Package {inti}

Title:	Tools and Statistical Procedures in Plant Science
Version:	0.7.3
Description:	The 'inti' package is part of the 'inkaverse' project for developing different procedures and tools used in plant science and experimental designs. The mean aim of the package is to support researchers during the planning of experiments and data collection (tarpuy()), data analysis and graphics (yupana()) , and scientific writing. Learn more about the 'inkaverse' project at https://inkaverse.com/.
License:	GPL-3 | file LICENSE
URL:	https://inkaverse.com/, https://github.com/Flavjack/inti
BugReports:	https://github.com/Flavjack/inti/issues
Depends:	shiny, ggplot2, dplyr, tidyr, tibble, R (≥ 4.1.0)
Imports:	lme4, agricolae, FactoMineR, emmeans, purrr, stringr, googlesheets4, DT, cowplot, cli, systemfonts
Suggests:	gsheet, huito, knitr, rmarkdown, quarto
VignetteBuilder:	knitr, quarto
LazyData:	true
Encoding:	UTF-8
SystemRequirements:	pandoc
Config/roxygen2/version:	8.1.0
NeedsCompilation:	no
Packaged:	2026-08-19 23:55:11 UTC; LENOVO
Author:	Flavio Lozano-Isla [aut, cre], Yoel Diaz-Saucedo [aut], María Belén Kistner [ctb], QuipoLab [ctb], Inkaverse [cph]
Maintainer:	Flavio Lozano-Isla <flozanoisla@gmail.com>
Repository:	CRAN
Date/Publication:	2026-08-20 10:20:02 UTC

Broad-sense heritability in plant breeding

Description

Heritability in plant breeding on a genotype difference basis

Usage

H2cal(
  data,
  trait,
  gen.name,
  rep.n,
  env.n = 1,
  year.n = 1,
  env.name = NULL,
  year.name = NULL,
  fixed.model,
  random.model,
  summary = FALSE,
  emmeans = FALSE,
  weights = NULL,
  plot_diag = FALSE,
  outliers.rm = FALSE,
  trial = NULL
)

Arguments

Details

The function allows to made the calculation for individual or multi-environmental trials (MET) using fixed and random model.

1. The variance components based in the random model and the population summary information based in the fixed model (BLUEs).

2. Heritability under three approaches: Standard (ANOVA), Cullis (BLUPs) and Piepho (BLUEs).

3. Best Linear Unbiased Estimators (BLUEs), fixed effect.

4. Best Linear Unbiased Predictors (BLUPs), random effect.

5. Table with the outliers removed for each model.

For individual experiments is necessary provide the {trait}, {gen.name}, {rep.n}.

For MET experiments you should {env.n} and {env.name} and/or {year.n} and {year.name} according your experiment.

The BLUEs calculation based in the pairwise comparison could be time consuming with the increase of the number of the genotypes. You can specify {emmeans = FALSE} and the calculate of the BLUEs will be faster.

If {emmeans = FALSE} you should change 1 by 0 in the fixed model for exclude the intersect in the analysis and get all the genotypes BLUEs.

For more information review the references.

Value

list

Author(s)

Maria Belen Kistner

Flavio Lozano Isla

References

Bernal Vasquez, Angela Maria, et al. “Outlier Detection Methods for Generalized Lattices: A Case Study on the Transition from ANOVA to REML.” Theoretical and Applied Genetics, vol. 129, no. 4, Apr. 2016.

Buntaran, H., Piepho, H., Schmidt, P., Ryden, J., Halling, M., and Forkman, J. (2020). Cross validation of stagewise mixed model analysis of Swedish variety trials with winter wheat and spring barley. Crop Science, 60(5).

Schmidt, P., J. Hartung, J. Bennewitz, and H.P. Piepho. 2019. Heritability in Plant Breeding on a Genotype Difference Basis. Genetics 212(4).

Schmidt, P., J. Hartung, J. Rath, and H.P. Piepho. 2019. Estimating Broad Sense Heritability with Unbalanced Data from Agricultural Cultivar Trials. Crop Science 59(2).

