Get Started with ggforestplotR

library(ggforestplotR)
library(ggplot2)

ggforestplotR is built for coefficient-driven forest plots that stay inside a normal ggplot2 workflow.

Choose a workflow

Use the package in one of two ways:

Start from a coefficient table and map the required columns directly to the plot.
Start from a fitted model and let its as_forest_data() method or ggforestplot() create the validated forest data and plot.

This article covers some basic examples and the minimum data you need.

Start from a coefficient table

The simplest input is a data frame with a column for terms, estimates, and confidence limits. If your columns use different names, you can map them explicitly.

There is also functionality to rename term labels and sort terms to your liking, among other things.

basic_coefs <- data.frame(
  term = c("Age", "BMI", "Treatment"),
  estimate = c(0.10, -0.08, 0.34),
  conf.low = c(0.02, -0.16, 0.12),
  conf.high = c(0.18, 0.00, 0.56)
)

ggforestplot(basic_coefs,
             term_labels = c("Age" = "age", "BMI" = "bmi", "Treatment" = "trt"),
             sort_terms = "descending")

Add grouped sections and row striping

Use facet when you want related variables separated into labeled panels. Add striped_rows = TRUE to color alternating rows in the plot.

sectioned_coefs <- data.frame(
  term = c("Age", "BMI", "Smoking", "Stage II", "Stage III", "Nodes"),
  estimate = c(0.10, -0.08, 0.20, 0.34, 0.52, 0.28),
  conf.low = c(0.02, -0.16, 0.05, 0.12, 0.20, 0.06),
  conf.high = c(0.18, 0.00, 0.35, 0.56, 0.84, 0.50),
  section = c("Clinical", "Clinical", "Clinical", "Tumor", "Tumor", "Tumor")
)

ggforestplot(
  sectioned_coefs,
  facet = "section",
  striped_rows = TRUE,
  stripe_fill = "grey94",
  facet_strip_position = "right",
  sort_terms = "ascending"
)

Add a summary table

Use add_forest_table() to add a summary table to your forest plot.

tabled_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)
)

ggforestplot(tabled_coefs, striped_rows = TRUE) +
  add_forest_table(
    position = "left",
    column_labels = c("term" = "Variable", "sample_size" = "N", "estimate" = "Beta (95% CI)"),
    columns = c("term", "sample_size", "estimate"),
    estimate_digits = 2,
    interval_digits = 3
  )

Add split summary tables

Use add_split_table() to create a more traditional looking forest plot, with summary data on either side of the plot.

ggforestplot(tabled_coefs, n = "sample_size", striped_rows = T) +
  add_split_table(
    left_columns = c("term", "n"),
    right_columns = c("estimate"),
    column_labels = c("term" = "Variable", "estimate" = "Beta (95% CI)")
  )

Start from a fitted model

If broom is installed, ggforestplot() can work directly from a fitted model.

fit <- lm(mpg ~ wt + hp + qsec, data = mtcars)

ggforestplot(fit, sort_terms = "descending",
             term_labels = c("wt" = "Weight"),
             striped_rows = T) +
  scale_x_continuous(breaks = seq(-6,2,1)) +
  add_forest_table()

For more detail, see: