ggstatsplot: ggplot2 Based Plots with Statistical Details

# <code>ggstatsplot</code>: <code>ggplot2</code> Based Plots with Statistical Details
## An Introductory Tutorial
### Indrajeet Patil (Center for Humans and Machines, Max Planck Institute, Berlin)
### 2021-03-01

---

---

# Plan

---

- Why `ggstatsplot`?

- Primary functions

- Customizability

- Benefits

- Misconceptions

- Limitations and Future scope

---

# Why *ggstatsplot*?

---

# Raison d'être

---

.font120[
Current count of packages on the Comprehensive R Archive Network (**CRAN**)
** > 16,000**
]

]

.right-column[.font120[
Short answer: 
`ggstatsplot` provides a collection of *information-rich* plots with *statistical details* and is suitable for scholarly publications and **faster** (exploratory) data analysis.
]
]

---

# Simpler/faster data analysis workflow

---

In a typical **exploratory** data analysis workflow, data visualization and statistical modeling are two
different phases: visualization informs modeling, and modeling can suggest a
different visualization, and so on and so forth.

The central idea of `ggstatsplot` is simple: combine these two phases into one!

---

# Information-rich graphic is worth a thousand words

---

.footnote[[(Matejka & Fitzmaurice, *Autodesk Research*, 2017)](https://www.autodeskresearch.com/publications/samestats)]

**Graphical** summaries can reveal problems not visible from **numerical** statistics.
 
“I plotted my data and what I found surprised me!" - BuzzFeed

---

# Ready-made plot = no customization

The **grammar of graphics** is a powerful framework [(Wilkinson, 2011)](https://www.google.com/books/edition/_/iI1kcgAACAAJ?hl=en&sa=X&ved=2ahUKEwiGl8rJ2KztAhWyElkFHa8NAvkQre8FMBR6BAgMEAc) and can help you make
infinite number of graphics, each tailored for your specific data visualization
problem! But...

`$\sum_{time}$` (Needed time ↑ + Likelihood to graphical explore data ↓) = Avoidance habit

---

# And a LOT more!
...but we will come back to that later 📌

Let's get started first!

---

# Installation

Install the stable version (latest: `0.7.0`) of `ggstatsplot` from 
[CRAN](https://cran.r-project.org/web/packages/ggstatsplot/index.html):

```r
install.packages("ggstatsplot")
```
--

You can get the development version of the package from
[Github](https://github.com/IndrajeetPatil/ggstatsplot):

```r
remotes::install_github("IndrajeetPatil/ggstatsplot")
```

Load the needed packages-

```r
library(ggstatsplot)
library(ggplot2)
```

---

# Primary functions

---

# Hypothesis about group differences

🔬 *ggbetweenstats*, *ggwithinstats*: multiple groups

🔭 *gghistostats*, *ggdotplotstats*: single group

---

# ggbetweenstats - For between group comparisons

---

```r
ggbetweenstats(
  data = movies_long,
  x = mpaa,
  y = rating
)
```

- *t*-test if **2** groups
- ANOVA if **> 2** groups

📝 **Defaults** return

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

]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggbetweenstats_1-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# ggbetweenstats - pairwise comparisons

---

```r
ggbetweenstats(
  data = movies_long,
  x = mpaa,
  y = rating,
* type = "np",
* pairwise.display = "ns"
)
```

✅ `"p"` → **parametric** (default) 
✅ `"np"` → **non-parametric** 
✅ `"r"` → **robust** 
✅ `"bf"` → **Bayesian**

Changing pairwise comparisons displayed

ℹ️ `"ns"` → only **non-significant** 
ℹ️ `"s"` → only **significant** 
ℹ️ `"all"` → **all**
]

]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggbetweenstats_2-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# ggbetweenstats - outlier tagging

---

```r
ggbetweenstats(
  data = movies_long,
  x = mpaa,
  y = rating,
  type = "r",
  pairwise.comparisons = FALSE,
* outlier.tagging = TRUE,
* outlier.label = title
)
```

.font70[
[Tukey's fences](https://en.wikipedia.org/wiki/Outlier#Tukey's_fences) method using 
interquartile range flags outliers.

