Package 'inti'

Experimental design: Augmented

Description

Fieldbook generator for Augmented Designs.

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}"
)

Arguments

checks	Vector of check treatments.
entries	Vector of new entries.
blocks	Optional number of blocks. If NULL, it is calculated from entries, checks and eu_block.
eu_block	Number of experimental units per block.
random	Randomize entries allocation.
zigzag	Zigzag field layout.
dim	Optional layout dimensions c(nrows, ncols).
serie	Plot series number.
seed	Random seed.
project	Barcode prefix.
qrcode	QR code template.

Value

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

factors	Lists with names and factor vector ⁠[list]⁠.
type	Randomization in the list ⁠[character: "sorted", "unsorted"]⁠
zigzag	Experiment layout in zigzag ⁠[logic: FALSE]⁠.
nrows	Experimental design dimension by rows ⁠[numeric: value]⁠
serie	Number to start the plot id ⁠[numeric: 1000]⁠.
seed	Replicability from randomization ⁠[numeric: NULL]⁠.
project	Bar code prefix for data collection ⁠[character: "inkaverse"]⁠.
qrcode	Concatenate the QR code ⁠[character: "{project}{plots}{factors}"]⁠

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 experiment for CRD and RCBD

Usage

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

Arguments

nfactors	Number of factor in the experiment ⁠[numeric: 1]⁠.
factors	Lists with names and factor vector ⁠[list]⁠.
type	Type of experimental arrange ⁠[character: "crd" "rcbd" "lsd"]⁠
rep	Number of replications in the experiment ⁠[numeric: 3]⁠.
zigzag	Experiment layout in zigzag ⁠[logic: FALSE]⁠.
nrows	Experimental design dimension by rows ⁠[numeric: value]⁠
serie	Number to start the plot id ⁠[numeric: 100]⁠.
seed	Replicability from randomization ⁠[numeric: NULL]⁠.
project	Bar code prefix for data collection ⁠[character: "inkaverse"]⁠.
qrcode	Concatenate the QR code ⁠[character: "{project}{plots}{factors}"]⁠

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, 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 = T
                     , seed = 0
                     , nrows = 20
                     , 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

nfactors	Number of factors in the experiment.
factors	List with factor levels.
type	Split-plot design type.
rep	Number of replications.
zigzag	Field layout in zigzag.
nrows	Experimental design dimension by rows.
serie	Number to start the plot id.
seed	Seed for randomization.
project	Barcode prefix.
qrcode	String to concatenate the QR code.

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

nfactors	Number of factors in the experiment. For split-plot RCBD it must be 2.
factors	List with exactly two named factors. The first factor is the whole-plot factor and the second factor is the subplot factor.
type	Design type. Default is "split-rcbd".
rep	Number of replications or blocks.
zigzag	Field layout in vertical zigzag order. If TRUE, subplot row order is reversed in even whole-plot columns.
nrows	Experimental design dimension by rows. If NA, it is calculated automatically as rep * number_of_subplot_levels.
serie	Number used as base for plot numbering.
seed	Seed for reproducible randomization.
project	Barcode or QR code prefix.
qrcode	String used to concatenate QR code fields.

Details

The first factor is interpreted as the whole-plot factor and the second factor as the subplot factor. Factor column names are preserved in the final fieldbook, while their experimental role is stored in parameters.

Value

A list with the fieldbook design and parameters.

---

Footnotes in tables

Description

Include tables footnotes and symbols for kables in pandoc format

Usage

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

Arguments

table	Kable output in pandoc format.
notes	Footnotes for the table.
label	Label for start the footnote.
notation	Notation for the footnotes (default = "alphabet"). See details.

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

---

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

data	Experimental design data frame with the factors and traits.
trait	Name of the trait.
gen.name	Name of the genotypes.
rep.n	Number of replications in the experiment.
env.n	Number of environments (default = 1). See details.
year.n	Number of years (default = 1). See details.
env.name	Name of the environments (default = NULL). See details.
year.name	Name of the years (default = NULL). See details.
fixed.model	The fixed effects in the model (BLUEs). See examples.
random.model	The random effects in the model (BLUPs). See examples.
summary	Print summary from random model (default = FALSE).
emmeans	Use emmeans for calculate the BLUEs (default = FALSE).
weights	an optional vector of ‘prior weights’ to be used in the fitting process (default = NULL).
plot_diag	Show diagnostic plots for fixed and random effects (default = FALSE). Options: "base", "ggplot". .
outliers.rm	Remove outliers (default = FALSE). See references.
trial	Column with the name of the trial in the results (default = NULL).

