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Package {DOEpro}

Title: Analysis of Designed Agricultural Experiments
Version: 2.0.1
Description: A 'shiny' application and supporting functions for the analysis of designed agricultural experiments, following the procedures set out by Gomez and Gomez (1984, ISBN:9780471870920). Handles completely randomised, randomised complete block, Latin square, factorial (up to four factors), split-plot and strip-plot designs, and pooled (combined) analysis over environments including factorial treatments, after Yates and Cochran (1938) <doi:10.1017/S0021859600050978>; analyses several response variables simultaneously; recommends and applies variance-stabilising transformations using the profile likelihood of Box and Cox (1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>; reports standard errors and critical differences for every legitimate comparison, including the four distinct comparisons of a split plot, the mixed ones using the approximation of Satterthwaite (1946) <doi:10.2307/3002019>; and produces publication-format tables of means, diagnostic plots and a written interpretation.
License: GPL (≥ 3)
Encoding: UTF-8
Depends: R (≥ 4.0)
Imports: shiny, DT, ggplot2, rlang, stats, utils, graphics, grDevices
Suggests: pagedown, testthat (≥ 3.0.0)
Config/testthat/edition: 3
URL: https://github.com/Uzairkhan11w/DOEpro, https://doepro.pages.dev
BugReports: https://github.com/Uzairkhan11w/DOEpro/issues
Config/roxygen2/version: 8.0.0
NeedsCompilation: no
Packaged: 2026-07-25 09:54:27 UTC; Uzair
Author: Immad A. Shah ORCID iD [aut], Uzair Javid Khan ORCID iD [aut, cre], M. Iqbal Jeelani ORCID iD [aut]
Maintainer: Uzair Javid Khan <uzairkhan11w@gmail.com>
Repository: CRAN
Date/Publication: 2026-08-04 14:10:39 UTC

DOEpro: Analysis of Designed Agricultural Experiments

Description

DOEpro analyses the designs used in field and horticultural research: completely randomised, randomised complete block, Latin square, factorial (CRD or RCBD, two to four factors), split-plot, strip-plot, and pooled (combined) analysis over environments, including factorial treatments.

Details

For each design it fits the analysis of variance with the correct error term for every comparison, screens the data against the assumptions of the analysis, advises on a variance-stabilising transformation when one is needed, and reports means with their standard errors, critical differences and grouping letters. Several response variables may be analysed at once.

The quickest way in is the interactive application: 

run_DOEpro()

To work directly with the functions, see analyze for a single response, run_all for several responses at once, and build_report to render the result as an HTML report.

Author(s)

Maintainer: Uzair Javid Khan uzairkhan11w@gmail.com (ORCID)

Authors:

See Also

Useful links:

The designs DOEpro supports

Description

A named character vector: the names are the labels shown in the application and the values are the design codes passed to analyze.

Usage

DESIGNS

Format

A named character vector of length 11.

Value

A named character vector of length 11. Each element is the design code passed to the design argument of analyze or run_all; the corresponding name is the label shown in the application.

Examples

DESIGNS
DESIGNS[["Split Plot Design"]]

The transformations DOEpro offers

Description

A named list of the variance-stabilising transformations. Each entry holds a label (lab), the transformation (f) and its inverse (inv), which is what lets DOEpro report means on the original scale of measurement alongside the transformed ones.

Usage

TRANS

Format

A named list of length 9. The names are the keys used in the trans argument of run_all: "none", "log", "log1", "sqrt", "sqrt0.5", "arcsine", "arcsine01", "reciprocal" and "boxcox".

Value

A named list of length 9. Each element is itself a list with lab, the label shown in the application; f, a function applying the transformation; and b, a function applying its inverse, which is what allows means to be reported on the original scale of measurement.

Examples

names(TRANS)
vapply(TRANS, `[[`, character(1), "lab")

Analyse one response from a designed experiment

Description

Fits the analysis of variance for a single response variable and returns the means, standard errors and critical differences for every legitimate comparison the design allows.

Usage

analyze(d, design, map, alpha = 0.05)

Arguments

d

A data frame in long format: one row per plot, with columns for the design factors and the response.

design

The design code. One of the values of DESIGNS, for example "RCBD", "SPLIT" or "POOLFRCBD".

map

A named list mapping roles to column names of d. Always needs response. Then, by design: treat (CRD, RCBD, LSD); block (RCBD, factorial RCBD); row, col (LSD); factors, a character vector of two to four column names (factorial); rep, main, sub (split and strip plots); env together with treat or factors, and rep for an RCBD base (pooled designs).

alpha

The significance level for the critical differences and the grouping letters. Defaults to 0.05.

