freqdist.evaluate.core: Frequency Distribution Histogram

In EvaluateCore: Quality Evaluation of Core Collections

Frequency Distribution Histogram

Description

Plot stacked frequency distribution histogram to graphically compare the probability distributions of traits between entire collection (EC) and core set (CS).

Usage

freqdist.evaluate.core(
  data,
  names,
  quantitative,
  qualitative,
  selected,
  highlight = NULL,
  include.highlight = TRUE,
  highlight.se = NULL,
  highlight.col = "red"
)

Arguments

data	The data as a data frame object. The data frame should possess one row per individual and columns with the individual names and multiple trait/character data.
names	Name of column with the individual names as a character string
quantitative	Name of columns with the quantitative traits as a character vector.
qualitative	Name of columns with the qualitative traits as a character vector.
selected	Character vector with the names of individuals selected in core collection and present in the names column.
highlight	Individual names to be highlighted as a character vector.
include.highlight	If TRUE, the highlighted individuals are included in the frequency distribution histogram. Default is TRUE.
highlight.se	Optional data frame of standard errors for the individuals specified in highlight. It should have the same column names as in data.
highlight.col	The colour(s) to be used to highlighting individuals in the plot as a character vector of the same length as highlight. Must be valid colour values in R (named colours, hexadecimal representation, index of colours [1:8] in default R palette() etc.).

Value

A list with the ggplot objects of stacked frequency distribution histograms plots for each trait specified as quantitative and qualitative.

See Also

hist, geom_histogram

Examples

data("cassava_CC")
data("cassava_EC")

ec <- cbind(genotypes = rownames(cassava_EC), cassava_EC)
ec$genotypes <- as.character(ec$genotypes)
rownames(ec) <- NULL

core <- rownames(cassava_CC)

quant <- c("NMSR", "TTRN", "TFWSR", "TTRW", "TFWSS", "TTSW", "TTPW", "AVPW",
           "ARSR", "SRDM")
qual <- c("CUAL", "LNGS", "PTLC", "DSTA", "LFRT", "LBTEF", "CBTR", "NMLB",
          "ANGB", "CUAL9M", "LVC9M", "TNPR9M", "PL9M", "STRP", "STRC",
          "PSTR")

ec[, qual] <- lapply(ec[, qual],
                     function(x) factor(as.factor(x)))


freqdist.evaluate.core(data = ec, names = "genotypes",
                       quantitative = quant, qualitative = qual,
                       selected = core)

checks <- c("TMe-1199", "TMe-1957", "TMe-3596", "TMe-3392")

freqdist.evaluate.core(data = ec, names = "genotypes",
                       quantitative = quant, qualitative = qual,
                       selected = core,
                       highlight = checks, highlight.col = "red")

quant.se <- data.frame(genotypes = checks,
                       NMSR = c(0.107, 0.099, 0.106, 0.062),
                       TTRN = c(0.081, 0.072, 0.057, 0.049),
                       TFWSR = c(0.089, 0.031, 0.092, 0.097),
                       TTRW = c(0.064, 0.031, 0.071, 0.071),
                       TFWSS = c(0.106, 0.071, 0.121, 0.066),
                       TTSW = c(0.084, 0.045, 0.066, 0.054),
                       TTPW = c(0.098, 0.052, 0.111, 0.082),
                       AVPW = c(0.074, 0.038, 0.054, 0.061),
                       ARSR = c(0.104, 0.019, 0.204, 0.044),
                       SRDM = c(0.078, 0.138, 0.076, 0.079))

freqdist.evaluate.core(data = ec, names = "genotypes",
                       quantitative = quant,
                       selected = core,
                       highlight = checks, highlight.col = "red",
                       highlight.se = quant.se)

EvaluateCore documentation built on July 3, 2022, 5:06 p.m.