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

---

Include PDF in markdown documents

Description

Insert PDF files in markdown documents

Usage

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

Arguments

file	file path from pdf file.
width	width preview file.
height	height preview file.

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

table	Data frame.
caption	Table caption (default = NULL). See details.
notes	Footnotes for the table (default = NA). See details.
label	Label for start the footnote (default = NA).
notation	Notation for the symbols and footnotes (default = "none") Others: "alphabet", "number", "symbol".

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

data	Fieldbook data.
response	Model used for the experimental design.
model_factors	Factor in the model.
comparison	Significance level for the analysis (default = 0.05).
test_comp	Comparison test (default = "SNK"). Others: "TUKEY", "DUNCAN".
sig_level	Significance level for the analysis (default = 0.05).

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

fieldbook	Data frame with the original information.
dictionary	Data frame with new names and categories. See details.
from	Column of the dictionary with the original names.
to	Column of the dictionary with the new names.
index	Column of the dictionary with the type and level of the variables.
colnames	Character vector with the name of the columns.

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.

---

Remove outliers

Description

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

Usage

outliers_remove(data, trait, model, drop_na = TRUE)

Arguments

data	Experimental design data frame with the factors and traits.
trait	Name of the trait.
model	The fixed or random effects in the model.
drop_na	drop NA values from the data.frame

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 %>% outliers_remove(
  data = .
  , trait ="stemdw"
  , model = "0 + treat*geno + (1|bloque)"
  , drop_na = FALSE
  )

rmout

---

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
)

Arguments

data	Fieldbook data frame from an augmented design.
factor	Character. Column used to color experimental units. Default is "type" when available.
fill	Character vector. Column or columns used as labels inside each experimental unit. Default is "plots".
xlab	Character. Optional x axis title.
ylab	Character. Optional y axis title.
glab	Character. Optional legend title.

Details

This function is intended for augmented designs with checks and entries. It uses:

• cols as the x axis.

• block as the y axis when available.

• type to distinguish checks, tests and empty plots.

Empty plots are shown in grey when type is NA.

Value

A ggplot object.

---

Diagnostic plots

Description

Function to plot the diagnostic of models

Usage

plot_diag(model, title = NA)

Arguments

model	Statistical model
title	Plot title

Value

plots

Examples

## Not run: 

library(inti)

lm <- aov(stemdw ~ bloque + geno*treat, data = potato)

# lm <- potato %>% lme4::lmer(stemdw ~ (1|bloque) + geno*treat, data = .)
 
plot(lm, which = 1)
plot_diag(lm)[3]

plot(lm, which = 2)
plot_diag(lm)[2]

plot(lm, which = 3)
plot_diag(lm)[4]

plot(lm, which = 4)
plot_diag(lm)[1]


## End(Not run)

---

Diagnostic plots

Description

Function to plot the diagnostic of models

Usage

plot_diagnostic(data, formula, title = NA)

Arguments

data	Experimental design data frame with the factors and traits.
formula	Mixed model formula
title	Plot title

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

data	raw data
type	Type of graphic. "boxplot" or "scatterplot"
x	Axis x variable
y	Axis y variable
group	Group variable
xlab	Title for the axis x
ylab	Title for the axis y
glab	Title for the legend
ylimits	Limits and break of the y axis c(initial, end, brakes)
xlimits	For scatter plot. Limits and break of the x axis c(initial, end, brakes)
xrotation	Rotation in x axis c(angle, h, v)
legend	the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector)
xtext	Text labels in x axis using a vector
gtext	Text labels in groups using a vector
color	Colored figure (TRUE), black & white (FALSE) or color vector
linetype	Line type for regression. Default = 0
opt	Add new layers to the plot

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

data	Output from summary data
type	Type of graphic. "bar" or "line"
x	Axis x variable
y	Axis y variable
group	Group variable
xlab	Title for the axis x
ylab	Title for the axis y
glab	Title for the legend
ylimits	limits of the y axis c(initial, end, brakes)
xrotation	Rotation in x axis c(angle, h, v)
xtext	Text labels in x axis using a vector
gtext	Text labels in group using a vector
legend	the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector)
sig	Column with the significance
sigsize	Font size in significance letters
error	Show the error bar ("ste" or "std")
color	colored figure (TRUE), black & white (FALSE) or color vector
opt	Add news layer to the plot

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 split-plot RCBD fieldbook design

Description

Plot fieldbook sketches for split-plot designs under RCBD structure.

