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circlecorR

Circular correlation “wheel” plots in native R.

circlecorR draws correlation wheel plots with variables arranged around a ring, grouped and colour-tiled by category, connected by curved links whose colour maps to the correlation coefficient using circlize.

A traditional correlation matrix is mostly redundant. A diagonal of self-correlations, two mirror-image triangles, and blocks of within-category correlations bury the between-category relationships we care about. The wheel, first seen in Gharibans et al. (2019), keeps only the relationships of interest. Hiding self- and within-category correlations also removes them from the multiple-comparison family, which improves statistical power (see vignette("circlecorR")).

Installation

# install.packages("remotes")
remotes::install_github("kriz98/circlecorR", build_vignettes = TRUE)

(CRAN release pending.)

Dependencies (circlize and psych) install automatically with the command above.

Quick start — straight from your data

circlecorR is designed to work straight from a data frame, one row per subject, one column per variable, with no separate step to build correlation matrices yourself. Hand that data frame to corr_wheel() and it computes the correlations and p-values for you. The only other thing you supply is groups, which both selects the variables and orders them around the wheel (extra columns like IDs are ignored).

library(circlecorR)

# `gastro_symptoms` is a synthetic example dataset bundled with the package --
# swap it for your own data frame (one row per subject) to use your own data.
groups <- list(
  Demographics = c("Age", "BMI"),
  Metrics      = c("Amplitude", "Fed-Fasted AR", "Frequency", "GA-RI"),
  Symptoms     = c("Nausea", "Early satiety", "Bloating",
                   "Upper GI pain", "Lower GI pain", "Heartburn"),
  Scores       = c("GCSI", "PAGI-SYM", "PAGI-QoL", "EQ-5D")
)

corr_wheel(
  gastro_symptoms,          # one row per subject -- use your own data here
  groups      = groups,
  method      = "pearson",
  adjust      = "hochberg",
  sig_level   = 0.05,
  r_threshold = 0.3,
  r_limits    = c(-0.6, 0.6)
)

Save to file

png("correlation_wheel.png", width = 2400, height = 2000, res = 300)
corr_wheel(gastro_symptoms, groups = groups, r_threshold = 0.3, r_limits = c(-0.6, 0.6))
dev.off()

See vignette("circlecorR") for the full tour.

Flexibility

Everything the reference figure controls is a plain argument:

Built-in schemes are listed with corr_wheel_schemes() and inspected/tweaked with corr_wheel_scheme(); see vignette("circlecorR") for examples.

corr_wheel() returns (invisibly) the ordered variables, resolved group and colour maps, the colour function, and the masked matrix actually plotted — handy for reproducibility or building a caption.

Example data

gastro_symptoms is a bundled synthetic dataset — fully simulated, not patient data — mimicking a gastric-symptom study, one row per subject. It’s loaded automatically with the package (LazyData: true), so it’s available directly as soon as you library(circlecorR) — no data() call needed. See ?gastro_symptoms for details, or use it as a template for your own data’s shape.

How masking & statistics work

A link between variables i and j is drawn only if all hold:

1. they are in different categories (when hide_within_group = TRUE), and never the same variable (self-correlations are always excluded);
2. p_adjusted <= sig_level; and
3. |r| >= r_threshold.

The correlations left after step 1 form the family of comparisons. The adjust correction is applied over only that family — so redundant self- and within-category correlations don’t inflate the count, and power improves. corr_wheel() returns n_tests (the family size) and the family-adjusted p-matrix for reporting.

References

The correlation wheel was introduced in:

Gharibans AA, Coleman TP, Mousa H, Kunkel DC. Spatial patterns from high-resolution electrogastrography correlate with severity of symptoms in patients with functional dyspepsia and gastroparesis. Clin Gastroenterol Hepatol. 2019 Dec;17(13):2668–77.

It has since been used in several other gastric physiology studies:

• Gharibans AA, Calder S, Varghese C, Waite S, Schamberg G, Daker C, et al. Gastric dysfunction in patients with chronic nausea and vomiting syndromes defined by a noninvasive gastric mapping device. Sci Transl Med. 2022 Sep 21;14(663):eabq3544.
• Wang TH-H, Varghese C, Calder S, Gharibans A, Schamberg G, Bartlett A, et al. Long-term evaluation of gastric electrophysiology, symptoms and quality of life after pancreaticoduodenectomy. HPB (Oxford). 2025 Dec;27(12):1535–42.
• Xu W, Wang T, Foong D, Schamberg G, Evennett N, Beban G, et al. Characterization of gastric dysfunction after fundoplication using body surface gastric mapping. J Gastrointest Surg. 2024 Mar;28(3):236–45.
• Xu W, Gharibans AA, Calder S, Schamberg G, Walters A, Jang J, et al. Defining and phenotyping gastric abnormalities in long-term type 1 diabetes using a novel body surface gastric mapping device. Gastro Hep Adv. 2023 Aug 18;2(8):1120–32.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.