Statistical Visualizations with {ggstatsplot}: A Biography

Indrajeet Patil

Genesis

Why a new software?

Life in the trenches (c. 2017, Harvard)

External Stimulus

Reporting errors:

“half of all published psychology papers contained at least one p-value that was inconsistent”¹

Interpretation errors:

“in 72% of cases, nonsignificant results were misinterpreted [to mean] that effect was absent”²

Replication crisis:

“39% of effects were subjectively rated to have replicated the original result”³

and more…

Internal Response

How to:

avoid reporting errors?
improve quality of statistical reporting?
emphasize the importance of the effect?
interpret null results?
easily assess validity of model assumptions?
increase replicability?

Proposal

Information-rich, ready-made statistical visualizations

(minimal effort and maximum transparency)

A visualization with statistical summary

💡 Visualizations reveal problems not discernible from model summaries!

Ready-made plots with one-line syntax

The grammar of graphics framework can prepare any visualization! But building plots from scratch can be time-consuming.

💡 Using ready-made plots lowers the effort needed for visualizing data!

Action Plan

{ggstatsplot} was born!

(open-sourced on GitHub in 2017; still actively developed)

Example function

E.g., for hypothesis about differences between groups

ggbetweenstats(iris, Species, Sepal.Length)

Important

Information-rich defaults

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

Statistical approaches available

parametric
non-parametric
robust
Bayesian

And there is more!

Promised Land

Does it deliver?

Show, don’t tell

Without {ggstatsplot}

Pearson’s correlation test revealed that, across 142 participants, variable x was negatively correlated with variable y: \(t(140)=-0.76, p=0.446\). The effect size \((r=-0.06, 95\% CI [-0.23,0.10])\) was small, as per Cohen’s (1988) conventions. The Bayes Factor for the same analysis revealed that the data were 5.81 times more probable under the null hypothesis as compared to the alternative hypothesis. This can be considered moderate evidence (Jeffreys, 1961) in favor of the null hypothesis (absence of any correlation between x and y).

With {ggstatsplot}

✅ No need to worry about reporting or interpretation errors!

Thoughtful Defaults

Data Visualization

Statistical Reporting

(Doorn et al., 2020; APA Manual)

✅ Follows best practices in data visualization and statistical reporting!

Impact

I can haz users?!

User Love

Total downloads > 500K (97 percentile)

Second most starred {ggplot2}-extension!

Total citations > 1000

From publications across a wide range of fields:
biology, medicine, psychology, economics, etc.

Improving Psychological Science Award (2020)

Pleasant Side Effects

Maybe the real treasure was the skills we acquired along the way!

Software Architecture

Breaking down the monolith: \(20K_{(2017)} \rightarrow 1K_{(2024)}\) lines of code

flowchart LR
    ggstatsplot[ggstatsplot]
    statsExpressions[statsExpressions]
    note["backend engine"]
    
    subgraph easystats[easystats]
        effectsize[effectsize]
        insight[insight]
        parameters[parameters]
        performance[performance]
        bayestestR[bayestestR]
    end
    
    %% Main dependencies
    ggstatsplot --> statsExpressions
    ggstatsplot --> dots[Other dependencies]
    
    %% Add note connecting to the main relationship
    note -.-> statsExpressions
    
    %% statsExpressions dependencies on easystats packages
    statsExpressions --> effectsize
    statsExpressions --> insight
    statsExpressions --> parameters
    statsExpressions --> performance
    statsExpressions --> bayestestR
    
    %% Styling using custom colors
    style easystats fill:#EED3B1
    
    classDef main fill:#FCF596,stroke:#333,stroke-width:3px
    classDef note fill:#ffffff,stroke:#333,stroke-width:1px,stroke-dasharray: 5 5
    
    class ggstatsplot main
    class note note

Collaborative Solutions

While re-architecting {ggstatsplot}, I started contributing upstream.

As part of {easystats} core team

leadership skills to steer the project
long-term vision for the project
API design
CI infrastructure
code review
documentation
scouting for new talent
developer advocacy
community engagement

Making it a habit

co-maintainer of {ggsignif}
contributor to {WRS2}, {ggcorrplot}

co-author of {lintr} (linter for R)
co-author of {styler} (code formatter)

Quality Assurance

“The only way to go fast, is to go well.”
- Robert C. Martin

CI Checks (GitHub Actions)

Unit tests (random-order)
Code coverage (100%)
Linting (0 lints)
Formatting (0 issues)
Documentation (website, no link rot, plenty examples)
Pre-commit hooks (0 issues)
Zero user-facing warnings
Portability (Linux, macOS, Windows)
Robustness (dependencies, language versions)
CRAN checks (0 notes, 0 warnings, 0 errors)

Healthy and active code base

Communication

Training material on best practices in software/package development to support community contributions keeping in mind the diverse backgrounds of contributors.

