# 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:

1. Start from a coefficient table and map the required columns directly to the plot.
2. Start from a fitted model and let `tidy_forest_model()` or `ggforestplot()` call `broom::tidy()` and create the 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()

## Next articles

For more detail, see:

- `ggforestplotR-plot-customization` for enhanced customization of the plots and summary tables.
- `ggforestplotR-data-helpers` for `as_forest_data()` and `tidy_forest_model()`.