Generate Samples with a Variety of Probability Distributions.
samplezoo 
The {samplezoo} package simplifies the generation of samples from various probability distributions, enabling users to quickly create datasets for demonstrations, troubleshooting, or teaching. By prioritizing simplicity and speed over the customization of sample parameters, {samplezoo} is ideal for beginners or anyone looking to save time when working with data.
Installation
{samplezoo} is available on CRAN. Install using:
install.packages("samplezoo")
You can install the development version of samplezoo from GitHub with:
# install.packages("pak")
pak::pak("nvietto/samplezoo")
Motivation
Creating or reprexing datasets on the Posit Community help forum can be challenging, especially for beginners. Similarly, building quick datasets for lectures, troubleshooting, or homework assignments often involves repetitive and time-consuming code that’s easy to forget. To simplify these tasks and save some time, {samplezoo} provides a streamlined approach to generating data sets.
For example, generating a dataset with various probability distributions typically looks like this:
numeric_data <- data.frame(
norm = rnorm(n = 100, mean = 50, sd = 15),
norm2 = rnorm(n = 100, mean = 60, sd = 10),
norm3 = rnorm(n = 100, mean = 40, sd = 20),
binom = rbinom(n = 100, size = 1, prob = 0.20),
neg = rnbinom(n = 100, size = 1, prob = 0.50),
pois = rpois(n = 100, lambda = 3),
exp = rexp(n = 100, rate = 0.10),
unif = runif(n = 100, min = 0, max = 1),
beta = rbeta(n = 100, shape1 = 2, shape2 = 5),
gamma = rgamma(n = 100, shape = 2, scale = 2),
chisq = rchisq(n = 100, df = 2),
t_dist = rt(n = 100, df = 10)
)
numeric_data <- round(numeric_data, 2)
head(numeric_data)
norm norm2 norm3 binom neg pois exp unif beta gamma chisq t_dist
1 46.38 73.25 34.66 0 4 4 8.49 0.89 0.32 1.30 2.94 -0.71
2 55.60 60.73 34.37 0 0 2 7.26 0.08 0.20 2.98 1.23 -0.88
3 42.81 68.03 18.15 0 1 0 0.02 0.70 0.17 2.18 3.89 -0.75
4 26.75 62.36 -3.14 0 0 3 11.92 0.05 0.20 8.72 2.16 -1.24
5 51.77 67.96 13.18 0 0 2 3.90 0.34 0.04 5.58 1.22 -0.74
6 65.95 64.32 38.59 0 0 2 6.85 0.04 0.29 8.12 4.32 0.88
With {samplezoo}, you can use one line of code:
library(samplezoo)
small_data <- samplezoo("small")
small_data <- round(small_data, 2)
head(small_data)
norm norm2 norm3 binom neg pois exp unif beta gamma chisq t_dist
1 21.96 57.23 24.31 0 1 3 0.66 0.95 0.20 2.97 3.75 -0.05
2 51.54 60.12 55.61 0 1 3 24.26 0.01 0.25 2.25 8.63 -0.36
3 46.76 63.80 15.81 0 0 7 16.84 0.27 0.14 3.47 6.60 -0.49
4 44.86 58.63 37.32 1 1 4 8.58 0.70 0.22 1.26 0.92 -0.04
5 47.39 43.17 72.43 0 1 2 0.83 0.13 0.11 1.28 0.68 -1.04
6 57.07 71.30 19.79 0 1 4 1.14 0.47 0.18 1.35 3.35 1.44