ggplot2 Based Plots with Statistical Details • ggstatsplot

`{ggstatsplot}`: `{ggplot2}` Based Plots with Statistical Details

Status	Usage	Miscellaneous

Raison d’être

“What is to be sought in designs for the display of information is the clear portrayal of complexity. Not the complication of the simple; rather … the revelation of the complex.” - Edward R. Tufte

{ggstatsplot} is an extension of {ggplot2} package for creating graphics with details from statistical tests included in the information-rich plots themselves. In a typical exploratory data analysis workflow, data visualization and statistical modeling are two different phases: visualization informs modeling, and modeling in its turn can suggest a different visualization method, and so on and so forth. The central idea of ggstatsplot is simple: combine these two phases into one in the form of graphics with statistical details, which makes data exploration simpler and faster.

Installation

Type	Source	Command
Release		`install.packages("ggstatsplot")`
Development		`pak::pak("IndrajeetPatil/ggstatsplot")`

Citation

If you want to cite this package in a scientific journal or in any other context, run the following code in your R console:

citation("ggstatsplot")

To cite package 'ggstatsplot' in publications use:

  Patil, I. (2021). Visualizations with statistical details: The
  'ggstatsplot' approach. Journal of Open Source Software, 6(61), 3167,
  doi:10.21105/joss.03167

A BibTeX entry for LaTeX users is

  @Article{,
    doi = {10.21105/joss.03167},
    url = {https://doi.org/10.21105/joss.03167},
    year = {2021},
    publisher = {{The Open Journal}},
    volume = {6},
    number = {61},
    pages = {3167},
    author = {Indrajeet Patil},
    title = {{Visualizations with statistical details: The {'ggstatsplot'} approach}},
    journal = {{Journal of Open Source Software}},
  }

Acknowledgments

I would like to thank all the contributors to ggstatsplot who pointed out bugs or requested features I hadn’t considered. I would especially like to thank other package developers (especially Daniel Lüdecke, Dominique Makowski, Mattan S. Ben-Shachar, Brenton Wiernik, Patrick Mair, Salvatore Mangiafico, etc.) who have patiently and diligently answered my relentless questions and supported feature requests in their projects. I also want to thank Chuck Powell for his initial contributions to the package.

The hexsticker was generously designed by Sarah Otterstetter (Max Planck Institute for Human Development, Berlin). This package has also benefited from the larger #rstats community on Twitter, LinkedIn, and StackOverflow.

Thanks are also due to my postdoc advisers (Mina Cikara and Fiery Cushman at Harvard University; Iyad Rahwan at Max Planck Institute for Human Development) who patiently supported me spending hundreds (?) of hours working on this package rather than what I was paid to do. 😁

Documentation and Examples

To see the detailed documentation for each function in the stable CRAN version of the package, see:

Summary of available plots

Function	Plot	Description
`ggbetweenstats()`	violin plots	for comparisons between groups/conditions
`ggwithinstats()`	violin plots	for comparisons within groups/conditions
`gghistostats()`	histograms	for distribution about numeric variable
`ggdotplotstats()`	dot plots/charts	for distribution about labeled numeric variable
`ggscatterstats()`	scatterplots	for correlation between two variables
`ggcorrmat()`	correlation matrices	for correlations between multiple variables
`ggpiestats()`	pie charts	for categorical data
`ggbarstats()`	bar charts	for categorical data
`ggcoefstats()`	dot-and-whisker plots	for regression models and meta-analysis

In addition to these basic plots, ggstatsplot also provides grouped_ versions (see below) that makes it easy to repeat the same analysis for any grouping variable.

