In R, a numeric variable is either a number (like 1, 2.1, or -3.14) or one of the four special values: **NA**, **NaN**, **Inf**, or **-Inf**. The **Inf** and **-Inf** are positive and negative `**infinity**‘, whereas **NA** means “**Not Available**” and NaN, which we will learn in this tutorial.

**NaN in R**

NaN in R means **“Not a Number”** which means there is something or some value, but it cannot be described in the computer. NaN designates a result that cannot be calculated for whatever reason, or it is not a floating-point number.

If you divide 0 / 0, then it will return **NaN. **The other example is to take a square root of a negative number or perform calculations with infinities that lead to **undefined** results.

**NaN** in R is distinct from NA.

```
rv <- 0 / 0
rv
```

**Output**

`[1] NaN`

As you can see that NaN is usually the product of some arithmetic operation, such as 0/0.

**How to check NaN values in R**

To check NaN values in R, use the is.nan() function. The is.nan() is a built-in R function that tests the object’s value and returns **TRUE** if it finds the NaN value; otherwise, it returns **FALSE**.

```
rv <- 0 / 0
rv
is.nan(rv)
```

**Output**

```
[1] NaN
[1] TRUE
```

And we get the **TRUE** for **NaN** value. The **is.nan()** function is provided to check specifically for NaN, and function **is.na()** also returns **TRUE** for **NaN**. Compelling NaN to logical or integer type gives an NA of the appropriate type.

The is.nan() method returns a boolean value for all the components of the vector.

```
rv2 <- c(100, NaN, 101, 102, 103, NaN)
rv2
is.nan(rv2)
```

**Output**

```
[1] 100 NaN 101 102 103 NaN
[1] FALSE TRUE FALSE FALSE FALSE TRUE
```

As you can see that it returns **TRUE** when it finds NaN values; otherwise, **FALSE**.

**Na, NaN, infinity, and -infinity: Table Comparison in R**

Function | Typical number | NA | NaN | Inf | -Inf |
---|---|---|---|---|---|

is.double() | TRUE | FALSE | TRUE | TRUE | TRUE |

is.finite() | TRUE | FALSE | FALSE | FALSE | FALSE |

is.infinite() | FALSE | FALSE | FALSE | TRUE | TRUE |

is.integer() | TRUE (if integer) or FALSE | FALSE | FALSE | FALSE | FALSE |

is.na() | FALSE | TRUE | TRUE | FALSE | FALSE |

is.nan() | FALSE | FALSE | TRUE | FALSE | FALSE |

is.null() | FALSE | FALSE | FALSE | FALSE | FALSE |

is.numeric() | TRUE | * | TRUE | TRUE | TRUE |

The * represents different values for different circumstances. R has different types of NAs. For example, **is.numeric(NA)** function returns **FALSE**, but **is.numeric(NA_integer_)** and **is.numeric(NA_real_)** functions return **TRUE**. Furthermore, **is.numeric(NA_complex_)** function returns **FALSE**.

That’s it for this tutorial.

Krunal Lathiya is an Information Technology Engineer by education and web developer by profession. He has worked with many back-end platforms, including Node.js, PHP, and Python. In addition, Krunal has excellent knowledge of Data Science and Machine Learning, and he is an expert in R Language. Krunal has written many programming blogs, which showcases his vast expertise in this field.