Usage

plot_split_rcbd_design(
  data,
  factor = NA,
  fill = "plots",
  xlab = NULL,
  ylab = NULL,
  glab = NULL
)

Arguments

data	Fieldbook data frame from design_split_rcbd().
factor	Character. Column used to color experimental units.
fill	Character vector. Column or columns used as labels inside each experimental unit.
xlab	Character. Optional x axis title.
ylab	Character. Optional y axis title.
glab	Character. Optional legend title.

Value

A ggplot object.

---

Plot standard fieldbook experimental designs

Description

Plot standard fieldbook sketches for simple experimental designs generated in Tarpuy. This function is intended for designs with a regular fieldbook layout, such as completely randomized designs, randomized complete block designs, sorted designs and unsorted designs.

Usage

plot_standard_design(
  data,
  factor = NA,
  fill = "plots",
  xlab = NULL,
  ylab = NULL,
  glab = NULL
)

Arguments

data	A fieldbook data frame. It must contain at least rows and cols. For RCBD/DBCA designs, it should also contain block.
factor	Character. Name of the column used to color the experimental units. For example: "geno", "acc", "nacl", "block" or "ntreat". If NA, the function uses "block" when available; otherwise, it uses the third column of data.
fill	Character vector. Name of one or more columns used as labels inside each experimental unit. For example: "plots", c("plots", "ntreat") or c("plots", "acc", "nacl"). When ntreat is used, it is shown as T1, T2, etc.
xlab	Character. Title for the x axis. If NULL, "columns" is used.
ylab	Character. Title for the y axis. If NULL, "row" is used for non-RCBD designs. For RCBD/DBCA designs, the y axis is shown as "blocks".
glab	Character. Title for the legend. If NULL, the value of factor is used.

Details

The function does not calculate the experimental design. It only plots an existing fieldbook. Therefore, if the fieldbook was generated with zigzag = TRUE, the zigzag layout is respected because the function uses the existing rows, cols and block columns.

For non-blocked standard designs, such as CRD/DCA, sorted and unsorted designs, the sketch is plotted using:

• cols as the x axis.

• rows as the y axis.

For RCBD/DBCA designs, the sketch is plotted using:

• cols as the x axis.

• block as the y axis.

In this way, each row represents one block, which makes the DBCA sketch easier to interpret in field layout previews.

The argument factor controls the fill color, while fill controls the text printed inside each plot. For example, factor = "nacl" and fill = c("plots", "acc", "nacl") colors plots by NaCl level and writes plot ID, accession and NaCl level inside each experimental unit.

Value

A ggplot object.

Examples

## Not run: 

library(dplyr)
library(ggplot2)

# Example 1: sorted design without replications
factores <- list(
  geno = paste0("G", 1:12)
)

fb <- design_noreps(
  factors = factores,
  type = "sorted",
  zigzag = FALSE,
  nrows = 3,
  serie = 1000,
  seed = 123,
  project = "TEST",
  qrcode = "{project}{plots}"
)

dsg <- fb$fieldbook

plot_standard_design(
  data = dsg,
  factor = "geno",
  fill = c("plots", "ntreat")
)

# Example 2: DCA / CRD
factores_dca <- list(
  geno = paste0("G", 1:6)
)

fb_dca <- design_repblock(
  nfactors = 1,
  factors = factores_dca,
  type = "crd",
  rep = 4,
  zigzag = TRUE,
  nrows = 4,
  serie = 1000,
  seed = 123,
  project = "DCA",
  qrcode = "{project}{plots}"
)

plot_standard_design(
  data = fb_dca$fieldbook,
  factor = "geno",
  fill = c("plots", "ntreat")
)

