---
title: "Sparse Allocation"
output: rmarkdown::html_vignette
bibliography: references.bib
vignette: >
  %\VignetteIndexEntry{Sparse Allocation}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = NA,
  warning = FALSE, 
  message = FALSE
)
```

```{r, echo = FALSE}
library(FielDHub)
library(magrittr)
library(knitr)
library(kableExtra)
```


## Sparse Allocation/Testing

This vignette shows how to generate un-replicated designs leveraging the **sparse allocation method** 
by using the FielDHub Shiny App and the scripting function `sparse_allocation()` from the `FielDHub` 
R package.

## Overview

Sparse allocation is a valuable strategy in plant breeding experiments, as it allows researchers to 
evaluate a large number of treatments over multiple locations in a single experiment. Sparse allocation 
can increase efficiency and reduce the number of experimental units required, making it a cost-effective 
option. One standard method for implementing sparse allocation in plant breeding experiments is 
incomplete block designs (IBD) [@EdmondsonIBDs]. 

The following key points summarize the advantages and disadvantages of sparse 
allocation [@MontesinosLopezSparse]:

**Increased efficiency**: By using sparse allocation, breeders can evaluate a large number of genotypes or 
treatments in a single experiment across multiple environments, which can accelerate the breeding program 
and reduce the time and resources needed for evaluation.

**Selection intensity**: The large number of genotypes or treatments evaluated in sparse allocation 
experiments can increase the genetic diversity in the breeding program and increase the chances 
of identifying superior genotypes or treatments.

**Cost-effective**: Sparse allocation experiments are generally less expensive compared to fully 
replicated experiments since fewer experimental units are needed.

**Less accurate predictions**: The limited number of experimental units means that the estimates 
of treatment effects are less precise compared to fully replicated designs. However, an increase 
in selection intensity may compensate for the loss of accuracy (Trade off problem).

FielDHub includes a function to run the sparse allocation strategy and the multi-location 
randomization, as well as an interface for creating a sparse allocation design on the 
FielDHub app. 

## Use Case

The plant breeding project aims to test 260 entries across five environments, but due to 
limited seed availability, only four replications for each genotype can be created across all
five locations. As a result, not all genotypes will be present in all environments. 
Additionally, the project includes four checks that will be replicated in all environments. 
To address the seed shortage, the sparse allocation strategy will be used. 

The table below illustrates the allocation of the first ten genotypes across the five environments.

```{r, include = FALSE}
allocation <- data.frame(
  ENV1 = c(1, 0, 1, 1, 1, 1, 1, 0, 1, 1),
  ENV2 = c(1, 1, 1, 1, 1, 1, 0, 1, 1, 1),
  ENV3 = c(1, 1, 0, 1, 1, 0, 1, 1, 1, 1),
  ENV4 = c(1, 1, 1, 1, 1, 1, 1, 1, 0, 0),
  ENV5 = c(0, 1, 1, 0, 0, 1, 1, 1, 1, 1)
  )
 rownames(allocation) <- paste0("Gen-", 1:10)
```

```{r, echo = FALSE}
allocation %>%
  kbl() %>%
  kable_styling()
```

The table illustrates the allocation of genotypes across different environments, with genotypes listed
in rows and environments in columns. Specifically, it indicates that Genotype 1 (Gen-1) has been 
assigned to locations 1, 2, 3, and 4, but not to environment 5. The process of allocating genotypes to 
locations is achieved through an optimization process that employs IBD principles.

## Running the Shiny App

To launch the app you need to run either

```{r, eval=FALSE}
FielDHub::run_app()
```

or

```{r, eval=FALSE}
library(FielDHub)
run_app()
```

## 1. Using the FielDHub Shiny App

Once the app is running, go to **Unreplicated Designs** > **Sparse Allocation**

Then, follow the following steps where we will show how to generate a sparse allocation experiment.

##  Inputs 

1. **Import entries' list?** Choose whether to import a list with entry numbers and names for genotypes  
or treatments.

    * If the selection is `No`, the app will generate synthetic data for entries and 
    names of the treatment/genotypes based on the user inputs.

    * If the selection is `Yes`, the entries list must fulfill a specific format and must be a `.csv` 
    file. The file must have the columns `ENTRY` and `NAME`. The `ENTRY` column must have a unique 
    integer number entry for each treatment/genotype. The column `NAME` must have a unique name 
    that identifies each treatment/genotype. Both `ENTRY` and `NAME` must be unique, duplicates 
    are not allowed. The following table shows an example of the entries list format. 
    Any checks must appear in the first rows of the `.csv` file. 

