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forrest

Lifecycle: experimental R-CMD-check Codecov test coverage Buy Me A Coffee

forrest creates publication-ready forest plots from any data frame that contains point estimates and confidence intervals — regression models, subgroup analyses, meta-analyses, dose-response patterns, and more. A single dependency (tinyplot) keeps the footprint minimal.

Key features:

Installation

install.packages("forrest")

# Development version from GitHub:
# install.packages("pak")
pak::pak("lorenzoFabbri/forrest")

Quick start

Introduction

The minimal call requires only three column names — estimate, lower, and upper. Use group and dodge = TRUE to place multiple CIs per row when comparing results across categories (e.g., time periods, sexes, models). See the Introduction vignette for the full feature tour.

library(forrest)

# Two estimates per exposure (e.g., two time windows)
periods <- data.frame(
  exposure = rep(c("Metals", "OPPs", "PFAS", "Phenols", "Phthalates"), each = 2),
  period   = rep(c("Pregnancy", "Childhood"), 5),
  est      = c( 0.03, -0.04, -0.03,  0.04, -0.17, -0.02,  0.10, -0.02,  0.07,  0.10),
  lo       = c(-0.25, -0.26, -0.19, -0.12, -0.40, -0.24, -0.06, -0.10, -0.15, -0.08),
  hi       = c( 0.30,  0.19,  0.13,  0.21,  0.06,  0.19,  0.26,  0.07,  0.29,  0.28)
)

forrest(
  periods,
  estimate = "est",
  lower    = "lo",
  upper    = "hi",
  label    = "exposure",
  group    = "period",
  dodge    = TRUE,
  header   = "Exposure class",
  ref_line = 0,
  xlab     = "Coefficient (95% CI)"
)

Meta-analysis

Use log_scale = TRUE and ref_line = 1 for ratio measures, is_summary = TRUE to draw the pooled row as a filled diamond, and weight to scale point sizes by inverse-variance weights. See the Meta-analysis vignette for subgroup analyses and multi-exposure layouts.

ma <- data.frame(
  study  = c("Adams 2016", "Baker 2018", "Chen 2019",
             "Davis 2020", "Evans 2021", "Pooled"),
  est    = c(1.45, 0.88, 1.21, 1.07, 0.95, 1.09),
  lo     = c(1.10, 0.65, 0.92, 0.80, 0.72, 0.94),
  hi     = c(1.91, 1.19, 1.59, 1.43, 1.25, 1.26),
  weight = c(180, 95, 140, 210, 160, NA),
  is_sum = c(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE)
)
ma$or_ci <- sprintf("%.2f (%.2f, %.2f)", ma$est, ma$lo, ma$hi)

forrest(
  ma,
  estimate   = "est",
  lower      = "lo",
  upper      = "hi",
  label      = "study",
  is_summary = "is_sum",
  weight     = "weight",
  log_scale  = TRUE,
  ref_line   = 1,
  header     = "Study",
  cols       = c("OR (95% CI)" = "or_ci"),
  widths     = c(2.2, 4, 2.5),
  xlab       = "Odds ratio (95% CI)"
)

Section headers

Pass a column name to section to group rows under automatic bold headers. forrest() inserts a header wherever the section value changes, indents the row labels, and adds a blank spacer after each group — no manual data manipulation required.

subgroups <- data.frame(
  domain   = c("Lifestyle",  "Lifestyle",  "Lifestyle",
               "Clinical",   "Clinical",
               "Socioeconomic"),
  exposure = c("Physical activity", "Diet quality", "Sleep duration",
               "Systolic BP (per 10 mmHg)", "BMI (per 5 kg/m\u00b2)",
               "Area deprivation"),
  est      = c(-0.31, -0.18, 0.09, 0.25,  0.19, 0.14),
  lo       = c(-0.51, -0.36, -0.08, 0.08, -0.02, -0.04),
  hi       = c(-0.11, -0.00,  0.26, 0.42,  0.40,  0.32)
)

forrest(
  subgroups,
  estimate = "est",
  lower    = "lo",
  upper    = "hi",
  label    = "exposure",
  section  = "domain",
  header   = "Exposure",
  ref_line = 0,
  xlab     = "Regression coefficient (95% CI)"
)

Regression models

Use broom::tidy() to extract parameters from a fitted model and pass the result directly to forrest(). The cols argument adds a formatted text column alongside the plot; header labels the row-label panel. See the Regression models vignette for progressive adjustment, multiple outcomes, dose-response, and causal inference (g-computation, IPW) examples.

set.seed(1)
n   <- 300
dat <- data.frame(
  sbp    = 120 + rnorm(n, sd = 15),
  age    = runif(n, 30, 70),
  female = rbinom(n, 1, 0.5),
  bmi    = rnorm(n, 26, 4),
  smoker = rbinom(n, 1, 0.2)
)
dat$sbp <- dat$sbp + 0.3 * dat$age - 4 * dat$female +
           0.5 * dat$bmi - 2 * dat$smoker + rnorm(n, sd = 6)

fit   <- lm(sbp ~ age + female + bmi + smoker, data = dat)
coefs <- broom::tidy(fit, conf.int = TRUE)
coefs <- coefs[coefs$term != "(Intercept)", ]
coefs$term <- c("Age (per 1 y)", "Female sex",
                "BMI (per 1 kg/m²)", "Current smoker")

coefs$coef_ci <- sprintf(
  "%.2f (%.2f, %.2f)",
  coefs$estimate, coefs$conf.low, coefs$conf.high
)

forrest(
  coefs,
  estimate = "estimate",
  lower    = "conf.low",
  upper    = "conf.high",
  label    = "term",
  header   = "Predictor",
  cols     = c("Coef (95% CI)" = "coef_ci"),
  widths   = c(3, 4, 2.5),
  xlab     = "Regression coefficient (95% CI)",
  stripe   = TRUE
)

Compared with other packages

Feature forrest forestplot forestploter ggforestplot
Minimal dependencies ✅ ❌ ❌ ❌
General (not meta-analysis only) ✅ ✅ ✅ ⚠️
Study weight sizing ✅ ✅ ✅ ❌
Auto section headers from grouping column ✅ ⚠️ ❌ ❌
Two-level nested section hierarchy ✅ ❌ ❌ ❌
Summary diamonds ✅ ✅ ✅ ❌
Text columns ✅ ✅ ✅ ❌
Alternating row stripes ✅ ✅ ✅ ✅
CI clipping with arrows ✅ ✅ ⚠️ ❌
Group colouring + legend ✅ ✅ ✅ ✅
Log-scale axis ✅ ✅ ✅ ✅
Export helper ✅ ❌ ⚠️ ❌
data.table support ✅ ❌ ❌ ❌
Actively maintained ✅ ✅ ✅ ❌

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.