Summary of types of statistical analyses

The table below summarizes all the different types of analyses currently supported in this package-

Functions	Description	Parametric	Non-parametric	Robust	Bayesian
`ggbetweenstats()`	Between group/condition comparisons	✅	✅	✅	✅
`ggwithinstats()`	Within group/condition comparisons	✅	✅	✅	✅
`gghistostats()`, `ggdotplotstats()`	Distribution of a numeric variable	✅	✅	✅	✅
`ggcorrmat`	Correlation matrix	✅	✅	✅	✅
`ggscatterstats()`	Correlation between two variables	✅	✅	✅	✅
`ggpiestats()`, `ggbarstats()`	Association between categorical variables	✅	✅	❌	✅
`ggpiestats()`, `ggbarstats()`	Equal proportions for categorical variable levels	✅	✅	❌	✅
`ggcoefstats()`	Regression model coefficients	✅	✅	✅	✅
`ggcoefstats()`	Random-effects meta-analysis	✅	❌	✅	✅

Summary of Bayesian analysis

Analysis	Hypothesis testing	Estimation
(one/two-sample) t-test	✅	✅
one-way ANOVA	✅	✅
correlation	✅	✅
(one/two-way) contingency table	✅	✅
random-effects meta-analysis	✅	✅

Statistical reporting

For all statistical tests reported in the plots, the default template abides by the gold standard for statistical reporting. For example, here are results from Yuen’s test for trimmed means (robust t-test):

Summary of statistical tests and effect sizes

Statistical analysis is carried out by statsExpressions package, and thus a summary table of all the statistical tests currently supported across various functions can be found in article for that package: https://indrajeetpatil.github.io/statsExpressions/articles/stats_details.html

Primary functions

`ggbetweenstats()`

This function creates either a violin plot, a box plot, or a mix of two for between-group or between-condition comparisons with results from statistical tests in the subtitle. The simplest function call looks like this-

set.seed(123)

ggbetweenstats(
  data  = iris,
  x     = Species,
  y     = Sepal.Length,
  title = "Distribution of sepal length across Iris species"
)

Defaults return

✅ raw data + distributions
✅ descriptive statistics
✅ inferential statistics
✅ effect size + CIs
✅ pairwise comparisons
✅ Bayesian hypothesis-testing
✅ Bayesian estimation

A number of other arguments can be specified to make this plot even more informative or change some of the default options. Additionally, there is also a grouped_ variant of this function that makes it easy to repeat the same operation across a single grouping variable:

set.seed(123)

grouped_ggbetweenstats(
  data             = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
  x                = mpaa,
  y                = length,
  grouping.var     = genre,
  outlier.tagging  = TRUE,
  outlier.label    = title,
  outlier.coef     = 2,
  ggsignif.args    = list(textsize = 4, tip_length = 0.01),
  p.adjust.method  = "bonferroni",
  palette          = "default_jama",
  package          = "ggsci",
  plotgrid.args    = list(nrow = 1),
  annotation.args  = list(title = "Differences in movie length by mpaa ratings for different genres")
)

Note here that the function can be used to tag outliers!

Details about underlying functions used to create graphics and statistical tests carried out can be found in the function documentation: https://indrajeetpatil.github.io/ggstatsplot/reference/ggbetweenstats.html

For more, also read the following vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggbetweenstats.html

`ggwithinstats()`

ggbetweenstats() function has an identical twin function ggwithinstats() for repeated measures designs that behaves in the same fashion with a few minor tweaks introduced to properly visualize the repeated measures design. As can be seen from an example below, the only difference between the plot structure is that now the group means are connected by paths to highlight the fact that these data are paired with each other.

set.seed(123)
library(WRS2) ## for data
library(afex) ## to run ANOVA

ggwithinstats(
  data    = WineTasting,
  x       = Wine,
  y       = Taste,
  title   = "Wine tasting"
)

Defaults return

✅ raw data + distributions
✅ descriptive statistics
✅ inferential statistics
✅ effect size + CIs
✅ pairwise comparisons
✅ Bayesian hypothesis-testing
✅ Bayesian estimation

As with the ggbetweenstats(), this function also has a grouped_ variant that makes repeating the same analysis across a single grouping variable quicker. We will see an example with only repeated measurements-

set.seed(123)

grouped_ggwithinstats(
  data            = dplyr::filter(bugs_long, region %in% c("Europe", "North America"), condition %in% c("LDLF", "LDHF")),
  x               = condition,
  y               = desire,
  type            = "np",
  xlab            = "Condition",
  ylab            = "Desire to kill an artrhopod",
  grouping.var    = region,
  outlier.tagging = TRUE,
  outlier.label   = education
)

For more, also read the following vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggwithinstats.html

`gghistostats()`

To visualize the distribution of a single variable and check if its mean is significantly different from a specified value with a one-sample test, gghistostats() can be used.

set.seed(123)

gghistostats(
  data       = ggplot2::msleep,
  x          = awake,
  title      = "Amount of time spent awake",
  test.value = 12,
  binwidth   = 1
)

Defaults return

✅ counts + proportion for bins
✅ descriptive statistics
✅ inferential statistics
✅ effect size + CIs
✅ Bayesian hypothesis-testing
✅ Bayesian estimation

There is also a grouped_ variant of this function that makes it easy to repeat the same operation across a single grouping variable:

set.seed(123)

grouped_gghistostats(
  data              = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
  x                 = budget,
  test.value        = 50,
  type              = "nonparametric",
  xlab              = "Movies budget (in million US$)",
  grouping.var      = genre,
  normal.curve      = TRUE,
  normal.curve.args = list(color = "red", size = 1),
  ggtheme           = ggthemes::theme_tufte(),
  ## modify the defaults from `{ggstatsplot}` for each plot
  plotgrid.args     = list(nrow = 1),
  annotation.args   = list(title = "Movies budgets for different genres")
)

For more, also read the following vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/gghistostats.html

`ggdotplotstats()`

This function is similar to gghistostats(), but is intended to be used when the numeric variable also has a label.

set.seed(123)

ggdotplotstats(
  data       = dplyr::filter(gapminder::gapminder, continent == "Asia"),
  y          = country,
  x          = lifeExp,
  test.value = 55,
  type       = "robust",
  title      = "Distribution of life expectancy in Asian continent",
  xlab       = "Life expectancy"
)

Defaults return

✅ descriptives (mean + sample size)
✅ inferential statistics
✅ effect size + CIs
✅ Bayesian hypothesis-testing
✅ Bayesian estimation

As with the rest of the functions in this package, there is also a grouped_ variant of this function to facilitate looping the same operation for all levels of a single grouping variable.

set.seed(123)

grouped_ggdotplotstats(
  data            = dplyr::filter(ggplot2::mpg, cyl %in% c("4", "6")),
  x               = cty,
  y               = manufacturer,
  type            = "bayes",
  xlab            = "city miles per gallon",
  ylab            = "car manufacturer",
  grouping.var    = cyl,
  test.value      = 15.5,
  point.args      = list(color = "red", size = 5, shape = 13),
  annotation.args = list(title = "Fuel economy data")
)

For more, also read the following vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggdotplotstats.html

`ggscatterstats()`

This function creates a scatterplot with marginal distributions overlaid on the axes and results from statistical tests in the subtitle:

ggscatterstats(
  data  = ggplot2::msleep,
  x     = sleep_rem,
  y     = awake,
  xlab  = "REM sleep (in hours)",
  ylab  = "Amount of time spent awake (in hours)",
  title = "Understanding mammalian sleep"
)

Defaults return

✅ raw data + distributions
✅ marginal distributions
✅ inferential statistics
✅ effect size + CIs
✅ Bayesian hypothesis-testing
✅ Bayesian estimation

There is also a grouped_ variant of this function that makes it easy to repeat the same operation across a single grouping variable.

set.seed(123)

grouped_ggscatterstats(
  data             = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
  x                = rating,
  y                = length,
  grouping.var     = genre,
  label.var        = title,
  label.expression = length > 200,
  xlab             = "IMDB rating",
  ggtheme          = ggplot2::theme_grey(),
  ggplot.component = list(ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))),
  plotgrid.args    = list(nrow = 1),
  annotation.args  = list(title = "Relationship between movie length and IMDB ratings")
)

For more, also read the following vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggscatterstats.html

`ggcorrmat`

ggcorrmat makes a correlalogram (a matrix of correlation coefficients) with minimal amount of code. Just sticking to the defaults itself produces publication-ready correlation matrices. But, for the sake of exploring the available options, let’s change some of the defaults. For example, multiple aesthetics-related arguments can be modified to change the appearance of the correlation matrix.

set.seed(123)

## as a default this function outputs a correlation matrix plot
ggcorrmat(
  data     = ggplot2::msleep,
  colors   = c("#B2182B", "white", "#4D4D4D"),
  title    = "Correlalogram for mammals sleep dataset",
  subtitle = "sleep units: hours; weight units: kilograms"
)

Defaults return

✅ effect size + significance
✅ careful handling of NAs

If there are NAs present in the selected variables, the legend will display minimum, median, and maximum number of pairs used for correlation tests.

There is also a grouped_ variant of this function that makes it easy to repeat the same operation across a single grouping variable:

set.seed(123)

grouped_ggcorrmat(
  data         = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
  type         = "robust",
  colors       = c("#cbac43", "white", "#550000"),
  grouping.var = genre,
  matrix.type  = "lower"
)

For more, also read the following vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcorrmat.html

`ggpiestats()`

This function creates a pie chart for categorical or nominal variables with results from contingency table analysis (Pearson’s chi-squared test for between-subjects design and McNemar’s chi-squared test for within-subjects design) included in the subtitle of the plot. If only one categorical variable is entered, results from one-sample proportion test (i.e., a chi-squared goodness of fit test) will be displayed as a subtitle.

To study an interaction between two categorical variables:

set.seed(123)

ggpiestats(
  data         = mtcars,
  x            = am,
  y            = cyl,
  package      = "wesanderson",
  palette      = "Royal1",
  title        = "Dataset: Motor Trend Car Road Tests",
  legend.title = "Transmission"
)

Defaults return

✅ descriptives (frequency + %s)
✅ inferential statistics
✅ effect size + CIs
✅ Goodness-of-fit tests
✅ Bayesian hypothesis-testing
✅ Bayesian estimation

There is also a grouped_ variant of this function that makes it easy to repeat the same operation across a single grouping variable. Following example is a case where the theoretical question is about proportions for different levels of a single nominal variable:

set.seed(123)

grouped_ggpiestats(
  data         = mtcars,
  x            = cyl,
  grouping.var = am,
  label.repel  = TRUE,
  package      = "ggsci",
  palette      = "default_ucscgb"
)

For more, also read the following vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggpiestats.html

`ggbarstats()`

In case you are not a fan of pie charts (for very good reasons), you can alternatively use ggbarstats() function which has a similar syntax.

N.B. The p-values from one-sample proportion test are displayed on top of each bar.

set.seed(123)
library(ggplot2)

ggbarstats(
  data             = movies_long,
  x                = mpaa,
  y                = genre,
  title            = "MPAA Ratings by Genre",
  xlab             = "movie genre",
  legend.title     = "MPAA rating",
  ggplot.component = list(ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge = 2))),
  palette          = "Set2"
)

Defaults return

✅ descriptives (frequency + %s)
✅ inferential statistics
✅ effect size + CIs
✅ Goodness-of-fit tests
✅ Bayesian hypothesis-testing
✅ Bayesian estimation

And, needless to say, there is also a grouped_ variant of this function-

## setup
set.seed(123)

grouped_ggbarstats(
  data         = mtcars,
  x            = am,
  y            = cyl,
  grouping.var = vs,
  package      = "wesanderson",
  palette      = "Darjeeling2" # ,
  # ggtheme      = ggthemes::theme_tufte(base_size = 12)
)

For more, also read the following vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggbarstats.html

`ggcoefstats()`

The function ggcoefstats() generates dot-and-whisker plots for regression models saved in a tidy data frame. The tidy data frames are prepared using parameters::model_parameters(). Additionally, if available, the model summary indices are also extracted from performance::model_performance().

