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# Tidy Randomly Generated Gaussian Distribution Tibble

Source: `R/random-tidy-normal.R`
`tidy_normal.Rd`

This function will generate `n` random points from a Gaussian distribution with a user provided, `.mean`, `.sd` - standard deviation 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 `dnorm`, `pnorm` and `qnorm` 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_normal(.n = 50, .mean = 0, .sd = 1, .num_sims = 1, .return_tibble = TRUE)

## Arguments

.n

  The number of randomly generated points you want.

.mean

  The mean of the randomly generated data.

.sd

  The standard deviation of the randomly generated data.

.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::rnorm()`, `stats::pnorm()`, and `stats::qnorm()` functions to generate data from the given parameters. For more information please see `stats::rnorm()`

## See also

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_lognormal()`, `tidy_paralogistic()`, `tidy_pareto()`, `tidy_pareto1()`, `tidy_t()`, `tidy_triangular()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

Other Gaussian: `tidy_inverse_normal()`, `util_normal_param_estimate()`, `util_normal_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_normal()
    #> # A tibble: 50 × 7
    #>    sim_number     x      y    dx       dy       p      q
    #>    <fct>      <int>  <dbl> <dbl>    <dbl>   <dbl>  <dbl>
    #>  1 1              1 -1.86  -4.13 0.000243 0.0313  -1.86
    #>  2 1              2 -1.29  -3.97 0.000638 0.0977  -1.29
    #>  3 1              3 -0.915 -3.81 0.00152  0.180   -0.915
    #>  4 1              4 -2.67  -3.66 0.00329  0.00382 -2.67
    #>  5 1              5  0.465 -3.50 0.00651  0.679    0.465
    #>  6 1              6  2.16  -3.34 0.0118   0.985    2.16
    #>  7 1              7  1.03  -3.18 0.0197   0.849    1.03
    #>  8 1              8 -0.129 -3.02 0.0306   0.449   -0.129
    #>  9 1              9 -0.420 -2.86 0.0444   0.337   -0.420
    #> 10 1             10 -1.53  -2.71 0.0609   0.0625  -1.53
    #> # ℹ 40 more rows