https://github.com/easystats/see
:art: Visualisation toolbox for beautiful and publication-ready figures
https://github.com/easystats/see
data-visualization easystats ggplot2 hacktoberfest plotting r rstats see statistics visualisation visualization
Last synced: 4 days ago
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:art: Visualisation toolbox for beautiful and publication-ready figures
- Host: GitHub
- URL: https://github.com/easystats/see
- Owner: easystats
- License: other
- Created: 2019-04-09T03:28:04.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2025-04-01T06:17:35.000Z (13 days ago)
- Last Synced: 2025-04-03T11:05:10.037Z (11 days ago)
- Topics: data-visualization, easystats, ggplot2, hacktoberfest, plotting, r, rstats, see, statistics, visualisation, visualization
- Language: R
- Homepage: https://easystats.github.io/see/
- Size: 2.87 GB
- Stars: 907
- Watchers: 21
- Forks: 43
- Open Issues: 35
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Support: .github/SUPPORT.md
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README
---
output: github_document
bibliography: paper/paper.bib
csl: apa.csl
---
# see: Model Visualisation Toolbox for 'easystats' and 'ggplot2' 
```{r, echo=FALSE, warning=FALSE, message=FALSE}
library(see)
knitr::opts_chunk$set(
dpi = 150,
collapse = TRUE,
out.width = "100%",
fig.path = "man/figures/",
warning = FALSE,
message = FALSE
)
```
[](https://doi.org/10.21105/joss.03393)
[](https://cran.r-project.org/package=see)
[](https://cranlogs.r-pkg.org/)
***"Damned are those who believe without seeing"***
*easystats* is a collection of packages that operate in synergy to provide a consistent and intuitive syntax when working with statistical models in the R programming language [@base2021]. Most *easystats* packages return comprehensive numeric summaries of model parameters and performance. The *see* package complements these numeric summaries with a host of functions and tools to produce a range of publication-ready visualizations for model parameters, predictions, and performance diagnostics. As a core pillar of *easystats*, the *see* package helps users to utilize visualization for more informative, communicable, and well-rounded scientific reporting.
# Installation
[](https://cran.r-project.org/package=see) [](https://easystats.r-universe.dev) [](https://github.com/easystats/see/actions) [](https://app.codecov.io/gh/easystats/see)
The *see* package is available on CRAN, while its latest development version is available on R-universe (from _rOpenSci_).
Type | Source | Command
---|---|---
Release | CRAN | `install.packages("see")`
Development | r-universe | `install.packages("see", repos = "https://easystats.r-universe.dev")`
Development | GitHub | `remotes::install_github("easystats/see")`
Once you have downloaded the package, you can then load it using:
```{r}
library("see")
```
> **Tip**
>
> Instead of `library(see)`, use `library(easystats)`.
> This will make all features of the easystats-ecosystem available.
>
> To stay updated, use `easystats::install_latest()`.
# Plotting functions for 'easystats' packages
[](https://easystats.github.io/see/)
[](https://easystats.github.io/blog/posts/)
[](https://easystats.github.io/see/reference/index.html)
Below we present one or two plotting methods for each *easystats* package, but many other methods are available. Interested readers are encouraged to explore the range of examples on the package [website](https://easystats.github.io/see/articles/).
## [parameters](https://github.com/easystats/parameters)
The *parameters* package converts summaries of regression model objects into data frames [@Lüdecke2020parameters]. The *see* package can take this transformed object and, for example, create a dot-and-whisker plot for the extracted regression estimates simply by passing the `parameters` class object to `plot()`.
```{r parameters1}
library(parameters)
library(see)
model <- lm(wt ~ am * cyl, data = mtcars)
plot(parameters(model))
```
As *see* outputs objects of class `ggplot`, *ggplot2* functions can be added as layers to the plot the same as with all other *ggplot2* visualizations. For example, we might add a title using `labs()` from *ggplot2*.
```{r parameters2}
library(parameters)
library(see)
model <- lm(wt ~ am * cyl, data = mtcars)
plot(parameters(model)) +
ggplot2::labs(title = "A Dot-and-Whisker Plot")
```
Plotting functions for the **parameters** package are demonstrated [in this vignette](https://easystats.github.io/see/articles/parameters.html).
## [bayestestR](https://github.com/easystats/bayestestR)
Similarly, for Bayesian regression model objects, which are handled by the *bayestestR* package [@Makowski2019], the *see* package provides special plotting methods relevant for Bayesian models (e.g., Highest Density Interval, or *HDI*). Users can fit the model and pass the model results, extracted via *bayestestR*, to `plot()`.
```{r bayestestR}
library(bayestestR)
library(rstanarm)
library(see)
set.seed(123)
model <- stan_glm(wt ~ mpg, data = mtcars, refresh = 0)
result <- hdi(model, ci = c(0.5, 0.75, 0.89, 0.95))
plot(result)
```
Plotting functions for the **bayestestR** package are demonstrated [in this vignette](https://easystats.github.io/see/articles/bayestestR.html).
## [performance](https://github.com/easystats/performance)
The *performance* package is primarily concerned with checking regression model assumptions [@Lüdecke2020performance]. The *see* package offers tools to visualize these assumption checks, such as the normality of residuals. Users simply pass the fit model object to the relevant *performance* function (`check_normality()` in the example below). Then, this result can be passed to `plot()` to produce a *ggplot2* visualization of the check on normality of the residuals.
