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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# `ggstats`: extension to `ggplot2` for plotting stats
<!-- badges: start -->
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/larmarange/ggstats/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/larmarange/ggstats)
[](https://CRAN.R-project.org/package=ggstats)
[](https://zenodo.org/badge/latestdoi/547360047)
<!-- badges: end -->
The `ggstats` package provides new statistics, new geometries and new positions for `ggplot2` and a suite of functions to facilitate the creation of statistical plots.
## Installation & Documentation
To install **stable version**:
```{r eval=FALSE}
install.packages("ggstats")
```
Documentation of stable version: <https://larmarange.github.io/ggstats/>
To install **development version**:
```{r eval=FALSE}
remotes::install_github("larmarange/ggstats")
```
Documentation of development version: <https://larmarange.github.io/ggstats/dev/>
## Plot model coefficients
```{r}
library(ggstats)
mod1 <- lm(Fertility ~ ., data = swiss)
ggcoef_model(mod1)
ggcoef_table(mod1)
```
## Comparing several models
```{r}
mod2 <- step(mod1, trace = 0)
mod3 <- lm(Fertility ~ Agriculture + Education * Catholic, data = swiss)
models <- list(
"Full model" = mod1,
"Simplified model" = mod2,
"With interaction" = mod3
)
ggcoef_compare(models, type = "faceted")
```
## Compute custom proportions
```{r}
library(ggplot2)
ggplot(as.data.frame(Titanic)) +
aes(x = Class, fill = Survived, weight = Freq, by = Class) +
geom_bar(position = "fill") +
geom_text(stat = "prop", position = position_fill(.5)) +
facet_grid(~Sex)
```
## Compute weighted mean
```{r}
data(tips, package = "reshape")
ggplot(tips) +
aes(x = day, y = total_bill, fill = sex) +
stat_weighted_mean(geom = "bar", position = "dodge") +
ylab("Mean total bill per day and sex")
```
## Compute cross-tabulation statistics
```{r}
ggplot(as.data.frame(Titanic)) +
aes(
x = Class, y = Survived, weight = Freq,
size = after_stat(observed), fill = after_stat(std.resid)
) +
stat_cross(shape = 22) +
scale_fill_steps2(breaks = c(-3, -2, 2, 3), show.limits = TRUE) +
scale_size_area(max_size = 20)
```
## Plot survey objects taking into account weights
```{r}
library(survey, quietly = TRUE)
dw <- svydesign(
ids = ~1,
weights = ~Freq,
data = as.data.frame(Titanic)
)
ggsurvey(dw) +
aes(x = Class, fill = Survived) +
geom_bar(position = "fill") +
ylab("Weighted proportion of survivors")
```
## Plot Likert-type items
```{r}
library(dplyr)
likert_levels <- c(
"Strongly disagree",
"Disagree",
"Neither agree nor disagree",
"Agree",
"Strongly agree"
)
set.seed(42)
df <-
tibble(
q1 = sample(likert_levels, 150, replace = TRUE),
q2 = sample(likert_levels, 150, replace = TRUE, prob = 5:1),
q3 = sample(likert_levels, 150, replace = TRUE, prob = 1:5),
q4 = sample(likert_levels, 150, replace = TRUE, prob = 1:5),
q5 = sample(c(likert_levels, NA), 150, replace = TRUE),
q6 = sample(likert_levels, 150, replace = TRUE, prob = c(1, 0, 1, 1, 0))
) |>
mutate(across(everything(), ~ factor(.x, levels = likert_levels)))
gglikert(df)
```
## Cascade plot (*experimental*)
```{r}
ggplot2::diamonds |>
ggcascade(
all = TRUE,
big = carat > .5,
"big & ideal" = carat > .5 & cut == "Ideal"
)
```