> “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`](https://indrajeetpatil.github.io/ggstatsplot/) is an
extension of [`ggplot2`](https://github.com/tidyverse/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.
# Summary of available plots
It, therefore, produces a limited kinds of plots for the supported
analyses:
| 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 |
| `ggpiestats` | **pie charts** | for categorical data |
| `ggbarstats` | **bar charts** | for categorical data |
| `ggscatterstats` | **scatterplots** | for correlations between two variables |
| `ggcorrmat` | **correlation matrices** | for correlations between multiple variables |
| `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
Currently, it supports only the most common types of statistical tests:
**parametric**, **nonparametric**, **robust**, and **bayesian** versions
of **t-test**/**anova**, **correlation** analyses, **contingency table**
analysis, **meta-analysis**, and **regression** analyses.
The table below summarizes all the different types of analyses currently
supported in this package-
| Functions | Description | Parametric | Non-parametric | Robust | Bayes Factor |
| -------------------------------- | ------------------------------------------------- | ------------------------------ | ------------------------------ | ------------------------------ | ------------------------------ |
| `ggbetweenstats` | Between group/condition comparisons | Yes | Yes | Yes | Yes |
| `ggwithinstats` | Within group/condition 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 model coefficients | Yes | Yes | Yes | Yes |
| `ggcoefstats` | Random-effects meta-analysis | Yes | No | Yes | Yes |
# Statistical reporting
For **all** statistical tests reported in the plots, the default
template abides by the [APA](https://my.ilstu.edu/~jhkahn/apastats.html)
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
Here is a summary table of all the statistical tests currently supported
across various functions:
Note that this function returns object of class `ggplot` and thus can be
further modified using `ggplot2` functions.
A number of other arguments can be specified to make this plot even more
informative or change some of the default options. Additionally, this
time we will use a grouping variable that has only two levels. The
function will automatically switch from carrying out an ANOVA analysis
to a *t*-test.
The `type` (of test) argument also accepts the following abbreviations:
`"p"` (for *parametric*) or `"np"` (for *nonparametric*) or `"r"` (for
*robust*) or `"bf"` (for *Bayes Factor*). Additionally, the type of plot
to be displayed can also be modified (`"box"`, `"violin"`, or
`"boxviolin"`).
A number of other arguments can be specified to make this plot even more
informative or change some of the default options.
``` r
# for reproducibility
set.seed(123)
library(ggplot2)
# plot
ggstatsplot::ggbetweenstats(
data = ToothGrowth,
x = supp,
y = len,
notch = TRUE, # show notched box plot
mean.ci = TRUE, # whether to display confidence interval for means
k = 3, # number of decimal places for statistical results
outlier.tagging = TRUE, # whether outliers need to be tagged
outlier.label = dose, # variable to be used for the outlier tag
xlab = "Supplement type", # label for the x-axis variable
ylab = "Tooth length", # label for the y-axis variable
title = "The Effect of Vitamin C on Tooth Growth", # title text for the plot
ggtheme = ggthemes::theme_fivethirtyeight(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off `ggstatsplot` theme layer
package = "wesanderson", # package from which color palette is to be taken
palette = "Darjeeling1", # choosing a different color palette
messages = FALSE
)
```
Additionally, there is also a `grouped_` variant of this function that
makes it easy to repeat the same operation across a **single** grouping
variable:
``` r
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggbetweenstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
y = length,
grouping.var = genre, # grouping variable
pairwise.comparisons = TRUE, # display significant pairwise comparisons
p.adjust.method = "bonferroni", # method for adjusting p-values for multiple comparisons
# adding new components to `ggstatsplot` default
ggplot.component = list(ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())),
k = 3,
title.prefix = "Movie genre",
caption = substitute(paste(italic("Source"), ":IMDb (Internet Movie Database)")),
palette = "default_jama",
package = "ggsci",
messages = FALSE,
plotgrid.args = list(nrow = 2),
title.text = "Differences in movie length by mpaa ratings for different genres"
)
```
### Summary of tests
Following (between-subjects) tests are carried out for each type of
analyses-
