---
title: "Visualise Regression Results"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Visualise Regression Results}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Visualise Regression Results

Regression tables are the evidence. Plots are the vibe check. Use
`plot_reg()`, `plot_reg_combine()`, `forest_df()`, and `forest_reg()` to make
the results easier to scan.

```{r plot-setup, message=FALSE, warning=FALSE}
library(gtregression)
library(dplyr)

data("data_birthwt", package = "gtregression")

birthwt_data <- data_birthwt |>
  mutate(
    race = factor(race, levels = c(1, 2, 3),
                  labels = c("White", "Black", "Other")),
    smoke = factor(smoke, levels = c(0, 1), labels = c("No", "Yes")),
    ht = factor(ht, levels = c(0, 1), labels = c("No", "Yes")),
    ui = factor(ui, levels = c(0, 1), labels = c("No", "Yes")),
    low = factor(low, levels = c(0, 1), labels = c("Normal BW", "Low BW")),
    ptl_cat = factor(ifelse(ptl > 0, "Yes", "No"), levels = c("No", "Yes")),
    ftv_cat = factor(case_when(
      ftv == 0 ~ "None",
      ftv == 1 ~ "One",
      ftv >= 2 ~ "Two or more"
    ), levels = c("None", "One", "Two or more"))
  )

birthwt_exposures <- c(
  "age", "lwt", "race", "smoke", "ht", "ui", "ptl_cat", "ftv_cat"
)

birthwt_desc <- descriptive_table(
  birthwt_data,
  exposures = birthwt_exposures,
  by = "low",
  show_overall = "last"
)
birthwt_uni <- uni_reg(
  birthwt_data,
  outcome = "low",
  exposures = birthwt_exposures,
  approach = "logit"
)
birthwt_multi <- multi_reg(
  birthwt_data,
  outcome = "low",
  exposures = c("smoke", "ht", "ui", "ptl_cat", "ftv_cat"),
  adjust_for = c("age", "lwt", "race"),
  approach = "logit"
)
```

## One Regression Plot

```{r one-plot, message=FALSE, warning=FALSE}
plot_reg(
  birthwt_multi,
  title = "Adjusted Regression for Low Birth Weight"
)
```

## Compare Crude and Adjusted Effects

```{r combined-plot, message=FALSE, warning=FALSE}
plot_reg_combine(
  tbl_uni = birthwt_uni,
  tbl_multi = birthwt_multi,
  title_uni = "Crude Effects",
  title_multi = "Adjusted Effects"
)
```

## Publication-Style Forest Table

`forest_df()` prepares the data. `forest_reg()` draws the forest table.

```{r forest-table, message=FALSE, warning=FALSE}
forest_data <- forest_df(
  uni = birthwt_uni,
  multi = birthwt_multi,
  desc = birthwt_desc
)

forest_reg(forest_data, quiet = TRUE)
```

## What To Inspect

- `plot_reg()` returns a `ggplot`.
- `plot_reg_combine()` returns a combined `ggplot`.
- `forest_df()` returns the plotting data frame.
- `forest_reg()` returns `plot`, `data`, `input_data`, and `meta`.