---
title: "Generating Small, Medium, and Large Datasets"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Example}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Overview 

This vignette demonstrates how to use the {samplezoo} package to generate datasets of varying sizes (small, medium, and large) with variables from multiple probability distributions.

Each dataset contains:

* Variables/columns from common distributions such as Normal, Binomial, Poisson, and others.

* Adjustable sample sizes to meet needs.


```{r setup}
library(samplezoo)

```

### Generate a small dataset (i.e., 100 rows)
```{r}
data_small <- samplezoo("small")
head(data_small)

```

### Generate a medium sized dataset (i.e., 1,000 rows)
```{r}
data_medium <- samplezoo("medium")
head(data_medium)

```

### Generate a large sized dataset (i.e., 10,000 rows)
```{r}
data_large <- samplezoo("large")
head(data_large)

```

### Adding Variation or Ensuring Reproducibility with set.seed()

To ensure reproducibility and introduce controlled variation in your dataset, use set.seed() before generating random data.

```{r}
set.seed(123)
data_large <- samplezoo("large")
head(data_large)
```

```{r}
set.seed(456)
data_large <- samplezoo("large")
head(data_large)
```