In mathematics, the inverse hyperbolic functions are the inverse functions of the hyperbolic functions. The acosh(x) returns the inverse hyperbolic cosine of the elements of x when x is a REAL scalar, vector, matrix, or array. The result has the same shape as x. The inverse hyperbolic cosine function is defined by x == cosh(y).

**asinh in R**

The asinh() is a built-in R function that calculates the inverse hyperbolic sine of a number. The inverse hyperbolic sine is the value whose hyperbolic sine is the number.

To calculate the hyperbolic arcsine in R, use the asinh() function.

**Syntax**

`asinh(x)`

**Parameters**

**x:** It is a numeric value, array, or vector.

**Example**

Let’s calculate the asinh value of 1 and 0.

```
asinh(0)
asinh(1)
```

**Output**

```
[1] 0
[1] 0.8813736
```

**Calculate asinh() of complex number**

Define a complex value and pass that value to the asinh() function.

```
dt <- 8 + 9i
asinh(dt)
```

**Output**

`[1] 3.181316+0.842441i`

**Plotting the asinh() function to a graph**

We can use the seq() function to create a series of values and pass that to the plot() function, which will create a line chart.

```
dt <- seq(-1, 1, by = 0.01)
plot(dt, asinh(dt), type = "l", col = "red")
```

**Output**

The function returns the NaN value that is why it can’t draw a graph based on that value.

**Applying asinh() function to a Vector**

To create a Vector in R, use the c() function. Then pass that vector to the asinh() function.

```
rv <- c(-1, 0.5, 0, 0.5, 1)
asinh(rv)
```

**Output**

`[1] -0.8813736 0.4812118 0.0000000 0.4812118 0.8813736`

**Passing a pi to the asinh() function**

The pi is an inbuilt constant in R programming, and its value is 3.141593.

Let’s find the pi constant’s asinh() value.

`asinh(pi)`

**Output**

`[1] 1.862296`

Let’s see another example of **pi.**

`asinh(pi / 4)`

**Output**

`[1] 0.7212255`

That is it for this tutorial.

**See also**

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.