Extended: More uses of `config` to control ggpedigree plots

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

Every gg-based plotting function in ggpedigree accepts a config argument. This is an easier way to control plot behavior without rewriting the plotting code.

As already discussed, a config is a named list. Each element corresponds to one plotting, layout, or aesthetic option. You pass the list to the plotting function and the plot is drawn using those values.

You do not need to supply every option. You only provide the options you want to change. Any options you do not specify will use the package defaults. You can see a full list of supported options and their defaults by reviewing the documentation for getDefaultPlotConfig().

Basic usage of `config` in `ggPedigree()`

As before, we will use the potter pedigree dataset bundled in BGmisc.

library(BGmisc)
data("potter")

A basic pedigree plot uses defaults:

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID"
)

Sex coding: `code_male` and `code_female`

ggPedigree() (and other ggpedigree plots that use sex) need to know how sex is encoded in your data so they can assign the correct shapes (and optionally colors) for female, male, and unknown.

The code_male and code_female config options define which values in your sex column should be treated as male vs female. The defaults assume:

code_female = 0
code_male = 1

If your dataset uses different codes (for example 1/2 or "M"/"F"), override these in config.

# Example: sex coded as 1 = male, 2 = female
ggPedigree(
  ped,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    code_male = 1,
    code_female = 2,
    code_unknown = 3
  )
)

# Example: sex coded as "M" / "F"
ggPedigree(
  ped,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    code_male = "M",
    code_female = "F"
  )
)

Once the sex codes are interpreted correctly, the plot uses the corresponding shape settings (sex_shape_female, sex_shape_male, sex_shape_unknown) and, when enabled, sex-based coloring (sex_color_include, sex_color_palette).

Customizing specific plot components via `config`

The rest of this section demonstrates how config affects specific components of the pedigree plot.

1) Labels

Label behavior is controlled by keys such as:

label_include
label_column
label_method
label_max_overlaps
label_text_color, label_text_family
label_text_size
label_nudge_x, label_nudge_y
label_text_angle

This example customizes labels. Here we label individuals by first_name, enlarge label text, and nudge the labels down slightly.

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    label_include = TRUE,
    label_column = "first_name",
    label_text_size = 3.2,
    label_nudge_y = 0.15
  )
)

To turn labels off completely:

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(label_include = FALSE)
)

You can also use repelled labels to avoid overlaps:

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    label_include = TRUE,
    label_method = "geom_text_repel",
    label_max_overlaps = 10,
    label_text_size = 9,
    label_segment_color = "grey50"
  )
)

Note that short labels are less likely to overlap, so consider abbreviating labels if your pedigree is dense. In this example, I enlarged the text size to demonstrate repulsion more clearly.

2) Points and outlines

Point size and whether points scale automatically are controlled by:

point_size
point_scale_by_pedigree

Outlines are controlled by:

outline_include
outline_multiplier
outline_color
outline_alpha

This example disables automatic point scaling and adds black outlines to points.

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    point_scale_by_pedigree = FALSE,
    point_size = 6,
    outline_include = TRUE,
    outline_color = "maroon",
    outline_multiplier = 1.5,
    outline_alpha = 1
  )
)

3) Segments (relationships)

Segments are controlled by options such as:

segment_linewidth, segment_linetype
segment_offspring_color, segment_parent_color, segment_spouse_color
segment_self_* for self-loops
segment_mz_* for MZ twin segments

This example thickens relationship segments and changes the spouse segment color.

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    segment_linewidth = 2,
    segment_spouse_color = "steelblue",
    segment_parent_color = "black",
    segment_offspring_color = "black"
  )
)

Self-loop geometry is also configurable:

ggPedigree(
  inbreeding %>% filter(famID %in% 5),
  famID = "famID",
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    segment_self_linetype = "dotdash",
    segment_self_color = "hotpink",
    segment_self_alpha = 0.6,
    segment_self_linewidth = 1.5,
    segment_self_curvature = -0.2,
    segment_self_angle = 80,
    code_male = 0
  )
) + ggtitle("Custom self-loop appearance")

4) Sex appearance

Sex is controlled by:

sex_color_include
sex_color_palette
sex_shape_female, sex_shape_male, sex_shape_unknown
sex_legend_show, sex_legend_title

This example shows sex legend and customizes shapes.

