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 0.306 -3.65 0.000261 0.620 0.306
#> 2 1 2 -0.119 -3.51 0.000820 0.453 -0.119
#> 3 1 3 1.33 -3.37 0.00218 0.909 1.33
#> 4 1 4 0.0612 -3.23 0.00491 0.524 0.0612
#> 5 1 5 0.620 -3.09 0.00942 0.732 0.620
#> 6 1 6 -0.733 -2.95 0.0155 0.232 -0.733
#> 7 1 7 -0.148 -2.81 0.0221 0.441 -0.148
#> 8 1 8 -0.364 -2.67 0.0282 0.358 -0.364
#> 9 1 9 -0.502 -2.53 0.0340 0.308 -0.502
#> 10 1 10 -0.529 -2.39 0.0412 0.298 -0.529
#> # … with 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")