Centrality measures

✅ `"p"` → `$\mu_{mean}$` 
✅ `"np"` → `$\mu_{median}$` 
✅ `"r"` → `$\mu_{trimmed}$` 
✅ `"bf"` → `$\mu_{MAP}$`
]

]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggbetweenstats_3-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# ggwithinstats - repeated measures equivalent

---

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

Changing the `type` of test

✅ `"p"` → **parametric** (default) 
✅ `"np"` → **non-parametric** 
✅ `"r"` → **robust** 
✅ `"bf"` → **Bayesian**
]
]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggwithinstats_1-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# gghistostats - Distribution of a numeric variable

---

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

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

Centrality measures

✅ `"p"` → `$\mu_{mean}$` 
✅ `"np"` → `$\mu_{median}$` 
✅ `"r"` → `$\mu_{trimmed}$` 
✅ `"bf"` → `$\mu_{MAP}$`
]
]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/gghistostats_1-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# ggdotplotstats - Labeled numeric variable

---

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

✅ descriptive statistics 
✅ inferential statistics 
✅ effect size + CIs 
✅ Bayesian hypothesis-testing 
✅ Bayesian estimation

Centrality measures

✅ `"p"` → `$\mu_{mean}$` 
✅ `"np"` → `$\mu_{median}$` 
✅ `"r"` → `$\mu_{trimmed}$` 
✅ `"bf"` → `$\mu_{MAP}$`
]
]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggdotplotstats_1-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Hypothesis about correlation
 
🔬 *ggscatterstats*: Two numeric variables
 
🧪 *ggcorrmat*: Multiple numeric variables

---

# ggscatterstats - Two numeric variables

---

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

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

Changing the `type` of test

✅ `"p"` → **parametric** (default) 
✅ `"np"` → **non-parametric** 
✅ `"r"` → **robust** 
✅ `"bf"` → **Bayesian**
]
]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggscatterstats_1-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# ggscatterstats - conditional point tagging

---

```r
ggscatterstats(
  data = movies_long,
  x = budget,
  y = rating,
  type = "r",
* label.var = title,
* label.expression = budget > 150
* & rating > 7.5,
* marginal.type = "boxplot"
)
```

ℹ️ **histogram** 
ℹ️ **boxplot** 
ℹ️ **density** 
ℹ️ **violin** 
ℹ️ **densigram**
]
]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggscatterstats_2-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# ggcorrmat - multiple numeric variables

---

```r
ggcorrmat(dplyr::starwars)
```

✅ effect size + significance 
✅ careful handling of `NA`s

Changing the `type` of test

✅ `"p"` → **parametric** (default) 
✅ `"np"` → **non-parametric** 
✅ `"r"` → **robust** 
✅ `"bf"` → **Bayesian**

]
]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggcorrmat_1-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# ggcorrmat - getting a dataframe

---

```r
library(ggplot2) # for data
options(digits = 2)

ggcorrmat(
  data = dplyr::select(msleep, sleep_rem, awake, brainwt),
  type = "bayes",
* output = "dataframe"
)
```

