ggforestplotR

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Overview

ggforestplotR provides a ggplot2-first workflow for building forest plots from tidy coefficient tables or fitted model objects.

Installation

CRAN

install.packages("ggforestplotR")

Development

#install.packages("remotes")
# remotes::install_github("thatoneguy006/ggforestplotR")
install.packages("ggforestplotR", repos = c('https://thatoneguy006.r-universe.dev', 'https://cloud.r-project.org'))

Supported workflows

ggforestplotR currently supports two core workflows:

• Plot directly from a table of coefficient data.
• Plot using data from a fitted model object.

Both workflows are converted to a validated forest_data object before plotting. This object records the effect scale, confidence level, reference value, and source provenance independently of the fitted-model class.

Basic example

library(ggforestplotR)
library(ggplot2)

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"
)

Add a summary table

ggforestplot(
  sectioned_coefs,
  striped_rows = TRUE,
  stripe_fill = "grey94"
) +
  add_forest_table()

Add a split summary table

ggforestplot(
  sectioned_coefs,
  striped_rows = TRUE,
  stripe_fill = "grey94"
) +
  add_split_table()

Learn more

• Package website: https://thatoneguy006.github.io/ggforestplotR/
• Get started: https://thatoneguy006.github.io/ggforestplotR/articles/ggforestplotR-get-started.html
• Plot & Table customization: https://thatoneguy006.github.io/ggforestplotR/articles/ggforestplotR-plot-customization.html
• Data helpers: https://thatoneguy006.github.io/ggforestplotR/articles/ggforestplotR-data-helpers.html

Main functions

• ggforestplot() builds the plotting panel from a data frame or supported model object.
• add_forest_table() attaches a summary table to the left or right side of the plot.
• add_split_table() creates a more traditional forestplot layout with table columns on both sides of the plot.
• bind_forest_models() binds output from several models for grouped plotting.
• as_forest_data() is the S3 conversion interface for custom coefficient data and supported fitted models.
• forest_metadata() inspects effect-scale and source metadata.
• tidy_forest_model() is used as a post-estimation interface for fitted models.