Although the statistical models displayed in the plot may differ based on the class of models being investigated, there are few aspects of the plot that will be invariant across models:

The dot-whisker plot contains a dot representing the estimate and their confidence intervals (95% is the default). The estimate can either be effect sizes (for tests that depend on the F-statistic) or regression coefficients (for tests with t-, χ²-, and z-statistic), etc. The function will, by default, display a helpful x-axis label that should clear up what estimates are being displayed. The confidence intervals can sometimes be asymmetric if bootstrapping was used.
The label attached to dot will provide more details from the statistical test carried out and it will typically contain estimate, statistic, and p-value.e
The caption will contain diagnostic information, if available, about models that can be useful for model selection: The smaller the Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values, the “better” the model is.
The output of this function will be a ggplot2 object and, thus, it can be further modified (e.g. change themes) with ggplot2 functions.

set.seed(123)

## model
mod <- stats::lm(formula = mpg ~ am * cyl, data = mtcars)

ggcoefstats(mod)

Defaults return

✅ inferential statistics
✅ estimate + CIs
✅ model summary (AIC and BIC)

Supported models

Most of the regression models that are supported in the underlying packages are also supported by ggcoefstats().

insight::supported_models()
#>   [1] "aareg"                   "afex_aov"               
#>   [3] "AKP"                     "Anova.mlm"              
#>   [5] "anova.rms"               "aov"                    
#>   [7] "aovlist"                 "Arima"                  
#>   [9] "averaging"               "bamlss"                 
#>  [11] "bamlss.frame"            "bayesQR"                
#>  [13] "bayesx"                  "BBmm"                   
#>  [15] "BBreg"                   "bcplm"                  
#>  [17] "betamfx"                 "betaor"                 
#>  [19] "betareg"                 "BFBayesFactor"          
#>  [21] "bfsl"                    "BGGM"                   
#>  [23] "bife"                    "bifeAPEs"               
#>  [25] "bigglm"                  "biglm"                  
#>  [27] "blavaan"                 "blrm"                   
#>  [29] "bracl"                   "brglm"                  
#>  [31] "brmsfit"                 "brmultinom"             
#>  [33] "btergm"                  "censReg"                
#>  [35] "cgam"                    "cgamm"                  
#>  [37] "cglm"                    "clm"                    
#>  [39] "clm2"                    "clmm"                   
#>  [41] "clmm2"                   "clogit"                 
#>  [43] "coeftest"                "complmrob"              
#>  [45] "confusionMatrix"         "coxme"                  
#>  [47] "coxph"                   "coxph.penal"            
#>  [49] "coxr"                    "cpglm"                  
#>  [51] "cpglmm"                  "crch"                   
#>  [53] "crq"                     "crqs"                   
#>  [55] "crr"                     "dep.effect"             
#>  [57] "DirichletRegModel"       "draws"                  
#>  [59] "drc"                     "eglm"                   
#>  [61] "elm"                     "epi.2by2"               
#>  [63] "ergm"                    "feglm"                  
#>  [65] "feis"                    "felm"                   
#>  [67] "fitdistr"                "fixest"                 
#>  [69] "flexsurvreg"             "gam"                    
#>  [71] "Gam"                     "gamlss"                 
#>  [73] "gamm"                    "gamm4"                  
#>  [75] "garch"                   "gbm"                    
#>  [77] "gee"                     "geeglm"                 
#>  [79] "glht"                    "glimML"                 
#>  [81] "glm"                     "Glm"                    
#>  [83] "glmm"                    "glmmadmb"               
#>  [85] "glmmPQL"                 "glmmTMB"                
