The goal of {TidyDensity}
is to make working with random numbers from different distributions easy. All tidy_
distribution functions provide the following components:
r_
]d_
]q_
]p_
]You can install the released version of {TidyDensity}
from CRAN with:
And the development version from GitHub with:
This is a basic example which shows you how to solve a common problem:
library(TidyDensity)
library(dplyr)
library(ggplot2)
tidy_normal()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.261 -3.68 0.000208 0.603 0.261
#> 2 1 2 0.491 -3.53 0.000571 0.688 0.491
#> 3 1 3 0.220 -3.37 0.00139 0.587 0.220
#> 4 1 4 -0.801 -3.21 0.00303 0.212 -0.801
#> 5 1 5 0.732 -3.06 0.00591 0.768 0.732
#> 6 1 6 -1.87 -2.90 0.0104 0.0308 -1.87
#> 7 1 7 -0.898 -2.74 0.0168 0.185 -0.898
#> 8 1 8 0.157 -2.59 0.0252 0.562 0.157
#> 9 1 9 -1.06 -2.43 0.0359 0.145 -1.06
#> 10 1 10 0.411 -2.27 0.0495 0.660 0.411
#> # ℹ 40 more rows
An example plot of the tidy_normal
data.
We can also take a look at the plots when the number of simulations is greater than nine. This will automatically turn off the legend as it will become too noisy.