statsExpressions: Dataframes and expressions with statistical details

Package	Status	Usage	GitHub	References

Introduction

statsExpressions produces dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses.

The functions are:

• pipe-friendly
• provide a consistent syntax to work with tidy data
• provide expressions with statistical details for graphing packages

This package forms the statistical processing backend for ggstatsplot package.

Please note that the API for the package has changed significantly in 1.0.0 release and will break any code that used prior versions of this package.

See: https://indrajeetpatil.github.io/statsExpressions/news/index.html

Installation

To get the latest, stable CRAN release:

install.packages("statsExpressions")

You can get the development version of the package from GitHub. To see what new changes (and bug fixes) have been made to the package since the last release on CRAN, you can check the detailed log of changes here: https://indrajeetpatil.github.io/statsExpressions/news/index.html

If you are in hurry and want to reduce the time of installation, prefer-

# needed package to download from GitHub repo
install.packages("remotes")

# downloading the package from GitHub
remotes::install_github(
  repo = "IndrajeetPatil/statsExpressions", # package path on GitHub
  dependencies = FALSE, # assumes you have already installed needed packages
  quick = TRUE # skips docs, demos, and vignettes
)

If time is not a constraint-

remotes::install_github(
  repo = "IndrajeetPatil/statsExpressions", # package path on GitHub
  dependencies = TRUE, # installs packages which statsExpressions depends on
  upgrade_dependencies = TRUE # updates any out of date dependencies
)

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("statsExpressions")
#> 
#>   Patil, I. (2018). ggstatsplot: 'ggplot2' Based Plots with Statistical
#>   Details. CRAN. Retrieved from
#>   https://cran.r-project.org/web/packages/ggstatsplot/index.html
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {{ggstatsplot}: 'ggplot2' Based Plots with Statistical Details},
#>     author = {Indrajeet Patil},
#>     year = {2018},
#>     journal = {CRAN},
#>     url = {https://CRAN.R-project.org/package=ggstatsplot},
#>     doi = {10.5281/zenodo.2074621},
#>   }

Documentation and Examples

To see the documentation relevant for the development version of the package, see the dedicated website for statsExpressions, which is updated after every new commit: https://indrajeetpatil.github.io/statsExpressions/.

Summary of types of statistical analyses

Currently, it supports only the most common types of statistical tests. Specifically, parametric, non-parametric, robust, and bayesian versions of:

• t-test
• anova
• correlation tests
• contingency table analysis
• meta-analysis

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

Statistical reporting

For all statistical test expressions, the default template abides by the APA 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: https://indrajeetpatil.github.io/statsExpressions/articles/stats_details.html

Dataframe as output

The returned dataframe will contain the following columns (the exact columns will depend on the test and the statistical approach):

• statistic: the numeric value of a statistic.

• df: the numeric value of a parameter being modeled (often degrees of freedom for the test); note that if no.parameters = 0L (e.g., for non-parametric tests), this column will be irrelevant.

• df.error, df: relevant only if the statistic in question has two degrees of freedom (e.g., anova).

• p.value: the two-sided p-value associated with the observed statistic.

• method: the details of the statistical test carried out.

• estimate: estimated value of the effect size.

• conf.low: lower bound for effect size estimate.

• conf.high: upper bound for effect size estimate.

• conf.level: width of the confidence interval.

• effectsize: the details of the effect size.

All possible outputs from all functions are tabulated here: https://indrajeetpatil.github.io/statsExpressions/articles/web_only/dataframe_outputs.html

But here is one quick example:

# setup
library(statsExpressions)
set.seed(123)

# one-way ANOVA - parametric
mtcars %>% oneway_anova(x = cyl, y = wt)
#> # A tibble: 1 x 11
#>   statistic    df df.error   p.value
#>       <dbl> <dbl>    <dbl>     <dbl>
#> 1      20.2     2     19.0 0.0000196
#>   method                                                   estimate conf.level
#>   <chr>                                                       <dbl>      <dbl>
#> 1 One-way analysis of means (not assuming equal variances)    0.637       0.95
#>   conf.low conf.high effectsize expression
#>      <dbl>     <dbl> <chr>      <list>    
#> 1    0.309     0.785 Omega2     <language>