#>  [87] "glmrob"                  "glmRob"                 
#>  [89] "glmx"                    "gls"                    
#>  [91] "gmnl"                    "HLfit"                  
#>  [93] "htest"                   "hurdle"                 
#>  [95] "iv_robust"               "ivFixed"                
#>  [97] "ivprobit"                "ivreg"                  
#>  [99] "lavaan"                  "lm"                     
#> [101] "lm_robust"               "lme"                    
#> [103] "lmerMod"                 "lmerModLmerTest"        
#> [105] "lmodel2"                 "lmrob"                  
#> [107] "lmRob"                   "logistf"                
#> [109] "logitmfx"                "logitor"                
#> [111] "logitr"                  "LORgee"                 
#> [113] "lqm"                     "lqmm"                   
#> [115] "lrm"                     "manova"                 
#> [117] "MANOVA"                  "marginaleffects"        
#> [119] "marginaleffects.summary" "margins"                
#> [121] "maxLik"                  "mblogit"                
#> [123] "mclogit"                 "mcmc"                   
#> [125] "mcmc.list"               "MCMCglmm"               
#> [127] "mcp1"                    "mcp12"                  
#> [129] "mcp2"                    "med1way"                
#> [131] "mediate"                 "merMod"                 
#> [133] "merModList"              "meta_bma"               
#> [135] "meta_fixed"              "meta_random"            
#> [137] "metaplus"                "mhurdle"                
#> [139] "mipo"                    "mira"                   
#> [141] "mixed"                   "MixMod"                 
#> [143] "mixor"                   "mjoint"                 
#> [145] "mle"                     "mle2"                   
#> [147] "mlm"                     "mlogit"                 
#> [149] "mmclogit"                "mmlogit"                
#> [151] "model_fit"               "multinom"               
#> [153] "mvord"                   "negbinirr"              
#> [155] "negbinmfx"               "ols"                    
#> [157] "onesampb"                "orm"                    
#> [159] "pgmm"                    "plm"                    
#> [161] "PMCMR"                   "poissonirr"             
#> [163] "poissonmfx"              "polr"                   
#> [165] "probitmfx"               "psm"                    
#> [167] "Rchoice"                 "ridgelm"                
#> [169] "riskRegression"          "rjags"                  
#> [171] "rlm"                     "rlmerMod"               
#> [173] "RM"                      "rma"                    
#> [175] "rma.uni"                 "robmixglm"              
#> [177] "robtab"                  "rq"                     
#> [179] "rqs"                     "rqss"                   
#> [181] "rvar"                    "Sarlm"                  
#> [183] "scam"                    "selection"              
#> [185] "sem"                     "SemiParBIV"             
#> [187] "semLm"                   "semLme"                 
#> [189] "slm"                     "speedglm"               
#> [191] "speedlm"                 "stanfit"                
#> [193] "stanmvreg"               "stanreg"                
#> [195] "summary.lm"              "survfit"                
#> [197] "survreg"                 "svy_vglm"               
#> [199] "svychisq"                "svyglm"                 
#> [201] "svyolr"                  "t1way"                  
#> [203] "tobit"                   "trimcibt"               
#> [205] "truncreg"                "vgam"                   
#> [207] "vglm"                    "wbgee"                  
#> [209] "wblm"                    "wbm"                    
#> [211] "wmcpAKP"                 "yuen"                   
#> [213] "yuend"                   "zcpglm"                 
#> [215] "zeroinfl"                "zerotrunc"

Although not shown here, this function can also be used to carry out parametric, robust, and Bayesian random-effects meta-analysis.

