From 97967b64937bed41604fe252c9d76fd3fe969c1b Mon Sep 17 00:00:00 2001 From: Indrajeet Patil Date: Fri, 11 Jun 2021 17:43:38 +0200 Subject: [PATCH] adding more examples to README and statement of need --- README.Rmd | 195 ++- README.md | 428 ++++++- apa.csl | 1916 ++++++++++++++++++++++++++++ man/figures/bayestestR-1.png | Bin 0 -> 149805 bytes man/figures/correlation-1.png | Bin 0 -> 125602 bytes man/figures/effectsize-1.png | Bin 0 -> 95273 bytes man/figures/modelbased1-1.png | Bin 0 -> 175924 bytes man/figures/modelbased2-1.png | Bin 0 -> 126466 bytes man/figures/parameters1-1.png | Bin 0 -> 95559 bytes man/figures/parameters2-1.png | Bin 0 -> 108140 bytes man/figures/performance-1.png | Bin 0 -> 182289 bytes man/figures/unnamed-chunk-10-1.png | Bin 217162 -> 220889 bytes man/figures/unnamed-chunk-11-1.png | Bin 502387 -> 217162 bytes man/figures/unnamed-chunk-12-1.png | Bin 182391 -> 502387 bytes man/figures/unnamed-chunk-13-1.png | Bin 246515 -> 182391 bytes man/figures/unnamed-chunk-14-1.png | Bin 56608 -> 246515 bytes man/figures/unnamed-chunk-5-1.png | Bin 150973 -> 148250 bytes man/figures/unnamed-chunk-6-1.png | Bin 150611 -> 150973 bytes man/figures/unnamed-chunk-7-1.png | Bin 157210 -> 150611 bytes man/figures/unnamed-chunk-8-1.png | Bin 374904 -> 157210 bytes paper.bib | 139 ++ 21 files changed, 2600 insertions(+), 78 deletions(-) create mode 100644 apa.csl create mode 100644 man/figures/bayestestR-1.png create mode 100644 man/figures/correlation-1.png create mode 100644 man/figures/effectsize-1.png create mode 100644 man/figures/modelbased1-1.png create mode 100644 man/figures/modelbased2-1.png create mode 100644 man/figures/parameters1-1.png create mode 100644 man/figures/parameters2-1.png create mode 100644 man/figures/performance-1.png create mode 100644 paper.bib diff --git a/README.Rmd b/README.Rmd index d30e15a73..31902245c 100644 --- a/README.Rmd +++ b/README.Rmd @@ -1,5 +1,7 @@ --- output: github_document +bibliography: paper.bib +csl: apa.csl --- @@ -25,6 +27,37 @@ options(knitr.kable.NA = "", digits = 4) ***"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. + +# Statement of Need + +The grammar of graphics [@wilkinson2012grammar], largely due to its implementation in the *ggplot2* package [@Wickham2016], has become the dominant approach to visualization in R. Building a model visualization with *ggplot2* is somewhat disconnected from the model fitting and evaluation process. Generally, this process entails: + + 1. Fitting a model. + 2. Extracting desired results from the model (e.g., model parameters and intervals, model predictions, diagnostic statistics) and arranging them into a dataframe. + 3. Passing the results dataframe to `ggplot()` and specifying the graphical parameters. For example: + +```{r, eval=FALSE} +library(ggplot2) + +# step-1 +model <- lm(mpg ~ factor(cyl) * wt, data = mtcars) + +# step-2 +results <- fortify(model) + +# step-3 +ggplot(results) + + geom_point(aes(x = wt, y = mpg, color = factor(cyl))) + + geom_line(aes(x = wt, y = .fitted, color = `factor(cyl)`)) +``` + +A number of packages have been developed to extend *ggplot2* and assist with model visualization (for a sampling of these packages, visit ). Some of these packages provide functions for additional geoms, annotations, or common visualization types without linking them to a specific statistical analysis or fundamentally changing the *ggplot2* workflow (e.g., *ggrepel*, *ggalluvial*, *ggridges*, *ggdist*, *ggpubr*, etc.). Other *ggplot2* extensions provide functions to generate publication-ready visualizations for specific types of models (e.g., *metaviz*, *tidymv*, *sjPlot*, *survminer*). For example, the *ggstatsplot* package [@Patil2021] offers visualizations for statistical analysis of one-way factorial designs, and the *plotmm* package [@Waggoner2020] supports specific types of mixture model objects. + +The aim of the *see* package is to produce visualizations for a wide variety of models and statistical analyses in a way that is tightly linked with the model fitting process and requires minimal interruption of users' workflow. *see* accomplishes this aim by providing a single `plot()` method for objects created by the other *easystats* packages, such as *parameters* tables, *modelbased* predictions, *performance* diagnostic tests, *correlation* matrices, and so on. The *easystats* packages compute numeric results for a wide range of statistical models, and the *see* package acts as a visual support to the entire *easystats* ecosystem. As such, visualizations corresponding to all stages of statistical analysis, from model fitting to diagnostics to reporting, can be easily created using *see*. *see* plots are compatible with other *ggplot2* functions for further customization (e.g., `labs()` for a plot title). In addition, *see* provides several aesthetic utilities to embellish both *easystats* plots and other *ggplot2* plots. The result is a package that minimizes the barrier to producing high-quality statistical