This vignette shows how to generate a Latin square
design using both the FielDHub Shiny App and the scripting
function latin_square() from the FielDHub
package.
1. Using the FielDHub Shiny App
To launch the app you need to run either
FielDHub::run_app()or
Once the app is running, go to Other Designs > Latin Square Design
Then, follow the following steps where we show how to generate this kind of design by an example with 5 treatments and 2 reps.
Inputs
-
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.csvfile. The file must have the three columns:ROW,COLUMNandTREATMENT. All of those columns contain a list of unique names that identify each treatment. 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 5 treatments.
| ROW | COLUMN | TREATMENT |
|---|---|---|
| Period1 | Cow1 | Diet1 |
| Period2 | Cow2 | Diet2 |
| Period3 | Cow3 | Diet3 |
| Period4 | Cow4 | Diet4 |
| Period5 | Cow5 | Diet5 |
Input the number of treatments in the Input # of Treatments box. In the alpha lattice design, the number of treatments must be a composite number.
Select the number of replications of these treatments with the Input # of Full Reps box. The number of treatments and the number of full reps set the dimensions of the field.
Select
serpentineorcartesianin the Plot Order Layout. For this example we will use the defaultserpentinelayout.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.
Enter a name for the location of the experiment in the Input Location box. A completely randomized design can only be run in a single location at a time.
To ensure that randomizations are consistent across sessions, we can set a random seed in the box labeled random seed. In this example, we will set it to
123.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 Latin square 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 Latin square 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. 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:
latin_square()
You can run the same design with a function in the FielDHub package,
latin_square().
First, you need to load the FielDHub package typing,
Then, you can enter the information describing the above design like this:
lsd <- latin_square(
t = 5,
reps = 2,
plotNumber = 101,
planter = "serpentine",
seed = 1238
)Details on the inputs entered in latin_square()
above
-
t = 5is the number of treatments. -
reps = 2is the number of replications (squares). -
plotNumber = 101is the starting plot number. -
planter = "cartesian"is the plot order layout. -
locationNames = "FARGO"is an optional name for the location. -
seed = 1238is the random seed to replicate identical randomizations.
Print lsd object
print(lsd)Latin Square Design:
Information on the design parameters:
List of 4
$ treatments : int 5
$ squares : num 2
$ locationName: NULL
$ seed : num 1238
10 First observations of the data frame with the latin_square field book:
ID LOCATION PLOT SQUARE ROW COLUMN TREATMENT
1 1 1 101 1 Row 1 Column 1 T5
2 2 1 102 1 Row 1 Column 2 T1
3 3 1 103 1 Row 1 Column 3 T2
4 4 1 104 1 Row 1 Column 4 T4
5 5 1 105 1 Row 1 Column 5 T3
6 6 1 110 1 Row 2 Column 1 T4
7 7 1 109 1 Row 2 Column 2 T2
8 8 1 108 1 Row 2 Column 3 T3
9 9 1 107 1 Row 2 Column 4 T1
10 10 1 106 1 Row 2 Column 5 T5Access to lsd object
The latin_square() 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. lsd$layoutRandom or lsd$fieldBook.
lsd$fieldBook is a list containing information about
every plot in the field, with information about the location of the plot
and the treatment in each plot. As seen in the output below, the field
book has columns for ID, LOCATION,
PLOT, SQUARE, ROW,
COLUMN, and TREATMENT.
field_book <- lsd$fieldBook
head(lsd$fieldBook, 10) ID LOCATION PLOT SQUARE ROW COLUMN TREATMENT
1 1 1 101 1 Row 1 Column 1 T5
2 2 1 102 1 Row 1 Column 2 T1
3 3 1 103 1 Row 1 Column 3 T2
4 4 1 104 1 Row 1 Column 4 T4
5 5 1 105 1 Row 1 Column 5 T3
6 6 1 110 1 Row 2 Column 1 T4
7 7 1 109 1 Row 2 Column 2 T2
8 8 1 108 1 Row 2 Column 3 T3
9 9 1 107 1 Row 2 Column 4 T1
10 10 1 106 1 Row 2 Column 5 T5