B_Multiple_colonies.Rmd

---
title: "Multiple colonies"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Multiple colonies}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
editor_options:
  markdown:
    wrap: 80
    canonical: true
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  include = TRUE
)
```

# Introduction

We have already introduced the Colony class that holds colony-specific
information and caste individuals. However, when working with honeybees, we
usually do not work with a single colony, but with apiaries or even whole
populations of colonies. To cater for this, SIMplyBee provides a `MultiColony`
class. It behaves as a list of `Colony` objects but with additional
functionality - you can apply function directly to the `MultiColony` objects. A
`MultiColony` can represent different apiaries or sub-populations in terms of
either age of the queens or geographical location of the apiaries etc. This
vignette demonstrates creating and working with `MultiColony` objects. First, we
again load the package.

```{r}
library(package = "SIMplyBee")
```

# Initial settings

We first initiate our simulation with founders genomes, simulation parameters,
base population of virgin queens and a drone congregation area (DCA).

```{r}
# Create 20 founder genomes
founderGenomes <- quickHaplo(nInd = 30, nChr = 1, segSites = 100)
# Set up new global simulation parameters
SP <- SimParamBee$new(founderGenomes)
# Create a base population of 20 virgin queens
basePop <- createVirginQueens(founderGenomes)
# Create a DCA from the drones of the first 10 queens
DCA <- createDrones(basePop[1:10], nInd = 100)
```

# Creating a MultiColony object

We create a `MultiColony` object with `createMultiColony()` function. Let's say
you want to create a `MultiColony` object that represents a single apiary. The
first option is to initialise an empty `MultiColony` object that represents an
empty apiary without any colonies and individuals within them.

```{r}
# Create an empty apiary
emptyApiary <- createMultiColony()
emptyApiary
```

Let's inspect the printout of the `MultiColony` object. This tells how many
colonies are within, how many of them are `empty` and contain no individuals,
how many are `NULL` objects, how many have experienced a split, swarm,
supersedure, or a collapse (you can read more about these events in the Colony
events vignette), and how many of them are productive, meaning that we can
collect a production phenotype from them such as honey yield.

The second option is again to create an empty `MultiColony` object that
represents an empty apiary without any individuals within, but with a defined
number of colony slots.

```{r}
# Create an empty apiary with 10 colony slots
emptyApiary1 <- createMultiColony(n = 10)
emptyApiary1
```

The third option is to create a `MultiColony` object with a population of either
virgin or mated queens. For this, we first have to initialise the simulation
with founder genomes and creating a base population of virgin queens. We will
use 10 virgin queens to produce drones and create a DCA - we will take these
from the initial settings above.

We will now create an apiary with 10 virgin colonies with the
`createMultiColony()` function by providing the second set of 10 virgin queens
as the input parameter. Let's call this apiary `apiary1` and say that it is
positioned at the location `(1,1)`.

```{r}
# Create an apiary with the remaining virgin queens
apiary1 <- createMultiColony(x = basePop[11:20])
# Set the location of the apiary
apiary1 <- setLocation(apiary1, c(1,1))
```

Let's now use functions `isQueenPresent()` and `isVirginQueensPresent()` to
confirm all the colonies are virgin.

```{r}
# Check whether all the colonies are virgin
isQueenPresent(apiary1)
isVirginQueensPresent(apiary1)
```

# MultiColony operations

Once we have a non-empty `MultiColony` object, we can do basic operations on it.
First, we can select some colonies by either specifying their IDs, desired
number or percentage of randomly selected colonies.

```{r}
# Get the IDs of the colonies
getId(apiary1)
# Select colonies according to IDs
selectColonies(apiary1, ID = c(1,2))
# Randomly select a given percentage of colonies
selectColonies(apiary1, p = 0.1)
```

Second, we can pull some colonies from the `MultiColony` object. This means,
that the pulled colonies are removed from the original object. The function
`pullColonies()` therefore returns two object - the pulled colonies and the
remnant colonies.

```{r}
# Pull one colony - returns a list with $remnant and $pulled nodes
pullColonies(apiary1, n = 1)
```

Third, we can also remove some colonies from the `MultiColony` object with
`removeColonies()` function.

```{r}
removeColonies(apiary1, ID = 13)
```

These three functions can also select, pull, and remove colonies based on some
values (phenotypes, genetic values ...). You can read more about that in the
Quantitative genetics vignette.

# Crossing a MultiColony

Next, we will cross all the virgin queens in the apiary with the `cross()`
function to groups of drones that we collected from the DCA with the
`pullDroneGroupsFromDCA()` function. We have to collect at least as many groups
of drones as we have colonies in our `MultiColony`.

```{r}
# Pull 10 groups of drones from the DCA
droneGroups <- pullDroneGroupsFromDCA(DCA, n = 10, nDrones = nFathersPoisson)
# Cross all virgin queens in the apiary to the selected drones
apiary1 <- cross(apiary1, drones = droneGroups, checkCross = "warning")
# Check whether the queens are present (and hence mated)
isQueenPresent(apiary1)
```

Once we have mated queens in the apiary, we can apply all the event functions
directly to the `MultiColony` object: `buildUp()`, `downsize()`, `swarm()`,
`split()`, `supersede()`, `collapse()` but also all the functions that either
add, replace, or remove individuals from the castes. Let's say we want to
build-up all the colonies in our apiary.

```{r}
# Build-up all the colonies in the apiary1
apiary1 <- buildUp(apiary1, nWorkers = 1000, nDrones = 100)
```

Furthermore, we can use the `pullColonies()` or `selectColonies()` to subset the
colonies that will for example swarm, collapse, or supersede (presented in the
Colony events vignette), or the ones that we decided to split (check out the
Colony events vignette).

# Working with multiple MultiColony objects

Let's now initiate another `MultiColony` named as `apiary2` that is placed at
location `(2,2)`. Here, we define different `MultiColony` object according to
the location of the apiary, but the objects could also be defined according to
the age of the queens (such as `age0`, `age1`...). `apiary2` contains only
virgin queens and we want to mate them to a DCA made of drones from `apiary1`.

```{r}
# Initiate apiary2 at the location (2,2)
apiary2 <- createMultiColony(basePop[21:30])
apiary2 <- setLocation(apiary2, c(2,2))
```

Since some time has passed, we want to first replace the drones in `apiary1`
with new drones. We can do that with `replaceDrones()` function.

```{r}
apiary1 <- replaceDrones(apiary1)
```

Now that we have a new set of drones, we can create a DCA with the function
`createDCA()` and mate virgin queens in apiary2 to the DCA.

```{r}
# Check whether all colonies in apiary2 are virgin
isQueenPresent(apiary2)
isVirginQueensPresent(apiary2)
# Create a DCA from all the drones in apiary
DCA <- createDCA(apiary1)
# Check how big is the DCA
DCA
# Sample drones groups from the DCA
droneGroups <- pullDroneGroupsFromDCA(DCA, 
                                      n = nColonies(apiary2), 
                                      nDrones = nFathersPoisson)
# Cross virgin queens in apiary2 to selected drones
apiary2 <- cross(apiary2, drones = droneGroups, checkCross = "warning")
```

To learn more about the `nFathersPoisson()` function and other similar
functions, read the Sampliong functions vignette.