Tanaka, E., and Hui, F. K. C. (2019). Symbolic Formulae for Linear Mixed Models. In H. Nguyen (Ed.), Statistics and Data Science. Springer.

Zystro, J., Colley, M., and Dawson, J. (2018). Alternative Experimental Designs for Plant Breeding. In Plant Breeding Reviews. John Wiley and Sons, Ltd.

Examples

library(inti)

dt <- potato

hr <- H2cal(data = dt
            , trait = "stemdw"
            , gen.name = "geno"
            , rep.n = 5
            , fixed.model = ~ 0 + (1|bloque) + geno
            , random.model = ~ 1 + (1|bloque) + (1|geno)
            , emmeans = TRUE
            , plot_diag = FALSE
            , outliers.rm = TRUE
            )

 hr$tabsmr
 hr$blues
 hr$blups
 hr$outliers
 

Experimental design: Augmented

Description

Fieldbook generator for augmented experimental designs. Every check occurs once in each block and every test entry occurs once in the complete design.

Usage

design_augmented(
  checks,
  entries,
  blocks = NULL,
  eu_block = NULL,
  random = TRUE,
  zigzag = FALSE,
  dim = NA,
  serie = 1000,
  seed = NULL,
  project = "inkaverse",
  qrcode = "{project}{plots}{entry}",
  separate_checks = TRUE
)

Arguments

Value

A list with fieldbook and parameters.

Experimental design without replications

Description

Function to deploy field-book experiment without replications

Usage

design_noreps(
  factors,
  type = "sorted",
  zigzag = FALSE,
  nrows = NA,
  serie = 1000,
  seed = NULL,
  project = "inkaverse",
  qrcode = "{project}{plots}"
)

Arguments

Value

A list with the field-book design and parameters

Examples

## Not run: 

library(inti)

factores <- list("geno" = c(1:99))

fb <- design_noreps(factors = factores
                     , type = "sorted"
                     , zigzag = F
                     , nrows = 10
                     )
                     
dsg <- fb$fieldbook

fb %>%   
  tarpuy_plotdesign(fill = "plots") 

fb$parameters


## End(Not run)

Experimental design in CRD and RCBD

Description

Function to deploy field-book experiments for completely randomized designs (CRD/DCA) and randomized complete block designs (RCBD/DBCA).

Usage

design_repblock(
  nfactors = 1,
  factors,
  type = "crd",
  rep = 3,
  zigzag = FALSE,
  nrows = NA,
  serie = 1000,
  seed = NULL,
  project = "inkaverse",
  qrcode = "{project}{plots}"
)

Arguments

Value

A list with the field-book design and parameters.

Examples

## Not run: 

library(inti)

factores <- list(
  "geno" = c("A", "B", "C", "D", "D", 1, NA, NULL, "NA"),
  "salt stress" = c(0, 50, 200, 200, "T0", NA, NULL, "NULL"),
  "time" = c(30, 60, 90)
)

fb <- design_repblock(
  nfactors = 3,
  factors = factores,
  type = "rcbd",
  rep = 5,
  zigzag = TRUE,
  seed = 123,
  nrows = 5,
  qrcode = "{project}{plots}"
)

dsg <- fb$fieldbook

fb %>%
  tarpuy_plotdesign(fill = "plots")

fb$parameters


## End(Not run)

Split-plot experimental designs

Description

Dispatch split-plot experimental designs.

Usage

design_split(
  nfactors = 2,
  factors,
  type = "split-rcbd",
  rep = 3,
  zigzag = FALSE,
  nrows = NA,
  serie = 1000,
  seed = NULL,
  project = "inkaverse",
  qrcode = "{project}{plots}{factors}"
)

Arguments

Value

A list with the fieldbook design and parameters.

Split-plot RCBD experimental design

Description

Generate a split-plot design under a randomized complete block design (RCBD) structure for TARPUY.

Usage

design_split_rcbd(
  nfactors = 2,
  factors,
  type = "split-rcbd",
  rep = 3,
  zigzag = FALSE,
  nrows = NA,
  serie = 1000,
  seed = NULL,
  project = "inkaverse",
  qrcode = "{project}{plots}{factors}"
)

Arguments

Details

The first factor is the whole-plot factor and the second factor is the subplot factor. Whole plots are randomized within each block and subplot levels are randomized independently within every whole plot.