| Type | No. of groups | Test |
| -------------- | ------------- | ----------------------------------------------- |
| Parametric | \> 2 | Fisher’s or Welch’s one-way ANOVA |
| Non-parametric | \> 2 | Kruskal-Wallis one-way ANOVA |
| Robust | \> 2 | Heteroscedastic one-way ANOVA for trimmed means |
| Bayes Factor | \> 2 | Fisher’s ANOVA |
| Parametric | 2 | Student’s or Welch’s *t*-test |
| Non-parametric | 2 | Mann-Whitney *U* test |
| Robust | 2 | Yuen’s test for trimmed means |
| Bayes Factor | 2 | Student’s *t*-test |
The omnibus effect in one-way ANOVA design can also be followed up with
more focal pairwise comparison tests. Here is a summary of multiple
pairwise comparison tests supported in *ggbetweenstats*-
| Type | Equal variance? | Test | *p*-value adjustment? |
| -------------- | --------------- | ------------------------- | ------------------------------ |
| Parametric | No | Games-Howell test | Yes |
| Parametric | Yes | Student’s *t*-test | Yes |
| Non-parametric | No | Dunn test | Yes |
| Robust | No | Yuen’s trimmed means test | Yes |
| Bayes Factor | `NA` | Student’s *t*-test | `NA` |
For more, see the `ggbetweenstats` vignette:
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-
``` r
# common setup
set.seed(123)
# plot
ggstatsplot::grouped_ggwithinstats(
data = dplyr::filter(
.data = ggstatsplot::bugs_long,
region %in% c("Europe", "North America"),
condition %in% c("LDLF", "LDHF")
),
x = condition,
y = desire,
xlab = "Condition",
ylab = "Desire to kill an artrhopod",
grouping.var = region,
outlier.tagging = TRUE,
outlier.label = education,
ggtheme = hrbrthemes::theme_ipsum_tw(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
```
### Summary of tests
Following (within-subjects) tests are carried out for each type of
analyses-
| Type | No. of groups | Test |
| -------------- | ------------- | ----------------------------------------------------------------- |
| Parametric | \> 2 | One-way repeated measures ANOVA |
| Non-parametric | \> 2 | Friedman’s rank sum test |
| Robust | \> 2 | Heteroscedastic one-way repeated measures ANOVA for trimmed means |
| Bayes Factor | \> 2 | One-way repeated measures ANOVA |
| Parametric | 2 | Student’s *t*-test |
| Non-parametric | 2 | Wilcoxon signed-rank test |
| Robust | 2 | Yuen’s test on trimmed means for dependent samples |
| Bayes Factor | 2 | Student’s *t*-test |
The omnibus effect in one-way ANOVA design can also be followed up with
more focal pairwise comparison tests. Here is a summary of multiple
pairwise comparison tests supported in *ggwithinstats*-
| Type | Test | *p*-value adjustment? |
| -------------- | ------------------------- | ------------------------------ |
| Parametric | Student’s *t*-test | Yes |
| Non-parametric | Durbin-Conover test | Yes |
| Robust | Yuen’s trimmed means test | Yes |
| Bayes Factor | Student’s *t*-test | `NA` |
For more, see the `ggwithinstats` vignette:
The available marginal distributions are-
- histograms
- boxplots
- density
- violin
- densigram (density + histogram)
Number of other arguments can be specified to modify this basic plot-
``` r
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggscatterstats(
data = dplyr::filter(.data = ggstatsplot::movies_long, genre == "Action"),
x = budget,
y = rating,
type = "robust", # type of test that needs to be run
xlab = "Movie budget (in million/ US$)", # label for x axis
ylab = "IMDB rating", # label for y axis
label.var = "title", # variable for labeling data points
label.expression = "rating < 5 & budget > 100", # expression that decides which points to label
title = "Movie budget and IMDB rating (action)", # title text for the plot
caption = expression(paste(italic("Note"), ": IMDB stands for Internet Movie DataBase")),
ggtheme = hrbrthemes::theme_ipsum_ps(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off `ggstatsplot` theme layer
marginal.type = "density", # type of marginal distribution to be displayed
xfill = "pink", # color fill for x-axis marginal distribution
yfill = "#009E73", # color fill for y-axis marginal distribution
centrality.parameter = "median", # central tendency lines to be displayed
messages = FALSE # turn off messages and notes
)
```
Additionally, there is also a `grouped_` variant of this function that
makes it easy to repeat the same operation across a **single** grouping
variable. Also, note that, as opposed to the other functions, this
function does not return a `ggplot` object and any modification you want
to make can be made in advance using `ggplot.component` argument
(available for all functions, but especially useful for this particular
function):
``` r
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggscatterstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = rating,
y = length,