Here I use shapes 17 (triangle) for males, 18 (diamond) for females, and 16 (circle) for unknown. You can find a full list of shape codes in the pch documentation (?points). You can also use shapes from the ggplot2 shape palette (e.g., 21-25 for filled shapes). Here I also disable sex coloring to focus on shapes alone.

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    sex_legend_show = TRUE,
    sex_shape_female = 18,
    sex_shape_male = 17,
    sex_shape_unknown = 16,
    sex_color_include = FALSE
  )
)

Below, I use a custom color palette for sex as well as emoji shapes for fun.

plot_ped <- ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    sex_color_palette = c("purple", "orange", "grey50"),
    sex_shape_female = "🥰",
    sex_shape_male = "🚗"
  )
)

ggplot2::ggsave(
  filename = "custom_sex_emoji_pedigree.png",
  plot = plot_ped,
  width = 8,
  height = 6,
  dpi = 300
)

Note that when using emoji shapes, it is best to use ggsave() to save the plot to a file, as some R graphics devices may render emoji differently. Notice how the emoji shapes appear in the saved PNG file below compares to the image rendered during the preview above. These may vary because of differences in font rendering.

5) Affected status overlay

Affected status behavior is controlled by keys such as:

status_include
status_code_affected, status_code_unaffected
status_alpha_affected, status_alpha_unaffected
status_color_affected, status_color_unaffected
status_legend_show

If your dataset includes an affected status column, you can control how affected status is drawn.

Below is a template showing the relevant config keys. Here I use the hazard dataset from BGmisc. The affected column uses 1 for affected and 0 for unaffected.

ggPedigree(
  hazard,
  famID = "famID",
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  status_column = "affected",
  config = list(
    code_male = 0,
    status_include = TRUE,
    status_code_affected = 1,
    status_code_unaffected = 0,
    status_alpha_affected = .6,
    status_alpha_unaffected = 0,
    status_color_affected = "red",
    status_shape_affected = 8,
    status_legend_show = TRUE
  )
)

Multiple overlays with per-overlay customization

When a single status marker is not enough, you can pass a list of overlay specs to the overlays argument. Each element of the list is itself a list that names a column and optionally overrides any overlay config key — shape, color, stroke, size, and code_affected — for that layer only. Config defaults are used for any key you leave out.

The hazard dataset already contains deathYr and onsetYr, which map naturally onto two independent overlays — deceased individuals (a cross) and those with a recorded disease onset (a slash):

# Derive binary flags from columns already present in hazard
hazard$deceased <- ifelse(!is.na(hazard$deathYr), 1, 0)
hazard$onset <- ifelse(!is.na(hazard$onsetYr), 1, 0)

ggPedigree(
  hazard,
  famID = "famID",
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  overlays = list(
    list(column = "deceased", code_affected = 1, shape = "cross", color = "purple"),
    list(column = "onset", code_affected = 1, shape = "slash", color = "red", stroke = 2)
  ),
  config = list(
    code_male = 0,
    overlay_include = TRUE,
    overlay_mode = "shape",
    reduce_variables = F,
    sex_color_include = FALSE
  )
)

A few things to note:

overlay_mode = "shape" must be set in config (or via a preset such as "clinical") to activate shape-based rendering. Without it the overlay loop falls back to alpha transparency.
Each spec can carry any combination of per-layer overrides. Specs with no override for a key inherit the matching config$overlay_* default, so you only need to specify what differs.
The specs are rendered as separate geom_point() layers in list order, so later specs draw on top of earlier ones for individuals who satisfy both conditions.

This means that when we have multiple conditions to show, they can be layered on top of each other with different shapes and colors. In the example above, we have two overlays: one for deceased individuals (a purple cross) and another for disease onset (a red slash). Individuals who are both deceased and have a recorded disease onset will have both shapes drawn on top of their node, allowing us to visually distinguish between different combinations of conditions.