```{r performance}
library(performance)
library(see)
model <- lm(wt ~ mpg, data = mtcars)
check <- check_normality(model)
plot(check, type = "qq")
```
Plotting functions for the **performance** package are demonstrated [in this vignette](https://easystats.github.io/see/articles/performance.html).
## [effectsize](https://github.com/easystats/effectsize)
The *effectsize* package computes a variety of effect size metrics for fitted models to assesses the practical importance of observed effects [@Ben-Shachar2020]. In conjunction with *see*, users are able to visualize the magnitude and uncertainty of effect sizes by passing the model object to the relevant *effectsize* function (`omega_squared()` in the following example), and then to `plot()`.
```{r effectsize}
library(effectsize)
library(see)
model <- aov(wt ~ am * cyl, data = mtcars)
plot(omega_squared(model))
```
Plotting functions for the **effectsize** package are demonstrated [in this vignette](https://easystats.github.io/see/articles/effectsize.html).
## [modelbased](https://github.com/easystats/modelbased)
The *modelbased* package computes model-based estimates and predictions from fitted models [@Makowski2020modelbased]. *see* provides methods to quickly visualize these model predictions. For the following example to work, you need to have installed the *emmeans* package first.
```{r modelbased1}
library(modelbased)
library(see)
data(mtcars)
mtcars$gear <- as.factor(mtcars$gear)
model <- lm(mpg ~ wt * gear, data = mtcars)
predicted <- estimate_expectation(model, data = "grid")
plot(predicted, show_data = TRUE)
```
One can also visualize *marginal means* (i.e., the mean at each factor level averaged over other predictors) using `estimate_means()`, that is then passed to `plot()`.
```{r modelbased2, error=TRUE}
means <- estimate_means(model)
plot(means)
```
Plotting functions for the **modelbased** package are demonstrated [in this vignette](https://easystats.github.io/see/articles/modelbased.html).
## [correlation](https://github.com/easystats/correlation)
The *correlation* package provides a unified syntax and human-readable code to carry out many types of correlation analysis [@Makowski2020]. A user can run `summary(correlation(data))` to create a construct a correlation matrix for the variables in a dataframe. With *see*, this matrix can be passed to `plot()` to visualize these correlations in a correlation matrix.
```{r correlation, error=FALSE}
library(correlation)
library(see)
results <- summary(correlation(iris))
plot(results, show_data = "points")
```
Plotting functions for the **correlation** package are demonstrated [in this vignette](https://easystats.github.io/see/articles/correlation.html).
# Themes
### Modern
```{r}
library(ggplot2)
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
geom_point2() +
theme_modern()
```
### Lucid
```{r}
library(ggplot2)
p <- ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
geom_point2()
p + theme_lucid()
```
### Blackboard
```{r}
p + theme_blackboard()
```
### Abyss
```{r}
p + theme_abyss()
```
# Palettes
This is just one example of the available palettes. See [this vignette](https://easystats.github.io/see/articles/seecolorscales.html) for a detailed overview of palettes and color scales.
### Material design
```{r}
p1 <- ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern(axis.text.angle = 45) +
scale_fill_material_d()
p2 <- ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern(axis.text.angle = 45) +
scale_fill_material_d(palette = "ice")
p3 <- ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point2() +
theme_modern() +
scale_color_material(discrete = FALSE)
```
## Multiple plots
The `plots()` function allows us to plot the figures side by side.
```{r}
plots(p1, p2, p3, n_columns = 2)
```
The `plots()` function can also be used to add **tags** (*i.e.*, labels for subfigures).
```{r}
plots(p1, p2, p3,
n_columns = 2,
tags = paste("Fig. ", 1:3)
)
```
# Geoms
## Better looking points
`geom_points2()` and `geom_jitter2()` allow points without borders and contour.
```{r, fig.width=9.5, fig.height=5.8}
normal <- ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) +
geom_point(size = 8, alpha = 0.3) +
theme_modern()
new <- ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) +
geom_point2(size = 8, alpha = 0.3) +
theme_modern()
plots(normal, new, n_columns = 2)
```
## Half-violin Half-dot plot
Create a half-violin half-dot plot, useful for visualising the distribution and the sample size at the same time.
```{r}
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violindot(fill_dots = "black") +
theme_modern() +
scale_fill_material_d()
```
## Radar chart (Spider plot)
```{r}
library(datawizard)
# prepare the data in tidy format
data <- iris |>
datawizard::data_group("Species") |>
datawizard::data_summary(
Sepal.Length = mean(Sepal.Length),
Sepal.Width = mean(Sepal.Width),
Petal.Length = mean(Petal.Length),
Petal.Width = mean(Petal.Width)
) |>
datawizard::reshape_longer(c(
"Sepal.Length",
"Sepal.Width",
"Petal.Length",
"Petal.Width"
))
data |>
ggplot(aes(
x = name,
y = value,
color = Species,
group = Species,
fill = Species
)) +
geom_polygon(linewidth = 1, alpha = 0.1) +
coord_radar() +
theme_radar()
```
# Contributing and Support
In case you want to file an issue or contribute in another way to the package, please follow [this guide](https://github.com/easystats/see/blob/master/.github/CONTRIBUTING.md). For questions about the functionality, you may either contact us via email or also file an issue.
# Code of Conduct
Please note that this project is released with a
[Contributor Code of Conduct](https://easystats.github.io/see/CODE_OF_CONDUCT.html). By participating in this project you agree to abide by its terms.
# References