Customize Forest Plots and Tables • ggforestplotR

library(ggforestplotR)
library(ggplot2)

This article focuses on grouped displays, separator lines, table composition, and ggplot2 styling.

Base plotting data

coefs <- data.frame(
  term = c("Age", "BMI", "Smoking", "Stage II", "Stage III"),
  estimate = c(0.12, -0.10, 0.18, 0.30, 0.46),
  conf.low = c(0.03, -0.18, 0.04, 0.10, 0.18),
  conf.high = c(0.21, -0.02, 0.32, 0.50, 0.74),
  sample_size = c(120, 115, 98, 87, 83),
  p_value = c(0.04, 0.15, 0.29, 0.001, 0.75),
  section = c("Clinical", "Clinical", "Clinical", "Tumor", "Tumor")
)

Group rows and control strip placement

grouping creates section panels, and grouping_strip_position controls which side gets the strip labels.

ggforestplot(
  coefs,
  grouping = "section",
  grouping_strip_position = "right",
  striped_rows = TRUE
) +
  ggplot2::labs(title = "Grouped forest plot with right-side strips")

Add separator lines around a multi-level variable

Use separator_group and separator_lines when a categorical variable expands into several levels and you want visible boundaries around that block.

race_coefs <- data.frame(
  term = c("race_black", "race_white", "race_other", "age", "bmi"),
  label = c("Black", "White", "Other", "Age", "BMI"),
  estimate = c(0.24, 0.08, -0.04, 0.12, -0.09),
  conf.low = c(0.10, -0.04, -0.18, 0.03, -0.17),
  conf.high = c(0.38, 0.20, 0.10, 0.21, -0.01),
  variable_block = c("Race", "Race", "Race", "Age", "BMI")
)

ggforestplot(
  race_coefs,
  label = "label",
  separator_group = "variable_block",
  separator_lines = TRUE,
  striped_rows = TRUE
) +
  ggplot2::labs(title = "Separator lines around a multi-level Race variable")

Add a side table

add_forest_table() is the simpler composition helper when all summary columns should sit on one side of the plot. Add it last, after the main plot styling, because it returns a patchwork composition rather than a plain ggplot.

ggforestplot(
  coefs,
  grouping = "section",
  n = "sample_size",
  p.value = "p_value",
  striped_rows = TRUE
) +
  ggplot2::labs(title = "Left-side summary table") +
  add_forest_table(
    position = "left",
    show_n = TRUE,
    show_p = TRUE,
    estimate_label = "Beta"
  )

Use split tables

add_split_table() is better when term labels should stay on the left and the formatted statistics should move to the right. Like add_forest_table(), it should be added after any plot-level styling.

ggforestplot(
  coefs,
  n = "sample_size",
  p.value = "p_value",
  striped_rows = TRUE
) +
  ggplot2::labs(title = "Split table layout") +
  add_split_table(
    left_columns = c("term", "n"),
    right_columns = c("estimate", "p"),
    estimate_label = "Beta"
  )

You can also specify split-table columns by position instead of name.

ggforestplot(
  coefs,
  n = "sample_size",
  p.value = "p_value"
) +
  add_split_table(
    left_columns = c(1, 2),
    right_columns = c(3, 4),
    estimate_label = "Beta"
  )

Plot odds ratios from logistic regression

For logistic regression models, use exponentiate = TRUE so the x-axis is on an odds-ratio scale and the null reference stays at 1.

set.seed(123)

logit_data <- data.frame(
  age = rnorm(250, mean = 62, sd = 8),
  bmi = rnorm(250, mean = 28, sd = 4),
  treatment = factor(rbinom(250, 1, 0.45), labels = c("Control", "Treatment"))
)

linpred <- -9 +
  0.09 * logit_data$age +
  0.11 * logit_data$bmi +
  0.9 * (logit_data$treatment == "Treatment")

logit_data$event <- rbinom(250, 1, plogis(linpred))

logit_fit <- glm(event ~ age + bmi + treatment, data = logit_data, family = binomial())

ggforestplot(logit_fit, exponentiate = TRUE) +
  ggplot2::labs(
    title = "Odds ratios from a logistic regression model",
    x = "Odds ratio"
  )

You can attach a table here as well and relabel the estimate column for odds ratios explicitly.

ggforestplot(logit_fit, exponentiate = TRUE) +
  ggplot2::labs(title = "Logistic regression with an odds-ratio table") +
  add_forest_table(position = "right", estimate_label = "OR", show_p = TRUE)

Compare multiple estimates per term

The group argument is for multiple estimates on the same row.

comparison_coefs <- data.frame(
  term = rep(c("Age", "BMI", "Smoking", "Stage II", "Stage III"), 2),
  estimate = c(0.12, -0.10, 0.18, 0.30, 0.46, 0.08, -0.05, 0.24, 0.40, 0.58),
  conf.low = c(0.03, -0.18, 0.04, 0.10, 0.18, 0.00, -0.13, 0.10, 0.20, 0.30),
  conf.high = c(0.21, -0.02, 0.32, 0.50, 0.74, 0.16, 0.03, 0.38, 0.60, 0.86),
  model = rep(c("Model A", "Model B"), each = 5),
  section = rep(c("Clinical", "Clinical", "Clinical", "Tumor", "Tumor"), 2)
)

ggforestplot(
  comparison_coefs,
  group = "model",
  grouping = "section",
  striped_rows = TRUE,
  dodge_width = 0.5
) +
  ggplot2::labs(title = "Comparing two model specifications")

Finish with ggplot2 styling

The returned object is still a normal ggplot until you add a table helper.

ggforestplot(
  comparison_coefs,
  group = "model",
  grouping = "section",
  striped_rows = TRUE,
  stripe_fill = "#F3F4F6",
  zero_line_colour = "#9CA3AF",
  zero_line_linetype = 3,
  point_size = 2.8,
  line_size = 0.6
) +
  ggplot2::labs(
    title = "Customized grouped forest plot",
    x = "Effect estimate",
    subtitle = "Clinical and tumor terms shown in separate sections"
  ) +
  ggplot2::scale_colour_manual(
    values = c("Model A" = "#1D4ED8", "Model B" = "#D97706")
  ) +
  ggplot2::theme(
    legend.position = "top",
    panel.grid.major.y = ggplot2::element_blank(),
    strip.background = ggplot2::element_rect(fill = "#E5E7EB", colour = NA),
    strip.text.y.left = ggplot2::element_text(face = "bold"),
    plot.title.position = "plot"
  )