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# Tidy Randomly Generated Gamma Distribution Tibble

Source: `R/random-tidy-gamma.R`
`tidy_gamma.Rd`

This function will generate `n` random points from a gamma distribution with a user provided, `.shape`, `.scale`, 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_gamma(
      .n = 50,
      .shape = 1,
      .scale = 0.3,
      .num_sims = 1,
      .return_tibble = TRUE
    )

## Arguments

.n

  The number of randomly generated points you want.

.shape

  This is strictly 0 to infinity.

.scale

  The standard deviation of the randomly generated data. This is strictly from 0 to infinity.

.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::rgamma()`, and its underlying `p`, `d`, and `q` functions. For more information please see `stats::rgamma()`

## See also

https://www.statology.org/fit-gamma-distribution-to-dataset-in-r/

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

Other Continuous Distribution: `tidy_beta()`, `tidy_burr()`, `tidy_cauchy()`, `tidy_chisquare()`, `tidy_exponential()`, `tidy_f()`, `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 Gamma: `tidy_inverse_gamma()`, `util_gamma_param_estimate()`, `util_gamma_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_gamma()
    #> # A tibble: 50 × 6
    #>    sim_number     x      y       dx      dy      p
    #>    <fct>      <int>  <dbl>    <dbl>   <dbl>  <dbl>
    #>  1 1              1 0.100  -0.302   0.00433 0.100
    #>  2 1              2 0.0332 -0.259   0.0148  0.0332
    #>  3 1              3 0.0908 -0.217   0.0435  0.0908
    #>  4 1              4 0.230  -0.174   0.110   0.230
    #>  5 1              5 0.312  -0.132   0.242   0.312
    #>  6 1              6 0.431  -0.0896  0.461   0.431
    #>  7 1              7 0.178  -0.0472  0.764   0.178
    #>  8 1              8 0.480  -0.00481 1.11    0.480
    #>  9 1              9 1.13    0.0376  1.42    1.13
    #> 10 1             10 1.15    0.0800  1.62    1.15
    #> # ℹ 40 more rows