Building on the previous example, we can also leverage other config options to further customize the plot. For instance we can facet by family ID to separate the pedigrees, and we can add labels for maternal IDs with custom colors. Here I use ped2maternal() to add a matID column to the hazard dataset, which identifies each individual’s maternal lineage. Then I use geom_text() to label each individual with their matID, coloring the labels randomly in grey or black for visual interest.

hazard <- ped2maternal(hazard,
  famID = "famID",
  personID = "ID",
  momID = "momID",
  dadID = "dadID"
)
hazard$label_color <- sample(c("grey", "black"), nrow(hazard), replace = TRUE)


ggPedigree(
  hazard,
  famID = "famID",
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  overlays = list(
    list(column = "deceased", code_affected = 1, shape = "cross", color = "purple"),
    list(column = "onset", code_affected = 1, shape = "slash", color = "red", stroke = 2)
  ),
  config = list(
    code_male = 0,
    overlay_include = TRUE,
    overlay_mode = "shape",
    reduce_variables = F,
    sex_color_palette = c("steelblue", "salmon", "grey50")
  )
) +
  facet_wrap(~famID,
    scales = "free_x"
  ) +
  geom_text(aes(label = matID),
    color = hazard$label_color,
    nudge_x = 0.15,
    nudge_y = 0.15, size = 3
  )
#> Warning: Removed 4 rows containing missing values or values outside the scale range
#> (`geom_text()`).

If you want to use standard ggplot2 geoms such as geom_text() to add labels, I recommend setting reduce_variables = FALSE in config to prevent the automatic reduction of variables. This allows you to retain the all the columns for labeling and faceting. In this example, we use geom_text() to label each individual with their matID, and we color the labels randomly in grey or black for visual interest.

To illustrate the effect of reduce_variables, let us facet by generation instead of family ID. With reduce_variables = FALSE, the black segments connecting individuals follow the nodes to the correct panel. In contrast, when reduce_variables = TRUE, the segments are drawn on all the panels because the generation variable isn’t present in the dataframe used for segment drawing.

ggPedigree(
  hazard,
  famID = "famID",
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  overlays = list(
    list(column = "deceased", code_affected = 1, shape = "cross", color = "purple"),
    list(column = "onset", code_affected = 1, shape = "slash", color = "red", stroke = 2)
  ),
  config = list(
    code_male = 0,
    overlay_include = TRUE,
    overlay_mode = "shape",
    reduce_variables = F,
    sex_color_palette = c("steelblue", "salmon", "grey50")
  )
) +
  facet_wrap(~gen,
    scales = "free_x"
  )


ggPedigree(
  hazard,
  famID = "famID",
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  overlays = list(
    list(column = "deceased", code_affected = 1, shape = "cross", color = "purple"),
    list(column = "onset", code_affected = 1, shape = "slash", color = "red", stroke = 2)
  ),
  config = list(
    code_male = 0,
    overlay_include = TRUE,
    overlay_mode = "shape",
    reduce_variables = T,
    sex_color_palette = c("steelblue", "salmon", "grey50")
  )
) +
  facet_wrap(~gen,
    scales = "free_x"
  )

6) Focal fill: highlighting relatives of a focal individual

A common analysis task is to pick a focal individual and visually emphasize how strongly other individuals are related to that focal person. In ggpedigree, this is handled by focal fill. When focal fill is enabled, node fill colors are mapped to a focal-based value (for example additive genetic relatedness or another focal-derived scalar).

Focal fill is controlled entirely through config. The minimal ingredients are:

focal_fill_include = TRUE
focal_fill_personID = <ID of focal person>
focal_fill_component = <component to use for focal calculation>. It can be "additive" (default), "mitochondrial", "patID", or "matID".
focal_fill_method is the Method used for focal fill gradient. Options are ‘steps’, ‘steps2’, ‘step’, ‘step2’, ‘viridis_c’, ‘viridis_d’, ‘viridis_b’, ‘manual’, ‘hue’, ‘gradient2’, ‘gradient’.

Turning focal fill on

Below we choose an individual as the focal person, enable focal fill, and disable sex coloring to highlight the focal fill pattern clearly. The exact person identifier must match the personID column used in the plot.

