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 1.02 -3.45 0.000231 0.5 0.334
#> 2 1 2 0.807 -3.29 0.000697 0.508 0.229
#> 3 1 3 0.558 -3.14 0.00181 0.516 0.108
#> 4 1 4 -0.113 -2.98 0.00401 0.524 -0.216
#> 5 1 5 0.519 -2.83 0.00764 0.533 0.0890
#> 6 1 6 0.615 -2.68 0.0126 0.541 0.136
#> 7 1 7 -1.01 -2.52 0.0181 0.549 -0.698
#> 8 1 8 0.180 -2.37 0.0233 0.557 -0.0738
#> 9 1 9 0.0577 -2.21 0.0285 0.565 -0.133
#> 10 1 10 -1.09 -2.06 0.0354 0.573 -0.747
#> # … 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")