row column design: plots = row by col; equal rep for each treatment

row column design: plots = row by col; equal rep for each treatment

Usage

randomize_row_col(
  clones,
  tot,
  trial,
  rowD,
  rowsinR,
  colsinR,
  n_dummies = 0,
  rep,
  season,
  path = t_dir,
  check = c("Shangi", "Unica", "Sagitta", "Sherekea"),
  dummy = c("Unica", "Shangi"),
  to_add = 4
)

Arguments

clones

a dataframe with geno column

tot

integer. total number of unique clones/genotypes to be randomized to field

trial

character. trial

rowD

integer. number of rows in the field

rowsinR

integer. number of rows in template replicate block; for blocking

colsinR

integer. number of columns in template replicate block; for blocking

n_dummies

integer. number of dummies to complete a rectangular layout

rep

integer. number of replication

season

season of trial

path

character specifying path to write the design

check

a character vector of checks to fill rectangular grid

dummy

a character vector of dummy checks to fill rectangular grid

to_add

integer. number of checks to add to complete the rectangular grid
Note: To optimize on the row or column, remember that rep * rowsinR = rowD or rep * colsinR = colsD.

Examples

df <- data.frame(geno = LETTERS[1:4])
rcD <-
  randomize_row_col(
    clones = df,
    trial = "KE24ILR-BW-ST01",
    tot = 6,
    rowD = 3,
    rowsinR = 3,
    colsinR = 2,
    n_dummies = 0,
    to_add = 2,
    rep = 3,
    path = NULL
  )
#>      Phase,    Search%,    A-measure
#> [1] 1.0000000 0.0000000 0.8121212
#> [1]  1.0000000 10.0000000  0.8121212
#> [1]  1.0000000 20.0000000  0.8121212
#> [1]  1.0000000 30.0000000  0.8121212
#> [1]  1.0000000 40.0000000  0.8121212
#> [1]  1.0000000 50.0000000  0.8121212
#> [1]  1.0000000 60.0000000  0.8121212
#> [1]  1.0000000 70.0000000  0.8121212
#> [1]  1.0000000 80.0000000  0.8121212
#> [1]  1.0000000 90.0000000  0.8121212
#> [1]   1.0000000 100.0000000   0.8121212
#>  [1] 0.8121212 0.8121212 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#>  [8] 0.0000000 0.0000000 0.0000000
#> 3 rows by 1 column
#>      Phase,    Search%,    A-measure
#> [1] 2.000000 0.000000 0.944703
#> [1]  2.0000000 10.0000000  0.8121212
#> [1]  2.0000000 20.0000000  0.8121212
#> [1]  2.0000000 30.0000000  0.8121212
#> [1]  2.0000000 40.0000000  0.8121212
#> [1]  2.0000000 50.0000000  0.8121212
#> [1]  2.0000000 60.0000000  0.8121212
#> [1]  2.0000000 70.0000000  0.8121212
#> [1]  2.0000000 80.0000000  0.8121212
#> [1]  2.0000000 90.0000000  0.8121212
#> [1]   2.0000000 100.0000000   0.8121212
#>  [1] 0.8121212 0.8121212 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#>  [8] 0.0000000 0.0000000 0.0000000
head(rcD$fieldbook)
#>                    plot     geno entry row column rep
#> 1 KE24ILR-BW-ST01-00001        C     3   1      1   1
#> 2 KE24ILR-BW-ST01-00002   Shangi     5   2      1   1
#> 3 KE24ILR-BW-ST01-00003        D     4   3      1   1
#> 4 KE24ILR-BW-ST01-00004        A     1   1      2   1
#> 5 KE24ILR-BW-ST01-00005 Sherekea     6   2      2   1
#> 6 KE24ILR-BW-ST01-00006        B     2   3      2   1