Two-Factors Design: RCBD

Planning an experiment follows a reproducible routine:

  1. Load required libraries: Load inti, knitr, and dplyr packages.
  2. Define factor levels: Set up lists with genotypes, treatments, and management factors.
  3. Dispatch design generator: Choose between CRD, RCBD, Split-plot, or Augmented designs.
  4. Plot the field sketch: Verify spatial layouts and serpentine/zigzag sequences.
  5. Label design: Design the experimental labels to facilitate the data collection.
  6. Export to Field Book app: Generate field-ready sheets with trait parameters.
# Install packages and dependencies

library(inti)
library(dplyr)
library(huito)

Designs with Two Factors

When evaluating two factors, four designs become available: CRD, RCBD, Split-plot RCBD, and Augmented.

Factorial Randomized Complete Block Design (RCBD)

Recommended for multi-factor trials where field spatial variability or environmental gradients require blocking to control experimental error.

# 1. Define factors: Bean genotypes and fertilization levels
factors <- list(
  Genotype = c("Bean_01", "Bean_02", "Bean_03"),
  Fertilization = c("0", "50", "100")
)

# 2. Generate factorial RCBD layout
design <- design_repblock(
  nfactors = 2,
  factors = factors,
  type = "rcbd",
  rep = 4,
  zigzag = TRUE,
  seed = 2026
)

# Fieldbook preview
fb <- design$fieldbook

fb %>%
  knitr::kable(caption = "Fieldbook preview")
Fieldbook preview
qrcode plots ntreat Genotype Fertilization sort block rows cols design
inkaverse_1001 1001 2 Bean_02 0 1 1 1 1 rcbd
inkaverse_1002 1002 9 Bean_03 100 2 1 1 2 rcbd
inkaverse_1003 1003 5 Bean_02 50 3 1 1 3 rcbd
inkaverse_1004 1004 6 Bean_03 50 4 1 1 4 rcbd
inkaverse_1005 1005 4 Bean_01 50 5 1 1 5 rcbd
inkaverse_1006 1006 3 Bean_03 0 6 1 1 6 rcbd
inkaverse_1007 1007 8 Bean_02 100 7 1 1 7 rcbd
inkaverse_1008 1008 7 Bean_01 100 8 1 1 8 rcbd
inkaverse_1009 1009 1 Bean_01 0 9 1 1 9 rcbd
inkaverse_2001 2001 5 Bean_02 50 1 2 2 9 rcbd
inkaverse_2002 2002 1 Bean_01 0 2 2 2 8 rcbd
inkaverse_2003 2003 3 Bean_03 0 3 2 2 7 rcbd
inkaverse_2004 2004 6 Bean_03 50 4 2 2 6 rcbd
inkaverse_2005 2005 4 Bean_01 50 5 2 2 5 rcbd
inkaverse_2006 2006 9 Bean_03 100 6 2 2 4 rcbd
inkaverse_2007 2007 8 Bean_02 100 7 2 2 3 rcbd
inkaverse_2008 2008 2 Bean_02 0 8 2 2 2 rcbd
inkaverse_2009 2009 7 Bean_01 100 9 2 2 1 rcbd
inkaverse_3001 3001 9 Bean_03 100 1 3 3 1 rcbd
inkaverse_3002 3002 1 Bean_01 0 2 3 3 2 rcbd
inkaverse_3003 3003 7 Bean_01 100 3 3 3 3 rcbd
inkaverse_3004 3004 4 Bean_01 50 4 3 3 4 rcbd
inkaverse_3005 3005 2 Bean_02 0 5 3 3 5 rcbd
inkaverse_3006 3006 6 Bean_03 50 6 3 3 6 rcbd
inkaverse_3007 3007 5 Bean_02 50 7 3 3 7 rcbd
inkaverse_3008 3008 3 Bean_03 0 8 3 3 8 rcbd
inkaverse_3009 3009 8 Bean_02 100 9 3 3 9 rcbd
inkaverse_4001 4001 7 Bean_01 100 1 4 4 9 rcbd
inkaverse_4002 4002 2 Bean_02 0 2 4 4 8 rcbd
inkaverse_4003 4003 1 Bean_01 0 3 4 4 7 rcbd
inkaverse_4004 4004 4 Bean_01 50 4 4 4 6 rcbd
inkaverse_4005 4005 3 Bean_03 0 5 4 4 5 rcbd
inkaverse_4006 4006 8 Bean_02 100 6 4 4 4 rcbd
inkaverse_4007 4007 6 Bean_03 50 7 4 4 3 rcbd
inkaverse_4008 4008 5 Bean_02 50 8 4 4 2 rcbd
inkaverse_4009 4009 9 Bean_03 100 9 4 4 1 rcbd 

# Spatial layout visualization
tarpuy_plotdesign(
  data = design,
  factor = "Genotype",
  fill = c("plots", "Fertilization")
)

Label

The experimental field book generated by the design is used as the input data for label creation. Each row represents an experimental unit, allowing the automatic generation of individualized labels.

Customize the label layout

The label layout can be customized by combining text, images and QR codes. Each layer can use values from the experimental field book, allowing automatic generation of labels for every experimental plot.

Load package and import fonts.

font <- c("Permanent Marker", "Tillana", "Courgette")

huito_fonts(font)

You can find more fonts in https://fonts.google.com/

Label design

label <- fb %>%
  label_layout(
    size = c(5.2, 10)
    , border_color = "#5C0000"
    , border_width = 1.5
  ) %>%
  include_image(
    value = "https://inkaverse.com/img/inkaverse.png"
    , size = c(1.3, 1.5)
    , position = c(0.8, 9.1)
  )  %>%
  include_text(
    value = "plots"
    , position = c(4.2, 9.1)
    , size = 20
    , color = "black"
    , fontface = "bold"
    , font = font[1]
  )  %>%
  include_image(value = "https://huito.inkaverse.com/img/scale.pdf"
                , size = c(5, 1)
                , position = c(2.6, 7.7)) %>%
  include_barcode(value = "qrcode"
                  , size = c(5, 5)
                  , position = c(2.6, 4.7)) %>%
  include_text(
    value = "Genotype"
    ,
    position = c(2.6, 2)
    ,
    size = 12
    ,
    prefix = "Genotype: "
    ,
    color = "blue"
    ,
    font = font[2]
    , 
    fontface = "bold"
  )  %>%
    include_text(
    value = "Fertilization"
    ,
    position = c(2.6, 1.5)
    ,
    size = 12
    ,
    prefix = "Fertilization: "
    ,
    color = "red"
    ,
    font = font[2]
    , 
    fontface = "bold"
  ) |> 
  include_image(value = "https://huito.inkaverse.com/img/scale.pdf"
                ,
                size = c(5, 1)
                ,
                position = c(2.6, 0.6))

Label preview

The preview mode label_print(mode = "preview") generate a example of the label design from a random row of the data set.

label %>% 
  label_print(mode = "preview")

Generate the complete labels

If you want generate the complete labels list, change: label_print(mode = "complete").

label %>% 
  label_print(mode = "complete"
              , filename = "vertical-DBCA-2"
              , nlabels = 12)