# Example 3: DBCA / RCBD
factores_dbca <- list(
  acc = paste0("acc", 1:6),
  nacl = c(0, 100, 200, 300)
)

fb_dbca <- design_repblock(
  nfactors = 2,
  factors = factores_dbca,
  type = "rcbd",
  rep = 4,
  zigzag = TRUE,
  serie = 1000,
  seed = 123,
  project = "DBCA",
  qrcode = "{project}{plots}"
)

plot_standard_design(
  data = fb_dbca$fieldbook,
  factor = "nacl",
  fill = c("plots", "acc", "nacl"),
  glab = "NaCl"
)


## 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)

---

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)

Arguments

data	Experimental design data frame with the factors and traits.
formula	mixed model formula.
drop_na	drop NA values from the data.frame
plot_diag	Diagnostic plot based in the raw and clean data

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 = FALSE
  , drop_na = FALSE
  )

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 = "draft.md", export = "files", type = c("asis", "list"))

Arguments

file	Character string indicating the path to the Markdown file. Default is "draft.md".
export	Character string specifying the output directory where the generated .qmd file will be saved. If NULL, the directory name is derived from the input file name.
type	Character string indicating how the manuscript should be organized. "asis" preserves the original structure, whereas "list" rearranges the content into text, figures, and tables.

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

folder	Path of folder to split (path).
export	Path to export the split folders (path).
units	Units to split folder (string: "megas", "number").
size	Folder size by the units selected (numeric).
zip	Zip split folders (logical).
remove	Remove the split folder after zip (logical).

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

dependencies

Install package dependencies for run the app

Details

Tarpuy allow to create experimental designs under an interactive app.

Value

Shiny app

Examples

if(interactive()){

 inti::tarpuy()

}

---

Fieldbook experimental designs

Description

Function to deploy experimental designs

Usage

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

Arguments

data	Experimental design data frame with the factors and level. See examples.
nfactors	Number of factor in the experiment(default = 1). See details.
type	Type of experimental arrange ⁠[default = "crd"]⁠. See details.
rep	Number of replications in the experiment (default = 3).
zigzag	Experiment layout in zigzag ⁠[logic: FALSE]⁠.
nrows	Experimental design dimension by rows ⁠[numeric: value]⁠.
serie	Number to start the plot id ⁠[numeric: 100]⁠.
seed	Replicability of draw results ⁠[default = 0]⁠ always random. See details.
project	Barcode prefix for data collection.
qrcode	String to concatenate the QR code ⁠[character: {project}{plots}{factors}]⁠.

Details

The function allows to include the arguments in the sheet that have the information of the design. You should include 2 columns in the sheet: {arguments} and {values}. See examples. The information will be extracted automatically and deploy the design. nfactors = 1: crd, rcbd, lsd, lattice. nfactors = 2 (factorial): split-crd, split-rcbd split-lsd nfactors >= 2 (factorial): crd, rcbd, lsd.

Value

A list with the fieldbook design

Examples

## Not run: 

library(inti)
library(gsheet)

url <- paste0("https://docs.google.com/spreadsheets/d/"
              , "1510fOKj0g4CDEAFkrpFbr-zNMnle_Hou9O_wuf7Vdo4/edit?gid=1479851579#gid=1479851579")
# browseURL(url)

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

data	Data with the fieldbook information.
title	Project title.
short_title	Short description of the project.
objective	The objectives of the project.
references	References.
plan	General description of the project (M & M).
institutions	Institutions involved in the project.
researchers	Persons involved in the project.
manager	Persons responsible of the collection of the data.
location	Location of the project.
altitude	Altitude of the experiment (m.a.s.l).
georeferencing	Georeferencing information.
environment	Environment of the experiment (greenhouse, lab, etc).
start	The date of the start of the experiments.
end	The date of the end of the experiments.
project	Name or ID for the fieldbook/project.
repository	link to the repository.
manuscript	link for manuscript.
album	link with the photos of the project.
nfactor	Number of factors for the design.
design	Type of design.
rep	Number of replication.
zigzag	Experiment layout in zigzag ⁠[logic: FALSE]⁠
nrows	Experimental design dimension by rows ⁠[numeric: value]⁠
serie	Number of digits in the plots.
seed	Seed for the randomization.
qrcode	QR code template used to concatenate fieldbook identifiers.
aug_blocks	Number of blocks for augmented design.
aug_eu_block	Number of plots per block for augmented design.
aug_random	Logical. Randomize entries allocation in augmented design.

