EvaluateCore (version 0.1.4)

bar.evaluate.core: Bar Plots

Description

Plot Bar plots to graphically compare the frequency distributions of qualitative traits between entire collection (EC) and core set (CS).

Usage

bar.evaluate.core(
  data,
  names,
  qualitative,
  selected,
  na.omit = TRUE,
  show.count = FALSE
)

Value

A list with the ggplot objects of relative frequency bar plots of CS and EC for each trait specified as qualitative.

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.

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.

na.omit

logical. If TRUE, missing values (NA) are ignored and not included in the plot as a distinct factor level. Default is TRUE.

show.count

logical. If TRUE, the accession count excluding missing values will also be displayed. Default is FALSE.

See Also

barplot, geom_bar

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)))

# \donttest{
bar.evaluate.core(data = ec, names = "genotypes",
                  qualitative = qual, selected = core)
bar.evaluate.core(data = ec, names = "genotypes",
                  qualitative = qual, selected = core,
                  show.count = TRUE)
# }