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---
#title: "Statistical Visualizations with {ggstatsplot}: A Biography"
author: "Indrajeet Patil"
#affiliation:
format:
revealjs:
theme: simple
slide-number: c
embed-resources: true
preview-links: auto
code-link: true
#footer: "Source code for these slides can be found [on GitHub](https://github.com/IndrajeetPatil/intro-to-ggstatsplot){target='_blank'}."
fig-dpi: 300
fig-align: center
reference-location: document
css: styles.css
lang: "en-GB"
execute:
echo: true
eval: true
warning: false
message: false
cache: false
keywords: ["ggstatsplot", "data visualization", "statistical reporting", "software development", "R package"]
license: "CC0 1.0 Universal"
pagetitle: "Statistical Visualizations with {ggstatsplot}: A Biography"
description-meta: "A presentation on the development of {ggstatsplot} R package for statistical visualizations."
author-meta: "Indrajeet Patil"
date-meta: "`r Sys.time()`"
---
```{r}
#| include: false
#| echo: false
#| label: "setup"
library(ggstatsplot)
library(ggplot2)
set.seed(123)
```
## Statistical Visualizations with {ggstatsplot}: A Biography {style="margin-top: 1em;"}
<!-- Need to install extension from https://github.com/quarto-ext/fontawesome -->
<!-- Run `quarto install extension quarto-ext/fontawesome` in terminal -->
:::: {.columns}
::: {.column width='40%'}
::: {style="margin-top: 0.5em; margin-bottom: 0.5em; font-size: 1em"}
Indrajeet Patil<br>
:::
:::
::: {.column width='60%'}
{.relative fig-align="center" width="350" height="350"}
:::
::::
:::aside
Or How I Learned to Stop Worrying about Data Visualization and Statistical Reporting
:::
# Genesis
Why a new software?
## Life in the trenches (c. 2017, Harvard) {.smaller}
:::: {.columns}
::: {.column width='50%'}
:::{.callout-caution collapse=false appearance='default' icon=true}
## External Stimulus
- **Reporting errors**:
> "half of all published psychology papers contained at least one *p*-value that was inconsistent"^[[(Nuijten et al., *Behavior Research Methods*, 2016)](https://link.springer.com/article/10.3758/s13428-015-0664-2)]
- **Interpretation errors**:
> "in 72% of cases, nonsignificant results were misinterpreted [to mean] that effect was absent"^[[(Aczel et al., *AMPPS*, 2018)](https://journals.sagepub.com/doi/pdf/10.1177/2515245918773742)]
- **Replication crisis**:
> "39% of effects were subjectively rated to have replicated the original result"^[[Open Science Collaboration, *Science*, 2015](https://www.science.org/doi/10.1126/science.aac4716)]
and more...
:::
:::
::: {.column width='50%'}
:::{.callout-warning collapse=false appearance='default' icon=true}
## Internal Response
{.relative fig-align="center"}
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
:::{style="font-size: 20px;"}
(minimal effort and maximum transparency)
:::
## A visualization with statistical summary {.smaller}
:::{style="background-color: #FDE7BB; padding: 20px; border-radius: 25px; text-align: center;"}
💡 **Visualizations** reveal problems not discernible from **model summaries**!
:::
:::footer
[(Matejka & Fitzmaurice, *Autodesk Research*,2017)](https://www.autodeskresearch.com/publications/samestats)
:::
## Ready-made plots with one-line syntax {.smaller}
::::: columns
::: {.column width="40%"}
<br>
:::{style="background-color: #FFDCA9; padding: 20px; border-radius: 25px; text-align: center;"}
The **grammar of graphics** framework can prepare **any** visualization! But building plots from scratch can be time-consuming.
:::
:::
::: {.column width="60%"}
{.relative fig-align="center" width="500" height="300"}
:::
:::::
::::: columns
::: {.column width="40%"}
{.relative fig-align="center" width="350" height="350}
:::
::: {.column width="60%"}
<br>
:::{style="background-color: #FDE7BB; padding: 20px; border-radius: 25px; text-align: center;"}
💡 Using **ready-made plots** lowers the effort needed for visualizing data!