Value

A list with fieldbook and parameters.

Examples

## Not run: 

factors <- list(
  Soil = c("S1", "S2", "S3", "S4"),
  Fertilizer = c("N1", "N2", "N3", "N4", "N5", "N6")
)

design_split_rcbd(
  factors = factors,
  rep = 3,
  zigzag = TRUE,
  seed = 123
)$fieldbook

## End(Not run)

Footnotes in tables

Description

Include tables footnotes and symbols for kables in pandoc format

Usage

footnotes(table, notes = NULL, label = "Note:", notation = "alphabet")

Arguments

Details

You should use the pandoc format kable(format = "pipe"). You can add the footnote symbol using {hypen} in your table. notation could be use: "alphabet", "number", "symbol", "none".

Value

Table with footnotes for word and html documents

Include PDF in markdown documents

Description

Insert PDF files in markdown documents

Usage

include_pdf(file, width = "100%", height = "600")

Arguments

Value

html code for markdown

Table with footnotes

Description

Include tables with title and footnotes for word and html documents

Usage

include_table(table, caption = NA, notes = NA, label = NA, notation = "none")

Arguments

Value

Table with caption and footnotes

Examples

library(inti)

table <- data.frame(
x = rep_len(1, 5)
, y = rep_len(3, 5)
, z = rep_len("c", 5)
)

table %>% inti::include_table(
  caption = "Title caption b) line 0
  a) line 1
  b) line 2"
  , notes = "Footnote"
  , label = "Where:"
  )
  

Mean comparison test

Description

Function to compare treatment from lm or aov using data frames

Usage

mean_comparison(
  data,
  response,
  model_factors,
  comparison,
  test_comp = "SNK",
  sig_level = 0.05
)

Arguments

Value

list

Examples

## Not run: 

library(inti)
library(gsheet)

url <- paste0("https://docs.google.com/spreadsheets/d/"
              , "15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/"
              , "edit#gid=172957346")
# browseURL(url)

fb <- gsheet2tbl(url)

mc <- mean_comparison(data = fb
                      , response = "spad_29"
                      , model_factors = "bloque* geno*treat"
                      , comparison = c("geno", "treat")
                      , test_comp = "SNK"
                      )
mc$comparison
mc$stat


## End(Not run)

Swedish cultivar trial data

Description

The datasets were obtained from official Swedish cultivar tests. Dry matter yield was analyzed. All trials were laid out as alpha-designs with two replicates. Within each replicate, there were five to seven incomplete blocks.

Usage

met

Format

A data frame with 1069 rows and 8 variables:

zone

Sweden is divided into three different agricultural zones: South, Middle, and North

location

Locations: 18 location in the Zones

rep

Replications (4): number of replication in the experiment

alpha

Incomplete blocks (8) in the alpha-designs

cultivar

Cultivars (30): genotypes evaluated

yield

Yield in kg/ha

year

Year (1): 2016

env

enviroment (18): combination zone + location + year

Source

doi:10.1002/csc2.20177

Transform fieldbooks based in a dictionary

Description

Transform entire fieldbook according to data a dictionary

Usage

metamorphosis(fieldbook, dictionary, from, to, index, colnames)

Arguments

Details

The function require at least three columns.

1. Original names (from).

2. New names (to).

3. Variable type (index).

Value

List with two objects. 1. New data frame. 2. Dictionary.

Plot augmented fieldbook design

Description

Plot fieldbook sketches for augmented experimental designs generated by design_augmented().

Usage

plot_augmented_design(
  data,
  factor = NA,
  fill = "plots",
  xlab = NULL,
  ylab = NULL,
  glab = NULL,
  text_size = NULL,
  wrap_width = NULL,
  font_family = "Open Sans",
  font_face = "plain"
)

Arguments

Details

The function always uses the physical rows and cols coordinates stored in the fieldbook. Therefore, custom dimensions and zigzag layouts are preserved. The block column never replaces rows as a plotting coordinate. When each row represents exactly one block, only the visible y-axis title changes from "Rows" to "Blocks". The statistical block remains available as a color factor and, when a block occupies a complete rectangular region, its external border is highlighted.

