PROBLEM SOLVING USING R - UNIT 4 MCQS

What is a data frame in programming?

• A data frame is a type of database.

• A data frame is a tabular data structure used for storing and manipulating data in rows and columns.

• A data frame is a programming language.

• A data frame is a graphical user interface.

How do you access components of a list in R?

• Access elements with . operator

• Use {} for named access

• Use () for index access

• Use [[ ]] for index access or $ for named access.

What is the purpose of naming columns in a data frame?

• To identify and access data easily, improve readability, and facilitate data manipulation.

• To ensure data is stored in a specific format

• To limit the amount of data in the frame

• To create a visual representation of data only

Explain the concept of nesting in data structures.

• Nesting prevents the use of multiple data types within a structure.

• Nesting simplifies data structures by flattening them into a single level.

• Nesting allows for hierarchical organization of data structures, enabling complex data representation.

• Nesting is only applicable to arrays and not to other data structures.

How can you add a new data column to an existing data frame?

• Use df['new_column_name'] = values to add a new column.

• Use df.add_column('new_column_name', values) to add a new column.

• Apply df.append({'new_column_name': values}) to add a new column.

• Use df.insert('new_column_name', values) to add a new column.

What is the function to combine two data frames in R?

• combine()

• merge()

• join()

• concat()

Define logical record subsets in the context of data frames.

• Logical record subsets are segments of a data frame defined by specific criteria or conditions.

• Logical record subsets are the entire data frame without any conditions.

• Logical record subsets are random selections of data from a data frame.

• Logical record subsets refer to the physical storage of data frames.

What does NaN stand for and when is it used?

• Not a Name

• Null and NaN

• Not a Number

• Number as NaN

How do NA values differ from NULL in R?

• NA is for missing values; NULL is for non-existent values.

• NA is used for all types of missing data; NULL is only for numeric data.

• NA is a placeholder for any data type; NULL is specifically for character data.

• NA represents a value that is zero; NULL represents an empty string.

What is the significance of infinity in data analysis?

• Infinity represents unboundedness, aiding in modeling extreme values and understanding limits in data analysis.

• Infinity is a measure of time in data analysis.

• Infinity is used to calculate averages in data analysis.

• Infinity indicates the maximum value in datasets.