Many times, for instance when teaching, I needed to quickly and simply generate a **perfectly normally distributed sample** to illustrate or show some of its characteristics.

This is now very easy to do with the new `bayestestR`

package, which includes the `rnorm_perfect`

function. This function is very similar to the classic `rnorm`

(same arguments), with the difference that the generated sample is *perfectly* normal.

## Example

`bayestestR`

can be installed as follows:

```
install.packages("bayestestR") # Install the package
library(bayestestR) # Load it
```

```
# Generate a perfect sample
x <- rnorm_perfect(n = 100, mean = 0, sd = 1)
# Visualise it
library(tidyverse)
x %>%
density() %>% # Compute density function
as.data.frame() %>%
ggplot(aes(x=x, y=y)) +
geom_line()
```

We can also easily color some of the parts of the curve, for instance, the observations lying beyond +2 standard deviations.

```
x %>%
density() %>% # Compute density function
as.data.frame() %>%
mutate(outlier = ifelse(x > 2, "Extreme", "Not extreme")) %>%
ggplot(aes(x=x, y=y, fill=outlier)) +
geom_ribbon(aes(ymin=0, ymax=y)) +
theme_classic()
```

## bayestestR and easystats

More details about `bayestestR`

’s features are comming soon, stay tuned ;)

**Don’t forget to check out the****documentation here****for more!**

Feel free to let us know how we could further improve this package! Also, note that *easystats*, the project supporting `bayestestR`

is in active development. Thus, do not hesitate to contact us if **you want to get involved :)**

**Check out our other blog posts**!*here*