<table>
 <thead>
 <tr>
 <th style="text-align:left;"> parameter1 </th>
 <th style="text-align:left;"> parameter2 </th>
 <th style="text-align:right;"> estimate </th>
 <th style="text-align:right;"> conf.level </th>
 <th style="text-align:right;"> conf.low </th>
 <th style="text-align:right;"> conf.high </th>
 <th style="text-align:right;"> pd </th>
 <th style="text-align:right;"> rope.percentage </th>
 <th style="text-align:left;"> prior.distribution </th>
 <th style="text-align:right;"> prior.location </th>
 <th style="text-align:right;"> prior.scale </th>
 <th style="text-align:right;"> bayes.factor </th>
 <th style="text-align:left;"> method </th>
 <th style="text-align:right;"> n.obs </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> sleep_rem </td>
 <td style="text-align:left;"> awake </td>
 <td style="text-align:right;"> -0.73 </td>
 <td style="text-align:right;"> 0.95 </td>
 <td style="text-align:right;"> -0.82 </td>
 <td style="text-align:right;"> -0.63 </td>
 <td style="text-align:right;"> 1.00 </td>
 <td style="text-align:right;"> 0.00 </td>
 <td style="text-align:left;"> beta </td>
 <td style="text-align:right;"> 1.4 </td>
 <td style="text-align:right;"> 1.4 </td>
 <td style="text-align:right;"> 3.0e+09 </td>
 <td style="text-align:left;"> Bayesian Pearson correlation </td>
 <td style="text-align:right;"> 61 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> sleep_rem </td>
 <td style="text-align:left;"> brainwt </td>
 <td style="text-align:right;"> -0.21 </td>
 <td style="text-align:right;"> 0.95 </td>
 <td style="text-align:right;"> -0.41 </td>
 <td style="text-align:right;"> 0.01 </td>
 <td style="text-align:right;"> 0.93 </td>
 <td style="text-align:right;"> 0.19 </td>
 <td style="text-align:left;"> beta </td>
 <td style="text-align:right;"> 1.4 </td>
 <td style="text-align:right;"> 1.4 </td>
 <td style="text-align:right;"> 6.5e-01 </td>
 <td style="text-align:left;"> Bayesian Pearson correlation </td>
 <td style="text-align:right;"> 48 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> awake </td>
 <td style="text-align:left;"> brainwt </td>
 <td style="text-align:right;"> 0.34 </td>
 <td style="text-align:right;"> 0.95 </td>
 <td style="text-align:right;"> 0.17 </td>
 <td style="text-align:right;"> 0.53 </td>
 <td style="text-align:right;"> 1.00 </td>
 <td style="text-align:right;"> 0.02 </td>
 <td style="text-align:left;"> beta </td>
 <td style="text-align:right;"> 1.4 </td>
 <td style="text-align:right;"> 1.4 </td>
 <td style="text-align:right;"> 7.3e+00 </td>
 <td style="text-align:left;"> Bayesian Pearson correlation </td>
 <td style="text-align:right;"> 56 </td>
 </tr>
</tbody>
</table>

</div>
]

---

# Hypothesis of composition of categorical variables
 
🧪 *ggpiestats*: If you like 🍕
 
🔬 *ggbarstats*: Otherwise

---

# ggpiestats - goodness-of-fit test

---

```r
ggpiestats(
  data = as.data.frame(Titanic),
  x = Class,
* counts = Freq,
* label = "both"
)
```

✅ descriptive statistics 
✅ inferential statistics 
✅ effect size + CIs 
✅ Bayesian hypothesis-testing 
🤞 Bayesian estimation

**Note** 
If the data is in *tabled* 
format, you can use the `counts` 
argument.
]

]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggpiestats_1-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# ggpiestats - association between categorical variables

---

```r
# let's use subset of data
ggpiestats(
  data = dplyr::filter(
    movies_long,
    genre %in% c("Drama", "Comedy")
  ),
  x = mpaa,
  y = genre
)
```

✅ descriptive statistics 
✅ inferential statistics 
✅ effect size + CIs 
✅ Goodness-of-fit tests 
✅ Bayesian hypothesis-testing 
✅ Bayesian estimation

Test by design

- `paired = FALSE` → Pearson's `$\chi^2$`
- `paired = TRUE` → McNemar's `$\chi^2$`

]
]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggpiestats_2-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# ggbarstats - association between categorical variables

