---
title: "Partially Replicated Design"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Partially Replicated Design}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

This vignette shows how to generate a **partially replicated design** using both the FielDHub Shiny App and the scripting function `partially_replicated()` from the `FielDHub` R package.

## Overview

Partially replicated designs are commonly employed in early generation field trials. This type of design is characterized by replication of a portion of the entries, with the remaining entries only appearing once in the experiment. Commonly, the part of treatments with reps is due to an arbitrary decision by the research, also in some cases, it is due to technical reasons. In this design, it is recommended to replicate at least 1/3 of the treatments.

In this design you can set the number of entries that will have reps, as well as the number of entries that will only appear once. You can also choose to run the same experiment over multiple locations. 

## Use case

Consider a plant breeding field trial with 300 plots containing 75 entries appearing two times each, and 130 entries only appearing once. This field trial is arranged in a field of 15 rows by 20 columns. In this case, the breeder decided to replicate the genotypes that do not share significant generic information with each other (75), as well as leave with just one copy the genotypes that are siblings (130).

## 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 the tab **Partially Replicated Design**

Then, follow the following steps where we will show how to generate a partially replicated design.

## 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`, that means the app is going to 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`, `NAME`, and `REPS`. The `ENTRY` column must have a unique entry integer number for each treatment/genotype. The column `NAME` must have a unique name that identifies each treatment/genotype. The `REPS` column must have an integer number for the replications of the groups. Both ENTRY and NAME must be unique, duplicates are not allowed. In the following table, we show an example of the entries list format. This example has an entry list with four treatments/genotypes that will appear twice and 8 that appear just once. 
    
```{r, include=FALSE}
ENTRY <- 1:12
NAME <- c(paste0("Genotype", LETTERS[1:12]))
REPS <- c(rep(2,4),rep(1,8))
df <- data.frame(ENTRY,NAME,REPS)
```

```{r, echo = FALSE, results='asis'}
library(knitr)
kable(df)
```

2. Enter the number of entries per replicate group in the **# of Entries per Rep Group** box as a comma separated list. In our example we will have 2 groups with 85 and 130 entries. So, we enter `75, 130` in the box for our sample experiment. 

3. Enter the number of replications per group in the **# of Rep per Group** box. In our example we will have 2 and 1 replications for the 2 groups, so we enter `2, 1` in this box. 

4. Enter the number of locations in **Input # of Locations**. We will run this experiment over a single location, so set **Input # of Locations** to `1`.

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

6. To ensure that randomizations are consistent across sessions, we can set a seed number in the box labeled **Seed Number**. In this example, we will set it to `1245`. 

7. Enter the starting plot number in the **Starting Plot Number** box. If the experiment has multiple locations, you must enter a comma separated list of numbers the length of the number of locations for the input to be valid. 

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

9. 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 `15 x 20`. 

10. Click the **Randomize!** button to randomize the experiment with the set field dimensions and to see the output plots. If you change the dimensions again, you must re-randomize.

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 single diagonal arrangement in FielDHub and set the dimensions of the field, there are several ways to display the information contained in the field book. The first tab, **Get Random**, shows the option to change the dimensions of the field and re-randomize, as well as a reference guide for experiment design. 

### Data Input

On the second tab, **Data Input**, you can see all the entries in the randomization in a list, as well as a table of the checks with the number of times they appear in the field. In the list of entries, the reps for each check is included as well.


### 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 the green colored cells, with the  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. 

### 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. 

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

You can run the same design with the function `partially_replicated()` in the `FielDHub` package.

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}
prep <- partially_replicated(
  nrows = 15,
  ncols = 20, 
  repGens = c(75,150),
  repUnits = c(2,1), 
  planter = "serpentine", 
  plotNumber = 101,
  l = 1, 
  exptName = "Expt1",
  locationNames = "PALMIRA",
  seed = 1245, 
)
```

#### Details on the inputs entered in `optimized_arrangement()` above

The description for the inputs that we used to generate the design,

*   `nrows = 15` is the number of rows in the field.
*   `ncols = 20` is the number of columns in the field.
*   `repGens = c(75,150)` are the values for the groups to replicate
*   `repUnits = c(2,1)` are the values for representing respective replicates of each group.
*   `planter = "serpentine"` is the layout order. 
*   `plotNumber = 101` is the starting plot number for the experiment.
*   `l = 1` is the number of locations.
*   `exptName = "Expt1"` is an optional name for experiment.
*   `locationNames = "PALMIRA"` is the optional name for the locations.
*   `seed = 1245` is the seed number to replicate identical randomizations.

### Print `prep` object

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

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

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

### Access to `prep` output

The `partially_replicated()` function returns 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. These are Accessible by the `$` operator, i.e. `prep$layoutRandom` or `prep$fieldBook`. 

`prep$fieldBook` is a list 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 this experiment.

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

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

### Plot field layout

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 follow,


```{r, fig.align='center', fig.width=7.2, fig.height=5.5}
plot(prep)
```

In the figure above, green plots contain replicated entries, and gray plots contain entries that only appear once.

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