Biography (2017-)

(Or how developing {ggstatsplot} continues to help me grow as a software developer)

graph LR
    Project[ggstatsplot]

    %% Technical Skills Branch
    Project --> TechSkills[Technical Skills]
    TechSkills --> CodeQuality[Code Quality]
    TechSkills --> ArchDesign[Architecture Design]
    TechSkills --> TechDebt[Technical Debt]

    %% Soft Skills Branch
    Project --> SoftSkills[Soft Skills]
    SoftSkills --> Collab[Collaboration]
    SoftSkills --> Leadership[Leadership]
    SoftSkills --> Communication[Communication]

    %% Styling using colorblind-friendly palette
    classDef mainNode fill:#FCF596,stroke:#000000,stroke-width:3px
    classDef broardSkillNode fill:#D0E8C5,stroke:#333,stroke-width:1px
    classDef skillNode fill:#ffffff,stroke:#333,stroke-width:1px,stroke-dasharray: 5 5

    class Project mainNode
    class TechSkills,SoftSkills broardSkillNode
    class CodeQuality,ArchDesign,TechDebt,Collab,Leadership,Communication skillNode

Conclusion

{ggstatsplot} offers an intuitive interface for creating detailed statistical visualizations, enabling users to adopt rigorous, reliable, and robust workflows for data exploration and reporting across various academic and industrial disciplines. It is a well-maintained tool with high-quality infrastructure and widespread adoption.

Thank You 😊

Source code for these slides can be found on GitHub.

For more

If you are interested in good programming and software development practices, check out my other slide decks.

Find me at…

Session information

sessioninfo::session_info(include_base = TRUE)