Details

The error term is chosen to match the design. A split plot is fitted with Error(rep/main) and a strip plot with Error(rep/(A+B)), so the main-plot and sub-plot comparisons are each tested against their own error; the two mixed comparisons use a Satterthwaite-weighted t. In a pooled (combined) analysis over environments, each treatment effect is tested against its own interaction with the environment, and each environment by treatment interaction against the pooled error.

Value

A list with, among others: anova (the analysis of variance table), effects (one entry per effect, each holding the table of means with sem, sed, cd5, cd1 and the grouping letters), mse and dfe (the error mean square and its degrees of freedom), cv, grand, resid and lm.

See Also

run_all to analyse several responses at once, and build_report to render the result.

Examples

d <- demo_data("RCBD")
res <- analyze(d, "RCBD", list(response = "Yield", treat = "Variety",
                               block = "Block"))
res$anova
res$effects[["Variety"]]$means

Render an analysis as an HTML report

Description

Turns the result of run_all into a complete, self-contained HTML report: the analysis of variance, the tables of means with their standard errors and critical differences, the checks of the assumptions, and a plain-English interpretation. The report carries the DOEpro citation in its footer.

Usage

build_report(rr, letters_on = TRUE, detailed = TRUE, screen = FALSE)

Arguments

rr

The object returned by run_all.

letters_on

Show the grouping letters. Letters are suppressed automatically for any effect whose F-test is not significant, whatever this is set to.

detailed

If TRUE, include the full per-effect tables of means. If FALSE, give the compact summary tables only.

screen

If TRUE, return a fragment styled for display inside the application. If FALSE, return a complete standalone HTML document.

Value

A character string of HTML, of length one.

Examples

rr <- run_all(demo_data("CRD"), "CRD", list(treat = "Treatment"), "Yield")
html <- build_report(rr)
substr(html, 1, 60)

# writeLines(html, "report.html")

Example datasets for each design

Description

Generates a small, balanced example dataset for a design, so you can try DOEpro without your own data. These are the datasets behind the Load example button in the application.

Usage

demo_data(which)

Arguments

which

The example to build: "CRD", "RCBD", "LSD", "FRCBD" (factorial), "SPLIT", "STRIP", "POOLRCBD" or "POOLCRD" (a single treatment factor over environments), "POOLFACT" or "POOLFACTC" (factorial treatments over environments, RCBD and CRD base respectively).

Value

A data frame in long format, ready to pass to analyze.

Examples

head(demo_data("RCBD"))
str(demo_data("SPLIT"))

The DOEpro server logic

Description

The Shiny server function. Called by run_DOEpro; you should not normally need to call it yourself.

Usage

doepro_server(input, output, session)

Arguments

input, output, session

Standard Shiny server arguments.

Value

Invisibly NULL; called for its side effects.

The DOEpro user interface

Description

Builds the Shiny UI object. Called by run_DOEpro; you should not normally need to call it yourself.

Usage

doepro_ui()

Value

A Shiny UI definition.

Run the DOEpro application

Description

Launches the DOEpro Shiny application, which analyses designed agricultural experiments: paste or upload your data, choose the design, and press Run analysis.

Usage

run_DOEpro(...)

Arguments

...

Further arguments passed to shinyApp, for example options = list(port = 8080).

Details

The application returns the analysis of variance, tables of means with standard errors and critical differences, checks of the assumptions with advice on transformations, post-hoc comparisons, diagnostic plots, a plain-English interpretation, and a downloadable report.

Value

An object of class shiny.appobj. Called for its side effect of starting the application.

Examples

if (interactive()) {
  run_DOEpro()
}

Analyse several responses from a designed experiment

Description

Runs analyze for every response variable in turn, using the same design and mapping, and optionally applying a variance-stabilising transformation to each. This is what the application calls when more than one response is selected.

Usage

run_all(d, design, map, responses, alpha = 0.05, trans = NULL, dtype = "auto")

Arguments

d

A data frame in long format.

design

The design code; see DESIGNS.

map

A named list mapping roles to columns; see analyze. The response element is set for each response in turn and need not be supplied.

responses

A character vector of response column names.

alpha

The significance level. Defaults to 0.05.

trans

Either NULL (analyse every response untransformed) or a named list or character vector giving a transformation for each response. The names are the response columns and the values are keys of TRANS, for example list(Incidence = "arcsine").

dtype

Passed to the transformation adviser; "auto" lets DOEpro judge the type of each response from its name and its mean-variance behaviour.

Value

A list with fits (one entry per response, each containing the fitted analysis in final), together with the design, the mapping and the significance level.

Examples

d <- demo_data("FRCBD")
rr <- run_all(d, "FRCBD",
              list(factors = c("Nitrogen", "Variety"), block = "Block"),
              responses = "Yield")
rr$fits[["Yield"]]$final$anova

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.