:::
:::
:::::
# Action Plan
`{ggstatsplot}` was born!
:::{style="font-size: 20px;"}
(open-sourced on [GitHub](https://github.com/IndrajeetPatil/ggstatsplot){target='_blank'} in 2017; still actively developed)
:::
## Example function {.smaller}
E.g., for hypothesis about differences *between* groups
:::: {.columns}
::: {.column width='65%'}
```{.r}
ggbetweenstats(iris, Species, Sepal.Length)
```
```{r}
#| label: "ggbetweenstats_1"
#| echo: false
#| fig-width: 6
#| fig-height: 5
ggbetweenstats(iris, Species, Sepal.Length)
```
:::
::: {.column width='35%'}
:::{.callout-important collapse=false appearance='default' icon=true}
##
**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! {.smaller}
:::footer
[Appendix](https://indrajeetpatil.github.io/intro-to-ggstatsplot/#/examples-of-other-functions) provides more details.
:::
# Promised Land
Does it deliver?
## Show, don't tell {.smaller}
:::: {.columns}
::: {.column width='50%'}
**Without `{ggstatsplot}`**
:::{style="font-size: 23px; text-align: justify; text-justify: inter-word;"}
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 `r round(exp(1.76), 2)` 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`).
:::
:::
::: {.column width='50%'}
**With `{ggstatsplot}`**
{.relative fig-align="center"}
:::
::::
:::{style="background-color: #FDE7BB; padding: 15px; border-radius: 25px; text-align: center;"}
✅ No need to worry about reporting or interpretation errors!
:::
## Thoughtful Defaults {.smaller}
:::: {.columns}
::: {.column width='40%'}
:::{.callout-note collapse=false appearance='default' icon=true}
## Data Visualization
{.relative fig-align="center" height="400"}
:::
:::
::: {.column width='60%'}
:::{.callout-note collapse=false appearance='default' icon=true}
## Statistical Reporting
{.relative fig-align="center"}
:::{style="font-size: 15px; text-align: center;"}
([Doorn et al., 2020]([https://link.springer.com/article/10.3758/s13423-020-01798-5]){target="_blank"};
[APA Manual](https://apastyle.apa.org/instructional-aids/numbers-statistics-guide.pdf){target="_blank"})
:::
:::
:::
::::
:::{style="background-color: #FDE7BB; padding: 15px; border-radius: 25px; text-align: center;"}
✅ Follows best practices in data visualization and statistical reporting!
:::
# Impact
*I can haz users?!*
## User Love {.smaller}
:::: {.columns}
::: {.column width='50%'}
:::{.callout-tip collapse=false appearance='default' icon=true}
## Total downloads > **500K** (97 percentile)
```{r}
#| label: "cran_downloads"
#| fig-align: center
#| echo: false
#| include: false
library(packageRank)
p_downloads <- plot(
cranDownloads("ggstatsplot", from = "2018-04-03", to = Sys.Date()),
graphics = "ggplot2", smooth = TRUE
)
```
```{r}
#| label: "cran_downloads_print"
#| fig-align: center
#| echo: false
p_downloads +
ggplot2::labs(subtitle = NULL) +
ggplot2::theme_minimal(base_size = 24)
```
:::{style="background-color: #FFDCA9; padding: 10px; border-radius: 25px; text-align: center; font-size:20px;"}
Second most starred `{ggplot2}`-extension!
:::
{.relative fig-align="center"}
:::
:::
::: {.column width='50%'}
:::{.callout-tip collapse=false appearance='default' icon=true}
## Total citations > **1000**
From publications across a wide range of fields: <br>
biology, medicine, psychology, economics, etc.
{.relative fig-align="center"}
:::{style="background-color: #FFDCA9; padding: 10px; border-radius: 25px; text-align: center; font-size:20px;"}
**[Improving Psychological Science Award](https://improvingpsych.org/mission/awards/){target='_blank'}** (2020)
:::
<br>
:::
:::
::::
# Pleasant Side Effects
*Maybe the real treasure was the skills we acquired along the way!*
## Software Architecture {.smaller}
Breaking down the monolith: $20K_{(2017)} \rightarrow 1K_{(2024)}$ lines of code
```{mermaid}
%%| echo: false
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 {.smaller}
While re-architecting `{ggstatsplot}`, I started contributing upstream.
