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Generate Randomized Experimental Layouts and Greenhouse/Field Planting Maps

Source: R/generate_layout.R
generate_layout.Rd

Generates fully randomized field or greenhouse layouts for single-factor, 2-way, and 3-way factorials across CRD (DCA), RCBD (DBCA), Split-Plot, and Split-Split-Plot designs, providing unique plot IDs, randomized spatial coordinates (Row, Col, Table/Block), exportable data-collection CSV sheets, and publication-grade 2D bench croquis.

Usage

generate_layout(
  design = c("RCBD", "CRD", "Split-Plot", "Split-Split-Plot"),
  treatments = list(),
  replications = 4,
  seed = NULL,
  output_csv = NULL
)

Arguments

design

Design type: `"RCBD"` (DBCA / Bloques Completos al Azar), `"CRD"` (DCA / Completamente al Azar), `"Split-Plot"` (Parcelas Divididas, 2 factors with physical restriction), or `"Split-Split-Plot"` (Parcelas Sub-Subdivididas, 3 factors).

treatments

Named list of treatment factor names and their levels. For Split-Plot: list with main-plot first, then sub-plot. For Split-Split-Plot: list with main-plot first, sub-plot second, sub-sub-plot third.

replications

Number of blocks (for RCBD/Split-Plot) or replicates (for CRD).

seed

Optional integer seed for reproducible random allocation.

output_csv

Optional file path to export the ready-to-fill phenotyping template (e.g., `"planting_plan.csv"`).

Value

An S3 object of class `"agri_layout"` containing:

layout_table

Data frame with plot IDs, block, spatial coordinates, treatment assignments, and an empty response column.

design

Experimental design type.

treatments

Treatment factors list.

replications

Number of blocks or replicates.

seed

Random seed used.

Examples

# Generate greenhouse DBCA layout for 5 genotyes across 4 tables/blocks
lay_rcbd <- generate_layout(
  design = "RCBD",
  treatments = list(genotype = paste0("G", 1:5)),
  replications = 4,
  seed = 42
)
print(lay_rcbd)
#> 
#> ======================================================================
#>   agriDesignR: Randomized Experimental Layout (RCBD)
#> ======================================================================
#>   Design Type    : RCBD
#>   Factor Count   : 1-Way Factorial (genotype)
#>   Replications   : 4 blocks / benches / reps
#>   Total Units    : 20 experimental plots / pots
#>   Random Seed    : 42
#> 
#> 
#> First 15 Randomized Planting Units (Preview)
#> ----------------------------------------------------------------------
#>  Plot_ID             Block Position_in_Block genotype response_value
#>    P_001 Mesa_1 (Bloque 1)                 1       G1             NA
#>    P_002 Mesa_1 (Bloque 1)                 2       G5             NA
#>    P_003 Mesa_1 (Bloque 1)                 3       G4             NA
#>    P_004 Mesa_1 (Bloque 1)                 4       G3             NA
#>    P_005 Mesa_1 (Bloque 1)                 5       G2             NA
#>    P_006 Mesa_2 (Bloque 2)                 1       G4             NA
#>    P_007 Mesa_2 (Bloque 2)                 2       G2             NA
#>    P_008 Mesa_2 (Bloque 2)                 3       G5             NA
#>    P_009 Mesa_2 (Bloque 2)                 4       G1             NA
#>    P_010 Mesa_2 (Bloque 2)                 5       G3             NA
#>    P_011 Mesa_3 (Bloque 3)                 1       G4             NA
#>    P_012 Mesa_3 (Bloque 3)                 2       G1             NA
#>    P_013 Mesa_3 (Bloque 3)                 3       G5             NA
#>    P_014 Mesa_3 (Bloque 3)                 4       G2             NA
#>    P_015 Mesa_3 (Bloque 3)                 5       G3             NA
#>   ... [Total: 20 pots/plots. Access full data with '$layout_table']
#> 
#> ======================================================================
#>   Tip: Print or export to CSV. Fill 'response_value' during harvest, then run 'fit_experiment()'.
#> 
# plot(lay_rcbd)