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
)

Arguments

data	Fieldbook data frame or design object containing a fieldbook.
factor	Character. Column used to color experimental units.
fill	Character vector. Column or columns used as labels inside experimental units.
xlab	Character. Optional x axis title.
ylab	Character. Optional y axis title.
glab	Character. Optional legend title.

Details

This function works as a dispatcher. It detects the design type from the fieldbook and sends the data to the corresponding plotting function.

Value

A ggplot object.

---

Field book traits

Description

Function to export field book and traits for be used in field book app.

Usage

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

Arguments

fieldbook	Experiment field book ⁠[dataframe]⁠.
last_factor	Last factor in the field book ⁠[character: colname]⁠
traits	Traits information ⁠[dataframe or list]⁠.

Details

For the traits parameters you can used shown in the Field Book app

Value

list

Examples

library(inti)

fieldbook <- inti::potato

traits <- list(
  list(variable = "altura de planta"
       , trait = "altp"
       , format = "numeric"
       , when = "30, 40, 50"
       , samples = 3
       , 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 = 1
          , units = "image"
          , details = NA
  )
  ,  list(variable = "germinacion"
          , trait = "ger"
          , format = "boolean"
          , when = "30, 40, 50"
          , samples = 1
          , units = "logical"
          , details = NA
  )
) 

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

## Not run:  

library(inti)
library(gsheet)

url_fb <- paste0("https://docs.google.com/spreadsheets/d/"
       , "1510fOKj0g4CDEAFkrpFbr-zNMnle_Hou9O_wuf7Vdo4/edit?gid=1607116093#gid=1607116093")
       
fb <- gsheet2tbl(url_fb) 

url_ds <- paste0("https://docs.google.com/spreadsheets/d/"
       , "1510fOKj0g4CDEAFkrpFbr-zNMnle_Hou9O_wuf7Vdo4/edit?gid=1278145622#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#gid=1665653985")
       
traits <- gsheet2tbl(url_trt) 

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

dsg <- fbapp[[1]]


## 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

data	Dataset.
caption	Title for the table.
digits	Digits number in the table exported.
rnames	Row names.
buttons	Buttons: "excel", "copy" or "none". Default c("excel", "copy")
file_name	Excel file name
scrolly	Windows height to show the table. Default "45vh"
columnwidth	Column width. Default '200px'
width	Width in pixels or percentage (Defaults to automatic sizing)

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

dependencies

Install package dependencies for run the app

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

data	Field book data.
last_factor	The last factor in your fieldbook.
response	Response variable.
model_factors	Model used for the experimental design.
comparison	Factors to compare
test_comp	Comprasison test c("SNK", "TUKEY", "DUNCAN")
sig_level	Significal test (default: p = 0.005)
plot_dist	Plot data distribution (default = "boxplot")
plot_diag	Diagnostic plots for model (default = FALSE).
digits	Digits number in the table exported.

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

data	Result from yupana_analysis or yupana_import.
type	Plot type
xlab	Title for the axis x
ylab	Title for the axis y
glab	Title for the legend
ylimits	limits of the y axis
xrotation	Rotation in x axis c(angle, h, v)
xtext	Text labels in x axis
gtext	Text labels in group
legend	the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector)
sig	Column with the significance
error	Show the error bar ("ste" or "std").
color	colored figure (TRUE), otherwise black & white (FALSE)
opt	Add news layer to the plot
dimension	Dimension of graphs

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

data	Summary information with options

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

data	Field book data.
last_factor	The last factor in your fieldbook ⁠[string: NULL]⁠.
summary_by	Variables for group the analysis.
groups	Groups for color in PCA.
variables	Variables to be use in the analysis ⁠[string: NULL]⁠.

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

data	Field book raw data.
last_factor	The last factor in your field book.
sep	Character that separates the last value.
new_colname	The new name for the column created.
from_var	The first variable in case you want to exclude several. variables.
to_var	The last variable in case you want to exclude several variables.
exc_factors	Factor to exclude during the reshape.

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

---