freqdist.evaluate.core {EvaluateCore} | R Documentation |
Plot stacked frequency distribution histogram to graphically compare the probability distributions of traits between entire collection (EC) and core set (CS).
freqdist.evaluate.core(
data,
names,
quantitative,
qualitative,
selected,
highlight = NULL,
include.highlight = TRUE,
highlight.se = NULL,
highlight.col = "red"
)
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 |
highlight |
Individual names to be highlighted as a character vector. |
include.highlight |
If |
highlight.se |
Optional data frame of standard errors for the
individuals specified in |
highlight.col |
The colour(s) to be used to highlighting individuals in
the plot as a character vector of the same length as |
A list with the ggplot
objects of stacked frequency
distribution histograms plots for each trait specified as
quantitative
and qualitative
.
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)