Empty experimental units are represented by the level "empty". When factor = "type", checks, test entries and empty plots receive stable colors. Other factor columns use the regular TARPUY color palette.

Automatic wrapping and formatting affect only the displayed text. They do not modify entry, plots, ntreat, QR codes or any other fieldbook value.

Value

A ggplot object.

Examples

## Not run: 

plot_augmented_design(
  data = fieldbook,
  factor = "type",
  fill = c("plots", "entry"),
  text_size = 9,
  font_family = "Open Sans",
  font_face = "plain"
)


## End(Not run)

Diagnostic plots

Description

Function to plot the diagnostic of models

Usage

plot_diagnostic(data, formula, title = NA)

Arguments

Value

plots

Examples

## Not run: 

library(inti)

plot_diagnostic(data = potato
                , formula = stemdw ~ (1|bloque) + geno*treat)


## End(Not run)

Plot raw data

Description

Function use the raw data for made a boxplot graphic

Usage

plot_raw(
  data,
  type = "boxplot",
  x,
  y,
  group = NULL,
  xlab = NULL,
  ylab = NULL,
  glab = NULL,
  ylimits = NULL,
  xlimits = NULL,
  xrotation = NULL,
  legend = "top",
  xtext = NULL,
  gtext = NULL,
  color = TRUE,
  linetype = 1,
  opt = NULL
)

Arguments

Details

You could add additional layer to the plot using "+" with ggplot2 options

Value

plot

Examples

## Not run: 

library(inti)

fb <- potato

fb %>%
  plot_raw(type = "box"
           , x = "geno"
           , y = "twue"
           #, group = "treat"
           , ylab = NULL
           , xlab = NULL
           , glab = ""
           ) 
           
fb %>%
  plot_raw(type = "sca"
           , x = "hi"
           , y = "twue"
           , group = "geno"
           ) 
           

## End(Not run)

Plot summary data

Description

Graph summary data into bar o line plot

Usage

plot_smr(
  data,
  type = NULL,
  x = NULL,
  y = NULL,
  group = NULL,
  xlab = NULL,
  ylab = NULL,
  glab = NULL,
  ylimits = NULL,
  xrotation = c(0, 0.5, 0.5),
  xtext = NULL,
  gtext = NULL,
  legend = "top",
  sig = NULL,
  sigsize = 3,
  error = NULL,
  color = TRUE,
  opt = NULL
)

Arguments

Details

If the table is a out put of mean_comparison(graph_opts = TRUE) function. Its contain all the parameter for the plot.

You could add additional layer to the plot using "+" with ggplot2 options

Value

plot

Examples

## Not run: 

library(inti)

fb <- potato

yrs <- yupana_analysis(data = fb
                       , response = "hi"
                       , model_factors = "geno*treat"
                       , comparison = c("geno", "treat")
                       )

yrs$meancomp %>% 
  plot_smr(type = "bar"
           , x = "geno"
           , y = "hi"
           , xlab = ""
           , group = "treat"
           , glab = "Tratamientos"
           , error = "ste"
           , sig = "sig"
           #, ylimits = c(0, 1, 0.2)
           , color = T #c("red", "black")
           , gtext = c("Irrigado", "Sequia")
           )
           
yrs$meancomp %>% 
  plot_smr(type = "bar"
           , x = "treat"
           , y = "hi"
           , group = "geno"
           , glab = "Genotipo"
           , error = "ste"
           , sig = "sig"
           )
           

## End(Not run)

Plot Splitplot-RCBD fieldbook design

Description

Plot fieldbook sketches for Splitplot-RCBD experimental designs generated by design_split_rcbd().

Usage

plot_split_rcbd_design(
  data,
  factor = NA,
  fill = "plots",
  xlab = NULL,
  ylab = NULL,
  glab = NULL,
  text_size = NULL,
  wrap_width = NULL,
  font_family = "Open Sans",
  font_face = "plain"
)

Arguments

Details

The function does not recalculate or rearrange the experimental design. It uses the existing rows and cols coordinates from the fieldbook, so the physical arrangement and zigzag order generated by Tarpuy are preserved.

