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# Combine Multiple Tidy Distributions of Different Types

Source: `R/combine-tidy-distributions-tbl.R`
`tidy_combine_distributions.Rd`

This allows a user to specify any `n` number of `tidy_` distributions that can be combined into a single tibble. This is the preferred method for combining multiple distributions of different types, for example a Gaussian distribution and a Beta distribution.

This generates a single tibble with an added column of dist_type that will give the distribution family name and its associated parameters.

## Usage

    tidy_combine_distributions(...)

## Arguments

...

  The `...` is where you can place your different distributions

## Value

A tibble

## Details

Allows a user to generate a tibble of different `tidy_` distributions

## See also

Other Multiple Distribution: `tidy_multi_single_dist()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tn <- tidy_normal()
    tb <- tidy_beta()
    tc <- tidy_cauchy()

    tidy_combine_distributions(tn, tb, tc)
    #> # A tibble: 150 × 8
    #>    sim_number     x      y    dx       dy      p      q dist_type
    #>    <fct>      <int>  <dbl> <dbl>    <dbl>  <dbl>  <dbl> <fct>
    #>  1 1              1 -0.199 -3.47 0.000222 0.421  -0.199 Gaussian c(0, 1)
    #>  2 1              2  0.142 -3.32 0.000638 0.556   0.142 Gaussian c(0, 1)
    #>  3 1              3  2.28  -3.16 0.00161  0.989   2.28  Gaussian c(0, 1)
    #>  4 1              4  1.63  -3.01 0.00355  0.949   1.63  Gaussian c(0, 1)
    #>  5 1              5  0.547 -2.86 0.00694  0.708   0.547 Gaussian c(0, 1)
    #>  6 1              6  0.215 -2.71 0.0121   0.585   0.215 Gaussian c(0, 1)
    #>  7 1              7 -1.42  -2.55 0.0193   0.0771 -1.42  Gaussian c(0, 1)
    #>  8 1              8 -0.850 -2.40 0.0288   0.198  -0.850 Gaussian c(0, 1)
    #>  9 1              9  0.886 -2.25 0.0411   0.812   0.886 Gaussian c(0, 1)
    #> 10 1             10 -1.44  -2.10 0.0576   0.0748 -1.44  Gaussian c(0, 1)
    #> # ℹ 140 more rows

    ## OR

    tidy_combine_distributions(
      tidy_normal(),
      tidy_beta(),
      tidy_cauchy(),
      tidy_logistic()
    )
    #> # A tibble: 200 × 8
    #>    sim_number     x      y    dx       dy     p      q dist_type
    #>    <fct>      <int>  <dbl> <dbl>    <dbl> <dbl>  <dbl> <fct>
    #>  1 1              1 -0.258 -3.24 0.000490 0.398 -0.258 Gaussian c(0, 1)
    #>  2 1              2  0.719 -3.12 0.00130  0.764  0.719 Gaussian c(0, 1)
    #>  3 1              3  0.720 -2.99 0.00309  0.764  0.720 Gaussian c(0, 1)
    #>  4 1              4 -0.697 -2.87 0.00648  0.243 -0.697 Gaussian c(0, 1)
    #>  5 1              5 -0.402 -2.75 0.0121   0.344 -0.402 Gaussian c(0, 1)
    #>  6 1              6  0.457 -2.63 0.0200   0.676  0.457 Gaussian c(0, 1)
    #>  7 1              7 -0.456 -2.50 0.0296   0.324 -0.456 Gaussian c(0, 1)
    #>  8 1              8  1.24  -2.38 0.0392   0.893  1.24  Gaussian c(0, 1)
    #>  9 1              9  0.183 -2.26 0.0473   0.573  0.183 Gaussian c(0, 1)
    #> 10 1             10  0.176 -2.14 0.0532   0.570  0.176 Gaussian c(0, 1)
    #> # ℹ 190 more rows