grouping.var = genre, # grouping variable
label.var = title,
label.expression = length > 200,
xfill = "#E69F00",
yfill = "#8b3058",
xlab = "IMDB rating",
title.prefix = "Movie genre",
ggtheme = ggplot2::theme_grey(),
ggplot.component = list(
ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
),
messages = FALSE,
plotgrid.args = list(nrow = 2),
title.text = "Relationship between movie length by IMDB ratings for different genres"
)
```
### Summary of tests
Following tests are carried out for each type of analyses. Additionally,
the correlation coefficients (and their confidence intervals) are used
as effect sizes-
| Type | Test | CI? |
| -------------- | --------------------------------------- | ----------------------------- |
| Parametric | Pearson’s correlation coefficient | Yes |
| Non-parametric | Spearman’s rank correlation coefficient | Yes |
| Robust | Percentage bend correlation coefficient | Yes |
| Bayes Factor | Pearson’s correlation coefficient | No |
For more, see the `ggscatterstats` vignette:
In case of repeated measures designs, setting `paired = TRUE` will
produce results from McNemar’s chi-squared test-
``` r
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = data.frame(
"before" = c("Approve", "Approve", "Disapprove", "Disapprove"),
"after" = c("Approve", "Disapprove", "Approve", "Disapprove"),
counts = c(794, 150, 86, 570),
check.names = FALSE
),
x = before,
y = after,
counts = counts,
title = "Survey results before and after the intervention",
label = "both",
paired = TRUE, # within-subjects design
package = "wesanderson",
palette = "Royal1"
)
#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.
#> # A tibble: 2 x 11
#> after counts perc N Approve Disapprove statistic p.value
#> 
Additionally, 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:
``` r
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggpiestats(
dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
grouping.var = genre, # grouping variable
title.prefix = "Movie genre", # prefix for the facetted title
messages = FALSE,
package = "ggsci", # package from which color palette is to be taken
palette = "default_jama", # choosing a different color palette
title.text = "Composition of MPAA ratings for different genres"
)
```
### Summary of tests
Following tests are carried out for each type of analyses-
| Type of data | Design | Test |
| ------------ | -------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| Unpaired |  contingency table | Pearson’s  test |
| Paired |  contingency table | McNemar’s  test |
| Frequency |  contingency table | Goodness of fit () |
Following effect sizes (and confidence intervals/CI) are available for
each type of test-
| Type | Effect size | CI? |
| -------------------------- | ------------ | ----------------------------- |
| Pearson’s chi-squared test | Cramér’s *V* | Yes |
| McNemar’s test | Cohen’s *g* | Yes |
| Goodness of fit | Cramér’s *V* | Yes |
For more, see the `ggpiestats` vignette:
And, needless to say, there is also a `grouped_` variant of this
function-
``` r
# setup
set.seed(123)
# smaller dataset
df <-
dplyr::filter(
.data = forcats::gss_cat,
race %in% c("Black", "White"),
relig %in% c("Protestant", "Catholic", "None"),
!partyid %in% c("No answer", "Don't know", "Other party")
)
# plot
ggstatsplot::grouped_ggbarstats(
data = df,
x = relig,
y = partyid,
grouping.var = race,
title.prefix = "Race",
xlab = "Party affiliation",
ggtheme = ggthemes::theme_tufte(base_size = 12),
ggstatsplot.layer = FALSE,
messages = FALSE,
title.text = "Race, religion, and political affiliation",
plotgrid.args = list(nrow = 2)
)
```
### Summary of tests
This is identical to the `ggpiestats` function summary of tests.
## `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.
``` r
# for reproducibility
set.seed(123)
# plot
ggstatsplot::gghistostats(
data = iris, # dataframe from which variable is to be taken
x = Sepal.Length, # numeric variable whose distribution is of interest
title = "Distribution of Iris sepal length", # title for the plot
caption = substitute(paste(italic("Source:"), "Ronald Fisher's Iris data set")),
bar.measure = "both",
test.value = 5, # default value is 0
test.value.line = TRUE, # display a vertical line at test value
centrality.parameter = "mean", # which measure of central tendency is to be plotted
centrality.line.args = list(color = "darkred"), # aesthetics for central tendency line
binwidth = 0.10, # binwidth value (experiment)
messages = FALSE, # turn off the messages
ggtheme = hrbrthemes::theme_ipsum_tw(), # choosing a different theme
ggstatsplot.layer = FALSE # turn off ggstatsplot theme layer
)
```
As can be seen from the plot, Bayes Factor can be attached (`bf.message
= TRUE`) to assess evidence in favor of the null hypothesis.
