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 -1.63 -3.26 0.000576 0.0516 -1.63
#> 2 1 2 0.274 -3.11 0.00147 0.608 0.274
#> 3 1 3 -0.000218 -2.97 0.00338 0.500 -0.000218
#> 4 1 4 -0.636 -2.83 0.00704 0.263 -0.636
#> 5 1 5 1.17 -2.68 0.0132 0.878 1.17
#> 6 1 6 1.02 -2.54 0.0225 0.846 1.02
#> 7 1 7 -0.476 -2.40 0.0348 0.317 -0.476
#> 8 1 8 1.30 -2.25 0.0492 0.903 1.30
#> 9 1 9 1.26 -2.11 0.0640 0.895 1.26
#> 10 1 10 0.700 -1.97 0.0779 0.758 0.700
#> # ℹ 40 more rows
An 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")