plot_boxplot: 'ggplot' type boxplots for each vars.cont by partition level.

In LilithF/doMIsaul: Do Multiple Imputation-Based Semi-Supervised and Unsupervised Learning

Description

ggplot type boxplots for each vars.cont by partition level.

Usage

plot_boxplot(
  data,
  partition.name,
  vars.cont,
  vars.cont.names = NULL,
  unclass.name = "Unclassified",
  include.unclass = FALSE,
  add.n = FALSE,
  nc.facet = 10
)

Arguments

data	The dataset.
partition.name	string. Name of the partition (in data). The partition variable should be a factor.
vars.cont	vector of strings. variables to plot (continuous only).
vars.cont.names	Optional. Names for displaying the continuous variables. (given in the same order than vars.cont)
unclass.name	If applicable, name for the unclassified observations in the partition.
include.unclass	boolean, should boxplot be displayed for the unclassified or should they be excluded from the plot.
add.n	Boolean. Should the number of samples per cluster be indicated on the x axis and color legend.
nc.facet	integer. Number of columns in the facet_wrap()

Value

ggplot object.

Examples

data(cancer, package = "survival")
cancer$status <- factor(cancer$status)
plot_boxplot(data = cancer, partition.name = "status",
                  vars.cont = c("age", "meal.cal", "wt.loss"))

## With unclassifieds
cancer$status.2 <- as.character(cancer$status)
cancer$status.2[sample(1:nrow(cancer), 30)] <- "Unclassif."
cancer$status.2 <- factor(cancer$status.2)
plot_boxplot(data = cancer, partition.name = "status.2",
                  vars.cont = c("age", "meal.cal", "wt.loss"),
                  unclass.name = "Unclassif.", include.unclass = TRUE)

## With unclassifieds (as NA)
cancer$status.3 <- cancer$status
cancer$status.3[sample(1:nrow(cancer), 30)] <- NA
plot_boxplot(data = cancer, partition.name = "status.3",
                  vars.cont = c("age", "meal.cal", "wt.loss"),
                  unclass.name = NA, include.unclass = TRUE, add.n = TRUE)

LilithF/doMIsaul documentation built on Dec. 17, 2021, 12:08 a.m.