Additionally, there is also a `grouped_` variant of this function that
makes it easy to repeat the same operation across a **single** grouping
variable:
``` r
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_gghistostats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = budget,
xlab = "Movies budget (in million US$)",
type = "robust", # use robust location measure
grouping.var = genre, # grouping variable
normal.curve = TRUE, # superimpose a normal distribution curve
normal.curve.args = list(color = "red", size = 1),
title.prefix = "Movie genre",
ggtheme = ggthemes::theme_tufte(),
ggplot.component = list( # modify the defaults from `ggstatsplot` for each plot
ggplot2::scale_x_continuous(breaks = seq(0, 200, 50), limits = (c(0, 200)))
),
messages = FALSE,
plotgrid.args = list(nrow = 2),
title.text = "Movies budgets for different genres"
)
```
### Summary of tests
Following tests are carried out for each type of analyses-
| Type | Test |
| -------------- | ------------------------------- |
| Parametric | One-sample Student’s *t*-test |
| Non-parametric | One-sample Wilcoxon test |
| Robust | One-sample percentile bootstrap |
| Bayes Factor | One-sample Student’s *t*-test |
Following effect sizes (and confidence intervals/CI) are available for
each type of test-
| Type | Effect size | CI? |
| -------------- | ------------------------------------------------------------------------ | ----------------------------- |
| Parametric | Cohen’s *d*, Hedge’s *g* (central-and noncentral-*t* distribution based) | Yes |
| Non-parametric | *r* | Yes |
| Robust | robust location measure | Yes |
| Bayes Factor | No | No |
For more, including information about the variant of this function
`grouped_gghistostats`, see the `gghistostats` vignette:
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.
``` r
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggdotplotstats(
data = dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6")),
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
type = "nonparametric", # non-parametric test
grouping.var = cyl, # grouping variable
test.value = 15.5,
test.value.line = TRUE,
title.prefix = "cylinder count",
point.args = list(color = "red", size = 5, shape = 13),
messages = FALSE,
title.text = "Fuel economy data"
)
```
### Summary of tests
This is identical to summary of tests for `gghistostats`.
## `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.
``` r
# for reproducibility
set.seed(123)
# as a default this function outputs a correlation matrix plot
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
type = "robust", # correlation method
p.adjust.method = "holm", # p-value adjustment method for multiple comparisons
cor.vars = c(sleep_rem, awake:bodywt), # a range of variables can be selected
cor.vars.names = c(
"REM sleep", # variable names
"time awake",
"brain weight",
"body weight"
),
matrix.type = "upper", # type of visualization matrix
colors = c("#B2182B", "white", "#4D4D4D"),
title = "Correlalogram for mammals sleep dataset",
subtitle = "sleep units: hours; weight units: kilograms",
caption = "Source: `ggplot2` R package"
)
```
Two things to note:
- If there are `NA`s present in the selected variables, the legend
will display minimum, median, and maximum number of pairs used for
correlation tests.
- If `cor.vars` are not specified, all numeric variables will be used.
There is also a `grouped_` variant of this function that makes it easy
to repeat the same operation across a **single** grouping variable:
``` r
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggcorrmat(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
cor.vars = length:votes,
colors = c("#cbac43", "white", "#550000"),
grouping.var = genre, # grouping variable
k = 3L, # number of digits after decimal point
title.prefix = "Movie genre",
messages = FALSE,
plotgrid.args = list(nrow = 2)
)
```
You can also get a dataframe containing all relevant details from the
statistical tests:
``` r
# setup
set.seed(123)
# dataframe in long format
ggcorrmat(
data = ggplot2::msleep,
type = "bayes",
output = "dataframe"
)
#> # A tibble: 15 x 12
#> parameter1 parameter2 rho ci_low ci_high pd rope_percentage
#> 
This default plot can be further modified to one’s liking with
additional arguments (also, let’s use a different model now):
``` r
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggcoefstats(
x = MASS::rlm(formula = mpg ~ am * cyl, data = mtcars),