---

```r
ggbarstats(
  data = dplyr::filter(
    movies_long,
    genre %in% c("Drama", "Comedy")
  ),
  x = mpaa,
  y = genre
)
```

✅ descriptive statistics 
✅ inferential statistics 
✅ effect size + CIs 
✅ Goodness-of-fit tests 
✅ Bayesian hypothesis-testing 
✅ Bayesian estimation

Test by design

- `paired = FALSE` → Pearson's `$\chi^2$`
- `paired = TRUE` → McNemar's `$\chi^2$`
]
]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggbarstats_1-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Hypothesis about regression coefficients

🔭 *ggcoefstats*: Regression model object

---

# ggcoefstats

---

```r
# model
mod <- stats::lm(
 formula = rating ~ mpaa,
 data = movies_long
)

# plot
ggcoefstats(
  x = mod,
  title = "IMDB rating by MPAA rating"
)
```

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

]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggcoefstats_1-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# ggcoefstats: Supported models

---

.font60[
`aareg`, `afex_aov`, `anova`, `anova.mlm`, `anova`, `aov`, `aovlist`, `Arima`,
`bam`, `bayesx`, `bayesGARCH`, `BBmm`, `BBreg`, `bcplm`, `betamfx`, `betaor`,
`BFBayesFactor`, `bglmerMod`, `bife`, `bigglm`, `biglm`, `blavaan`, `bmlm`,
`blmerMod`, `bracl`, `brglm`, `brglm2`, `brmsfit`, `brmultinom`, `btergm`,
`cch`, `censReg`, `cgam`, `cgamm`, `cglm`, `clm`, `clm2`, `clmm`, `clmm2`,
`coeftest`, `complmrob`, `confusionMatrix`, `coxme`, `coxph`, `coxph.penal`,
`cpglm`, `cpglmm`, `crch`, `crq`, `crr`, `DirichReg`, `drc`, `eglm`, `elm`,
`emmGrid`, `epi.2by2`, `ergm`, `feis`, `felm`, `fitdistr`, `fixest`,
`flexsurvreg`, `gam`, `Gam`, `gamlss`, `garch`, `geeglm`, `glmc`, `glmerMod`,
`glmmTMB`, `gls`, `glht`, `glm`, `glmm`, `glmmadmb`, `glmmPQL`, `glmRob`,
`glmrob`, `glmx`, `gmm`, `HLfit`, `hurdle`, `ivFixed`, `ivprobit`, `ivreg`,
`iv_robust`, `lavaan`, `lm`, `lm.beta`, `lmerMod`, `lmerModLmerTest`, `lmodel2`,
`lmRob`, `lmrob`, `lm_robust`, `logitmfx`, `logitor`, `logitsf`, `LORgee`,
`lqm`, `lqmm`, `lrm`, `manova`, `maov`, `margins`, `mcmc`, `mcmc.list`,
`MCMCglmm`, `mclogit`, `mice`, `mmclogit`, `mediate`, `metafor`, `merMod`,
`merModList`, `metaplus`, `mixor`, `mjoint`, `mle2`, `mlm`, `multinom`,
`negbin`, `negbinmfx`, `negbinirr`, `nlmerMod`, `nlrq`, `nlreg`, `nls`,
`orcutt`, `orm`, `plm`, `poissonmfx`, `poissonirr`, `polr`, `ridgelm`,
`riskRegression`, `rjags`, `rlm`, `rlmerMod`, `robmixglm`, `rq`, `rqs`, `rqss`,
`rrvglm`, `scam`, `semLm`, `semLme`, `slm`, `speedglm`, `speedlm`, `stanfit`,
`stanreg`, `summary.lm`, `survreg`, `svyglm`, `svyolr`, `svyglm`, `tobit`,
`truncreg`, `varest`, `vgam`, `vglm`, `wbgee`, `wblm`, `zeroinfl`, etc.
]

Thanks to [`easystats`](https://easystats.github.io/easystats/)!