:::: {.columns}
::: {.column width='50%'}
:::{.callout-note collapse=false appearance='default' icon=true}
## 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
:::
:::
::: {.column width='50%'}
{.relative fig-align="center"}
:::
::::
#### Making it a habit
:::: {.columns}
::: {.column width='50%'}
- co-maintainer of `{ggsignif}`
- contributor to `{WRS2}`, `{ggcorrplot}`
:::
::: {.column width='50%'}
- co-author of `{lintr}` (linter for R)
- co-author of `{styler}` (code formatter)
:::
::::
## Quality Assurance {.smaller}
> "_The only way to go fast, is to go well._"<br>
> \- Robert C. Martin
:::: {.columns}
::: {.column width='50%'}
:::{.callout-note collapse=false appearance='default' icon=true}
## 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)
:::
:::
::: {.column width='50%'}
:::{.callout-important collapse=false appearance='default' icon=true}
## Healthy and active code base
{.relative fig-align="center"}
{.relative fig-align="center"}
:::
:::
::::
## Communication {.smaller}
[Training material](https://sites.google.com/site/indrajeetspatilmorality/presentations){target='_blank'} on best practices in software/package development to support community contributions keeping in mind the diverse backgrounds of contributors.
{.relative fig-align="center"}
## Biography (2017-) {.smaller}
:::{style="font-size: 20px;"}
(Or how developing `{ggstatsplot}` continues to help me grow as a software developer)
:::
```{mermaid}
%%| label: "biography"
%%| echo: false
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 <span style="background-color:#D0E8C5;">intuitive interface</span> for creating detailed statistical visualizations, enabling users to adopt <span style="background-color:#FFE31A;">rigorous, reliable, and robust workflows for data exploration and reporting</span> across various academic and industrial disciplines. It is a well-maintained tool with <span style="background-color:#F0C1E1;">high-quality infrastructure</span> and <span style="background-color:#D4F6FF;">widespread adoption</span>.
# Thank You 😊
:::{style="font-size: 20px;"}
Source code for these slides can be found [on GitHub](https://github.com/IndrajeetPatil/intro-to-ggstatsplot){target='_blank'}.
:::
# For more
If you are interested in good programming and software development practices, check out my other [slide
decks](https://sites.google.com/site/indrajeetspatilmorality/presentations){target="_blank"}.
# Find me at...
{{< fa brands twitter >}} [Twitter](http://twitter.com/patilindrajeets){target="_blank"}
{{< fa brands linkedin >}} [LikedIn](https://www.linkedin.com/in/indrajeet-patil-397865174/){target="_blank"}
{{< fa brands github >}} [GitHub](http://github.com/IndrajeetPatil){target="_blank"}
{{< fa solid link >}} [Website](https://sites.google.com/site/indrajeetspatilmorality/){target="_blank"}
{{< fa solid envelope >}} [E-mail](mailto:[email protected]){target="_blank"}
## Session information {.smaller}
```{r}
sessioninfo::session_info(include_base = TRUE)
```
# Appendix
# Examples of other functions
## `ggwithinstats()` {.smaller}
Hypothesis about group differences: repeated measures design
:::: {.columns}
::: {.column width='65%'}
```{r}
#| label: "ggwithinstats_1"
ggwithinstats(
data = WRS2::WineTasting,
x = Wine,
y = Taste
)
```
:::
::: {.column width='35%'}
:::{.callout-important collapse=false appearance='default' icon=true}
##
✏️ **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()` {.smaller}
Distribution of a numeric variable
:::: {.columns}
::: {.column width='65%'}
```{r}
#| label: "gghistostats_1"
gghistostats(
data = movies_long,
x = budget,
test.value = 30
)
```
:::
::: {.column width='35%'}
:::{.callout-important collapse=false appearance='default' icon=true}