```{r, include=FALSE}
ENTRY <- 1:9
NAME <- c(c("CHECK1","CHECK2","CHECK3"), c(paste0("Genotype", LETTERS[1:6])))
df <- data.frame(ENTRY,NAME)
```

```{r, echo = FALSE, results='asis'}
df %>%
  kbl() %>%
  kable_styling()
```

2. Enter the number of entries/treatments in the **Input # of Entries** box, which is 260 in our case.

3. Select 4 from the drop-down on the **Input # of Checks** box. 

4. Since we want to run this experiment over 5 locations, set **Input # of Locations** to 5.

5. Set the number of copies of each treatment in the **# of Copies Per Entry** dropdown box to 4. 

6. Select `serpentine` or `cartesian` in the **Plot Order Layout**. For this example we will use 
the `serpentine` layout.

7. To ensure that randomizations are consistent across sessions, we can set a seed number in the box 
labeled **Random Seed**. For instance, we will set it to `16`. 

8. Enter the name for the experiment in the **Input Experiment Name** box. For example, `SparseTest2023`.

9. Enter the starting plot number in the **Starting Plot Number** box. In this experiment we want the plot 
start at `1, 1001, 2001, 3001, 4001` for each location.

10. Enter the name of the site/location in the **Input the Location** box. For this experiment we will 
set the sites as `FARGO, CASSELTON, MINOT, PROSPER, WILLISTON`. 

11. Once we have entered all the information for our experiment on the left side panel, click the **Run!** 
button to run the design. 

12. You will then be prompted to select the dimensions of the field from the list of options in the 
drop-down in the middle of the screen with the box labeled **Select dimensions of field**. 
In our case, we will select `16 x 15`. We also can see the table with the sparse allocation.

13. Click the **Randomize!** button to randomize the experiment with the set field dimensions and to see 
the output plots. 

If you change any of the inputs on the left side panel after running an experiment initially, 
you have to click the Run and Randomize buttons again, to re-run with the new inputs.

## Outputs

After you run a sparse allocation design in FielDHub and set the dimensions of the field, there are 
several ways to display the information about the sparse process and the randomization. 

### Expt Design Info

On the first tab, **Expt Design Info**, you can see all the entries in the randomization displayed 
in a binary matrix with a column for each location, with a 1 indicating that the respective genotype 
is in the respective location, and a 0 indicating that it is not. This is the sparse genotype 
allocation characteristic of this method. There are buttons to copy, print, and save the table 
to an Excel file. 

### Randomized Field

The **Randomized Field** tab displays a graphical representation of the randomization of the entries 
in a field of the specified dimensions. The checks are each colored uniquely, showing the number of 
times they are distributed throughout the field. The display includes numbered labels for the rows 
and columns. You can copy the field as a table or save it directly as an Excel file with the _Copy_ 
and _Excel_ buttons at the top. 

In the **Choose % of Checks:** drop-down box, users can play with different options for the total 
amount of checks in the field. 

### Plot Number Field

On the **Plot Number Field** tab, there is a table display of the field with the plots numbered 
according to the Plot Order Layout specified, either _serpentine_ or _cartesian_. You can see the 
corresponding entries for each plot number in the field book. Like the Randomized Field tab, you 
can copy the table or save it as an Excel file with the _Copy_ and _Excel_ buttons. 

### Field Book

The **Field Book** displays all the information on the experimental design in a table format. 
It contains the specific plot number and the row and column address of each entry, as well as 
the corresponding treatment on that plot. This table is searchable, and we can filter the data 
in relevant columns. 

<br>

## 2. Using the `FielDHub` function: `sparse_allocation()`

You can run the same design with a function in the FielDHub package, `sparse_allocation()`.

First, you need to load the `FielDHub` package typing,