Every experimental unit is positioned with cols on the x axis and rows on the y axis. The block column is used only to identify the block panels; it never replaces the physical row coordinate.

Within each block, cols represents whole-plot positions. The visible y-axis tick labels are derived from the rank of the actual rows values inside each block, so the panels can display subplot positions while the geometry continues to use the original fieldbook coordinates. A black external border identifies each whole plot.

Automatic wrapping affects only the displayed labels. It does not change plots, ntreat, treatment factors, QR codes, rows, columns or any other fieldbook value.

Value

A ggplot object.

Examples

## Not run: 

plot_split_rcbd_design(
  data = fieldbook,
  factor = "wp_sp",
  fill = c("plots", "ntreat"),
  text_size = 9,
  font_family = "Open Sans",
  font_face = "plain"
)


## End(Not run)

Plot standard fieldbook experimental designs

Description

Plot standard fieldbook sketches for regular experimental designs generated in Tarpuy, including completely randomized designs (CRD/DCA) and randomized complete block designs (RCBD/DBCA).

Usage

plot_standard_design(
  data,
  factor = NA,
  fill = "plots",
  xlab = NULL,
  ylab = NULL,
  glab = NULL,
  text_size = NULL,
  wrap_width = NULL,
  font_family = "Open Sans",
  font_face = "plain"
)

Arguments

Details

The function does not calculate or rearrange an experimental design. It uses the existing rows and cols coordinates from the fieldbook, so the physical layout and the zigzag order generated by Tarpuy are preserved.

All standard designs are plotted with cols on the x axis and rows on the y axis. A DBCA/RCBD is therefore displayed according to its actual physical coordinates rather than replacing the row coordinate with the block number. When rows and blocks have a strict one-to-one relationship, only the visible y-axis title changes to "Blocks". The block can still be selected as the color factor.

Automatic wrapping affects only the sketch. It does not change entry, ntreat, QR codes, factor levels or any other fieldbook value.

Value

A ggplot object.

Examples

## Not run: 

plot_standard_design(
  data = fieldbook,
  factor = "geno",
  fill = c("plots", "entry"),
  text_size = 9,
  font_family = "Open Sans",
  font_face = "plain"
)


## End(Not run)

Water use efficiency in 15 potato genotypes

Description

Experiment to evaluate the physiological response from 15 potatos genotypes under water deficit condition. The experiment had a randomized complete block design with five replications. The stress started at 30 day after planting.

Usage

potato

Format

A data frame with 150 rows and 17 variables:

treat

Water deficit treatments: sequia, irrigado

geno

15 potato genotypes

bloque

blocks for the experimentl design

spad_29

Relative chlorophyll content (SPAD) at 29 day after planting

spad_83

Relative chlorophyll content (SPAD) at 84 day after planting

rwc_84

Relative water content (percentage) at 84 day after planting

op_84

Osmotic potential (Mpa) at 84 day after planting

leafdw

leaf dry weight (g)

stemdw

stem dry weight (g)

rootdw

root dry weight (g)

tubdw

tuber dry weight (g)

biomdw

total biomass dry weight (g)

hi

harvest index

ttrans

total transpiration (l)

wue

water use effiency (g/l)

twue

tuber water use effiency (g/l)

lfa

leaf area (cm2)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

dplyr

%>%

Remove outliers using mixed models

Description

Use the method M4 in Bernal Vasquez (2016). Bonferroni Holm test to judge residuals standardized by the re scaled MAD (BH MADR).

Usage

remove_outliers(
  data,
  formula,
  drop_na = FALSE,
  plot_diag = FALSE,
  title = "Diagnostic Plot"
)

Arguments

Details

Function to remove outliers in MET experiments

Value

list. 1. Table with date without outliers. 2. The outliers in the dataset.

References

Bernal Vasquez, Angela Maria, et al. “Outlier Detection Methods for Generalized Lattices: A Case Study on the Transition from ANOVA to REML.” Theoretical and Applied Genetics, vol. 129, no. 4, Apr. 2016.