Needless to say this will also work with the kable function to generate a table:

# setup
library(statsExpressions)
set.seed(123)

# one-sample robust t-test
# we will leave `expression` column out; it's not needed for using only the dataframe
mtcars %>%
  one_sample_test(wt, test.value = 3, type = "robust") %>%
  dplyr::select(-expression) %>%
  knitr::kable()

These functions also play nicely with dplyr function. For example, let’s say we want to run a one-sample t-test for all levels of a certain grouping variable. Here is how you can do it:

# for reproducibility
set.seed(123)
library(dplyr)

# grouped operation
mtcars %>%
  group_by(cyl) %>%
  group_modify(~ one_sample_test(.x, wt, test.value = 3), .keep = TRUE) %>%
  ungroup()
#> # A tibble: 3 x 12
#>     cyl    mu statistic df.error  p.value method            estimate conf.level
#>   <dbl> <dbl>     <dbl>    <dbl>    <dbl> <chr>                <dbl>      <dbl>
#> 1     4     3    -4.16        10 0.00195  One Sample t-test   -1.16        0.95
#> 2     6     3     0.870        6 0.418    One Sample t-test    0.286       0.95
#> 3     8     3     4.92        13 0.000278 One Sample t-test    1.24        0.95
#>   conf.low conf.high effectsize expression
#>      <dbl>     <dbl> <chr>      <list>    
#> 1   -1.97     -0.422 Hedges' g  <language>
#> 2   -0.419     1.01  Hedges' g  <language>
#> 3    0.565     1.98  Hedges' g  <language>

Using expressions in custom plots

A list of primary functions in this package can be found at the package website: https://indrajeetpatil.github.io/statsExpressions/reference/index.html

Following are few examples of how these functions can be used.

Example: Expressions for one-way ANOVAs

Between-subjects design

Let’s say we want to check differences in weight of the vehicle based on number of cylinders in the engine and wish to carry out Welch’s ANOVA:

# setup
set.seed(123)
library(ggplot2)
library(ggforce)
library(statsExpressions)

# plot with subtitle
ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_violin() +
  geom_sina() +
  labs(
    title = "Fisher's one-way ANOVA",
    subtitle = oneway_anova(iris, Species, Sepal.Length, var.equal = TRUE)$expression[[1]]
  )

In case you change your mind and now want to carry out a robust ANOVA instead. Also, let’s use a different kind of a visualization:

# setup
set.seed(123)
library(ggplot2)
library(statsExpressions)
library(ggridges)

# create a ridgeplot
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(
    jittered_points = TRUE, quantile_lines = TRUE,
    scale = 0.9, vline_size = 1, vline_color = "red",
    position = position_raincloud(adjust_vlines = TRUE)
  ) +
  labs(
    title = "A heteroscedastic one-way ANOVA for trimmed means",
    subtitle = oneway_anova(iris, Species, Sepal.Length, type = "robust")$expression[[1]]
  )

Needless to say, you can also use these functions to display results in ggplot-extension packages. For example, ggpubr:

set.seed(123)
library(ggpubr)
library(ggplot2)

# plot
ggboxplot(
  ToothGrowth,
  x = "dose",
  y = "len",
  color = "dose",
  palette = c("#00AFBB", "#E7B800", "#FC4E07"),
  add = "jitter",
  shape = "dose"
) + # adding results from stats analysis using `statsExpressions`
  labs(
    title = "Kruskall-Wallis test",
    subtitle = oneway_anova(ToothGrowth, dose, len, type = "np")$expression[[1]]
  )

Within-subjects design

Let’s now see an example of a repeated measures one-way ANOVA.

