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 -1.52 -3.17 0.000315 0.0640 -1.52
#> 2 1 2 -0.835 -3.04 0.000857 0.202 -0.835
#> 3 1 3 0.251 -2.90 0.00213 0.599 0.251
#> 4 1 4 1.50 -2.76 0.00481 0.933 1.50
#> 5 1 5 0.677 -2.63 0.00993 0.751 0.677
#> 6 1 6 -1.04 -2.49 0.0188 0.150 -1.04
#> 7 1 7 -1.92 -2.35 0.0326 0.0272 -1.92
#> 8 1 8 1.11 -2.21 0.0520 0.866 1.11
#> 9 1 9 -0.568 -2.08 0.0766 0.285 -0.568
#> 10 1 10 -0.257 -1.94 0.104 0.399 -0.257
#> # 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")