Examples

library(inti)

rmout <- potato %>%
  remove_outliers(data = .
  , formula = stemdw ~ 0 + (1|bloque) + treat*geno
  , plot_diag = TRUE
  , drop_na = FALSE
  , title = "Plot Diagnostic"
  )

rmout
  

Convert a Markdown manuscript into a Quarto document

Description

Reads a Markdown file exported from Google Docs and converts it into a Quarto-compatible manuscript. The function detects and separates text, figures, and tables, removes formatting artifacts, restores equations embedded as image placeholders, and inserts format-specific section breaks. The resulting document can be rendered directly using Quarto for HTML, Word, or PDF outputs.

Usage

rticle(file = "render2rticle.md", export = "files", type = c("asis", "list"))

Arguments

Value

A character vector containing the full path of the generated Quarto (.qmd) file.

Split folder

Description

Function to split folder by size or number of elements

Usage

split_folder(
  folder,
  export,
  units = "megas",
  size = 500,
  zip = TRUE,
  remove = FALSE
)

Arguments

Value

zip files

Examples

## Not run: 

split_folder("pictures/QUINOA 2018-2019 SC SEEDS EDWIN - CAMACANI/"
   , "pictures/split_num", remove = T, size = 400, units = "number")


## End(Not run)

Interactive fieldbook designs

Description

Invoke RStudio addin to create fieldbook designs

Usage

tarpuy(dependencies = FALSE)

Arguments

Details

TARPUY allows users to create experimental designs through an interactive Shiny application.

Value

A Shiny application launched with shiny::runApp().

Examples

if (interactive()) {
  inti::tarpuy()
}

Fieldbook experimental designs

Description

Function to deploy the experimental designs currently supported by TARPUY.

Usage

tarpuy_design(
  data,
  nfactors = 1,
  type = "crd",
  rep = 2,
  zigzag = FALSE,
  nrows = NA,
  serie = 100,
  seed = NULL,
  project = NA,
  qrcode = "{project}{plots}"
)

Arguments

Details

The design sheet can include two optional columns named {arguments} and {values}. Values supplied in those columns override the corresponding function arguments. Factor columns are the remaining columns whose names are not enclosed in braces ({}) or square brackets (⁠[]⁠).

TARPUY currently dispatches only designs with an implemented and validated generator: CRD/DCA, RCBD/DBCA, augmented, and split-plot RCBD. Other design identifiers are rejected explicitly instead of being routed to incomplete generators.

Value

A data frame containing the generated fieldbook.

Examples

## Not run: 

library(inti)
library(gsheet)

url <- paste0(
  "https://docs.google.com/spreadsheets/d/",
  "1510fOKj0g4CDEAFkrpFbr-zNMnle_Hou9O_wuf7Vdo4/edit"
)

fb <- gsheet2tbl(url)

dsg <- fb %>% tarpuy_design()

dsg %>% tarpuy_plotdesign()


## End(Not run)

Fieldbook plan information

Description

Information for build a plan for an experiment (PLEX)

Usage

tarpuy_plex(
  data = NULL,
  title = NULL,
  short_title = NULL,
  objective = NULL,
  references = NULL,
  plan = NULL,
  institutions = NULL,
  researchers = NULL,
  manager = NULL,
  location = NULL,
  altitude = NULL,
  georeferencing = NULL,
  environment = NULL,
  start = NA,
  end = NA,
  project = NULL,
  repository = NULL,
  manuscript = NULL,
  album = NULL,
  nfactor = 2,
  design = "rcbd",
  rep = 4,
  zigzag = FALSE,
  nrows = NA,
  serie = 1000,
  seed = 0,
  qrcode = "{project}{plots}",
  aug_blocks = NA,
  aug_eu_block = NA,
  aug_random = TRUE
)

Arguments

Details

Provide the information available.

Value

data frame or list of arguments:

1. info

2. variables

3. design

4. logbook

5. timetable

6. budget

Fieldbook plot experimental designs

Description

Plot fieldbook sketches according to the experimental design type.