# setup
set.seed(123)
library(ggplot2)
library(WRS2)
library(ggbeeswarm)
library(statsExpressions)

ggplot2::ggplot(WineTasting, aes(Wine, Taste, color = Wine)) +
  geom_quasirandom() +
  labs(
    title = "Friedman's rank sum test",
    subtitle = oneway_anova(
      WineTasting,
      Wine,
      Taste,
      paired = TRUE,
      subject.id = Taster,
      type = "np"
    )$expression[[1]]
  )

Example: Expressions for two-sample tests

Between-subjects design

# setup
set.seed(123)
library(ggplot2)
library(gghalves)
library(ggbeeswarm)
library(hrbrthemes)
library(statsExpressions)

# create a plot
ggplot(ToothGrowth, aes(supp, len)) +
  geom_half_boxplot() +
  geom_beeswarm(beeswarmArgs = list(side = 1)) +
  theme_ipsum_rc() +
  # adding a subtitle with
  labs(
    title = "Two-Sample Welch's t-test",
    subtitle = two_sample_test(ToothGrowth, supp, len)$expression[[1]]
  )

Example with ggpubr:

# setup
set.seed(123)
library(ggplot2)
library(ggpubr)
library(statsExpressions)

# basic plot
gghistogram(
  data.frame(
    sex = factor(rep(c("F", "M"), each = 200)),
    weight = c(rnorm(200, 55), rnorm(200, 58))
  ),
  x = "weight",
  add = "mean",
  rug = TRUE,
  fill = "sex",
  palette = c("#00AFBB", "#E7B800"),
  add_density = TRUE
) + # displaying stats results
  labs(
    title = "Yuen's two-sample test for trimmed means",
    subtitle = two_sample_test(
      data = data.frame(
        sex = factor(rep(c("F", "M"), each = 200)),
        weight = c(rnorm(200, 55), rnorm(200, 58))
      ),
      x = sex,
      y = weight,
      type = "robust"
    )$expression[[1]]
  )

Another example with ggiraphExtra:

# setup
set.seed(123)
library(ggplot2)
library(ggiraphExtra)
library(gcookbook)
library(statsExpressions)

# plot
ggDot(heightweight, aes(sex, heightIn, fill = sex),
  boxfill = "white",
  binwidth = 0.4
) +
  labs(
    title = "Mann-Whitney test",
    subtitle = two_sample_test(heightweight, sex, heightIn, type = "nonparametric")$expression[[1]]
  )

Within-subjects design

We can also have a look at a repeated measures design and the related expressions.

# setup
set.seed(123)
library(ggplot2)
library(statsExpressions)
library(tidyr)
library(PairedData)
data(PrisonStress)

# plot
paired.plotProfiles(PrisonStress, "PSSbefore", "PSSafter", subjects = "Subject") +
  # `statsExpressions` needs data in the tidy format
  labs(
    title = "Two-sample Wilcoxon paired test",
    subtitle = two_sample_test(
      data = pivot_longer(PrisonStress, starts_with("PSS"), "PSS", values_to = "stress"),
      x = PSS,
      y = stress,
      paired = TRUE,
      subject.id = Subject,
      type = "np"
    )$expression[[1]]
  )

Example: Expressions for one-sample tests

# setup
set.seed(123)
library(ggplot2)
library(statsExpressions)

# creating a histogram plot
ggplot(mtcars, aes(wt)) +
  geom_histogram(alpha = 0.5) +
  geom_vline(xintercept = mean(mtcars$wt), color = "red") +
  # adding a caption with a non-parametric one-sample test
  labs(
    title = "One-Sample Wilcoxon Signed Rank Test",
    subtitle = one_sample_test(mtcars, wt, test.value = 3, type = "nonparametric")$expression[[1]]
  )

Example: Expressions for correlation analyses

Let’s look at another example where we want to run correlation analysis:

# setup
set.seed(123)
library(ggplot2)
library(statsExpressions)

# create a scatter plot
ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  geom_smooth(method = "lm", formula = y ~ x) +
  labs(
    title = "Spearman's rank correlation coefficient",
    subtitle = corr_test(mtcars, mpg, wt, type = "nonparametric")$expression[[1]]
  )

Another example

# setup
set.seed(123)
library(ggplot2)
library(ggExtra)
library(statsExpressions)

# basic plot
p <-
  ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(
    title = "Pearson's correlation coefficient",
    subtitle = corr_test(mtcars, mpg, wt, type = "parametric")$expression[[1]]
  )

# add
ggMarginal(p, type = "histogram", xparams = list(binwidth = 1, fill = "orange"))

Example: Expressions for contingency table analysis

For categorical/nominal data - one-sample:

