Coming from meta::forest()

The same object, a ggplot result

meta::forest() draws directly to a graphics device with a grid-based layout. ggmeta::ggforest() takes the same meta object but returns a ggplot:

library(meta)

m <- metabin(
  event.e = c(14, 30, 15, 22), n.e = c(100, 150, 100, 120),
  event.c = c(10, 25, 12, 18), n.c = c(100, 150, 100, 120),
  studlab = c("Study A", "Study B", "Study C", "Study D"),
  sm = "RR"
)

# meta::forest(m)   # base-graphics forest plot
ggforest(m)         # the same analysis, as a ggplot

The practical difference: everything after ggforest() is ggplot2. You compose with +, restyle with theme(), add annotations, and save with ggsave().

What maps to what

Reproducing common tweaks

Relabel the axis and add a title. These are ordinary ggplot2 calls:

ggforest(m) +
  labs(title = "Risk of the event", x = "Risk ratio (log scale)")

Add the effect / CI / weight columns — the rightcols idea — with columns:

ggforest(m, columns = TRUE)

For a custom column (say a sample-size column of your own), reach for geom_forest_text(). tidy_meta() exposes the same tidy data frame ggforest() builds internally, so you can align your own text to the rows:

td <- tidy_meta(m)
studies <- td[!td$is_summary, ]
studies$effect_txt <- format_effect(studies$estimate, studies$ci_lower, studies$ci_upper)

ggforest(m) +
  geom_forest_text(aes(y = studlab, label = effect_txt), data = studies,
                   x = 4.2, hjust = 0) +
  expand_limits(x = 7)

When to use which

• Reach for meta::forest() when you want its exhaustive, print-ready defaults out of the box and don’t need to restyle.
• Reach for ggmeta when you want a ggplot you can theme, compose, facet, and drop into an existing ggplot2 workflow — or when you only have a tidy data frame of effect sizes and no meta object at all (see vignette("getting-started")).