For more, also read the following vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcoefstats.html

Extracting expressions and data frames with statistical details

ggstatsplot also offers a convenience function to extract data frames with statistical details that are used to create expressions displayed in ggstatsplot plots.

set.seed(123)

p <- ggbetweenstats(mtcars, cyl, mpg) 

# extracting expression present in the subtitle
extract_subtitle(p)
#> list(italic("F")["Welch"](2, 18.03) == "31.62", italic(p) == 
#>     "1.27e-06", widehat(omega["p"]^2) == "0.74", CI["95%"] ~ 
#>     "[" * "0.53", "1.00" * "]", italic("n")["obs"] == "32")

# extracting expression present in the caption
extract_caption(p)
#> list(log[e] * (BF["01"]) == "-14.92", widehat(italic(R^"2"))["Bayesian"]^"posterior" == 
#>     "0.71", CI["95%"]^HDI ~ "[" * "0.57", "0.79" * "]", italic("r")["Cauchy"]^"JZS" == 
#>     "0.71")

# a list of tibbles containing statistical analysis summaries
extract_stats(p)
#> $subtitle_data
#> # A tibble: 1 × 14
#>   statistic    df df.error    p.value
#>       <dbl> <dbl>    <dbl>      <dbl>
#> 1      31.6     2     18.0 0.00000127
#>   method                                                   effectsize estimate
#>   <chr>                                                    <chr>         <dbl>
#> 1 One-way analysis of means (not assuming equal variances) Omega2        0.744
#>   conf.level conf.low conf.high conf.method conf.distribution n.obs expression
#>        <dbl>    <dbl>     <dbl> <chr>       <chr>             <int> <list>    
#> 1       0.95    0.531         1 ncp         F                    32 <language>
#> 
#> $caption_data
#> # A tibble: 6 × 17
#>   term     pd prior.distribution prior.location prior.scale     bf10
#>   <chr> <dbl> <chr>                       <dbl>       <dbl>    <dbl>
#> 1 mu    1     cauchy                          0       0.707 3008850.
#> 2 cyl-4 1     cauchy                          0       0.707 3008850.
#> 3 cyl-6 0.780 cauchy                          0       0.707 3008850.
#> 4 cyl-8 1     cauchy                          0       0.707 3008850.
#> 5 sig2  1     cauchy                          0       0.707 3008850.
#> 6 g_cyl 1     cauchy                          0       0.707 3008850.
#>   method                          log_e_bf10 effectsize         estimate std.dev
#>   <chr>                                <dbl> <chr>                 <dbl>   <dbl>
#> 1 Bayes factors for linear models       14.9 Bayesian R-squared    0.714  0.0503
#> 2 Bayes factors for linear models       14.9 Bayesian R-squared    0.714  0.0503
#> 3 Bayes factors for linear models       14.9 Bayesian R-squared    0.714  0.0503
#> 4 Bayes factors for linear models       14.9 Bayesian R-squared    0.714  0.0503
#> 5 Bayes factors for linear models       14.9 Bayesian R-squared    0.714  0.0503
#> 6 Bayes factors for linear models       14.9 Bayesian R-squared    0.714  0.0503
#>   conf.level conf.low conf.high conf.method n.obs expression
#>        <dbl>    <dbl>     <dbl> <chr>       <int> <list>    
#> 1       0.95    0.574     0.788 HDI            32 <language>
#> 2       0.95    0.574     0.788 HDI            32 <language>
#> 3       0.95    0.574     0.788 HDI            32 <language>
#> 4       0.95    0.574     0.788 HDI            32 <language>
#> 5       0.95    0.574     0.788 HDI            32 <language>
#> 6       0.95    0.574     0.788 HDI            32 <language>
#> 
#> $pairwise_comparisons_data
#> # A tibble: 3 × 9
#>   group1 group2 statistic   p.value alternative distribution p.adjust.method
#>   <chr>  <chr>      <dbl>     <dbl> <chr>       <chr>        <chr>          
#> 1 4      6          -6.67 0.00110   two.sided   q            Holm           
#> 2 4      8         -10.7  0.0000140 two.sided   q            Holm           
#> 3 6      8          -7.48 0.000257  two.sided   q            Holm           
#>   test         expression
#>   <chr>        <list>    
#> 1 Games-Howell <language>
#> 2 Games-Howell <language>
#> 3 Games-Howell <language>
#> 
#> $descriptive_data
#> NULL
#> 
#> $one_sample_data
#> NULL
#> 
#> $tidy_data
#> NULL
#> 
#> $glance_data
#> NULL

Note that all of this analysis is carried out by statsExpressions package: https://indrajeetpatil.github.io/statsExpressions/

Using `{ggstatsplot}` statistical details with custom plots

Sometimes you may not like the default plots produced by ggstatsplot. In such cases, you can use other custom plots (from ggplot2 or other plotting packages) and still use ggstatsplot functions to display results from relevant statistical test.

