# Full Factorial Design

This vignette shows how to generate a Full Factorial Design using both the FielDHub Shiny App and the scripting function `full_factorial()` from the `FielDHub` package.

## 1. Using the FielDHub Shiny App

To launch the app you need to run either

    FielDHub::run_app()

or

    library(FielDHub)
    run_app()

Once the app is running, go to Other Designs > Full Factorial Designs

Then, follow the following steps where we show how to generate this kind of design by an example with a set of 3 treatments with levels `3, 3, 2` each. We will run this experiment 3 times.

## 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 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 two columns: `FACTORS` and `LEVEL`. Containing a list of unique names that identify each treatment and level. Duplicate values are not allowed, all entries must be unique. In the following table, we show an example of the entries list format. This example has an entry list with three treatments/factors, and 3, 3 and 2 levels each.

FACTOR | LEVEL
--- | ---
A | a0
A | a1
A | a2
B | b0
B | b1
B | b2
C | c0
C | c1

2. Choose whether to use the factorial design in a RCBD or CRD with the Select a Factorial Design Type box. Set it to `RCBD`.

3. Set the number of entries for each factor in a comma separated list in the Input # of Entries for Each Factor box. We want our example experiment to have 3 factors with 3, 3, and 2 levels respectively, so enter `3, 3, 2` in the box.

4. Set the number of replications of the squares with the Input # of Full Reps box. Set it to `3`.

5. Enter the number of locations in Input # of Locations. Set it to `1`.

6. 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. In this case, set it to `101`.

7. Optionally, you may enter a name for the location of the experiment in the Input Location box.

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

9. As with all the designs, we can set a random seed in the box labeled random seed. In this example, we will set it to `1239`.

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

## Outputs

After you run a full factorial design in FielDHub, there are several ways to display the information contained in the field book.

### Field Layout

When you first click the run button on a full factorial design, FielDHub displays the Field Layout tab, which shows the entries and their arrangement in the field. In the box below the display, you can change the layout of the field or change the location displayed. You can also display a heatmap over the field by changing Type of Plot to `Heatmap`. To view a heatmap, you must first simulate an experiment over the described field with the Simulate! button. A pop-up window will appear where you can enter what variable you want to simulate along with minimum and maximum values.

### 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. If we have simulated data for a heatmap, an additional column for that variable appears in the Field Book.

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

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

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

    library(FielDHub)

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

    factorial <- full_factorial(
      setfactors = c(3,3,2),
      reps = 3,
      l = 1,
      type = 2,
      plotNumber = 101,
      planter = "serpentine",
      locationNames = "FARGO",
      seed = 1239
    )

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

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

- `setfactors = c(3,3,2)` are the levels of each factor.
- `reps = 3` is the number of replications for each treatment.
- `l = 1` is the number of locations.
- `type = 2` means CRD or RCBD, 1 or 2 respectively.
- `plotNumber = 101` is the starting plot number.
- `planter = "serpentine"` is the order layout.
- `locationNames = "FARGO"` is an optional name for each location.
- `seed = 1239` is the random seed to replicate identical randomizations.

### Print `factorial` object

    print(factorial)

    Full Factorial Design

    Information on the design parameters:
    List of 9
     $ factors           : chr [1:3] "A" "B" "C"
     $ levels            : int [1:8] 0 1 2 0 1 2 0 1
     $ runs              : int 18
     $ all_treatments    :'data.frame': 18 obs. of  3 variables:
      ..$ A: int [1:18] 0 1 2 0 1 2 0 1 2 0 ...
      ..$ B: int [1:18] 0 0 0 1 1 1 2 2 2 0 ...
      ..$ C: int [1:18] 0 0 0 0 0 0 0 0 0 1 ...
     $ reps              : num 3
     $ locations         : num 1
     $ location_names    : chr "FARGO"
     $ kind              : chr "RCBD"
     $ levels_each_factor: num [1:3] 3 3 2

     10 First observations of the data frame with the full_factorial field book:
       ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB
    1   1    FARGO  101   1        0        1        0    0*1*0
    2   2    FARGO  102   1        1        1        0    1*1*0
    3   3    FARGO  103   1        2        1        0    2*1*0
    4   4    FARGO  104   1        2        1        1    2*1*1
    5   5    FARGO  105   1        2        2        0    2*2*0
    6   6    FARGO  106   1        1        0        1    1*0*1
    7   7    FARGO  107   1        0        0        1    0*0*1
    8   8    FARGO  108   1        1        2        0    1*2*0
    9   9    FARGO  109   1        0        2        0    0*2*0
    10 10    FARGO  110   1        0        1        1    0*1*1

### Access to `factorial` object

The `full_factorial()` 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 `$` operator, i.e. `factorial$layoutRandom` or `factorial$fieldBook`.

`factorial$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`, `LOCATION`, `PLOT`, `REP`, and `TRT_COMB`, and columns for each factor individually.

    field_book <- factorial$fieldBook
    head(factorial$fieldBook, 10)

       ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB
    1   1    FARGO  101   1        0        1        0    0*1*0
    2   2    FARGO  102   1        1        1        0    1*1*0
    3   3    FARGO  103   1        2        1        0    2*1*0
    4   4    FARGO  104   1        2        1        1    2*1*1
    5   5    FARGO  105   1        2        2        0    2*2*0
    6   6    FARGO  106   1        1        0        1    1*0*1
    7   7    FARGO  107   1        0        0        1    0*0*1
    8   8    FARGO  108   1        1        2        0    1*2*0
    9   9    FARGO  109   1        0        2        0    0*2*0
    10 10    FARGO  110   1        0        1        1    0*1*1

### Plot the field layout

For plotting the layout in function of the coordinates `ROW` and `COLUMN`, you can use the the generic function `plot()` as follow,

    plot(factorial)