Usage

tarpuy_plotdesign(
  data,
  factor = NA,
  fill = "plots",
  xlab = NULL,
  ylab = NULL,
  glab = NULL,
  text_size = NULL,
  wrap_width = NULL,
  font_family = "Open Sans",
  font_face = "plain"
)

Arguments

Details

This function is the common plotting interface used by TARPUY. It reads the design stored in the fieldbook, chooses the corresponding plotter and sends the same plotting arguments to all supported design types.

Supported designs and plotters are:

• CRD and RCBD: plot_standard_design().

• Augmented: plot_augmented_design().

• Splitplot-RCBD: plot_split_rcbd_design().

The function never recalculates or rearranges the design. All plotters use cols as the x coordinate and rows as the y coordinate. Columns such as block are used only for labels, grouping or faceting.

wrap_width, font_family and font_face remain available to avoid breaking existing programmatic calls, but the TARPUY frontend should expose only the general text_size control. Automatic label wrapping is used when wrap_width = NULL.

Value

A ggplot object.

Examples

## Not run: 

tarpuy_plotdesign(
  data = fieldbook,
  factor = "auto",
  fill = c("plots", "ntreat"),
  text_size = 9
)


## End(Not run)

Field book traits

Description

Function to export a field book and its trait definitions for use in the Field Book app.

Usage

tarpuy_traits(fieldbook = NULL, last_factor = NULL, traits = NULL)

Arguments

Details

The trait sheet can contain the columns variable, {trait}, {when}, {samples}, {format}, units, details, and categories.

Spaces inside {trait}, {when}, and the textual part of {samples} are removed. The components are joined with underscores. For example, {trait} = "G", {when} = "Dia 1", and {samples} = "plant3" generate G_Dia1_plant1, G_Dia1_plant2, and G_Dia1_plant3.

Value

A list with four elements:

• fieldbook: the field book including empty trait columns;

• traits: the trait definition table used to export the .trt file;

• fb: the base field book used to export the Field Book CSV;

• metadata: internal mapping between a stable Trait ID and every generated fieldbook column. This element is used by TARPUY and is not exported to the fieldbook or to Field Book mobile files.

Examples

library(inti)

fieldbook <- inti::potato

traits <- list(
  list(
    variable = "altura de planta",
    trait = "altp",
    format = "numeric",
    when = "Dia 30, Dia 40, Dia 50",
    samples = "plant3",
    units = "cm",
    details = NA,
    minimum = 0,
    maximum = 100
  ),
  list(
    variable = "severidad",
    trait = "svr",
    format = "scategorical",
    when = "30, 40, 50",
    samples = 1,
    units = "scale",
    details = NA,
    categories = "1, 3, 5, 7, 9"
  ),
  list(
    variable = "foto",
    trait = "foto",
    format = "photo",
    when = "hrv, pshrv",
    samples = NA,
    units = "image",
    details = NA
  ),
  list(
    variable = "germinacion",
    trait = "G",
    format = "boolean",
    when = "0, 1, 2",
    samples = 1,
    units = "logical",
    details = NA
  )
)

fbapp <- tarpuy_traits(fieldbook, last_factor = "bloque", traits)

## Not run: 

library(inti)
library(gsheet)

url_ds <- paste0(
  "https://docs.google.com/spreadsheets/d/",
  "1510fOKj0g4CDEAFkrpFbr-zNMnle_Hou9O_wuf7Vdo4/edit?gid=1278145622"
)

ds <- gsheet2tbl(url_ds)
fb <- ds |> tarpuy_design()

url_trt <- paste0(
  "https://docs.google.com/spreadsheets/d/",
  "1510fOKj0g4CDEAFkrpFbr-zNMnle_Hou9O_wuf7Vdo4/edit?gid=1665653985"
)

traits <- gsheet2tbl(url_trt)
fbapp <- tarpuy_traits(fb, last_factor = "cols", traits)

## End(Not run)

HTML tables for markdown documents

Description

Export tables with download, pasta and copy buttons

Usage

web_table(
  data,
  caption = NULL,
  digits = 2,
  rnames = FALSE,
  buttons = NULL,
  file_name = "file",
  scrolly = NULL,
  columnwidth = "200px",
  width = "100%"
)

Arguments

Value

table in markdown format for html documents

Examples

## Not run: 

library(inti)

met %>%
  web_table(caption = "Web table")


## End(Not run)

Interactive data analysis

Description

Invoke RStudio addin to analyze and graph experimental design data

Usage

yupana(dependencies = FALSE)

Arguments

Details

Yupana: data analysis and graphics for experimental designs.

