Example
This is a basic example which shows you how easy it is to generate
data with {TidyDensity}:
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 0.288 -3.37 0.000347 0.613 0.288
#> 2 1 2 -0.944 -3.23 0.000968 0.173 -0.944
#> 3 1 3 0.153 -3.10 0.00239 0.561 0.153
#> 4 1 4 1.05 -2.96 0.00519 0.854 1.05
#> 5 1 5 -0.577 -2.82 0.00997 0.282 -0.577
#> 6 1 6 2.25 -2.68 0.0169 0.988 2.25
#> 7 1 7 1.17 -2.54 0.0254 0.879 1.17
#> 8 1 8 -1.12 -2.40 0.0338 0.132 -1.12
#> 9 1 9 1.56 -2.26 0.0401 0.940 1.56
#> 10 1 10 0.868 -2.13 0.0430 0.807 0.868
#> # ℹ 40 more rowsAn example plot of the tidy_normal data.
tn <- tidy_normal(.n = 100, .num_sims = 6)
tidy_autoplot(tn, .plot_type = "density")
tidy_autoplot(tn, .plot_type = "quantile")
tidy_autoplot(tn, .plot_type = "probability")
tidy_autoplot(tn, .plot_type = "qq")
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.
tn <- tidy_normal(.n = 100, .num_sims = 20)
tidy_autoplot(tn, .plot_type = "density")
tidy_autoplot(tn, .plot_type = "quantile")
tidy_autoplot(tn, .plot_type = "probability")
tidy_autoplot(tn, .plot_type = "qq")