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.2 (2024-10-31)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 hostname fv-az1345-193
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2024-12-15
 pandoc   3.6 @ /opt/hostedtoolcache/pandoc/3.6/x64/ (via rmarkdown)
 quarto   1.7.3 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package          * version     date (UTC) lib source
 base             * 4.4.2       2024-10-31 [3] local
 BayesFactor        0.9.12-4.7  2024-01-24 [1] RSPM
 bayestestR         0.15.0      2024-10-17 [1] RSPM
 bitops             1.0-9       2024-10-03 [1] RSPM
 BWStest            0.2.3       2023-10-10 [1] RSPM
 cachem             1.1.0       2024-05-16 [1] RSPM
 cli                3.6.3       2024-06-21 [1] RSPM
 coda               0.19-4.1    2024-01-31 [1] RSPM
 colorspace         2.1-1       2024-07-26 [1] RSPM
 compiler           4.4.2       2024-10-31 [3] local
 correlation        0.8.6       2024-10-26 [1] RSPM
 cranlogs           2.1.1       2019-04-29 [1] RSPM
 curl               6.0.1       2024-11-14 [1] RSPM
 data.table         1.16.4      2024-12-06 [1] RSPM
 datasets         * 4.4.2       2024-10-31 [3] local
 datawizard         0.13.0      2024-10-05 [1] RSPM
 digest             0.6.37      2024-08-19 [1] RSPM
 dplyr              1.1.4       2023-11-17 [1] RSPM
 effectsize         1.0.0       2024-12-10 [1] RSPM
 evaluate           1.0.1       2024-10-10 [1] RSPM
 fansi              1.0.6       2023-12-08 [1] RSPM
 farver             2.1.2       2024-05-13 [1] RSPM
 fastmap            1.2.0       2024-05-15 [1] RSPM
 generics           0.1.3       2022-07-05 [1] RSPM
 ggplot2          * 3.5.1       2024-04-23 [1] RSPM
 ggrepel            0.9.6       2024-09-07 [1] RSPM
 ggsignif           0.6.4       2022-10-13 [1] RSPM
 ggstatsplot      * 0.13.0.9000 2024-12-15 [1] Github (IndrajeetPatil/ggstatsplot@1c57eac)
 glue               1.8.0       2024-09-30 [1] RSPM
 gmp                0.7-5       2024-08-23 [1] RSPM
 graphics         * 4.4.2       2024-10-31 [3] local
 grDevices        * 4.4.2       2024-10-31 [3] local
 grid               4.4.2       2024-10-31 [3] local
 gtable             0.3.6       2024-10-25 [1] RSPM
 htmltools          0.5.8.1     2024-04-04 [1] RSPM
 httr               1.4.7       2023-08-15 [1] RSPM
 insight            1.0.0       2024-11-26 [1] RSPM
 jsonlite           1.8.9       2024-09-20 [1] RSPM
 knitr              1.49        2024-11-08 [1] RSPM
 kSamples           1.2-10      2023-10-07 [1] RSPM
 labeling           0.4.3       2023-08-29 [1] RSPM
 lattice            0.22-6      2024-03-20 [3] CRAN (R 4.4.2)
 lifecycle          1.0.4       2023-11-07 [1] RSPM
 lubridate          1.9.4       2024-12-08 [1] RSPM
 magrittr           2.0.3       2022-03-30 [1] RSPM
 MASS               7.3-61      2024-06-13 [3] CRAN (R 4.4.2)
 Matrix             1.7-1       2024-10-18 [3] CRAN (R 4.4.2)
 MatrixModels       0.5-3       2023-11-06 [1] RSPM
 memoise            2.0.1       2021-11-26 [1] RSPM
 methods          * 4.4.2       2024-10-31 [3] local
 mgcv               1.9-1       2023-12-21 [3] CRAN (R 4.4.2)
 multcompView       0.1-10      2024-03-08 [1] RSPM
 munsell            0.5.1       2024-04-01 [1] RSPM
 mvtnorm            1.3-2       2024-11-04 [1] RSPM
 nlme               3.1-166     2024-08-14 [3] CRAN (R 4.4.2)
 packageRank      * 0.9.4       2024-11-13 [1] RSPM
 paletteer          1.6.0       2024-01-21 [1] RSPM
 parallel           4.4.2       2024-10-31 [3] local
 parameters         0.24.0      2024-11-27 [1] RSPM
 patchwork          1.3.0       2024-09-16 [1] RSPM
 pbapply            1.7-2       2023-06-27 [1] RSPM
 performance        0.12.4      2024-10-18 [1] RSPM
 pillar             1.9.0       2023-03-22 [1] RSPM
 pkgconfig          2.0.3       2019-09-22 [1] RSPM
 pkgsearch          3.1.3       2023-12-10 [1] RSPM
 PMCMRplus          1.9.12      2024-09-08 [1] RSPM
 prismatic          1.1.2       2024-04-10 [1] RSPM
 purrr              1.0.2       2023-08-10 [1] RSPM
 R.methodsS3        1.8.2       2022-06-13 [1] RSPM
 R.oo               1.27.0      2024-11-01 [1] RSPM
 R.utils            2.12.3      2023-11-18 [1] RSPM
 R6                 2.5.1       2021-08-19 [1] RSPM
 Rcpp               1.0.13-1    2024-11-02 [1] RSPM
 RcppParallel       5.1.9       2024-08-19 [1] RSPM
 RCurl              1.98-1.16   2024-07-11 [1] RSPM
 rematch2           2.1.2       2020-05-01 [1] RSPM
 rlang              1.1.4       2024-06-04 [1] RSPM
 rmarkdown          2.29        2024-11-04 [1] RSPM
 Rmpfr              1.0-0       2024-11-18 [1] RSPM
 rstantools         2.4.0       2024-01-31 [1] RSPM
 scales             1.3.0       2023-11-28 [1] RSPM
 sessioninfo        1.2.2.9000  2024-11-10 [1] Github (r-lib/sessioninfo@37c81af)
 splines            4.4.2       2024-10-31 [3] local
 stats            * 4.4.2       2024-10-31 [3] local
 statsExpressions   1.6.2.9000  2024-12-15 [1] Github (IndrajeetPatil/statsExpressions@04d47fe)
 stringi            1.8.4       2024-05-06 [1] RSPM
 stringr            1.5.1       2023-11-14 [1] RSPM
 sugrrants          0.2.9       2024-03-12 [1] RSPM
 SuppDists          1.1-9.8     2024-09-03 [1] RSPM
 tibble             3.2.1       2023-03-20 [1] RSPM
 tidyr              1.3.1       2024-01-24 [1] RSPM
 tidyselect         1.2.1       2024-03-11 [1] RSPM
 timechange         0.3.0       2024-01-18 [1] RSPM
 tools              4.4.2       2024-10-31 [3] local
 utf8               1.2.4       2023-10-22 [1] RSPM
 utils            * 4.4.2       2024-10-31 [3] local
 vctrs              0.6.5       2023-12-01 [1] RSPM
 withr              3.0.2       2024-10-28 [1] RSPM
 xfun               0.49        2024-10-31 [1] RSPM
 yaml               2.3.10      2024-07-26 [1] RSPM
 zeallot            0.1.0       2018-01-28 [1] RSPM

