Customising forest and funnel plots

ggforest() and ggfunnel() assemble several layers for you — study confidence intervals, summary diamonds, a prediction interval, reference lines, funnel points and contours. You restyle any of them with a matching *_args argument: a named list of arguments passed straight to the underlying geom.

This is the intended way to change those elements. Adding another geom_forest_*() layer to a ggforest() plot does not restyle the built-in one — it draws a second layer over every row.

library(ggmeta)
library(ggplot2)

library(meta)
#> Loading required package: metabook
#> Loading 'meta' package (version 8.5-0).
#> Type 'help(meta)' for a brief overview.

dat <- data.frame(
  study   = c("Adams 2019", "Baker 2020", "Chen 2020",
              "Diaz 2021", "Evans 2022", "Foster 2023"),
  event.e = c(12,  8, 25, 18, 30, 15), n.e = c(120,  90, 200, 150, 250, 130),
  event.c = c(20, 14, 30, 28, 35, 25), n.c = c(118,  92, 205, 148, 245, 128)
)
m <- metabin(event.e, n.e, event.c, n.c,
             data = dat, studlab = study, sm = "RR")

The prediction interval

predict_args controls the prediction interval — colour, linetype, linewidth, alpha, and the end-cap size cap_width:

ggforest(m, predict_args = list(
  cap_width = 0.1, colour = "firebrick", linewidth = 0.8, linetype = "solid"
))

Summary diamonds

Recolour the diamonds with diamond_colours — a named vector keyed by "common", "random", "subgroup_common", "subgroup_random" — and restyle their border or transparency with diamond_args:

ggforest(m,
  diamond_colours = c(common = "grey45", random = "#1B7837"),
  diamond_args    = list(colour = "grey20", alpha = 1)
)

Study intervals and reference lines

ci_args styles the study confidence intervals and their weight-proportional squares (including point_size_range). ref_args styles the null-effect line, and consensus / consensus_args control the dotted pooled-estimate line:

ggforest(m,
  ci_args   = list(colour = "grey30", point_size_range = c(1, 5)),
  ref_args  = list(linetype = "dashed"),
  consensus = FALSE
)

Everything together

The styling arguments combine freely, and work with the meta::forest()-style table columns too:

ggforest(m, columns = TRUE,
  predict_args    = list(cap_width = 0.1, colour = "firebrick"),
  diamond_colours = c(common = "grey45", random = "#1B7837"),
  ci_args         = list(colour = "grey30")
)

Funnel plots

ggfunnel() follows the same pattern with point_args (the study points), contour_args (the pseudo confidence-interval contours), and ref_args (the vertical reference line):

ggfunnel(m,
  point_args   = list(size = 3, fill = "#1B7837"),
  contour_args = list(colour = "grey70", linetype = "dotted", level = c(0.95, 0.99)),
  ref_args     = list(colour = "firebrick")
)