![](images/easystats.jpg)

---

# *grouped_* variants of all functions
Running the same function for 
all levels of a single grouping variable

---

# *grouped_* functions

---

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

.font70[
Available `grouped_` variants
- `grouped_ggbetweenstats`
- `grouped_ggwithinstats`
- `grouped_gghistostats`
- `grouped_ggdotplotstats`
- `grouped_ggscatterstats`
- `grouped_ggcorrmat`
- `grouped_ggpiestats`
- `grouped_ggbarstats`
]
]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/grouped_1-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Customizability of *ggstatsplot*
"What if I don't like the default plots?" 🤔

---

---

# Changing aesthetics (themes + palettes) 🖼🎨

---

Aesthetic preferences are not an excuse to not use `ggstatsplot`! 😻

```r
ggbetweenstats(
  data = movies_long,
  x = mpaa,
  y = rating,
* ggtheme = hrbrthemes::theme_ipsum_tw(),
* palette = "Darjeeling2",
* package = "wesanderson"
)
```

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggbetweenstats_4-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Further modification with *ggplot2* 🛠

---

You can modify `ggstatsplot` plots further using `ggplot2` functions. 🎉

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

]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/ggbetweenstats_5-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Too much information 🙈

---

`ggstatsplot` can be used to get **only plots**.

```r
# using `ggstatsplot` only for plot
ggbetweenstats(
  data = iris,
  x = Species,
  y = Sepal.Length,
  # turn off centrality measure
* centrality.plotting = FALSE,
  # turn off statistical analysis
* results.subtitle = FALSE,
  # turn off Bayesian message
* bf.message = FALSE,
  # turn off pairwise comparisons
* pairwise.comparisons = FALSE
)
```
]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/only_plot-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Expressions for custom plots 🀄️

---

`ggstatsplot` can be used to get **only the expressions**.

```r
# using `ggstatsplot` for stats
results <-
 ggstatsplot::ggpiestats(
 data = Titanic_full,
 x = Survived,
 y = Sex,
* output = "subtitle"
 )

# using `ggiraphExtra` for plot
*ggiraphExtra::ggSpine(
  data = Titanic_full,
  aes(x = Sex, fill = Survived),
  addlabel = TRUE,
  interactive = FALSE
) +
* labs(subtitle = results)
```
]

.right-plot[
<img src="ggstatsplot_presentation_files/figure-html/subtitle_1-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Why use *ggstatsplot*? 👍️

---

# Supports different statistical approaches

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

---

# One package to access it all 💍

📦 to carry out the test (e.g. `stats`) 
📦 computing effect size + CIs (e.g. `effectsize`) 
📦 for descriptives (e.g. `skimr`) 
📦 pairwise comparisons (e.g. `multcomp`) 
📦 Bayesian hypothesis testing (e.g. `BayesFactor`) 
📦 Bayesian estimation (e.g. `bayestestR`) 
📦 . 
📦 . 
]

]

🤔 accepts dataframe, vectors, matrix? 
🤔 long/wide format data? 
🤔 works with `NA`s? 
🤔 returns list, dataframe, arrays? 
🤔 works with tibbles?

]

---

# Toggling between statistical approaches 🔀

```r
# 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"
)
```
]

```r
# 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"
)
```
]

---

# Results *in context* of the underlying data 🕵️

**Without ggstatsplot**

The 20 participants who received the drug intervention `$(M = 24.14)$` compared to
the 20 participants in the control group `$(M = 41.78)$` demonstrated
significantly lower weight (Welch's `$t$`-test: `$t(19.03) = 2.33, p = .031$`). The
effect size `$(g_{Hedges} = 0.72, 95\% CI [0.09,1.35])$` for this analysis was
found to exceed Cohen’s (1988) convention for a medium effect `$(> .50)$`.
]

**With ggstatsplot**

![](images/after_ggstats.PNG)

]

---

# Best practices in statistical reporting 🏆

The expression template tries to follow the gold standard for statistical
reporting.