# setup
set.seed(123)
library(ggplot2)
library(statsExpressions)

# basic pie chart
ggplot(as.data.frame(table(mpg$class)), aes(x = "", y = Freq, fill = factor(Var1))) +
  geom_bar(width = 1, stat = "identity") +
  theme(axis.line = element_blank()) +
  # cleaning up the chart and adding results from one-sample proportion test
  coord_polar(theta = "y", start = 0) +
  labs(
    fill = "Class",
    x = NULL,
    y = NULL,
    title = "Pie Chart of class (type of car)",
    subtitle = contingency_table(as.data.frame(table(mpg$class)), Var1, counts = Freq)$expression[[1]],
    caption = "One-sample goodness of fit proportion test"
  )

Another example of contingency tabs analysis:

# setup
set.seed(123)
library(moonBook)
library(ggiraphExtra)
library(statsExpressions)

# plot
ggSpine(
  data = acs,
  aes(x = Dx, fill = smoking),
  addlabel = TRUE,
  interactive = FALSE
) +
  labs(
    x = "diagnosis",
    title = "Pearson's chi-squared contingency table test for counts",
    subtitle = contingency_table(acs, Dx, smoking, paired = FALSE)$expression[[1]]
  )

You can also use these function to get the expression in return without having to display them in plots:

# setup
set.seed(123)
library(ggplot2)
library(statsExpressions)

# Pearson's chi-squared test of independence
contingency_table(mtcars, am, cyl)$expression[[1]]
#> paste(chi["Pearson"]^2, "(", "2", ") = ", "8.74", ", ", italic("p"), 
#>     " = ", "0.013", ", ", widehat(italic("V"))["Cramer"], " = ", 
#>     "0.46", ", CI"["95%"], " [", "0.00", ", ", "0.78", "]", ", ", 
#>     italic("n")["obs"], " = ", "32")

Example: Expressions for meta-analysis

# setup
set.seed(123)
library(metaviz)
library(ggplot2)
library(metaplus)

# meta-analysis forest plot with results random-effects meta-analysis
viz_forest(
  x = mozart[, c("d", "se")],
  study_labels = mozart[, "study_name"],
  xlab = "Cohen's d",
  variant = "thick",
  type = "cumulative"
) + # use `statsExpressions` to create expression containing results
  labs(
    title = "Meta-analysis of Pietschnig, Voracek, and Formann (2010) on the Mozart effect",
    subtitle = meta_analysis(dplyr::rename(mozart, estimate = d, std.error = se))$expression[[1]]
  ) +
  theme(text = element_text(size = 12))

Customizing details to your liking

Sometimes you may not wish include so many details in the subtitle. In that case, you can extract the expression and copy-paste only the part you wish to include. For example, here only statistic and p-values are included:

# setup
set.seed(123)
library(ggplot2)
library(statsExpressions)

# extracting detailed expression
(res_expr <- oneway_anova(iris, Species, Sepal.Length, var.equal = TRUE)$expression[[1]])
#> paste(italic("F")["Fisher"], "(", "2", ",", "147", ") = ", "119.26", 
#>     ", ", italic("p"), " = ", "1.67e-31", ", ", widehat(omega["p"]^2), 
#>     " = ", "0.61", ", CI"["95%"], " [", "0.52", ", ", "0.68", 
#>     "]", ", ", italic("n")["obs"], " = ", "150")

# adapting the details to your liking
ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot() +
  labs(subtitle = ggplot2::expr(paste(
    NULL, italic("F"), "(", "2",
    ",", "147", ") = ", "119.26", ", ",
    italic("p"), " = ", "1.67e-31"
  )))

Usage in ggstatsplot

Note that these functions were initially written to display results from statistical tests on ready-made ggplot2 plots implemented in ggstatsplot.

For detailed documentation, see the package website: https://indrajeetpatil.github.io/ggstatsplot/

Here is an example from ggstatsplot of what the plots look like when the expressions are displayed in the subtitle-

Acknowledgments

The hexsticker was generously designed by Sarah Otterstetter (Max Planck Institute for Human Development, Berlin).

Code coverage

As the code stands right now, here is the code coverage for all primary functions involved: https://codecov.io/gh/IndrajeetPatil/statsExpressions/tree/master/R

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.