visualizations in R. + +The central goal of *easystats* is to make the task of doing statistics in R as easy as possible. This goal is realized through intuitive and consistent syntax, consistent and transparent argument names, comprehensive documentation, informative warnings and error messages, and smart functions with sensible default parameter values. The *see* package follows this philosophy by using a single access point---the generic `plot()` method---for visualization of all manner of statistical results supported by *easystats*. + ## Installation [![CRAN](http://www.r-pkg.org/badges/version/see)](https://cran.r-project.org/package=see) @@ -43,10 +76,6 @@ And then load it: library("see") ``` -## 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. - # Documentation This package provides visualisation toolbox for R packages in the `easystats` ecosystem and extra `geom`s, themes and color palettes for `ggplot2` @@ -55,7 +84,131 @@ This package provides visualisation toolbox for R packages in the `easystats` ec [![Blog](https://img.shields.io/badge/blog-easystats-orange.svg?colorB=FF9800)](https://easystats.github.io/blog/posts/) [![Features](https://img.shields.io/badge/features-see-orange.svg?colorB=2196F3)](https://easystats.github.io/see/reference/index.html) -## Themes +# Plotting functions for 'easystats' packages + +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, . + +## [parameters](https://github.com/easystats/parameters) + +The *parameters* package converts summaries of regression model objects into dataframes [@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, eval=FALSE} +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. + +```{r modelbased1} +library(modelbased) +library(see) + +model <- lm(mpg ~ wt * as.factor(cyl), data = mtcars) +predicted <- estimate_prediction(model) + +plot(predicted) +``` + +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} +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 corrgram.. + +```{r correlation, error=FALSE} +library(correlation) +library(see) + +results <- summary(correlation(iris)) + +plot(results) +``` + +Plotting functions for the **correlation** package are demonstrated [in this vignette](https://easystats.github.io/see/articles/correlation.html). + +# Themes ### Modern @@ -90,7 +243,7 @@ p + theme_blackboard() p + theme_abyss() ``` -## Palettes +# 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. @@ -130,6 +283,8 @@ plots(p1, p2, p3, ) ``` +# Geoms + ## Better looking points `geom_points2()` and `geom_jitter2()` allow points without borders and contour. @@ -177,33 +332,13 @@ data %>% theme_minimal() ``` -## Plot functions for easystats packages - -### [bayestestR](https://github.com/easystats/bayestestR) - -Plotting functions for the **bayestestR** package are demonstrated [in this vignette](https://easystats.github.io/see/articles/bayestestR.html). - -### [parameters](https://github.com/easystats/parameters) - -Plotting functions for the **parameters** package are demonstrated [in this vignette](https://easystats.github.io/see/articles/parameters.html). - -### [performance](https://github.com/easystats/performance) +# Contributing and Support -Plotting functions for the **performance** package are demonstrated [in this vignette](https://easystats.github.io/see/articles/performance.html). - -### [modelbased](https://github.com/easystats/modelbased) - -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) - -Plotting functions for the **correlation** package are demonstrated [in this vignette](https://easystats.github.io/see/articles/correlation.html). - -### [effectsize](https://github.com/easystats/effectsize) - -Plotting functions for the **effectsize** package are demonstrated [in this vignette](https://easystats.github.io/see/articles/effectsize.html). +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 \ No newline at end of file diff --git a/README.md b/README.md index ff37181d7..988648d48 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,88 @@ ***“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. + +# Statement of Need + +The grammar of graphics (Wilkinson, 2012), largely due to its +implementation in the *ggplot2* package (Wickham, 2016), has become the +dominant approach to visualization in R. Building a model visualization +with *ggplot2* is somewhat disconnected from the model fitting and +evaluation process. Generally, this process entails: + +1. Fitting a model. +2. Extracting desired results from the model (e.g., model parameters + and intervals, model predictions, diagnostic statistics) and + arranging them into a dataframe. +3. Passing the results dataframe to `ggplot()` and specifying the + graphical parameters. For example: + +``` r +library(ggplot2) + +# step-1 +model <- lm(mpg ~ factor(cyl) * wt, data = mtcars) + +# step-2 +results <- fortify(model) + +# step-3 +ggplot(results) + + geom_point(aes(x = wt, y = mpg, color = factor(cyl))) + + geom_line(aes(x = wt, y = .fitted, color = `factor(cyl)`)) +``` + +A number of packages have been developed to extend *ggplot2* and assist +with model visualization (for a sampling of these packages, visit +). Some of these packages +provide functions for additional geoms, annotations, or common +visualization types without linking them to a specific statistical +analysis or fundamentally changing the *ggplot2* workflow (e.g., +*ggrepel*, *ggalluvial*, *ggridges*, *ggdist*, *ggpubr*, etc.). Other +*ggplot2* extensions provide functions to generate publication-ready +visualizations for specific types of models (e.g., *metaviz*, *tidymv*, +*sjPlot*, *survminer*). For example, the *ggstatsplot* package (Patil, +2021) offers visualizations for statistical analysis of one-way +factorial designs, and the *plotmm* package (Waggoner, 2020) supports +specific types of mixture model objects. + +The aim of the *see* package is to produce visualizations for a wide +variety of models and statistical analyses in a way that is tightly +linked with the model fitting process and requires minimal interruption +of users’ workflow. *see* accomplishes this aim by providing a single +`plot()` method for objects created by the other *easystats* packages, +such as *parameters* tables, *modelbased* predictions, *performance* +diagnostic tests, *correlation* matrices, and so on. The *easystats* +packages compute numeric results for a wide range of statistical models, +and the *see* package acts as a visual support to the entire *easystats* +ecosystem. As such, visualizations corresponding to all stages of +statistical analysis, from model fitting to diagnostics to reporting, +can be easily created using *see*. *see* plots are compatible with other +*ggplot2* functions for further customization (e.g., `labs()` for a plot +title). In addition, *see* provides several aesthetic utilities to +embellish both *easystats* plots and other *ggplot2* plots. The result +is a package that minimizes the barrier to producing high-quality +statistical visualizations in R. + +The central goal of *easystats* is to make the task of doing statistics +in R as easy as possible. This goal is realized through intuitive and +consistent syntax, consistent and transparent argument names, +comprehensive documentation, informative warnings and error messages, +and smart functions with sensible default parameter values. The *see* +package follows this philosophy by using a single access point—the +generic `plot()` method—for visualization of all manner of statistical +results supported by *easystats*. + ## Installation [![CRAN](http://www.r-pkg.org/badges/version/see)](https://cran.r-project.org/package=see) @@ -25,14 +107,6 @@ And then load it: library("see") ``` -## 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. - # Documentation This package provides visualisation toolbox for R packages in the @@ -43,7 +117,192 @@ This package provides visualisation toolbox for R packages in the [![Blog](https://img.shields.io/badge/blog-easystats-orange.svg?colorB=FF9800)](https://easystats.github.io/blog/posts/) [![Features](https://img.shields.io/badge/features-see-orange.svg?colorB=2196F3)](https://easystats.github.io/see/reference/index.html) -## Themes +# Plotting functions for ‘easystats’ packages + +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, +. + +## [parameters](https://github.com/easystats/parameters) + +The *parameters* package converts summaries of regression model objects +into dataframes (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()`. + +``` r +library(parameters) +library(see) + +model <- lm(wt ~ am * cyl, data = mtcars) + +plot(parameters(model)) +``` + +![](man/figures/parameters1-1.png) + +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 +library(parameters) +library(see) + +model <- lm(wt ~ am * cyl, data = mtcars) + +plot(parameters(model)) + + ggplot2::labs(title = "A Dot-and-Whisker Plot") +``` + +![](man/figures/parameters2-1.png) + +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 (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()`. + + + + + + +``` r +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 (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. + +``` r +library(performance) +library(see) + +model <- lm(wt ~ mpg, data = mtcars) +check <- check_normality(model) +## Warning: Non-normality of residuals detected (p = 0.016). + +plot(check, type = "qq") +``` + +![](man/figures/performance-1.png) + +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-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()`. + +``` r +library(effectsize) +library(see) + +model <- aov(wt ~ am * cyl, data = mtcars) + +plot(omega_squared(model)) +``` + +![](man/figures/effectsize-1.png) + +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 (Makowski et al., 2020a). *see* provides methods to +quickly visualize these model predictions. + +``` r +library(modelbased) +library(see) + +model <- lm(mpg ~ wt * as.factor(cyl), data = mtcars) +predicted <- estimate_prediction(model) + +plot(predicted) +``` + +![](man/figures/modelbased1-1.png) + +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 +means <- estimate_means(model) + +plot(means) +``` + +![](man/figures/modelbased2-1.png) + +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 (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 corrgram.. + +``` r +library(correlation) +library(see) + +results <- summary(correlation(iris)) + +plot(results) +``` + +![](man/figures/correlation-1.png) + +Plotting functions for the **correlation** package are demonstrated [in +this +vignette](https://easystats.github.io/see/articles/correlation.html). + +# Themes ### Modern @@ -55,7 +314,7 @@ ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) + theme_modern() ``` -![](man/figures/unnamed-chunk-4-1.png) +![](man/figures/unnamed-chunk-5-1.png) ### Lucid @@ -68,7 +327,7 @@ p <- ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) + p + theme_lucid() ``` -![](man/figures/unnamed-chunk-5-1.png) +![](man/figures/unnamed-chunk-6-1.png) ### Blackboard @@ -76,7 +335,7 @@ p + theme_lucid() p + theme_blackboard() ``` -![](man/figures/unnamed-chunk-6-1.png) +![](man/figures/unnamed-chunk-7-1.png) ### Abyss @@ -84,9 +343,9 @@ p + theme_blackboard() p + theme_abyss() ``` -![](man/figures/unnamed-chunk-7-1.png) +![](man/figures/unnamed-chunk-8-1.png) -## Palettes +# Palettes This is just one example of the available palettes. See [this vignette](https://easystats.github.io/see/articles/seecolorscales.html) @@ -119,7 +378,7 @@ The `plots()` function allows us to plot the figures side by side. plots(p1, p2, p3, n_columns = 2) ``` -![](man/figures/unnamed-chunk-9-1.png) +![](man/figures/unnamed-chunk-10-1.png) The `plots()` function can also be used to add **tags** (*i.e.*, labels for subfigures). @@ -131,7 +390,9 @@ plots(p1, p2, p3, ) ``` -![](man/figures/unnamed-chunk-10-1.png) +![](man/figures/unnamed-chunk-11-1.png) + +# Geoms ## Better looking points @@ -150,7 +411,7 @@ new <- ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) + plots(normal, new, n_columns = 2) ``` -![](man/figures/unnamed-chunk-11-1.png) +![](man/figures/unnamed-chunk-12-1.png) ## Half-violin Half-dot plot @@ -164,7 +425,7 @@ ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) + scale_fill_material_d() ``` -![](man/figures/unnamed-chunk-12-1.png) +![](man/figures/unnamed-chunk-13-1.png) ## Radar chart (Spider plot) @@ -184,48 +445,119 @@ data %>% theme_minimal() ``` -![](man/figures/unnamed-chunk-13-1.png) +![](man/figures/unnamed-chunk-14-1.png) -## Plot functions for easystats packages +# Contributing and Support -### [bayestestR](https://github.com/easystats/bayestestR) +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. -Plotting functions for the **bayestestR** package are demonstrated [in -this -vignette](https://easystats.github.io/see/articles/bayestestR.html). +# Code of Conduct -### [parameters](https://github.com/easystats/parameters) +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. -Plotting functions for the **parameters** package are demonstrated [in -this -vignette](https://easystats.github.io/see/articles/parameters.html). +# References -### [performance](https://github.com/easystats/performance) +
-Plotting functions for the **performance** package are demonstrated [in -this -vignette](https://easystats.github.io/see/articles/performance.html). +
-### [modelbased](https://github.com/easystats/modelbased) +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. -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) +
-Plotting functions for the **correlation** package are demonstrated [in -this -vignette](https://easystats.github.io/see/articles/correlation.html). +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. + -### [effectsize](https://github.com/easystats/effectsize) +
-Plotting functions for the **effectsize** package are demonstrated [in -this -vignette](https://easystats.github.io/see/articles/effectsize.html). +
-# Code of Conduct +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. + -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. +
+ +
+ +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. + + +
+ +
+ +Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020a). +Estimation of model-based predictions, contrasts and means. *CRAN*. + + +
+ +
+ +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. + +
+ +
+ +Patil, I. (2021). Visualizations with statistical +details: The ’ggstatsplot’ approach. +*Journal of Open Source Software*, *6*(61), 3167. + + +
+ +
+ +R Core Team. (2021). *R: A language and environment for statistical +computing*. R Foundation for Statistical Computing. + + +
+ +
+ +Waggoner, P. D. (2020). *plotmm: Tidy tools +for visualizing mixture models*. + + +
+ +
+ +Wickham, H. (2016). *ggplot2: Elegant +graphics for data analysis*. Springer-Verlag New York. + +
+ +
+ +Wilkinson, L. (2012). The Grammar of +Graphics. In *Handbook of computational statistics* (pp. +375–414). Springer. + +
+ +
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