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    focal_fill_include = TRUE,
    sex_color_include = FALSE,
    focal_fill_component = "additive",
    focal_fill_personID = 8,
    focal_fill_legend_show = TRUE,
    focal_fill_legend_title = "Focal relatedness"
  )
)

If the plot is dense, it is often helpful to turn labels off or reduce their prominence, so the focal fill pattern reads cleanly. Note we can also choose different focal components such as "mitochondrial", which traces matrilineal relatedness.

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    focal_fill_include = TRUE,
    sex_color_include = FALSE,
    focal_fill_personID = 8,
    focal_fill_component = "mitochondrial",
    label_include = FALSE,
    point_scale_by_pedigree = FALSE,
    point_size = 6
  )
)

Choosing the focal fill scale and colors

Focal fill supports multiple scale methods via focal_fill_method. For continuous gradients, the most common choice is "gradient" (default) or "gradient2".

You can explicitly set the low/mid/high colors used by the focal gradient:

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    focal_fill_include = TRUE,
    sex_color_include = FALSE,
    focal_fill_personID = 8,
    focal_fill_method = "gradient2",
    focal_fill_low_color = "purple",
    focal_fill_mid_color = "orange",
    focal_fill_high_color = "red",
    focal_fill_scale_midpoint = 0.10,
    focal_fill_legend_show = TRUE
  )
)

Discrete focal fill palettes

If you prefer discrete bins rather than a continuous gradient, you can use step-based scales (for example "steps" / "steps2"). When using step-based methods, focal_fill_n_breaks controls the number of discrete breaks.

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    focal_fill_include = TRUE,
    sex_color_include = FALSE,
    focal_fill_personID = 8,
    focal_fill_method = "steps2",
    focal_fill_n_breaks = 9,
    focal_fill_legend_show = TRUE
  )
)

Handling missing and zero values

When focal fill is computed, some individuals can have missing focal values (for example if they are disconnected). You can control the color used for missing values with focal_fill_na_value. The focal_fill_force_zero option forces exact zeros to be treated as missing so they can be filled in using focal_fill_na_value.

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    focal_fill_include = TRUE,
    sex_color_include = FALSE,
    focal_fill_personID = 4,
    focal_fill_force_zero = TRUE,
    focal_fill_na_value = "grey75"
  )
)

Using viridis-based focal fill

If you want perceptually uniform color scaling, focal fill supports viridis options through focal_fill_method = "viridis_c" (continuous) or "viridis_d" (discrete). You can control the viridis option and range using:

focal_fill_viridis_option
focal_fill_viridis_begin
focal_fill_viridis_end
focal_fill_viridis_direction

Here the focal fill is drawn using paternal relatedness. Each individual’s fill color indicates which patriline they belong to, colored using a discrete viridis palette.

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    focal_fill_include = TRUE,
    sex_color_include = FALSE,
    focal_fill_personID = 5,
    focal_fill_method = "viridis_d",
    focal_fill_component = "patID",
    focal_fill_viridis_option = "D",
    focal_fill_viridis_begin = 0.05,
    focal_fill_viridis_end = 0.95,
    focal_fill_viridis_direction = 1,
    focal_fill_legend_show = TRUE
  )
)

7) Global greyscale / black-and-white switch

If you want a black-and-white plot, you can request it using:

color_theme = "greyscale" (also accepts "bw", "black", etc.)

This triggers coordinated adjustments so the plot remains coherent without manually changing multiple palettes.

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    color_theme = "bw",
    focal_fill_include = TRUE,
    sex_color_include = FALSE,
    focal_fill_personID = 5,
    segment_linewidth = 0.7,
    point_scale_by_pedigree = FALSE,
    point_size = 6
  )
)

8) Interactive pedigrees: `ggPedigreeInteractive()`

Interactive pedigrees usually require thinner segments and careful tooltip selection. Tooltips are controlled primarily through tooltip_columns, while most drawing options are still handled by config.

ggPedigreeInteractive(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  tooltip_columns = c("personID", "first_name", "sex"),
  config = list(
    label_include = FALSE,
    point_scale_by_pedigree = FALSE,
    point_size = 7,
    segment_linewidth = 0.5
  )
)

9) Layout and coordinate system

In addition to the above options, layout and coordinate system are also configurable via config. For example if you are interested in a circular layout, you can set coord_layout = "radial" and adjust the minimum radius with coord_radial_min_radius. This plot uses polar coordinates to create a circular pedigree layout.

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = list(
    coord_layout = "radial",
    point_scale_by_pedigree = FALSE,
    coord_radial_min_radius = 1,
    label_include = FALSE,
    spread_out_generations_factor = 12.5,
    spread_out_generations = TRUE
  )
) #+theme_classic()

Saving and loading a config file

If you want to reuse the same overrides across scripts or share them with collaborators, save your config list.

cfg <- list(
  point_scale_by_pedigree = FALSE,
  point_size = 6,
  segment_linewidth = 0.7,
  label_include = TRUE,
  label_text_size = 3,
  sex_color_palette = c("purple", "orange", "grey50")
)

saveRDS(cfg, file = "ggpedigree_config.rds")

cfg <- readRDS("ggpedigree_config.rds")

ggPedigree(
  potter,
  famID = "famID",
  personID = "personID",
  momID = "momID",
  dadID = "dadID",
  config = cfg
)

Config reference

The config argument accepts many options. Most users will only change a small subset, but the full list of supported keys can be printed programmatically.

Show all config keys (names only)

cfg_names <- sort(names(getDefaultPlotConfig("ggPedigree")))

tibble::tibble(Config_Key = cfg_names) %>%
  knitr::kable()

Config_Key
add_phantoms
affected_fill_code_affected
affected_fill_code_unaffected
affected_fill_color_affected
affected_fill_color_unaffected
affected_fill_include
affected_fill_label_affected
affected_fill_label_unaffected
affected_fill_shape_female
affected_fill_shape_male
affected_fill_shape_unknown
alpha
annotate_include
annotate_x_shift
annotate_y_shift
apply_default_scales
apply_default_theme
axis_text_angle_x
axis_text_angle_y
axis_text_color
axis_text_family
axis_text_size
axis_x_label
axis_y_label
ci_include
ci_ribbon_alpha
code_female
code_male
code_na
code_unknown
color_palette_default
color_palette_high
color_palette_low
color_palette_mid
color_scale_midpoint
color_scale_theme
color_theme
coord_layout
coord_radial_end_angle
coord_radial_min_radius
coord_radial_scale
coord_radial_start_angle
debug
drop_classic_kin
drop_non_classic_sibs
filter_degree_max
filter_degree_min
filter_n_pairs
focal_fill_chroma
focal_fill_component
focal_fill_force_zero
focal_fill_high_color
focal_fill_hue_direction
focal_fill_hue_range
focal_fill_include
focal_fill_legend_show
focal_fill_legend_title
focal_fill_lightness
focal_fill_low_color
focal_fill_method
focal_fill_mid_color
focal_fill_n_breaks
focal_fill_na_value
focal_fill_personID
focal_fill_scale_midpoint
focal_fill_shape
focal_fill_use_log
focal_fill_viridis_begin
focal_fill_viridis_direction
focal_fill_viridis_end
focal_fill_viridis_option
generation_height
generation_width
group_by_kin
grouping_column
hints
label_column
label_include
label_max_overlaps
label_method
label_nudge_x
label_nudge_y
label_nudge_y_flip
label_scale_by_pedigree
label_segment_color
label_text_angle
label_text_color
label_text_family
label_text_size
match_threshold_percent
matrix_diagonal_include
matrix_fill_legend_title
matrix_isChild_method
matrix_lower_triangle_include
matrix_sparse
matrix_upper_triangle_include
max_degree_levels
optimize_plotly
outline_additional_size
outline_alpha
outline_color
outline_color_affected
outline_color_code_affected
outline_color_code_unaffected
outline_color_include
outline_color_label_affected
outline_color_label_unaffected
outline_color_unaffected
outline_include
outline_multiplier
overlay_alpha_affected
overlay_alpha_unaffected
overlay_code_affected
overlay_code_unaffected
overlay_color
overlay_include
overlay_label_affected
overlay_label_unaffected
overlay_legend_show
overlay_legend_title
overlay_mode
overlay_shape
overlay_size
overlay_stroke
override_many2many
ped_align
ped_packed
ped_width
plot_subtitle
plot_title
point_scale_by_pedigree
point_size
preset
recode_missing_ids
recode_missing_sex
reduce_variables
relation
return_interactive
return_mid_parent
return_static
return_widget
segment_default_color
segment_lineend
segment_linejoin
segment_linetype
segment_linewidth
segment_mz_alpha
segment_mz_color
segment_mz_linetype
segment_mz_t
segment_offspring_color
segment_parent_color
segment_scale_by_pedigree
segment_self_alpha
segment_self_angle
segment_self_color
segment_self_curvature
segment_self_linetype
segment_self_linewidth
segment_sibling_color
segment_spouse_color
sex_color_include
sex_color_palette
sex_legend_show
sex_legend_title
sex_shape_female
sex_shape_include
sex_shape_labels
sex_shape_male
sex_shape_unknown
spread_out_generations
spread_out_generations_factor
status_alpha_affected
status_alpha_unaffected
status_code_affected
status_code_unaffected
status_color_affected
status_color_palette
status_color_unaffected
status_include
status_label_affected
status_label_unaffected
status_legend_show
status_legend_title
status_shape_affected
tile_cluster
tile_color_border
tile_color_palette
tile_geom
tile_interpolate
tile_linejoin
tile_na_rm
tooltip_columns
tooltip_include
use_only_classic_kin
use_relative_degree
value_rounding_digits

Show all config keys with defaults

df <- getDefaultPlotConfig("ggPedigree") %>%
  # is a list
  unlist() %>%
  as.data.frame() %>%
  rownames_to_column(var = "Config_Key") %>%
  rename(Default_Value = ".")
df %>%
  knitr::kable()

Config_Key	Default_Value
apply_default_scales	TRUE
segment_default_color	black
apply_default_theme	TRUE
color_theme	color
color_palette_default1	#440154FF
color_palette_default2	#7fd34e
color_palette_default3	#f1e51d
color_palette_low	#000004FF
color_palette_mid	#56106EFF
color_palette_high	#FCFDBFFF
color_scale_midpoint	0.5
color_scale_theme	ggthemes::calc
alpha	1
value_rounding_digits	3
code_male	1
code_na	NA
code_female	0
code_unknown	NA
filter_n_pairs	500
filter_degree_min	0
filter_degree_max	7
drop_classic_kin	FALSE
drop_non_classic_sibs	TRUE
use_only_classic_kin	TRUE
use_relative_degree	TRUE
group_by_kin	TRUE
match_threshold_percent	10
max_degree_levels	12
grouping_column	mtdna_factor
annotate_include	TRUE
annotate_x_shift	-0.1
annotate_y_shift	0.005
label_include	TRUE
label_column	personID
label_method	geom_text
label_max_overlaps	25
label_nudge_x	0
label_nudge_y	0.15
label_nudge_y_flip	TRUE
label_segment_color	NA
label_text_angle	0
label_text_size	3
label_text_color	black
label_text_family	sans
label_scale_by_pedigree	TRUE
point_size	6
point_scale_by_pedigree	TRUE
outline_include	FALSE
outline_multiplier	1.25
outline_color	black
outline_additional_size	0
outline_alpha	1
tooltip_include	TRUE
tooltip_columns1	ID1
tooltip_columns2	ID2
tooltip_columns3	value
axis_text_angle_x	90
axis_text_angle_y	0
axis_text_size	9
axis_text_color	black
axis_text_family	sans
generation_height	1
generation_width	1
segment_linewidth	0.8
segment_linetype	1
segment_lineend	round
segment_linejoin	round
segment_scale_by_pedigree	FALSE
segment_offspring_color	black
segment_parent_color	black
segment_self_color	black
segment_sibling_color	black
segment_spouse_color	black
segment_mz_color	black
segment_mz_linetype	1
segment_mz_alpha	1
segment_mz_t	0.6
segment_self_linetype	dotdash
segment_self_alpha	0.5
segment_self_angle	90
segment_self_curvature	-0.2
segment_self_linewidth	0.4
sex_color_include	TRUE
sex_legend_title	Sex
sex_shape_labels1	Female
sex_shape_labels2	Male
sex_shape_labels3	Unknown
sex_color_palette1	#440154FF
sex_color_palette2	#7fd34e
sex_color_palette3	#f1e51d
sex_shape_female	16
sex_shape_male	15
sex_shape_unknown	18
sex_legend_show	FALSE
sex_shape_include	TRUE
status_include	TRUE
status_code_affected	1
status_code_unaffected	0
status_label_affected	Affected
status_label_unaffected	Unaffected
status_alpha_affected	1
status_alpha_unaffected	0
status_color_palette1	#440154FF
status_color_palette2	#7fd34e
