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vistributions

Visualize probability distributions

vistributions visualizes the normal, t, F, chi-square and binomial distributions with ggplot2. Each family exposes the same grammar: _plot for the shape, _perc for quantiles from probabilities and _prob for probabilities from quantiles, with shaded tails and annotated cutoffs for teaching and publication.

Installation

# Install release version from CRAN
install.packages("vistributions")

# Install development version from GitHub
# install.packages("devtools")
devtools::install_github("rsquaredacademy/vistributions")

Supported distributions

Family	Shape	Quantile from p	Probability from q
Normal	`vdist_normal_plot()`	`vdist_normal_perc()`	`vdist_normal_prob()`
t	`vdist_t_plot()`	`vdist_t_perc()`	`vdist_t_prob()`
F	`vdist_f_plot()`	`vdist_f_perc()`	`vdist_f_prob()`
Chi-square	`vdist_chisquare_plot()`	`vdist_chisquare_perc()`	`vdist_chisquare_prob()`
Binomial	`vdist_binom_plot()`	`vdist_binom_perc()`	`vdist_binom_prob()`

All plotting functions print by default and return the ggplot object invisibly. Pass print_plot = FALSE to get a composable plot object for further customization with + labs(), + theme_minimal() or ggplot2::ggsave().

Articles

Explore Distributions

Usage

Normal distribution

# distribution shape
vdist_normal_plot()


# quantile from a probability (upper 10%)
vdist_normal_perc(0.10, mean = 60, sd = 3, type = "upper")


# probability from quantiles (middle 50%)
vdist_normal_prob(c(85, 100), mean = 90, sd = 4, type = "both")

t distribution

vdist_t_plot(df = 8)


# upper 15th percentile
vdist_t_perc(0.15, df = 8, type = "upper")


# probability between -2 and 2
vdist_t_prob(2, df = 6, type = "interval")

Chi-square distribution

vdist_chisquare_plot(df = 5)


# tenth percentile
vdist_chisquare_perc(0.10, df = 8, type = "lower")


# P(X > 8.79) with 12 df
vdist_chisquare_prob(8.79, df = 12, type = "upper")

F distribution

vdist_f_plot()


# upper twentieth percentile, F(4, 5)
vdist_f_perc(0.20, num_df = 4, den_df = 5, type = "upper")


# P(X > 3.89), F(4, 5)
vdist_f_prob(3.89, num_df = 4, den_df = 5, type = "upper")

Binomial distribution

vdist_binom_plot(12, 0.2)


# exactly 4 successes out of 12
vdist_binom_prob(12, 0.2, 4, type = "exact")


# at most 1 success
vdist_binom_prob(12, 0.2, 1, type = "lower")

Customize the plot object

library(ggplot2)

p <- vdist_normal_plot(mean = 60, sd = 3, print_plot = FALSE)
p + theme_minimal() + labs(title = "Normal(60, 3)")

How it compares

visualize covers more families with base graphics; vistributions focuses on five teaching families with annotated ggplot2 output.
mosaic (xpnorm() and friends) is course-framework bound; vistributions works standalone.
distributional + ggdist is the modeling-grade stack; vistributions is the lightweight classroom companion. Native distributional dispatch is planned for v1.0 (see roadmap/).

Getting Help

If you encounter a bug, please file a minimal reproducible example using reprex on GitHub. For questions and clarifications, please use Stack Overflow (tagged vistributions or r).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.