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:
install.packages("TidyDensity")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("spsanderson/TidyDensity")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 0.926 -3.70 0.000226 0.823 0.926
#> 2 1 2 -0.471 -3.55 0.000595 0.319 -0.471
#> 3 1 3 -0.438 -3.41 0.00141 0.331 -0.438
#> 4 1 4 1.65 -3.27 0.00304 0.950 1.65
#> 5 1 5 0.396 -3.13 0.00595 0.654 0.396
#> 6 1 6 -1.45 -2.99 0.0106 0.0736 -1.45
#> 7 1 7 0.247 -2.84 0.0174 0.597 0.247
#> 8 1 8 0.409 -2.70 0.0264 0.659 0.409
#> 9 1 9 0.799 -2.56 0.0372 0.788 0.799
#> 10 1 10 1.11 -2.42 0.0493 0.867 1.11
#> # 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")