statsExpressions: Expressions with statistical details

Introduction

statsExpressions provides statistical processing backend for the ggstatsplot package, which combines ggplot2 visualizations with expressions containing results from statistical tests. statsExpressions contains all functions needed to create these expressions.

Installation

To get the latest, stable CRAN release:

install.packages(pkgs = "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
utils::install.packages(pkgs = "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  upgrade_dependencies = TRUE # updates any out of date dependencies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")

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:

Primary functions

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-sample t-test

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

# create fake data
df <- data.frame(x = rnorm(1000, 0.1, 0.5))

# creating a histogram plot
p <- ggplot(df, aes(x)) +
  geom_histogram(alpha = 0.5) +
  geom_vline(xintercept = mean(df$x), color = "red")

# adding a caption with a non-parametric one-sample test
p + labs(
  title = "One-Sample Wilcoxon Signed Rank Test",
  caption = expr_t_onesample(df, x, type = "nonparametric")
)
#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.

Example: Expressions for two-sample t-test

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

# create a plot
p <-
  ggplot(ToothGrowth, aes(supp, len)) +
  geom_boxplot() +
  theme_ipsum_rc()

# adding a subtitle with
p + labs(
  title = "Two-Sample Welch's t-test",
  subtitle = expr_t_parametric(ToothGrowth, supp, len)
)

Example: Expressions for one-way ANOVA

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(statsExpressions)

# create a boxplot
ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot() +
  labs(
    title = "Welch's ANOVA",
    subtitle = expr_anova_parametric(iris, Species, Sepal.Length, messages = FALSE)
  )

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
p <-
  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)
  )

# create an expression containing details from the relevant test
results <- expr_anova_robust(iris, Species, Sepal.Length, messages = FALSE)

# display results on the plot
p + labs(
  title = "A heteroscedastic one-way ANOVA for trimmed means",
  subtitle = results
)

Example: Expressions for correlation analysis

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

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

# create a ridgeplot
p <-
  ggplot(mtcars, aes(x = mpg, y = wt)) +
  geom_point() +
  geom_smooth(method = "lm")

# create an expression containing details from the relevant test
results <- expr_corr_test(mtcars, mpg, wt, type = "nonparametric")

# display results on the plot
p + labs(
  title = "Spearman's rank correlation coefficient",
  subtitle = results
)

Example: Expressions for contingency table analysis

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

# data
df <- as.data.frame(table(mpg$class))
colnames(df) <- c("class", "freq")

# basic pie chart
p <-
  ggplot(df, aes(x = "", y = freq, fill = factor(class))) +
  geom_bar(width = 1, stat = "identity") +
  theme(
    axis.line = element_blank(),
    plot.title = element_text(hjust = 0.5)
  )

# cleaning up the chart and adding results from one-sample proportion test
p +
  coord_polar(theta = "y", start = 0) +
  labs(
    fill = "class",
    x = NULL,
    y = NULL,
    title = "Pie Chart of class",
    subtitle = expr_onesample_proptest(df, class, counts = freq),
    caption = "One-sample goodness of fit proportion test"
  )
#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.

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
expr_contingency_tab(mtcars, am, cyl, messages = FALSE)
#> paste(NULL, chi["Pearson"]^2, "(", "2", ") = ", "8.74", ", ", 
#>     italic("p"), " = ", "0.013", ", ", widehat(italic("V"))["Cramer"], 
#>     " = ", "0.46", ", CI"["95%"], " [", "0.08", ", ", "0.75", 
#>     "]", ", ", italic("n")["obs"], " = ", 32L)

Example: Expressions for meta-analysis

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

# rename columns to `statsExpressions` conventions
df <- dplyr::rename(mozart, estimate = d, std.error = se)

# 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 = expr_meta_parametric(df, k = 3)
  ) +
  theme(text = element_text(size = 12))

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-

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.