---
title: "Plotting pedigrees with ggPedigree()"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Plotting pedigrees with ggPedigree()}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)
```



# Introduction

This vignette demonstrates pedigree visualization with `ggPedigree()` from `ggpedigree`. Pedigree plots are central to fields like human genetics, behavioral science, and genealogy, where understanding lineage and familial relationships informs diagnosis, analysis, and interpretation. 

`ggPedigree()` builds on `ggplot2` and `kinship2`, leveraging modular helpers from both `{BGmisc}` and `{ggpedigree}` to streamline plot generation while retaining full aesthetic flexibility.

Unlike base R pedigree tools, this package returns a `ggplot2` object, meaning users can customize their output using familiar `ggplot2` syntax, themes, scales, and layering. Throughout this vignette, we walk through basic usage, layout control, status overlays, aesthetic customization, and faceting for multifamily visualization.

We use two bundled example datasets (from `BGmisc`) to illustrate the package's capabilities. The first dataset, `potter`, contains a fictional wizarding family tree, while the second dataset, `hazard`, includes a multigenerational historical pedigree with affected/unaffected status. 


# Basic usage

We begin by loading the required libraries. These include the main plotting package `ggpedigree`, helper utilities from `BGmisc`, and supporting tools for aesthetic and data manipulation.

```{r libraries, message=FALSE, warning=FALSE}
library(ggpedigree) # ggPedigree lives here
library(BGmisc) # helper utilities & example data
library(ggplot2) # ggplot2 for plotting
library(viridis) # viridis for color palettes
library(tidyverse) # for data wrangling
```


The simplest usage requires a data frame and column names for family and individual IDs. Here’s a basic pedigree plot using the `potter` data:

```{r basic-usage}
data("potter")
ggPedigree(potter,
  famID = "famID",
  personID = "personID"
)
```

Behind the scenes, `ggPedigree()`:

1. reshapes the data by family (`ped2fam()`),

2. recodes sex (e.g., 0/1/NA) into semantic labels via `recodeSex()`

3. extracts a layout grid with `calculateCoordinates()`

4. builds connection segments for spouses, siblings, parents, and offspring with `calculateConnections()`

The result is returned as a `ggplot2` object, which allows immediate post-processing with standard `ggplot` syntax.



# Customizing Aesthetics

Most appearance settings are managed through a `config` list, which allows for fine-tuned customization. If any options are omitted, they fall back to predefined defaults. For example, we can turn off sex-based fill color and assign specific colors to connection segments:

```{r customize-aesthetics}
ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  config = list(
    code_male = 1, # Here, 1 = male, 0 = female
    sex_color = FALSE,
    line_width = 1,
    segment_spouse_color = viridis_pal()(5)[1],
    segment_sibling_color = viridis_pal()(5)[2],
    segment_parent_color = viridis_pal()(5)[3],
    segment_offspring_color = viridis_pal()(5)[4],
    outline = TRUE,
    outline_color = viridis_pal()(5)[5]
  )
)
```

As with any `ggplot2` object, further theming and labeling can be applied using layers or themes. For instance:


```{r}
ggPedigree(potter,
  famID = "famID",
  personID = "personID"
) +
  theme_bw(base_size = 12)
```

This can be particularly useful for matching house styles in academic publications or removing clutter for presentations.

## Labeling Individuals

You can also label individuals in the pedigree using the `label_method` argument. The default is `geom_label_repel()`, which uses the `ggrepel` package to avoid overlapping labels. You can also use `geom_text()` for simpler labeling. Other configurable options include `label_col` for the label column, `label_text_angle` for text rotation, `label_nudge_y` for vertical adjustment, and `label_nudge_x` for horizontal adjustment.

```{r}
ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  config = list(
    label_col = "name",
    label_text_angle = -45,
    label_nudge_y = -.25,
    label_nudge_x = 0.45,
    label_method = "geom_text",
    sex_color = TRUE
  )
)
```

# Overlaying Status Information

In many applied settings, pedigrees include binary phenotypes such as affected/unaffected status. To incorporate this, use the `status_col` argument to point to a column containing the relevant variable.

```{r}
data("hazard")

p <- ggPedigree(
  hazard,
  famID = "famID",
  personID = "ID",
  status_col = "affected",
  config = list(
    code_male = 0,
    sex_color = TRUE,
    status_affected_lab = TRUE,
    status_unaffected_lab = FALSE,
    status_affected_shape = 4
  )
)

p
```

The `status_col` argument allows you to specify a column that identifies affected/unaffected individuals. This column should be a factor or character vector; if not, `ggPedigree()` will coerce it.

The behavior differs depending on whether `sex_color` is also enabled:

* If `sex_color = TRUE`, affected individuals are overlaid with a shape (controlled by `affected_shape`), preserving the fill color by sex.

* If `sex_color = FALSE`, affected and unaffected individuals are instead colored by status, using a default fill scale.

You can control how values are interpreted via the `affected` and `unaffected` parameters in `config`. By default, `TRUE` is considered affected and `FALSE` unaffected.


