“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 (R Core Team, 2021). 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.
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:
library("see")Tip
Instead of
library(see), uselibrary(easystats). This will make all features of the easystats-ecosystem available.To stay updated, use
easystats::install_latest().
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.
The parameters package converts summaries of regression model objects
into data frames (Lüdecke et al., 2020). 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().
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.
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.
Similarly, for Bayesian regression model objects, which are handled by
the bayestestR package (Makowski et al., 2019), 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().
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.
The performance package is primarily concerned with checking
regression model assumptions (Lüdecke et al., 2021). 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.
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.
The effectsize package computes a variety of effect size metrics for
fitted models to assesses the practical importance of observed effects
(Ben-Shachar et al., 2020). 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().
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.
The modelbased package computes model-based estimates and predictions from fitted models (Makowski et al., 2020a). see provides methods to quickly visualize these model predictions. For the following example to work, you need to have installed the emmeans package first.
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().
means <- estimate_means(model)
plot(means)
Plotting functions for the modelbased package are demonstrated in this vignette.
The correlation package provides a unified syntax and human-readable
code to carry out many types of correlation analysis (Makowski et al.,
2020b). 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.
library(correlation)
library(see)
results <- summary(correlation(iris))
plot(results, show_data = "points")
Plotting functions for the correlation package are demonstrated in this vignette.
library(ggplot2)
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
geom_point2() +
theme_modern()
library(ggplot2)
p <- ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
geom_point2()
p + theme_lucid()
p + theme_blackboard()
p + theme_abyss()
This is just one example of the available palettes. See this vignette for a detailed overview of palettes and color scales.
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_c(palette = "rainbow")The plots() function allows us to plot the figures side by side.
plots(p1, p2, p3, n_columns = 2)
The plots() function can also be used to add tags (i.e., labels
for subfigures).
plots(p1, p2, p3,
n_columns = 2,
tags = paste("Fig. ", 1:3)
)
geom_points2() and geom_jitter2() allow points without borders and
contour.
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)
Create a half-violin half-dot plot, useful for visualising the distribution and the sample size at the same time.
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violindot(fill_dots = "black") +
theme_modern() +
scale_fill_material_d()
library(poorman)
library(datawizard)
# prepare the data in tidy format
data <- iris %>%
group_by(Species) %>%
summarise(across(everything(), mean)) %>%
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()
In case you want to file an issue or contribute in another way to the package, please follow this guide. For questions about the functionality, you may either contact us via email or also file an issue.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
Ben-Shachar, M. S., Lüdecke, D., & Makowski, D. (2020). effectsize: Estimation of effect size indices and standardized parameters. Journal of Open Source Software, 5(56), 2815. https://doi.org/10.21105/joss.02815
Lüdecke, D., Ben-Shachar, M. S., Patil, I., & Makowski, D. (2020). Extracting, computing and exploring the parameters of statistical models using R. Journal of Open Source Software, 5(53), 2445. https://doi.org/10.21105/joss.02445
Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P., & Makowski, D. (2021). performance: An R package for assessment, comparison and testing of statistical models. Journal of Open Source Software, 6(60), 3139. https://doi.org/10.21105/joss.03139
Makowski, D., Ben-Shachar, M. S., & Lüdecke, D. (2019). bayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian framework. Journal of Open Source Software, 4(40), 1541. https://doi.org/10.21105/joss.01541
Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020a). Estimation of model-based predictions, contrasts and means. CRAN. https://github.com/easystats/modelbased
Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020b). Methods and algorithms for correlation analysis in R. Journal of Open Source Software, 5(51), 2306. https://doi.org/10.21105/joss.02306
R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/