipulate R objects directly
  • Which of the following is used for Statistical analysis in the R language?
  • Which of the following are examples of variable names that can be used in R?
  • Which of the following are best practices for creating data frames?

The strengths of R programming language lie in its statistical capabilities, data visualization tools (such as ggplot2), and a vast ecosystem of packages contributed by the community. R Language remains a popular choice for statisticians and data scientists working on a wide range of projects.

Basic Statistics and Data Analysis

JSON Files in R: Reading and Writing (2019)

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Introduction to JSON Files in R

A JSON file stores simple data structures and objects in JavaScript Object Notation (JSON) format. JSON is a standard data lightweight interchange format primarily used for transmitting data between a web application and a server. The JSON file is a text file that is language-independent, self-describing, and easy to understand. In this article, we will discuss reading and writing a JSON file in R Language in detail using the R package “rjson“.

Since JSON file format is text only, it can be sent to and from a server and used as a data format by any programming language. The data in the JSON file is nested and hierarchical. Let us start reading and writing JSON files in R.

Creating JSON File

Let’s create a JSON file. Copy the following lines into a text editor such as Notepad. Save the file with a .json extension and choose the file type as all files(*.*). Let the file name be “data.json”, stored on the “D:” drive.

{ 
"ID":["1","2","3","4","5","6","7","8" ],
"Name":["Rick","Dan","Michelle","Ryan","Gary","Nina","Simon","Guru" ],
"Salary":["623.3","515.2","611","729","843.25","578","632.8","722.5" ],
"StartDate":[ "1/1/2012","9/23/2013","11/15/2014","5/11/2014","3/27/2015","5/21/2013",
"7/30/2013","6/17/2014"],
"Dept":[ "IT","Operations","IT","HR","Finance","IT","Operations","Finance"]
}
Reading and Writing JSON files in R

Installing rjson R Package

The R language can also read the JSON files using the rjson package. To read a JSON data file, First, install the rjson package. Issue the following command in the R console, to install the rjson package.

install.packages("rjson")

The rjson package needs to be loaded after installation of the package.

Reading JSON Files in R

To read a JSON file, the rjson package needs to be loaded. Use the fromJSON( ) function to read the file.

# Give the data file name to the function.
result <- fromJSON(file = "D:\\data.json")
# Print the result.
print(result)

The JSON file now can be converted to a Data Frame for further analysis using the as.data.frame() function.

# Convert JSON file to a data frame.
json_data_frame <- as.data.frame(result)
print(json_data_frame)

Writing JSON objects to .Json file

To write JSON Object to file, the toJSON() function from the rjson library can be used to prepare a JSON object and then use the write() function for writing the JSON object to a local file.

Let’s create a list of objects as follows

list1 <- vector(mode="list", length=2)
list1[[1]] <- c("apple", "banana", "rose")
list1[[2]] <- c("fruit", "fruit", "flower")

read the above list to JSON

jsonData <- toJSON(list1)

write JSON object to a file

write(jsonData, "output.json")

Read more about importing and exporting data in R: see the post

MCQs General Knowledge

Input Data in R Language: c() & scan() Function

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Introduction to Input Data in R Language

There are many ways to input data in R Language. Here, I will concentrate only on typing data directly at the keyboard using c() and scan() functions, which are very common ways to input data in R language.

Traditional statistical computer software such as Minitab, SPSS, and SAS, etc., are designed to transform rectangular datasets (a dataset whose rows represent the observations and columns represent the variables) into printed reports and graphs. However, R and S languages are designed to transform data objects into other data objects (such as reports, and graphs).

S and R language both support rectangular datasets, in the form of data frames and other data structures. Here we will learn to know about data in R to work efficiently as a statistical data analyst.

Data Input Functions in R

There are many ways to input data in R and S-Plus. Let us learn to type data directly on the keyboard.

Input Data Using c() Function

The best choice is to enter small datasets directly on the keyboard. Remember that it is impractical to enter a large data set directly at the keyboard.

Let us use the c() function to enter the vector of numbers directly as:

x    <- c(1, 3, 5, 7, 9)
char <- c('a', 'b', 'c', 'd')
TF   <- c(TRUE, FALSE)
Input Data in R Language

Note that the character strings can be directly inputted in single or double quotation marks. For example, "a" and 'a' both are equivalent.

Input Data Using Scan() Function in R

It is also very convenient to use the scan() function in R, which prompts with the index of the next entry.  Consider the example,

xyz <- scan()
1: 10 20 30 35
5: 40 35 25
8: 9 100 50
11:
Read 10 items

The number before the colon on each of the inputted lines is the index of the next data entry point (observation) to be entered. Note that entering a blank line terminates the scan() function input behavior.

Click the following links to learn about data entry (import and export internal and external data) in R Language

R FAQS https://rfaqs.com

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