```{r}
ggPedigree(
  hazard,
  famID = "famID",
  personID = "ID",
  status_col = "affected",
  config = list(
    code_male = 0,
    sex_color = FALSE,
    status_affected_lab = TRUE,
    status_unaffected_lab = FALSE
  )
)
```

## Highlighting Specific Individuals

You can also highlight specific individuals in the pedigree by creating a new column in your data frame. For example, if you want to highlight Harry Potter and Dudley Dursley, you can create a new column called `proband` and set it to `TRUE` for those individuals. You can specify the `affected_shape` to be a star shape (8) and use the `status_col` argument to indicate the probands.

```{r}
df <- potter

df <- df %>%
  mutate(proband = ifelse(name %in% c("Harry Potter", "Dudley Dursley"), TRUE, FALSE))

ggPedigree(
  df,
  famID = "famID",
  personID = "personID",
  status_col = "proband",
  config = list(
    sex_color = TRUE,
    status_affected_lab = TRUE,
    status_unaffected_lab = FALSE,
    status_affected_shape = 8 # star shape
  )
)
```

# Multiple families in one graphic

If you have multiple families in your data frame, you can use the `facet_wrap()` function from `ggplot2` to create separate plots for each family. This is useful if you want to compare the pedigrees of different families side by side. Note that you can use `scales = "free_x"` to allow each family to have its own x-axis scale.

```{r facet_wrap}
p +
  facet_wrap(~famID, scales = "free_x")
```

# Adjusting Plot Appearance

The layout appearance can be refined using `theme()` elements. This doesn't change the pedigree structure but helps declutter the visualization.

```{r}
p +
  theme_bw(base_size = 12) +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background = element_blank(),
    axis.line        = element_line(colour = "black"),
    axis.text.x      = element_blank(),
    axis.text.y      = element_blank(),
    axis.ticks.x     = element_blank(),
    axis.ticks.y     = element_blank(),
    axis.title.x     = element_blank(),
    axis.title.y     = element_blank()
  ) + scale_color_viridis(
    discrete = TRUE,
    labels = c("Female", "Male", "Unknown")
  )
```

# Advanced Example: Large Pedigrees with self-loops

In this section, we demonstrate how to create a more complex pedigree plot with multiple families. We use the `inbreeding` dataset from the `BGmisc` package, which contains several multigenerational pedigrees with consanguinity. Note that in these plots that some individuals may appear in multiple places within the pedigree. This is common in large pedigrees, especially when there are overlapping generations or multiple marriages. Here the colors are set to be the same for all segments, except for self-loops, which are colored purple.

```{r self-loops, message=FALSE, warning=FALSE}
library(BGmisc) # helper utilities & example data

data("inbreeding")


df <- inbreeding # multigenerational pedigree with consanguinity


# df  <- dplyr::filter(df, famID %in% c(5, 7))


p <- ggPedigree(
  df,
  famID = "famID",
  personID = "ID",
  status_col = "proband",
  #  debug = TRUE,
  config = list(
    code_male = 0,
    sex_color = FALSE,
    status_affected_lab = TRUE,
    status_unaffected_lab = FALSE,
    generation_height = 4,
    generation_width = 2,
    status_affected_shape = 4,
    segment_self_color = "purple"
  )
)

p + facet_wrap(~famID, scales = "free") #+ scale_color_viridis(
#   discrete = TRUE,
#   labels = c("TRUE", "FALSE")
#  )  + theme_bw(base_size = 14)  +  guides(colour="none", shape="none")
```

# Example: Constructing Custom Pedigrees for Publication

Here we demonstrate how to create a custom pedigree using the `ggpedigree package`. The data shown here were generated using the `simulatePedigree()` function from the {BGmisc} package, which is the parent package to {ggpedigree}. These simulated pedigrees were used in a study evaluating statistical power and estimation bias for a variance decomposition model that includes mitochondrial DNA (mtDNA) effects.

The simulation generated thousands of extended pedigree structures varying in depth, sibship size, mating structure, and maternal lineage overlap. The example below shows one of the simulated pedigrees and is the version included in the final manuscript:

> Detecting mtDNA effects with an Extended Pedigree Model: An Analysis of Statistical Power and Estimation Bias
> Xuanyu Lyu, S. Alexandra Burt, Michael D. Hunter, Rachel Good, Sarah L. Carroll, S. Mason Garrison
> Preprint available at: https://doi.org/10.1101/2024.12.19.629449

The structure includes multiple generations, sibling sets, and overlapping parental lineages, and was chosen to illustrate the complexity of the simulated pedigrees used in the power study.

## Preparing the data

Each row represents one individual. Variables include `personID`, `momID`, `dadID`, `sex`, and `famID`. The proband variable is included to demonstrate status overlays. For plotting, we normalize identifiers in family 1 to avoid ID collisions across families. 

<details>
<summary>Click to expand pedigree setup</summary>

