The clustered correlogram
The workhorse figure: reorder the variables by hierarchical clustering so correlated variables sit together and the block structure is visible, and draw thin white separators between cells.
This is a gallery of finished figures — each with the code that produces it — for the kinds of correlograms that show up in papers. For the argument-by-argument tour of every option, see vignette("ggcorrplot").
Two facts make ggcorrplot well suited to publication work: it takes a correlation matrix you already have (so it never gets between you and your statistics), and it returns a plain ggplot object, so any figure here can be restyled, titled, and combined with +.
library(ggcorrplot)
library(ggplot2)
data(mtcars)
corr <- round(cor(mtcars), 1)
p.mat <- cor_pmat(mtcars)The workhorse figure: reorder the variables by hierarchical clustering so correlated variables sit together and the block structure is visible, and draw thin white separators between cells.
For a symmetric matrix the two triangles are redundant, so a paper usually shows one, often with the coefficients printed in the cells — a figure that replaces a correlation table.
Supply the p-value matrix from cor_pmat() and choose how to convey significance. insig = "blank" drops the non-significant cells; insig = "stars" instead marks the significant ones with */**/*** — a standalone significance map.
# non-significant cells left blank
ggcorrplot(corr, hc.order = TRUE, type = "lower", p.mat = p.mat, insig = "blank")
# significant cells starred
ggcorrplot(corr, p.mat = p.mat, insig = "stars")
The default, insig = "pch", crosses out the non-significant cells with an X instead.
method = "circle" encodes the correlation with the circle’s area, so strong correlations pop out — the familiar corrplot look, drawn in ggplot2.
scale.square = TRUE sizes the squares by the absolute correlation, so magnitude is encoded by both area and color at once — the classic corrplot square look. Near-zero cells shrink to small squares while the strong correlations dominate, which reads well for large matrices and for colorblind viewers.
cell.grid = TRUE draws a light rectangle around every cell and drops the gridlines that otherwise run through the glyph centers, so each sized glyph sits inside its own box. Combined with scale.square = TRUE (or method = "circle") this is the boxed-cell corrplot signature, drawn in ggplot2. It has no effect on a plain full-tile square heatmap, whose cells already have a border.
# size-scaled squares in boxed cells
ggcorrplot(corr, scale.square = TRUE, cell.grid = TRUE, hc.order = TRUE, outline.color = "white")
# circles in boxed cells
ggcorrplot(corr, method = "circle", cell.grid = TRUE, hc.order = TRUE)
preset = "publication" is a one-token beautiful default (white separators + the colorblind-safe RdBu palette). For a specific journal look, set colors and ggtheme yourself.
# one-token publication preset
ggcorrplot(corr, hc.order = TRUE, preset = "publication")
# a custom diverging palette on a minimal theme
ggcorrplot(corr,
hc.order = TRUE, type = "lower", outline.color = "white",
ggtheme = theme_minimal, colors = c("#6D9EC1", "white", "#E46726")
)
A correlogram of solid colored squares with no cell border — the look used in many module–trait and omics papers. method = "square" is a full-tile heatmap; outline.color = NA removes the border. Reverse the default gradient to c("red", "white", "blue") for red-negative / blue-positive, and put the variable names on top with a one-line scale.
ggcorrplot(corr,
outline.color = NA,
colors = c("red", "white", "blue"),
legend.title = "Correlation"
) +
scale_x_discrete(position = "top")
Correlations are not always a square symmetric matrix. To relate one set of variables to another — say engine/size variables against performance variables — pass a rectangular correlation matrix. Clustering and the triangle options need a square matrix, so use hc.order = FALSE.
rect <- round(cor(
mtcars[, c("mpg", "hp", "wt", "qsec")],
mtcars[, c("disp", "drat", "vs", "am", "gear")]
), 1)
ggcorrplot(rect, hc.order = FALSE, lab = TRUE, outline.color = "white")
Because ggcorrplot() returns a ggplot object, anything ggplot2 can do is available. Start from any correlogram and add a title, a clearer legend label, and theme tweaks; then save at print resolution.
The legend label is a ggcorrplot argument (legend.title); the title, subtitle and theme come from ggplot2.
p <- ggcorrplot(corr,
hc.order = TRUE, type = "lower", outline.color = "white",
legend.title = "Pearson r"
) +
labs(
title = "Correlations among car-design variables",
subtitle = "mtcars, Pearson correlation"
) +
theme(plot.title = element_text(face = "bold"))
p
sessionInfo()
#> R version 4.5.1 (2025-06-13)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Sequoia 15.6.1
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
#>
#> locale:
#> [1] C/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8
#>
#> time zone: Europe/Paris
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggcorrplot_0.3.0 ggplot2_4.0.3
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 jsonlite_2.0.0 dplyr_1.2.1 compiler_4.5.1
#> [5] tidyselect_1.2.1 Rcpp_1.1.1 stringr_1.6.0 jquerylib_0.1.4
#> [9] scales_1.4.0 yaml_2.3.12 fastmap_1.2.0 R6_2.6.1
#> [13] plyr_1.8.9 labeling_0.4.3 generics_0.1.4 knitr_1.51
#> [17] tibble_3.3.1 bslib_0.10.0 pillar_1.11.1 RColorBrewer_1.1-3
#> [21] rlang_1.2.0 cachem_1.1.0 stringi_1.8.7 xfun_0.57
#> [25] sass_0.4.10 S7_0.2.1 otel_0.2.0 cli_3.6.6
#> [29] withr_3.0.2 magrittr_2.0.4 digest_0.6.39 grid_4.5.1
#> [33] lifecycle_1.0.5 vctrs_0.7.2 evaluate_1.0.5 glue_1.8.0
#> [37] farver_2.1.2 reshape2_1.4.5 rmarkdown_2.31 tools_4.5.1
#> [41] pkgconfig_2.0.3 htmltools_0.5.9