 [1] /home/runner/work/_temp/Library
 [2] /opt/R/4.4.2/lib/R/site-library
 [3] /opt/R/4.4.2/lib/R/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

Appendix

Examples of other functions

`ggwithinstats()`

Hypothesis about group differences: repeated measures design

ggwithinstats(
  data = WRS2::WineTasting,
  x = Wine,
  y = Taste
)

Important

✏️ Defaults

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

Statistical approaches available

parametric
parametric
robust
Bayesian

`gghistostats()`

Distribution of a numeric variable

gghistostats(
  data = movies_long,
  x = budget,
  test.value = 30 
)

Important

✏️ Defaults

counts + proportion for bins
descriptive statistics
inferential statistics
effect size + uncertainty
pairwise comparisons
Bayesian hypothesis-testing
Bayesian estimation

Statistical approaches available

parametric
parametric
robust
Bayesian

`ggdotplotstats()`

Labeled numeric variable

ggdotplotstats(
  data = movies_long,
  x = budget,
  y = genre,
  test.value = 30 
)

Important

✏️ Defaults

descriptive statistics
inferential statistics
effect size + uncertainty
pairwise comparisons
Bayesian hypothesis-testing
Bayesian estimation

Statistical approaches available

parametric
parametric
robust
Bayesian

`ggscatterstats()`

Hypothesis about correlation: Two numeric variables

ggscatterstats(
  data = movies_long,
  x = budget,
  y = rating
)

Important

✏️ Defaults

joint distribution
marginal distribution
effect size + uncertainty
pairwise comparisons
Bayesian hypothesis-testing
Bayesian estimation

Statistical approaches available

parametric
parametric
robust
Bayesian

`ggcorrmat()`

Hypothesis about correlation: Multiple numeric variables

ggcorrmat(dplyr::starwars)

Important

✏️ Defaults

inferential statistics
effect size + uncertainty
careful handling of NAs
partial correlations

Statistical approaches available

parametric
parametric
robust
Bayesian

`ggpiestats()`

Hypothesis about composition of categorical variables

ggpiestats(
  data = mtcars,
  x = am,
  y = cyl
)

Important

✏️ Defaults

descriptive statistics
inferential statistics
effect size + uncertainty
goodness-of-fit tests
Bayesian hypothesis-testing
Bayesian estimation

`ggbarstats()`

Hypothesis about composition of categorical variables

ggbarstats(
  data = mtcars,
  x = am,
  y = cyl
)

Important

✏️ Defaults

descriptive statistics
inferential statistics
effect size + uncertainty
goodness-of-fit tests
Bayesian hypothesis-testing
Bayesian estimation

`ggcoefstats()`

Hypothesis about regression coefficients

mod <- lm(
  formula = rating ~ mpaa,
  data = movies_long
)

ggcoefstats(mod)

Important

✏️ Defaults

estimate + uncertainty
inferential statistics (\(t\), \(z\), \(F\), \(\chi^2\))
model fit indices (AIC + BIC)

Supports all regression models supported in {easystats} ecosystem.

Meta-analysis is also supported!

grouped_ variants

Iterating over a grouping variable

grouped_ functions

grouped_ggpiestats(
  data = mtcars,
  x = cyl,
  grouping.var = am 
)

Available grouped_ variants:

grouped_ggbetweenstats()
grouped_ggwithinstats()
grouped_gghistostats()
grouped_ggdotplotstats()
grouped_ggscatterstats()
grouped_ggcorrmat()
grouped_ggpiestats()
grouped_ggbarstats()

Customizability

“What if I don’t like the default plots?” 🤔

Modify the look 🎨

By changing theme and palette

ggbetweenstats(
  data = movies_long,
  x = mpaa,
  y = rating,
  ggtheme = ggthemes::theme_economist(), 
  palette = "Darjeeling2", 
  package = "wesanderson" 
)

By using {ggplot2} functions

ggbetweenstats(
  data = mtcars,
  x = am,
  y = wt,
  type = "bayes"
) +
  scale_y_continuous(sec.axis = dup_axis())

