install.packages("FielDHub")

remotes::install_github("DidierMurilloF/FielDHub")

A shiny design of experiments (DOE) app that aids in the creation of traditional, un-replicated, augmented and partially-replicated designs applied to agriculture, plant breeding, forestry, animal and biological sciences.

For more details and examples of all functions present in the FielDHub package. Please, go to https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html.

This is a basic example which shows you how to launch the app:

library(FielDHub)
run_app()

A project needs to test 280 genotypes in a field containing 16 rows and 20 columns of plots. In this example, these 280 genotypes are divided among three different experiments. In addition, four checks are included in a systematic diagonal arrangement across experiments to fill 40 plots representing 12.5% of the total number of experimental plots. An option to include filler plots is also available for fields where the number of experimental plots does not equal the number of available field plots.

The figure above shows a map of an experiment randomized along with multiple experiments (three) and checks on diagonals. Distinctively colored check plots are replicated throughout the field in a systematic diagonal arrangement.

The figure above shows the layout for the three experiments in the field.

To illustrate using FielDHub to build experimental designs through R code, the design produced in the R Shiny interface described above can also be created using the function diagonal_arrangement() in the R script below. Note, that to obtain identical results, users must include the same random seed in the script as was used in the Shiny app. In this case, the random seed is 1249.

diagonal <- diagonal_arrangement(
  nrows = 16, 
  ncols = 20, 
  lines = 280, 
  checks = 4, 
  plotNumber = 101, 
  splitBy = "row", 
  seed = 1249, 
  kindExpt = "DBUDC", 
  blocks = c(100, 100, 80), 
  exptName = c("Expt1", "Expt2", "Expt3")
)

Users can print the returned values from diagonal_arrangement() as follow,

print(diagonal)
Un-replicated Diagonal Arrangement Design 

Information on the design parameters: 
List of 11
 $ rows          : num 16
 $ columns       : num 20
 $ treatments    : num [1:3] 100 100 80
 $ checks        : int 4
 $ entry_checks  : int [1:4] 1 2 3 4
 $ rep_checks    : num [1:4] 11 9 9 11
 $ locations     : num 1
 $ planter       : chr "serpentine"
 $ percent_checks: chr "12.5%"
 $ fillers       : num 0
 $ seed          : num 1249

 10 First observations of the data frame with the diagonal_arrangement field book: 
   ID  EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT
1   1 Expt1        1 2023  101   1      1      0    51    Gen-51
2   2 Expt1        1 2023  102   1      2      0    67    Gen-67
3   3 Expt1        1 2023  103   1      3      0    50    Gen-50
4   4 Expt1        1 2023  104   1      4      0    29    Gen-29
5   5 Expt1        1 2023  105   1      5      0    39    Gen-39
6   6 Expt1        1 2023  106   1      6      0    92    Gen-92
7   7 Expt1        1 2023  107   1      7      1     1   Check-1
8   8 Expt1        1 2023  108   1      8      0    58    Gen-58
9   9 Expt1        1 2023  109   1      9      0    23    Gen-23
10 10 Expt1        1 2023  110   1     10      0    54    Gen-54