For example, in the following chunk, we will create our own plot using ggplot2 package, and use ggstatsplot function for extracting expression:

## loading the needed libraries
set.seed(123)
library(ggplot2)

## using `{ggstatsplot}` to get expression with statistical results
stats_results <- ggbetweenstats(morley, Expt, Speed) %>% extract_subtitle()

## creating a custom plot of our choosing
ggplot(morley, aes(x = as.factor(Expt), y = Speed)) +
  geom_boxplot() +
  labs(
    title = "Michelson-Morley experiments",
    subtitle = stats_results,
    x = "Speed of light",
    y = "Experiment number"
  )

Summary of benefits of using `{ggstatsplot}`

No need to use scores of packages for statistical analysis (e.g., one to get stats, one to get effect sizes, another to get Bayes Factors, and yet another to get pairwise comparisons, etc.).
Minimal amount of code needed for all functions (typically only data, x, and y), which minimizes chances of error and makes for tidy scripts.
Conveniently toggle between statistical approaches.
Truly makes your figures worth a thousand words.
No need to copy-paste results to the text editor (MS-Word, e.g.).
Disembodied figures stand on their own and are easy to evaluate for the reader.
More breathing room for theoretical discussion and other text.
No need to worry about updating figures and statistical details separately.

Misconceptions about `{ggstatsplot}`

This package is…

❌ an alternative to learning ggplot2
✅ (The better you know ggplot2, the more you can modify the defaults to your liking.)

❌ meant to be used in talks/presentations
✅ (Default plots can be too complicated for effectively communicating results in time-constrained presentation settings, e.g. conference talks.)

❌ the only game in town
✅ (GUI software alternatives: JASP and jamovi).

Extensions

In case you use the GUI software jamovi, you can install a module called jjstatsplot, which is a wrapper around ggstatsplot.

Contributing

I’m happy to receive bug reports, suggestions, questions, and (most of all) contributions to fix problems and add features. I personally prefer using the GitHub issues system over trying to reach out to me in other ways (personal e-mail, Twitter, etc.). Pull Requests for contributions are encouraged.

Here are some simple ways in which you can contribute (in the increasing order of commitment):

Read and correct any inconsistencies in the documentation
Raise issues about bugs or wanted features
Review code
Add new functionality (in the form of new plotting functions or helpers for preparing subtitles)

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.

{ggstatsplot}: {ggplot2} Based Plots with Statistical Details

Raison d’être

Installation

Citation

Acknowledgments

Documentation and Examples

Summary of available plots

Summary of types of statistical analyses

Statistical reporting

Summary of statistical tests and effect sizes

Primary functions

ggbetweenstats()

ggwithinstats()

gghistostats()

ggdotplotstats()

ggscatterstats()

ggcorrmat

ggpiestats()

ggbarstats()

ggcoefstats()

Supported models

Extracting expressions and data frames with statistical details

Using {ggstatsplot} statistical details with custom plots

Summary of benefits of using {ggstatsplot}

Misconceptions about {ggstatsplot}

Extensions

Contributing

`{ggstatsplot}`: `{ggplot2}` Based Plots with Statistical Details

`ggbetweenstats()`

`ggwithinstats()`

`gghistostats()`

`ggdotplotstats()`

`ggscatterstats()`

`ggcorrmat`

`ggpiestats()`

`ggbarstats()`

`ggcoefstats()`

Using `{ggstatsplot}` statistical details with custom plots

Summary of benefits of using `{ggstatsplot}`

Misconceptions about `{ggstatsplot}`