Too much information 🙈

Get only plots:

ggbetweenstats(
  data = iris,
  x = Species,
  y = Sepal.Length,
  # turn off statistical analysis
  centrality.plotting = FALSE, 
  results.subtitle = FALSE, 
  bf.message = FALSE, 
  # turn off pairwise comparisons
  pairwise.display = "none" 
)

Get only expressions:

stats_expr <- ggpiestats(
  Titanic_full, Survived, Sex,
) %>% 
  extract_subtitle()

ggiraphExtra::ggSpine( 
  data = Titanic_full,
  aes(x = Sex, fill = Survived)
) +
  labs(subtitle = stats_expr)

`{ggstatsplot}`: Details about statistical reporting

Supports different statistical approaches

Note

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

Toggling statistical approaches 🔀

Parametric

# anova
ggbetweenstats(
  data = mtcars,
  x = cyl,
  y = wt,
  type = "p" 
)

# correlation analysis
ggscatterstats(
  data = mtcars,
  x = wt,
  y = mpg,
  type = "p" 
)

# t-test
gghistostats(
  data = mtcars,
  x = wt,
  test.value = 2,
  type = "p" 
)

Non-parametric

# anova
ggbetweenstats(
  data = mtcars,
  x = cyl,
  y = wt,
  type = "np" 
)

# correlation analysis
ggscatterstats(
  data = mtcars,
  x = wt,
  y = mpg,
  type = "np" 
)

# t-test
gghistostats(
  data = mtcars,
  x = wt,
  test.value = 2,
  type = "np" 
)

Alternative: Pure Pain

Hunting for packages

📦 for inferential statistics ({stats})
📦 computing effect size + CIs ({effectsize})
📦 for descriptive statistics ({skimr})
📦 pairwise comparisons ({multcomp})
📦 Bayesian hypothesis testing ({BayesFactor})
📦 Bayesian estimation ({bayestestR})
📦 …

Inconsistent APIs

🤔 accepts data frame, vector, matrix?
🤔 long/wide format data?
🤔 works with NAs?
🤔 returns data frame, vector, matrix?
🤔 works with tibbles?
🤔 has all necessary details?
🤔 …

Benefits in details

{ggstatsplot} combines data visualization and statistical analysis in a single step.

It…

provides ready-made plots with information-rich defaults

minimizes the chances of making errors in statistical reporting

follows best practices in data visualization and statistical reporting

helps evaluate statistical analysis in the context of the underlying data

highlights the importance of the effect by providing effect size measures

provides an easy way to evaluate absence of an effect using Bayesian framework

extremely beginner-friendly

Simplified data analysis workflow

✅ Quick insight into data by combining visualization and modeling!

Community Involvement

11 contributors
3 reverse dependencies
Widely covered in YouTube videos and social media posts
Almost 100% resolution rate on StackOverflow (> 150 questions)
Over 100 daily visitors on GitHub repo
Usage in a wide range of fields: psychology, biology, medicine, economics, etc.
Usage in data science training programs

A grain of salt

The “Golem of Prague” problem

❌ Promotes mindless application of statistical tests.

Statistical Visualizations with {ggstatsplot}: A Biography

Genesis

Life in the trenches (c. 2017, Harvard)

Proposal

A visualization with statistical summary

Ready-made plots with one-line syntax

Action Plan

Example function

And there is more!

Promised Land

Show, don’t tell

Thoughtful Defaults

Impact

User Love

Pleasant Side Effects

Software Architecture

Collaborative Solutions

Making it a habit

Quality Assurance

Communication

Biography (2017-)

Conclusion

Thank You 😊

For more

Find me at…

Session information

Appendix

Examples of other functions

ggwithinstats()

gghistostats()

ggdotplotstats()

ggscatterstats()

ggcorrmat()

ggpiestats()

ggbarstats()

ggcoefstats()

grouped_ variants

grouped_ functions

Customizability

Modify the look 🎨

Too much information 🙈

{ggstatsplot}: Details about statistical reporting

Supports different statistical approaches

Toggling statistical approaches 🔀

Alternative: Pure Pain

Benefits in details

Simplified data analysis workflow

Community Involvement

A grain of salt

The “Golem of Prague” problem

No stable release yet.

Footnotes

`ggwithinstats()`

`gghistostats()`

`ggdotplotstats()`

`ggscatterstats()`

`ggcorrmat()`

`ggpiestats()`

`ggbarstats()`

`ggcoefstats()`

`{ggstatsplot}`: Details about statistical reporting