Type:	Package
Version:	0.6.7
Title:	Tools and Statistical Procedures in Plant Science
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 technical writing. Learn more about the 'inkaverse' project at https://inkaverse.com/.
Date:	2025-02-26
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
Suggests:	gsheet, cowplot, knitr, rmarkdown, bookdown
VignetteBuilder:	knitr
License:	GPL-3 | file LICENSE
LazyData:	true
Encoding:	UTF-8
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-02-26 11:15:53 UTC; floza
Author:	Flavio Lozano-Isla [aut, cre], QuipoLab [ctb], Inkaverse [cph]
Maintainer:	Flavio Lozano-Isla <flozanoisla@gmail.com>
Repository:	CRAN
Date/Publication:	2025-02-26 11:30:06 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
 

Colourise text for display in the terminal

Description

If R is not currently running in a system that supports terminal colours the text will be returned unchanged.

Usage

colortext(text, fg = "red", bg = NULL)

Arguments

Details

Allowed colours are: black, blue, brown, cyan, dark gray, green, light blue, light cyan, light gray, light green, light purple, light red, purple, red, white, yellow

Author(s)

testthat package

Examples

print(colortext("Red", "red"))
cat(colortext("Red", "red"), "\n")
cat(colortext("White on red", "white", "red"), "\n")

Experimental design without replications

Description

Function to deploy field-book experiment without replications

Usage

design_noreps(
  factors,
  type = "sorted",
  zigzag = FALSE,
  nrows = NA,
  serie = 100,
  seed = NULL,
  fbname = "inkaverse",
  qrcode = "{fbname}{plots}{factors}"
)

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 experiment for CRD and RCBD

Usage

design_repblock(
  nfactors = 1,
  factors,
  type = "crd",
  rep = 3,
  zigzag = FALSE,
  nrows = NA,
  serie = 100,
  seed = NULL,
  fbname = "inkaverse",
  qrcode = "{fbname}{plots}{factors}"
)

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, NA, NULL, "NA")
                 , "salt stress" = c(0, 50, 200, 200, "T0", NA, NULL, "NULL")
                 , time = c(30, 60, 90)
                 )

fb <-design_repblock(nfactors = 2
                     , factors = factores
                     , type = "rcbd"
                     , rep = 5
                     , zigzag = T
                     , seed = 0
                     , nrows = 20
                     , qrcode = "{fbname}{plots}{factors}"
                     )
                     
dsg <- fb$fieldbook

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

fb$parameters


## End(Not run)

Figure to Quarto format

Description

Use Articul8 Add-ons from Google docs to build Rticles

Usage

figure2qmd(text, path = ".", opts = NA)

Arguments

Details

Quarto option can be included in the title using "{{}}" separated by commas

Value

string mutated

Figure to Rmarkdown format

Description

Use Articul8 Add-ons from Google docs to build Rticles

Usage

figure2rmd(text, path = ".", opts = NA)

Arguments

Value

Mutated string

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

Google docs to Rmarkdown

Description

Use Articul8 Add-ons from Google docs to build Rticles

Usage

gdoc2qmd(file, export = NA, format = "qmd", type = "asis")

Arguments

Details

Document rendering until certain point: "#| end" Include for next page: "#| newpage" You can include the cover page params using "#|" in a Google docs table

Value

path

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:"
  )
  

Journal Club Tombola

Description

Function for arrange journal club schedule

Usage

jc_tombola(
  data,
  members,
  papers = 1,
  group = NA,
  gr_lvl = NA,
  status = NA,
  st_lvl = "active",
  frq = 7,
  date = NA,
  seed = NA
)

Arguments

Details

The function could consider n levels for gr_lvl. In the case of more levels using "both" or "all" will be the combination. The suggested levels for st_lvl are: active or spectator. Only the "active" members will enter in the schedule.

Value

data frame with the schedule for the JC

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.

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

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
  

Diagnostic plots

Description

Function to plot the diagnostic of models

Usage

plot_diag(model, title = NA)

Arguments

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

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 = NULL
           , ylab = NULL
           , xlab = NULL
           , glab = ""
           ) 
           
fb %>%
  plot_raw(type = "sca"
           , x = "geno"
           , y = "twue"
           , group = "treat"
           , color = c("red", "blue")
           ) 
           

## 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 = "lfa"
                       , model_factors = "geno*treat"
                       , comparison = c("geno", "treat")
                       )

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

## 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)

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 = FALSE
  , drop_na = FALSE
  )

rmout
  

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)

Table to Quarto format

Description

Use Articul8 Add-ons from Google docs to build Rticles

Usage

table2qmd(text, type = "asis")

Arguments

Value

string mutated

Table to Rmarkdown format

Description

Use Articul8 Add-ons from Google docs to build Rticles

Usage

table2rmd(text, opts = NA)

Arguments

Value

Mutated string

Interactive fieldbook designs

Description

Invoke RStudio addin to create fieldbook designs

Usage

tarpuy(dependencies = FALSE)

Arguments

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,
  fbname = NA,
  qrcode = "{fbname}{plots}{factors}"
)

Arguments

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,
  objectives = NULL,
  hypothesis = NULL,
  rationale = NULL,
  references = NULL,
  plan = NULL,
  institutions = NULL,
  researchers = NULL,
  manager = NULL,
  location = NULL,
  altitude = NULL,
  georeferencing = NULL,
  environment = NULL,
  start = NA,
  end = NA,
  about = NULL,
  fieldbook = NULL,
  project = NULL,
  repository = NULL,
  manuscript = NULL,
  album = NULL,
  nfactor = 2,
  design = "rcbd",
  rep = 3,
  zigzag = FALSE,
  nrows = NA,
  serie = 100,
  seed = 0,
  qrcode = "{fbname}{plots}{factors}"
)

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 sketch designs based in experimental design

Usage

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

Arguments

Details

The function allows to plot the experimental design according the field experiment design.

Value

plot

Examples

## Not run: 

library(inti)
library(gsheet)

url <- paste0("https://docs.google.com/spreadsheets/d/"
              , "1_BVzChX_-lzXhB7HAm6FeSrwq9iKfZ39_Sl8NFC6k7U/edit#gid=1834109539")
# browseURL(url)

fb <- gsheet2tbl(url) 

dsg <- fb %>% tarpuy_design() 

dsg

dsg %>% str()

dsg %>% 
  tarpuy_plotdesign()


## End(Not run)

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

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

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