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 1.69 -2.91 0.000292 0.954 1.69
#> 2 1 2 0.323 -2.78 0.000855 0.627 0.323
#> 3 1 3 -0.880 -2.65 0.00222 0.189 -0.880
#> 4 1 4 2.09 -2.53 0.00511 0.982 2.09
#> 5 1 5 0.226 -2.40 0.0105 0.589 0.226
#> 6 1 6 0.125 -2.27 0.0194 0.550 0.125
#> 7 1 7 -0.719 -2.14 0.0324 0.236 -0.719
#> 8 1 8 0.0652 -2.01 0.0494 0.526 0.0652
#> 9 1 9 1.55 -1.88 0.0694 0.940 1.55
#> 10 1 10 0.232 -1.75 0.0916 0.592 0.232
#> # 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")