```{r, echo = TRUE}
library(FielDHub)
```

Then, you can enter the information describing the above design like this:

```{r, echo = TRUE, warning=FALSE, comment=''}
sparse_example <- sparse_allocation(
   lines = 260, 
   l = 5, 
   copies_per_entry = 4, 
   checks = 4, 
   plotNumber = c(1, 1001, 2001, 3001, 4001),
   locationNames = c("FARGO", "CASSELTON", "MINOT", "PROSPER", "WILLISTON"), 
   exptName = "SparseTest2023",
   seed = 16
)
```

#### Details on the inputs entered in `sparse_allocation()` above:

*   `lines = 260` is the number of genotypes.
*   `l = 5` is the number of locations.
*   `copies_per_entry = 4` is the number of copies of each entry.
*   `checks = 4` is the number of checks.
*   `plotNumber = c(1, 1001, 2001, 3001, 4001)` are optional starting plot numbers
*   `locationNames = c("FARGO", "CASSELTON", "MINOT", "PROSPER", "WILLISTON")` are optional names for each location.
*   `exptName = "SparseTest2023"` is an optional name of the experiment
*   `seed = 16` is the random seed number to replicate identical randomizations.

### Print `sparse_example` object

To print a summary of the information that is in the object `sparse_example`, we can use the generic 
function `print()`. 

The `sparse_allocation()` function returns a list of objects, includes all the outputs from the function
diagonal_arrangement() and in addition `list_locs`, `allocation`, and `size_locations`. The `list_locs` object 
is a list of data frames. Each data frame has two columns; `ENTRY` and `NAME` with the information to randomize 
to each environment. The object `allocation` is the binary allocation matrix of genotypes to locations, 
and `size_locations` is a data frame with a column for  each location and a row indicating the size of 
the location (number of field plots). 

For example, we can display the `allocation` object. Let us print the first ten genotypes allocation.

```{r, echo=TRUE, eval=FALSE}
print(head(sparse_example$allocation, 10))
```

```{r, echo=FALSE, eval=TRUE}
print(head(sparse_example$allocation, 10))
```

We can manipulate the sparse_allocation object as any other list in R. For example, we can print the design 
information as following:

```{r, echo=TRUE, eval=FALSE}
print(sparse_example)
```

which outputs:

```{r, echo=FALSE, eval=TRUE}
print(sparse_example)
```

### Access to `sparse_example` object

The object `sparse_example` is a list consisting of all  the information displayed in the output tabs in 
the FielDHub app: design information, plot layout, 
plot numbering, entries list, and field book, indexed by each location in the experiment. These are 
accessible by  the `$` operator, i.e. `designs$layoutRandom[[1]]` for `LOC1` or `designs$fieldBook` for 
the whole field book.

`designs$fieldBook` is a data frame containing information about every plot in the field, with 
information about the location of the plot and the treatment in each plot. As seen in the output below, 
the field book has columns for `ID`, `EXPT`, `LOCATION`, `YEAR`, `PLOT`, `ROW`, `COLUMN`, `CHECKS`, 
`ENTRY`, and `TREATMENT`.

Let us see the first 10 rows of the field book for the first location in this experiment.

```{r, echo=TRUE, eval=FALSE}
field_book <- sparse_example$fieldBook
head(field_book, 10)
```

```{r, echo=FALSE, eval=TRUE}
field_book <- sparse_example$fieldBook
head(field_book, 10)
```

### Plot field layout

#### Plot field layout Location 1

For plotting the layout in function of the coordinates `ROW` and `COLUMN` in the field book object we 
can use the generic function `plot()` as follows,

```{r, fig.align='center', fig.width=7.2, fig.height=5}
plot(sparse_example, l = 1)
```

The above plot is for `LOC1`. We can plot any location in the experiment, like location 2 in this example:

#### Plot field layout Location 2

```{r, fig.align='center', fig.width=7.2, fig.height=5}
plot(sparse_example, l = 2)
```

The figure above shows a map of an experiment randomized as an unreplicated arrangement design. 
The blue plots represent the unreplicated treatments, while the yellow-boxed colored check plots are 
replicated throughout the field in a systematic diagonal arrangement. The red plots with 0s are are fillers. 

# References
