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 1.27 -3.47 0.000336 0.899 1.27
#> 2 1 2 1.18 -3.33 0.000903 0.881 1.18
#> 3 1 3 -0.955 -3.20 0.00217 0.170 -0.955
#> 4 1 4 0.909 -3.06 0.00463 0.818 0.909
#> 5 1 5 -0.0293 -2.92 0.00884 0.488 -0.0293
#> 6 1 6 0.631 -2.79 0.0151 0.736 0.631
#> 7 1 7 -1.35 -2.65 0.0230 0.0882 -1.35
#> 8 1 8 -0.930 -2.52 0.0316 0.176 -0.930
#> 9 1 9 0.487 -2.38 0.0393 0.687 0.487
#> 10 1 10 0.635 -2.24 0.0447 0.737 0.635
#> # … 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")