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 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.

ggcorrplot(corr, hc.order = TRUE, outline.color = "white")

Lower triangle with coefficients

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.

ggcorrplot(corr, hc.order = TRUE, type = "lower", lab = TRUE, lab_size = 3)

Marking significance

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.

Circles for magnitude

method = "circle" encodes the correlation with the circle’s area, so strong correlations pop out — the familiar corrplot look, drawn in ggplot2.

ggcorrplot(corr, method = "circle", hc.order = TRUE, type = "upper", outline.color = "white")

Size-scaled squares

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.

ggcorrplot(corr, scale.square = TRUE, hc.order = TRUE, outline.color = "white")

corrplot-style boxed cells

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)

A colorblind-safe palette

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 clean, edgeless heatmap

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")

A rectangular predictor-by-outcome matrix

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")

Going further: it is a ggplot

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

ggsave("correlogram.png", p, width = 7, height = 6, dpi = 300)

Session information

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] ggcorrplot_0.2.0.9000 ggplot2_4.0.3         rmarkdown_2.31       
#> 
#> loaded via a namespace (and not attached):
#>  [1] vctrs_0.7.3        cli_3.6.6          knitr_1.51         rlang_1.3.0       
#>  [5] xfun_0.60          stringi_1.8.7      otel_0.2.0         S7_0.2.2          
#>  [9] jsonlite_2.0.0     labeling_0.4.3     glue_1.8.1         buildtools_1.0.0  
#> [13] plyr_1.8.9         htmltools_0.5.9    maketools_1.3.2    sys_3.4.3         
#> [17] sass_0.4.10        scales_1.4.0       grid_4.6.1         evaluate_1.0.5    
#> [21] jquerylib_0.1.4    fastmap_1.2.0      reshape2_1.4.5     yaml_2.3.12       
#> [25] lifecycle_1.0.5    stringr_1.6.0      compiler_4.6.1     RColorBrewer_1.1-3
#> [29] Rcpp_1.1.2         farver_2.1.2       digest_0.6.39      R6_2.6.1          
#> [33] magrittr_2.0.5     bslib_0.11.0       withr_3.0.3        tools_4.6.1       
#> [37] gtable_0.3.6       cachem_1.1.0

Publication-ready correlation plots