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 0.991 -3.18 0.000487 0.839 0.991
#> 2 1 2 -0.163 -3.05 0.00163 0.435 -0.163
#> 3 1 3 2.19 -2.92 0.00454 0.986 2.19
#> 4 1 4 -0.226 -2.78 0.0106 0.411 -0.226
#> 5 1 5 -1.07 -2.65 0.0208 0.141 -1.07
#> 6 1 6 -0.708 -2.52 0.0345 0.239 -0.708
#> 7 1 7 0.343 -2.39 0.0488 0.634 0.343
#> 8 1 8 0.264 -2.26 0.0600 0.604 0.264
#> 9 1 9 -0.0531 -2.13 0.0667 0.479 -0.0531
#> 10 1 10 0.444 -2.00 0.0705 0.671 0.444
#> # ℹ 40 more rowsAn 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.
