## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 8,
  fig.height = 4.5
)

## ----setup--------------------------------------------------------------------
library(ggforestplotR)
library(ggplot2)

## ----as-forest-data-----------------------------------------------------------
raw_coefs <- data.frame(
  variable = c("Age", "BMI", "Treatment"),
  beta = c(0.10, -0.08, 0.34),
  lower = c(0.02, -0.16, 0.12),
  upper = c(0.18, 0.00, 0.56),
  display = c("Age", "BMI", "Treatment"),
  section = c("Clinical", "Clinical", "Treatment"),
  sample_size = c(120, 115, 98),
  p_value = c(0.04, 0.15, 0.001)
)

forest_ready <- as_forest_data(
  data = raw_coefs,
  term = "variable",
  estimate = "beta",
  conf.low = "lower",
  conf.high = "upper",
  label = "display",
  grouping = "section",
  n = "sample_size",
  p.value = "p_value",
  estimate_scale = "identity",
  effect_label = "Beta",
  conf.level = 0.95
)

## ----inspect-forest-metadata--------------------------------------------------
forest_metadata(forest_ready)[c(
  "estimate_scale", "axis_transform", "effect_label", "conf_level",
  "reference_value", "source_model", "source_package", "source_columns"
)]

## ----helper-to-plot-----------------------------------------------------------
ggforestplot(forest_ready)

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

model_ready <- as_forest_data(fit)

## ----helper-to-plot-model-----------------------------------------------------
ggforestplot(model_ready)

## ----model-subgroup-effects---------------------------------------------------
interaction_fit <- lm(wt ~ hp + mpg * factor(cyl) + qsec, data = mtcars)

subgroup_ready <- tidy_forest_model(
  interaction_fit,
  subgroup = "auto",
  focal = "mpg"
)

as.data.frame(subgroup_ready)[, c(
  "term", "subgroup", "estimate", "conf.low", "conf.high", "p.value"
)]

ggforestplot(subgroup_ready, striped_rows = TRUE) +
  add_forest_table(columns = c("term", "estimate", "p"))

## ----explicit-model-subgroup-effects------------------------------------------
tidy_forest_model(
  interaction_fit,
  subgroup = "cyl",
  focal = "mpg"
)

## ----model-subgroup-level-p-values--------------------------------------------
level_p_ready <- tidy_forest_model(
  interaction_fit,
  subgroup = "auto",
  focal = "mpg",
  p_method = "level"
)

ggforestplot(level_p_ready, striped_rows = TRUE) +
  add_forest_table(columns = c("term", "estimate", "p"))