point.args = list(color = "red", size = 3, shape = 15),
vline.args = list(size = 1, color = "#CC79A7", linetype = "dotdash"),
stats.label.color = c("#0072B2", "#D55E00", "darkgreen"),
title = "Car performance predicted by transmission & cylinder count",
subtitle = "Source: 1974 Motor Trend US magazine",
ggtheme = hrbrthemes::theme_ipsum_ps(),
ggstatsplot.layer = FALSE
) + # note the order in which the labels are entered
ggplot2::scale_y_discrete(labels = c("transmission", "cylinders", "interaction")) +
ggplot2::labs(x = "regression coefficient", y = NULL)
```
Most of the regression models that are supported in the underlying
packages are also supported by `ggcoefstats`. For example-
`aareg`, `anova`, `aov`, `aovlist`, `Arima`, `bayesx`, `bayesGARCH`,
`BBmm`, `BBreg`, `bcplm`, `bglmerMod`, `bife`, `bigglm`, `biglm`,
`blavaan`, `bmlm`, `blmerMod`, `bracl`, `brglm2`, `brmsfit`,
`brmultinom`, `btergm`, `cch`, `cgam`, `cgamm`, `cglm`, `clm`, `clm2`,
`clmm`, `clmm2`, `coeftest`, `complmrob`, `confusionMatrix`, `coxme`,
`coxph`, `cpglm`, `cpglmm`, `crch`, `DirichReg`, `drc`, `emmGrid`,
`epi.2by2`, `ergm`, `feis`, `felm`, `fitdistr`, `flexsurvreg`, `glmc`,
`glmerMod`, `glmmTMB`, `gls`, `gam`, `Gam`, `gamlss`, `garch`, `glm`,
`glmmadmb`, `glmmPQL`, `glmRob`, `glmrob`, `glmx`, `gmm`, `hurdle`,
`ivreg`, `iv_robust`, `lavaan`, `lm`, `lm.beta`, `lmerMod`,
`lmerModLmerTest`, `lmodel2`, `lmRob`, `lmrob`, `LORgee`, `lrm`, `mcmc`,
`mcmc.list`, `MCMCglmm`, `mclogit`, `mmclogit`, `mediate`, `mixor`,
`mjoint`, `mle2`, `mlm`, `multinom`, `negbin`, `nlmerMod`, `nlrq`,
`nlreg`, `nls`, `orcutt`, `plm`, `polr`, `ridgelm`, `rjags`, `rlm`,
`rlmerMod`, `robmixglm`, `rq`, `rqss`, `slm`, `speedglm`, `speedlm`,
`stanfit`, `stanreg`, `survreg`, `svyglm`, `svyolr`, `svyglm`, `tobit`,
`truncreg`, `vgam`, `wbgee`, `wblm`, `zcpglm`, `zeroinfl`, etc.
Although not shown here, this function can also be used to carry out
both frequentist and Bayesian random-effects meta-analysis.
For a more exhaustive account of this function, see the associated
vignette-
# Usage and syntax simplicity
As seen from these examples, `ggstatsplot` relies on non-standard
evaluation (NSE) - implemented via `rlang` - i.e., rather than looking
at the values of arguments (`x`, `y`), it instead looks at their
expressions. Therefore, the syntax is simpler and follows the following
principles-
- When a given function depends on variables in a dataframe, `data`
argument must always be specified.
- The `$` operator cannot be used to specify variables in a dataframe.
- All functions accept both string (`x = "var1"`) and symbol (`x =
var1`) arguments.
These set principles combined with the fact that almost all functions
produce publication-ready plots that require very few arguments if one
finds the aesthetic and statistical defaults satisfying make the syntax
much less cognitively demanding and easy to remember/reconstruct.
`ggstatsplot` is a very chatty package and will by default print helpful notes on assumptions about statistical tests, warnings, etc. If you don’t want your console to be cluttered with such messages, they can be turned off by setting argument `messages = FALSE` in the function call. Most functions share a `type` (of test) argument that is helpful to specify the type of statistical analysis: - `"p"` (for **parametric**) - `"np"` (for **non-parametric**) - `"r"` (for **robust**) - `"bf"` (for **Bayes Factor**) All relevant functions in `ggstatsplot` have a `output` argument which can be used to not only return plots (which is the default), but also to return a `subtitle` or `caption`, which are objects of type `call` and can be used to display statistical details in conjunction with a custom plot and at a custom location in the plot. Additionally, all functions share the `ggtheme` and `palette` arguments that can be used to specify your favorite `ggplot` theme and color palette. # `ggstatsverse`: Components of `ggstatsplot` To make the maintenance and development of `ggstatsplot` more manageable, it is being broken into smaller pieces. Currently, the package internally relies on the following packages that manage different aspects of statistical analyses:
## `statsExpressions`
The `statsExpressions` package forms the statistical backend that
processes data and creates expressions containing results from
statistical tests and are by default displayed in as plot **subtitle**.
For more exhaustive documentation for this package, see: