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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
fig.align = "center",
dpi = 300
)
```
# verbalisr
<!-- badges: start -->
[![CRAN status](https://www.r-pkg.org/badges/version/verbalisr)](https://CRAN.R-project.org/package=verbalisr)
[![](https://cranlogs.r-pkg.org/badges/grand-total/verbalisr?color=yellow)](https://cran.r-project.org/package=verbalisr)
[![](https://cranlogs.r-pkg.org/badges/last-month/verbalisr?color=yellow)](https://cran.r-project.org/package=verbalisr)
<!-- badges: end -->
The purpose of **verbalisr** is to describe pedigree relationships in plain language. This is often helpful in order to understand complex genealogies. Given two members of any pedigree, **verbalisr** spells out the connecting paths between them, using common terminology like *great-grandmother* and *half first cousins*.
To see **verbalisr** in action, check out the interactive app **QuickPed** for building and analysing pedigrees, available online here: <https://magnusdv.shinyapps.io/quickped>.
The algorithm behind **verbalisr** is described in detail in this paper: [QuickPed: an online tool for drawing pedigrees and analysing relatedness](https://doi.org/10.1186/s12859-022-04759-y) (Vigeland, BMC Bioinformatics, 2022).
The **verbalisr** package is part of the [pedsuite](https://magnusdv.github.io/pedsuite/) framework for pedigree analysis in R.
## Installation
To get the current official version of **verbalisr**, install from CRAN as follows:
```{r, eval = FALSE}
install.packages("verbalisr")
```
Alternatively, the development version can be installed from GitHub:
```{r, eval = FALSE}
# install.packages("devtools")
devtools::install_github("magnusdv/verbalisr")
```
## Example
```{r example}
library(verbalisr)
```
Here is an example involving a double-cousin-like relationship:
```{r dblcous, fig.height = 3.7, fig.width = 5.5, out.width = "65%"}
x = doubleCousins(degree1 = 1, removal1 = 1, half1 = TRUE, # half first cousins once removed
degree2 = 2, removal2 = 0) # second cousins
plot(x, hatched = leaves(x))
```
We apply `verbalise()` to describe the relationship between the children:
```{r}
verbalise(x, ids = 16:17)
```
This output shows that 16 and 17 are simultaneous first cousins once removed and second cousins. Below each description follows the corresponding path, with its top-most shared ancestor(s) indicated in brackets. The first path has one ancestor on top, `[4]`, indicating a *half* relationship, while the second has two ancestors on top, `[1,2]`, contributing a *full* relationship.
## A bigger example: The royal Habsburg family
The figure below shows a subset of the (infamously inbred) royal Habsburg family, contained in the built-in dataset `habsburg`.
Untangling all the loops in this pedigree *by hand* would be a daunting task, making it a good test case for **verbalisr**.
```{r habsburg, fig.width = 6, fig.height = 4.4, dpi = 600, out.width = "85%"}
plot(habsburg, hatched = "Charles II", cex = 0.6)
```
The inbreeding coefficient of King Charles II of Spain (the bottom individual) has been estimated to be around 25%, i.e., similar to a child produced by brother-sister incest. We can validate this with the **ribd** package, which provides the function `inbreeding()`:
```{r}
ribd::inbreeding(habsburg, "Charles II")
```
(The answer is a bit less than 25% since we are only looking at a subset of the historic family tree.)
The high inbreeding coefficient shows that the parents of Charles II were closely related. But *how* were they related? **verbalisr** gives the answer:
```{r}
rel = verbalise(habsburg, ids = c("Philip IV", "Mariana"))
rel
```
## Controlling the output
The output of `verbalise()` is actually a detailed list containing various data about each pedigree path. When the output is printed to the screen, however, a special `print` method is called, which formats the data into reader-friendly statements.
The print method accepts various arguments for controlling the output, including `simplify`, `abbreviate`, `includePaths` and `cap`. For example, setting `cap` to FALSE results in all-lowercase output, instead of the default first-letter capitalisation. If `simplify` is TRUE, a simplified description is printed. By default, this also removes the path details, but this may be overridden by the `includePaths` argument.
Here is a simplified description of Charles II's parents:
```{r}
print(rel, simplify = TRUE)
```
Even shorter descriptions are obtained by applying standard abbreviations. This leaves the most important words (such as `cousins`) intact, but shortens quantifiers like `once removed` into `1r`.
```{r}
print(rel, simplify = TRUE, abbreviate = TRUE)
```
Finally, if you want the output as a character vector instead of just the printout, replace `print()` with `format()`:
```{r, echo = -1}
options(width = 100)
format(rel, simplify = TRUE, abbreviate = TRUE)
```