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samplezoo

R-CMD-check CRAN status

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")

Example

Generating a dataset with various probability distributions typically looks like this:

numeric_data <- data.frame(
      norm_2 = rnorm(n = 100, mean = 60, sd = 10),
      norm_3 = rnorm(n = 100, mean = 40, sd = 20),
      binom = rbinom(n = 100, size = 1, prob = runif(n = 100, min = 0, max = 1)),
      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),
      chi_sq = rchisq(n = 100, df = 2),
      t_dist = rt(n = 100, df = 10),
      f_dist =rf(n = 100, df1 = 10, df2 = 10)
)

numeric_data <- round(numeric_data, 2)

head(numeric_data)

  norm_2 norm_3 binom neg pois   exp unif beta gamma chi_sq t_dist f_dist
1  54.11  28.67     1   0    3  5.02 0.18 0.40  9.38   0.91  -1.47   1.27
2  63.45  59.60     1   0    2  3.22 0.24 0.07  7.10   0.40  -0.71   1.43
3  61.65  44.14     0   3    4  1.82 0.89 0.04  2.37   1.10  -0.75   0.64
4  48.12  47.52     0   0    4  3.39 0.65 0.36  8.10   1.16  -1.23   1.83
5  49.70  26.29     0   0    1 31.68 0.93 0.18  3.09   0.59   0.59   0.90
6  63.17  20.99     1   0    5  1.70 0.40 0.31  2.78   0.18  -1.20   0.91

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 norm_2 norm_3 binom neg pois   exp unif beta gamma chi_sq t_dist f_dist
1 75.33  50.06  43.18     1   0    3 31.23 0.05 0.17  5.96   1.01   0.02   1.28
2 43.76  52.50   9.47     1   0    4  9.96 0.23 0.36  3.39   1.49  -0.70   1.15
3 44.48  61.26  22.44     0   1    3  1.18 0.17 0.41  4.99   0.47   0.42   0.78
4 47.62  56.66  22.36     1   0    6  6.27 0.43 0.22  0.66   3.54   0.99   0.72
5 55.92  46.84  59.63     0   1    3  0.54 0.94 0.33  5.86   4.26  -1.05   2.06
6 39.56  76.65  43.02     0   1    1 18.88 0.55 0.40  9.11   0.37   1.82   1.78

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.