To calculate the natural log in R, use the log() function. The default setting of this function is to return the natural logarithm of a value. But through a package called **SciViews, **you can use the ln() function, which also calculates the natural log in R. We will see about the ln() function in this tutorial.

**ln in R**

The ln() function comes with the SciViews package that takes a vector as an argument and returns the natural log of the input vector. We define ln() and ln1p() as wrappers for log()“ with defaultbase = exp(1) argument and for log1p(), respectively. The lg1p() is a convenient way to use the optimized code to calculate the logarithm of x + 1 but returning the result in base 10 logarithm.

```
ln(x)
ln1p()
lg()
lg1p(x)
E
lb()
```

**Arguments**

**x:** It is a numeric or complex vector.

**Examples**

R does not come with ln() function. R provides log10() function. To use ln() function, use the **SciViews** package.

```
library("SciViews")
ln(exp(2))
ln1p(c(0, 1, 11, 110))
lg(11 ^ 3)
lg1p(c(0, 1, 11, 110))
E ^ 4
lb(1:4)
```

**Output**

```
[1] 2
[1] 0.0000000 0.6931472 2.4849066 4.7095302
[1] 3.124178
[1] 0.000000 0.301030 1.079181 2.045323
[1] 54.59815
[1] 0.000000 1.000000 1.584963 2.000000
```

E is the Euler constant and is equal to exp(1).

**log in R**

The log() is a built-in R function that calculates logarithms of input values. The **log()** method calculates natural logarithms by default.

**Syntax**

`log(x, base)`

**Arguments**

**x –** It is numeric to which log has to be computed

**base –** It is the base of the log.

**Example**

Let’s find the natural log of 19 using the log() function.

```
data <- log(19)
cat("The natural logarithm of 19 is: ")
cat(data)
```

**Output**

`The natural logarithm of 19 is: 2.944439`

The natural log(ln) function is frequently used to rescale data for statistical and graphical analysis.

That is it for ln() function in R.

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.