```{r}
library(tibble)
library(dplyr)
pedigree_df <- tribble(
  ~personID, ~momID, ~dadID, ~sex, ~famID,
  10011, NA, NA, 0, 1,
  10012, NA, NA, 1, 1,
  10021, NA, NA, 1, 1,
  10022, 10011, 10012, 1, 1,
  10023, 10011, 10012, 0, 1,
  10024, NA, NA, 0, 1,
  10025, NA, NA, 0, 1,
  10026, 10011, 10012, 0, 1,
  10027, 10011, 10012, 1, 1,
  10031, 10023, 10021, 0, 1,
  10032, 10023, 10021, 1, 1,
  10033, 10023, 10021, 1, 1,
  10034, 10023, 10021, 1, 1,
  10035, 10023, 10021, 0, 1,
  10036, 10024, 10022, 1, 1,
  10037, 10024, 10022, 0, 1,
  10038, 10025, 10027, 1, 1,
  10039, 10025, 10027, 0, 1,
  10310, 10025, 10027, 1, 1,
  10311, 10025, 10027, 1, 1,
  10312, 10025, 10027, 0, 1,
  10011, NA, NA, 0, 2,
  10012, NA, NA, 1, 2,
  10021, NA, NA, 0, 2,
  10022, 10011, 10012, 0, 2,
  10023, 10011, 10012, 1, 2,
  10024, 10011, 10012, 1, 2,
  10025, NA, NA, 1, 2,
  10026, 10011, 10012, 0, 2,
  10027, NA, NA, 1, 2,
  10031, 10021, 10023, 1, 2,
  10032, 10021, 10023, 0, 2,
  10033, 10021, 10023, 1, 2,
  10034, 10022, 10025, 0, 2,
  10035, 10022, 10025, 0, 2,
  10036, 10022, 10025, 1, 2,
  10310, 10022, 10025, 1, 2,
  10037, 10026, 10027, 0, 2,
  10038, 10026, 10027, 0, 2,
  10039, 10026, 10027, 0, 2,
  10311, 10026, 10027, 1, 2,
  10312, 10026, 10027, 1, 2
) %>%
  mutate(
    cleanpersonID = personID - 10000,
    personID = ifelse(famID == 1, personID - 10000, personID),
    momID = ifelse(famID == 1 & !is.na(momID), momID - 10000, momID),
    dadID = ifelse(famID == 1 & !is.na(dadID), dadID - 10000, dadID),
    proband = case_when(
      personID %in% c(11, 22, 23, 26, 27, 31, 32, 33, 34, 35) ~ TRUE,
      personID %in% c(
        10011, 10022, 10022, 10023, 10024, 10026,
        10034, 10035, 10036, 10310,
        10037, 10038, 10039, 10311,
        10312
      ) ~ TRUE,
      TRUE ~ FALSE
    )
  )
```
</details>


## Plotting the pedigree

```{r}
p <- ggPedigree(
  pedigree_df,
  famID = "famID",
  personID = "personID",
  status_col = "proband",
  #  debug = TRUE,
  config = list(
    code_male = 1,
    sex_color = FALSE,
    apply_default_scales = FALSE,
    label_method = "geom_text",
    label_col = "cleanpersonID",
    status_affected_lab = TRUE,
    status_unaffected_lab = FALSE,
    generation_height = 1,
    generation_width = 1,
    status_affected_shape = 4,
    segment_spouse_color = "black",
    segment_sibling_color = "black",
    segment_parent_color = "black",
    segment_offspring_color = "black"
  )
)
```


We finish by adjusting the legend and shape scale for visual clarity:

```{r, message=FALSE, warning=FALSE}
p + scale_shape_manual(
  values = c(16, 15, 15),
  labels = c("Female", "Male", "Unknown")
) +
  guides(shape = "none") + scale_color_viridis(
    discrete = TRUE,
    labels = c("TRUE", "FALSE"),
    name = "Founding MtDNA Line"
  ) +
  facet_wrap(~famID, scales = "free", shrink = TRUE) +
  theme(
    strip.text = element_blank(),
    legend.position = "bottom"
  )
```
