In CIP-RIU/hidap: High Interactive Data Analysis Platform for Plant breeding

## Data

dfr <- params$dfr
colnames(dfr)[2:4] <- c("Appe", "Tast", "Text")

## Count number of men and women

temp <- dfr[dfr$Sex == "M" | dfr$Sex == "Male", ]
nm <- length(unique(temp$PanelNo))
temp <- dfr[dfr$Sex == "F" | dfr$Sex == "Female", ]
nw <- length(unique(temp$PanelNo))

## Aggregate data for principal components

adg <- docomp("sum", c("Appe", "Tast", "Text"), "INSTN", dfr = dfr)
temp <- docomp("sum", c("Appe", "Tast", "Text"), c("INSTN", "Sex"), dfr = dfr)
adm <- temp[temp$Sex == "M" | temp$Sex == "Male", ]
adf <- temp[temp$Sex == "F" | temp$Sex == "Female", ]
colnames(adm)[3:5] <- c("Appe-M", "Tast-M", "Text-M")
colnames(adf)[3:5] <- c("Appe-F", "Tast-F", "Text-F")
ads <- cbind(adm[, c(1, 3:5)], adf[, 3:5])

rownames(ads) <- ads$INSTN
rownames(adg) <- adg$INSTN

ads <- ads[, -1]
adg <- adg[, -1]

1. Gathering of data

Samples of all genotypes are boiled and presented on plates. Each genotype is evaluated about appearance and taste with the options:

5 excellent,
3 fair,
1 poor,

and about texture with:

5 mealy or floury,
3 intermediate,
1 soggy or watery.

For the graphs below, the following abbreviations are used:

Appe: Appearance.
Tast: Taste.
Text: Texture.
Appe-M: Men opinion on appearance.
Tast-M: Men opinion on taste.
Text-M: Men opinion on texture.
Appe-W: Women opinion on appearance.
Tast-W: Women opinion on taste.
Text-W: Women opinion on texture.

2. Results

A principal components analysis is shown to see the associations among the genotypes and the attributes, first with all the panelists together and then with panelists opinions differentiated by gender.

princip <- prcomp(adg, center = TRUE, scale. = TRUE)
summary(princip)
factoextra::fviz_pca(princip, repel = TRUE,
                     title = "Biplot of genotypes and attributes")

princip <- prcomp(ads, center = TRUE, scale. = TRUE)
summary(princip)
factoextra::fviz_pca(princip, repel = TRUE,
                     title = "Biplot of genotypes and attributes by gender")

3. Friedman test for genotypes

3.1. Analysis for appearance

ft <- with(dfr, friedman(PanelNo, INSTN, Appe, group = TRUE)) 
ft$statistics
ft$groups
ft <- with(dfr, friedman(PanelNo, INSTN, Appe, group = FALSE)) 
ft$comparison

3.2. Analysis for taste

ft <- with(dfr, friedman(PanelNo, INSTN, Tast, group = TRUE)) 
ft$statistics
ft$groups
ft <- with(dfr, friedman(PanelNo, INSTN, Tast, group = FALSE)) 
ft$comparison

3.3. Analysis for texture

ft <- with(dfr, friedman(PanelNo, INSTN, Text, group = TRUE)) 
ft$statistics
ft$groups
ft <- with(dfr, friedman(PanelNo, INSTN, Text, group = FALSE)) 
ft$comparison