Site icon R-bloggers

How to easily generate a perfectly normal distribution

[This article was first published on R on easystats, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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 😉

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!

To leave a comment for the author, please follow the link and comment on their blog: R on easystats.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.