# Which One of Following Is Not Atomic Data Type in R?

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Heather Bennett

When working with data in R, it is important to understand the different types of data that can be used. R has several atomic data types, which are the building blocks for storing and manipulating information.

These data types include numeric, logical, character, integer, complex, and raw. However, there is one type that does not fit into this category: data frame.

## What is a Data Frame?

A data frame is a tabular data structure in R that is similar to a spreadsheet or a database table. It consists of rows and columns, where each column can have a different data type. In other words, a data frame is a collection of vectors of equal length.

Data frames are commonly used for storing and manipulating structured data such as survey responses, sales records, or any other tabular information. They provide an efficient way to organize and analyze large datasets.

## Differences between Atomic Data Types and Data Frame

The main difference between atomic data types and the data frame is that atomic data types store individual values whereas the data frame stores collections of values organized in rows and columns.

• Numeric: Numeric data type represents real numbers (e.g., 3.14).
• Logical: Logical data type represents Boolean values (TRUE or FALSE).
• Character: Character data type represents text strings (e., “Hello World”).
• Integer: Integer data type represents whole numbers (e., 10).
• Complex: Complex data type represents complex numbers with real and imaginary parts (e., 3+2i).
• Raw: Raw data type represents bytes of raw data.

In contrast, a data frame can contain multiple columns, each of which can have a different atomic data type. For example, a data frame can have a column for names (character), ages (numeric), and salaries (numeric).

## Working with Data Frames in R

R provides several functions and operators to work with data frames. You can create a data frame using the `data.frame()` function or by importing external data files such as CSV or Excel files.

To access individual columns or rows of a data frame, you can use the indexing operator `[]`. For example, `df[, "name"]` will retrieve the column named “name” from the data frame `df`.

You can also perform various operations on data frames such as filtering rows based on specific conditions, adding or removing columns, summarizing data using aggregate functions, and merging multiple data frames together.

### In Conclusion

In summary, while R has several atomic data types like numeric, logical, character, integer, complex, and raw, the one that does not fit into this category is the data frame. Data frames are tabular structures that store collections of values in rows and columns. They are widely used for working with structured datasets in R due to their versatility and efficiency.

If you’re interested in learning more about R’s atomic types or how to work with data frames in R, be sure to check out our other tutorials!