Value

Shiny app

Examples

if(interactive()){

 inti::yupana()

}

Fieldbook analysis report

Description

Function to create a complete report of the fieldbook

Usage

yupana_analysis(
  data,
  last_factor = NULL,
  response,
  model_factors,
  comparison,
  test_comp = "SNK",
  sig_level = 0.05,
  plot_dist = "boxplot",
  plot_diag = FALSE,
  digits = 2
)

Arguments

Value

list

Examples

## Not run: 

library(inti)

fb <- potato

rsl <- yupana_analysis(data = fb
                       , last_factor = "bloque"
                       , response = "spad_83"
                       , model_factors = "geno * treat"
                       , comparison = c("geno", "treat")
                       )


## End(Not run)

Graph options to export

Description

Function to export the graph options and model parameters

Usage

yupana_export(
  data,
  type = NA,
  xlab = NA,
  ylab = NA,
  glab = NA,
  ylimits = NA,
  xrotation = c(0, 0.5, 0.5),
  xtext = NA,
  gtext = NA,
  legend = "top",
  sig = NA,
  error = NA,
  color = TRUE,
  opt = NA,
  dimension = c(20, 10, 100)
)

Arguments

Value

data frame

Examples

## Not run: 

library(inti)
library(gsheet)

url <- paste0("https://docs.google.com/spreadsheets/d/"
              , "15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=172957346")
# browseURL(url)

fb <- gsheet2tbl(url)

smr <- yupana_analysis(data = fb
                       , last_factor = "bloque"
                       , response = "spad_83"
                       , model_factors = "block + geno*riego"
                       , comparison = c("geno", "riego")
                       )
                       
gtab <- yupana_export(smr, type = "line", ylimits = c(0, 100, 2))

#> import

url <- paste0("https://docs.google.com/spreadsheets/d/"
              , "15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=1202800640")
# browseURL(url)

fb <- gsheet2tbl(url)

info <- yupana_import(fb)

etab <- yupana_export(info)

info2 <- yupana_import(etab)

etab2 <- yupana_export(info2)


## End(Not run)

Import information from data summary

Description

Graph summary data

Usage

yupana_import(data)

Arguments

Value

list

Examples

## Not run: 

library(inti)
library(gsheet)

url <- paste0("https://docs.google.com/spreadsheets/d/"
              , "15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit?gid=2137596914#gid=2137596914")
# browseURL(url)

fb <- gsheet2tbl(url)

info <- yupana_import(fb)


## End(Not run)

Multivariate Analysis

Description

Multivariate analysis for PCA and HCPC

Usage

yupana_mvr(
  data,
  last_factor = NULL,
  summary_by = NULL,
  groups = NULL,
  variables = NULL
)

Arguments

Details

Compute and plot information for multivariate analysis (PCA, HCPC and correlation).

Value

result and plots

Examples

## Not run: 

library(inti)

fb <- inti::potato 

mv <- yupana_mvr(data = fb
                 , last_factor = "geno"
                 , summary_by = c("geno", "treat")
                 , groups = "treat"
                 , variables = c("all")
                 #, variables = c("wue", "twue")
                 )
                 
mv$plot[1] 

mv$data



## End(Not run)

Fieldbook reshape

Description

Function to reshape fieldbook according a separation character

Usage

yupana_reshape(
  data,
  last_factor,
  sep,
  new_colname,
  from_var = NULL,
  to_var = NULL,
  exc_factors = NULL
)

Arguments

Details

If you variable name is variable_evaluation_rep. The reshape function will help to create the column rep and the new variable name will be variable_evaluation.

Value

data frame