##
✏️ **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()` {.smaller}
Labeled numeric variable
:::: {.columns}
::: {.column width='65%'}
```{r}
#| label: "ggdotplotstats_1"
ggdotplotstats(
data = movies_long,
x = budget,
y = genre,
test.value = 30
)
```
:::
::: {.column width='35%'}
:::{.callout-important collapse=false appearance='default' icon=true}
##
✏️ **Defaults**
- descriptive statistics
- inferential statistics
- effect size + uncertainty
- pairwise comparisons
- Bayesian hypothesis-testing
- Bayesian estimation
**Statistical approaches available**
- parametric
- parametric
- robust
- Bayesian
:::
:::
::::
## `ggscatterstats()` {.smaller}
Hypothesis about correlation: Two numeric variables
:::: {.columns}
::: {.column width='65%'}
```{r}
#| label: "ggscatterstats_1"
ggscatterstats(
data = movies_long,
x = budget,
y = rating
)
```
:::
::: {.column width='35%'}
:::{.callout-important collapse=false appearance='default' icon=true}
##
✏️ **Defaults**
- joint distribution
- marginal distribution
- effect size + uncertainty
- pairwise comparisons
- Bayesian hypothesis-testing
- Bayesian estimation
**Statistical approaches available**
- parametric
- parametric
- robust
- Bayesian
:::
:::
::::
## `ggcorrmat()` {.smaller}
Hypothesis about correlation: Multiple numeric variables
:::: {.columns}
::: {.column width='65%'}
```{r}
#| label: "ggcorrmat_1"
ggcorrmat(dplyr::starwars)
```
:::
::: {.column width='35%'}
:::{.callout-important collapse=false appearance='default' icon=true}
##
✏️ **Defaults**
- inferential statistics
- effect size + uncertainty
- careful handling of `NA`s
- partial correlations
**Statistical approaches available**
- parametric
- parametric
- robust
- Bayesian
:::
:::
::::
## `ggpiestats()` {.smaller}
Hypothesis about composition of categorical variables
:::: {.columns}
::: {.column width='65%'}
```{r}
#| label: "ggpiestats_1"
ggpiestats(
data = mtcars,
x = am,
y = cyl
)
```
:::
::: {.column width='35%'}
:::{.callout-important collapse=false appearance='default' icon=true}
##
✏️ **Defaults**
- descriptive statistics
- inferential statistics
- effect size + uncertainty
- goodness-of-fit tests
- Bayesian hypothesis-testing
- Bayesian estimation
:::
:::
::::
## `ggbarstats()` {.smaller}
Hypothesis about composition of categorical variables
:::: {.columns}
::: {.column width='65%'}
```{r}
#| label: "ggbarstats_1"
ggbarstats(
data = mtcars,
x = am,
y = cyl
)
```
:::
::: {.column width='35%'}
:::{.callout-important collapse=false appearance='default' icon=true}
##
✏️ **Defaults**
- descriptive statistics
- inferential statistics
- effect size + uncertainty
- goodness-of-fit tests
- Bayesian hypothesis-testing
- Bayesian estimation
:::
:::
::::
## `ggcoefstats()` {.smaller}
Hypothesis about regression coefficients
:::: {.columns}
::: {.column width='65%'}
```{r}
#| label: "ggcoefstats_1"
mod <- lm(
formula = rating ~ mpaa,
data = movies_long
)
ggcoefstats(mod)
```
:::
::: {.column width='35%'}
:::{.callout-important collapse=false appearance='default' icon=true}
##
✏️ **Defaults**
- estimate + uncertainty
- inferential statistics ($t$, $z$, $F$, $\chi^2$)
- model fit indices (AIC + BIC)
Supports all regression models supported in [`{easystats}`](https://easystats.github.io/insight/reference/is_model_supported.html) ecosystem.
Meta-analysis is also supported!
:::
:::
::::
# *grouped\_* variants
Iterating over a grouping variable
## *grouped\_* functions {.smaller}
:::: {.columns}
::: {.column width='60%'}
```{r grouped_1}
grouped_ggpiestats(
data = mtcars,
x = cyl,
grouping.var = am
)
```
:::
::: {.column width='40%'}
:::{.callout-tip collapse=false appearance='default' icon=true}
## 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?" 🤔