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# Tidy Randomly Generated Lognormal Distribution Tibble

Source: `R/random-tidy-lognormal.R`
`tidy_lognormal.Rd`

This function will generate `n` random points from a lognormal distribution with a user provided, `.meanlog`, `.sdlog`, 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_lognormal(
      .n = 50,
      .meanlog = 0,
      .sdlog = 1,
      .num_sims = 1,
      .return_tibble = TRUE
    )

## Arguments

.n

  The number of randomly generated points you want.

.meanlog

  Mean of the distribution on the log scale with default 0

.sdlog

  Standard deviation of the distribution on the log scale with default 1

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

## See also

https://www.itl.nist.gov/div898/handbook/eda/section3/eda3669.htm

Other Continuous Distribution: `tidy_beta()`, `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_normal()`, `tidy_paralogistic()`, `tidy_pareto()`, `tidy_pareto1()`, `tidy_t()`, `tidy_triangular()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

Other Lognormal: `util_lognormal_param_estimate()`, `util_lognormal_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_lognormal()
    #> # A tibble: 50 × 7
    #>    sim_number     x     y      dx       dy     p     q
    #>    <fct>      <int> <dbl>   <dbl>    <dbl> <dbl> <dbl>
    #>  1 1              1 4.14  -1.74   0.000468 0.922 4.14
    #>  2 1              2 0.387 -1.54   0.00141  0.171 0.387
    #>  3 1              3 3.86  -1.33   0.00379  0.912 3.86
    #>  4 1              4 3.34  -1.13   0.00917  0.886 3.34
    #>  5 1              5 3.15  -0.924  0.0200   0.875 3.15
    #>  6 1              6 2.10  -0.719  0.0393   0.771 2.10
    #>  7 1              7 0.964 -0.515  0.0698   0.485 0.964
    #>  8 1              8 1.86  -0.310  0.113    0.732 1.86
    #>  9 1              9 0.677 -0.105  0.166    0.348 0.677
    #> 10 1             10 0.734  0.0995 0.223    0.379 0.734
    #> # ℹ 40 more rows