![](images/stats_reporting_format.jpg)

---

# Avoiding reporting errors

> "half of all published psychology papers that use NHST contained at least one
p-value that was inconsistent with its test statistic and degrees of freedom.
One in eight papers contained a grossly inconsistent p-value that may have
affected the statistical conclusion"

> [(Nuijten et al., *Behavior Research Methods*, 2016)](https://link.springer.com/article/10.3758/s13428-015-0664-2)

Since the plot and the statistical analysis are yoked together, the chances of
making an error in reporting the results are minimized.

---

# Making sense of null results

`$p > 0.05$`: The null hypothesis (`H0`) can't be rejected

But can it be **accepted**?! NHST 🤫

> "In 72% of cases, nonsignificant results were misinterpreted, in that the
authors inferred that the effect was absent. A Bayesian reanalysis revealed
that fewer than 5% of the nonsignificant findings provided strong evidence
(i.e., `$BF_{01} > 10$`) in favor of the null hypothesis over the alternative
hypothesis."

> [(Aczel et al., *AMPPS*, 2018)](https://journals.sagepub.com/doi/pdf/10.1177/2515245918773742)

Juxtaposing frequentist and Bayesian statistics for the same analysis helps to
properly interpret the null results.

---

# A few additional benefits

---

- Minimal amount of code needed (typically only `data`, `x`,
  and `y`), which minimizes chances of error and makes for tidy scripts. 💅

- 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 and limitations

---

# Misconceptions about *ggstatsplot*

---

This package is...

❌ an alternative to learning `ggplot2` 
--
✅ (the more you know `ggplot2`, the better 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)

❌ meant for mass communication 
--
✅ LOL 😆

❌ the only game in town 
--
✅ (GUI open-source softwares: [JASP](https://jasp-stats.org/) and [jamovi](https://www.jamovi.org/))

---

# Limitations of *ggstatsplot* 👎️

---

- Limited kinds of plots available.

- Limited number of statistical tests available.
 This will **always** be the case. 🤷

- Expects a non-trivial level of statistical proficiency (but plots without
  statistics can still be useful).

- Faceting not implemented.

---

# Overcoming these limitations 👥

---

![](images/needs_you.jpg)

]

.pull-right[
.font90[
Ways in which you can [contribute](https://github.com/IndrajeetPatil/ggstatsplot)

- Read and correct any inconsistencies in the
[documentation](https://indrajeetpatil.github.io/ggstatsplot/) 📖

- Raise issues about bugs/features 🐞

- Either mention or cite software if used in a publication 📝

- Star the GitHub repo (increases visibility) ⭐

- Review code 🕵

- Add new functionality 👨‍💻
]
]

---

# Acknowledgments

Other developers 🙌

[Daniel Lüdecke](https://github.com/strengejacke), [Dominique Makowski](https://github.com/DominiqueMakowski), [Mattan S. Ben-Shachar](https://github.com/mattansb)

Support 💰

[Mina Cikara](http://www.intergroupneurosciencelaboratory.com/), [Fiery Cushman](http://cushmanlab.fas.harvard.edu/index.php), [Iyad Rahwan](https://rahwan.me/)

Community 🙏

Contributors to *ggstatsplot* &
*rstats* users and developers

---

# Find me at...

[🐦 @patilindrajeets](http://twitter.com/patilindrajeets)

[💻 @IndrajeetPatil](http://github.com/IndrajeetPatil)

[🔗 https://sites.google.com/site/indrajeetspatilmorality/](https://sites.google.com/site/indrajeetspatilmorality/)

[📫 patilindrajeet.science@gmail.com](mailto:patilindrajeet.science@gmail.com)

]

---

# The End 👋

For more information, see

https://indrajeetpatil.github.io/ggstatsplot/

---