status_color_affected	black
status_color_unaffected	#7fd34e
status_shape_affected	4
status_legend_title	Affected
status_legend_show	FALSE
overlay_shape	4
overlay_code_affected	1
overlay_code_unaffected	0
overlay_label_affected	Affected
overlay_label_unaffected	Unaffected
overlay_alpha_unaffected	0
overlay_color	black
overlay_alpha_affected	1
overlay_include	FALSE
overlay_mode	alpha
overlay_stroke	1.5
overlay_legend_title	Overlay
overlay_legend_show	FALSE
focal_fill_include	FALSE
focal_fill_legend_show	TRUE
focal_fill_personID	1
focal_fill_legend_title	Focal Fill
focal_fill_high_color	#FDE725FF
focal_fill_mid_color	#9F2A63FF
focal_fill_low_color	#0D082AFF
focal_fill_scale_midpoint	0.5
focal_fill_method	gradient
focal_fill_component	additive
focal_fill_shape	21
focal_fill_na_value	black
focal_fill_use_log	FALSE
focal_fill_force_zero	FALSE
focal_fill_hue_range1	0
focal_fill_hue_range2	360
focal_fill_chroma	50
focal_fill_lightness	50
focal_fill_hue_direction	horizontal
focal_fill_viridis_option	D
focal_fill_viridis_begin	0
focal_fill_viridis_end	1
focal_fill_viridis_direction	1
ci_include	TRUE
ci_ribbon_alpha	0.3
tile_color_palette1	white
tile_color_palette2	gold
tile_color_palette3	red
tile_color_border	NA
tile_cluster	TRUE
tile_interpolate	TRUE
tile_geom	geom_tile
tile_na_rm	FALSE
tile_linejoin	mitre
matrix_fill_legend_title	Relatedness
matrix_sparse	FALSE
matrix_isChild_method	partialparent
matrix_diagonal_include	TRUE
matrix_upper_triangle_include	FALSE
matrix_lower_triangle_include	TRUE
return_static	FALSE
return_widget	FALSE
return_interactive	FALSE
return_mid_parent	FALSE
reduce_variables	TRUE
ped_packed	TRUE
ped_align	TRUE
ped_width	15
coord_layout	cartesian
coord_radial_start_angle	-90
coord_radial_end_angle	270
coord_radial_scale	1.5
coord_radial_min_radius	0.75
spread_out_generations	TRUE
spread_out_generations_factor	0.5
override_many2many	FALSE
optimize_plotly	TRUE
recode_missing_ids	TRUE
recode_missing_sex	TRUE
add_phantoms	FALSE
debug	FALSE
affected_fill_include	FALSE
affected_fill_code_affected	1
affected_fill_code_unaffected	0
affected_fill_label_affected	Affected
affected_fill_label_unaffected	Unaffected
affected_fill_color_affected	black
affected_fill_color_unaffected	NA
affected_fill_shape_female	21
affected_fill_shape_male	22
affected_fill_shape_unknown	23
outline_color_include	FALSE
outline_color_code_affected	1
outline_color_code_unaffected	0
outline_color_label_affected	Highlighted
outline_color_label_unaffected	Default
outline_color_affected	blue
outline_color_unaffected	black
preset	none

Basic usage of config in ggPedigree()

Sex coding: code_male and code_female

Customizing specific plot components via config