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# Tidy Randomly Generated Beta Distribution Tibble

Source: `R/random-tidy-beta.R`
`tidy_beta.Rd`

This function will generate `n` random points from a beta distribution with a user provided, `.shape1`, `.shape2`, `.ncp` or `non-centrality parameter`, and number of random simulations to be produced. The function returns a tibble with the simulation number column the x column which corresponds to the n randomly generated points, the `d_`, `p_` and `q_` data points as well.

The data is returned un-grouped.

The columns that are output are:

- `sim_number` The current simulation number.

- `x` The current value of `n` for the current simulation.

- `y` The randomly generated data point.

- `dx` The `x` value from the `stats::density()` function.

- `dy` The `y` value from the `stats::density()` function.

- `p` The values from the resulting p_ function of the distribution family.

- `q` The values from the resulting q_ function of the distribution family.

## Usage

    tidy_beta(
      .n = 50,
      .shape1 = 1,
      .shape2 = 1,
      .ncp = 0,
      .num_sims = 1,
      .return_tibble = TRUE
    )

## Arguments

.n

  The number of randomly generated points you want.

.shape1

  A non-negative parameter of the Beta distribution.

.shape2

  A non-negative parameter of the Beta distribution.

.ncp

  The `non-centrality parameter` of the Beta distribution.

.num_sims

  The number of randomly generated simulations you want.

.return_tibble

  A logical value indicating whether to return the result as a tibble. Default is TRUE.

## Value

A tibble of randomly generated data.

## Details

This function uses the underlying `stats::rbeta()`, and its underlying `p`, `d`, and `q` functions. For more information please see `stats::rbeta()`

## See also

https://statisticsglobe.com/beta-distribution-in-r-dbeta-pbeta-qbeta-rbeta

https://en.wikipedia.org/wiki/Beta_distribution

Other Continuous Distribution: `tidy_burr()`, `tidy_cauchy()`, `tidy_chisquare()`, `tidy_exponential()`, `tidy_f()`, `tidy_gamma()`, `tidy_generalized_beta()`, `tidy_generalized_pareto()`, `tidy_geometric()`, `tidy_inverse_burr()`, `tidy_inverse_exponential()`, `tidy_inverse_gamma()`, `tidy_inverse_normal()`, `tidy_inverse_pareto()`, `tidy_inverse_weibull()`, `tidy_logistic()`, `tidy_lognormal()`, `tidy_normal()`, `tidy_paralogistic()`, `tidy_pareto()`, `tidy_pareto1()`, `tidy_t()`, `tidy_triangular()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

Other Beta: `tidy_generalized_beta()`, `util_beta_param_estimate()`, `util_beta_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_beta()
    #> # A tibble: 50 × 7
    #>    sim_number     x     y      dx      dy     p     q
    #>    <fct>      <int> <dbl>   <dbl>   <dbl> <dbl> <dbl>
    #>  1 1              1 0.359 -0.353  0.00273 0.359 0.359
    #>  2 1              2 0.988 -0.318  0.00644 0.988 0.988
    #>  3 1              3 0.295 -0.283  0.0141  0.295 0.295
    #>  4 1              4 0.724 -0.248  0.0284  0.724 0.724
    #>  5 1              5 0.729 -0.213  0.0532  0.729 0.729
    #>  6 1              6 0.301 -0.178  0.0925  0.301 0.301
    #>  7 1              7 0.423 -0.143  0.149   0.423 0.423
    #>  8 1              8 1.00  -0.107  0.224   1.00  1.00
    #>  9 1              9 0.816 -0.0724 0.315   0.816 0.816
    #> 10 1             10 0.784 -0.0373 0.416   0.784 0.784
    #> # ℹ 40 more rows