I will have to resort to manually replacing NA with a pre-defined color as opposed to using the fill.colour option, which is kind of a waste of a nice feature.
real.continuous.test.data <- t(data.frame(
continuous.data = c(NA, '#3FDADC', '#BFF2F3', '#1FD4D6', NA,'#7FE6E8', '#5FE0E2', '#DFF8F9', '#FFFFFF', '#9FECED')
));
categorical.test.data <- t(data.frame(
categorical.data = c(NA, 'red', 'blue', 'red', NA, 'green', 'yellow', 'purple', 'orange', 'pink')
));
# Ex1 Second column inherits NA from first column but the other NA is handled fine?
create.heatmap(
x = real.continuous.test.data,
input.colours = TRUE,
clustering.method = 'none',
same.as.matrix = TRUE,
print.colour.key = FALSE
);
# Ex2 Both second and fourth columns inherit NA from first and fifth column?
create.heatmap(
x = categorical.test.data,
input.colours = TRUE,
clustering.method = 'none',
same.as.matrix = TRUE,
print.colour.key = FALSE
)
# Ex3 Two rows, now the categorical data looks correct but continuous data remains contaminated
create.heatmap(
x = rbind(categorical.test.data, real.continuous.test.data),
input.colours = TRUE,
clustering.method = 'none',
same.as.matrix = TRUE,
print.colour.key = FALSE
)
I'm working with
create.heatmapand encountering odd behavior when dealing with NA values and single-row heatmaps.The weirdest thing is that the NA missing values seem to be contaminating adjacent cells in certain cases.
I will have to resort to manually replacing NA with a pre-defined color as opposed to using the
fill.colouroption, which is kind of a waste of a nice feature.For example:
Ex1:

Ex2:

Ex3:
