ClassBarPlot: ClassBarPlot

In DataVisualizations: Visualizations of High-Dimensional Data

ClassBarPlot

Description

Represent values for each class and instance as bar plot with optional error deviation, e.g., mean values of features depending on class with standard deviation.

Usage

ClassBarPlot(Values, Cls, Deviation, Names, ClassColors,

            ylab = "Values", xlab = "Instances", PlotIt = TRUE)

Arguments

Values	[1:n] Numeric vector with values (y-axis) in matching order to Cls, Deviation and Names.
Cls	[1:n] Numeric vector of classes in matching order to Values and Deviation and Names.
Deviation	[1:n] Numeric vector with deviation in matching order to Values and Cls and Names.
Names	[1:n] Character or numeric vector of instances (x-axis) in matching order to Values and Cls and Deviation.
ClassColors	Character vector of color names stating either the colors for each class or defining colors matching the class vector cls.
ylab	Character stating y label.
xlab	Character stating x label.
PlotIt	Logical value indicating visual output TRUE => create visual output FALSE => do not create visual output (Default: Boolean=TRUE).

Value

ggplot2 object

Author(s)

Quirin Stier

Examples

# Compute means and counts
tmpVar1 <- aggregate(Sepal.Length ~ Species, 

data = iris, FUN = function(x) c(mean = mean(x), n = length(x)))


tmpVar2 <- aggregate(Sepal.Width ~ Species, 

data = iris, FUN = function(x) c(mean = mean(x), n = length(x)))


tmpVar3 <- aggregate(Petal.Length ~ Species, 

data = iris, FUN = function(x) c(mean = mean(x), n = length(x)))


tmpVar4 <- aggregate(Petal.Width ~ Species, 

data = iris, FUN = function(x) c(mean = mean(x), n = length(x)))

# Extract mean and count
tmpVar1_mean <- tmpVar1$Sepal.Length[, "mean"]
tmpVar2_mean <- tmpVar2$Sepal.Width[, "mean"]
tmpVar3_mean <- tmpVar3$Petal.Length[, "mean"]
tmpVar4_mean <- tmpVar4$Petal.Width[, "mean"]

# Compute standard deviations
tmpVar5 <- aggregate(Sepal.Length ~ Species, data = iris, FUN = sd)
tmpVar6 <- aggregate(Sepal.Width ~ Species, data = iris, FUN = sd)
tmpVar7 <- aggregate(Petal.Length ~ Species, data = iris, FUN = sd)
tmpVar8 <- aggregate(Petal.Width ~ Species, data = iris, FUN = sd)

# Combine results
Values <- c(tmpVar1_mean, tmpVar2_mean, tmpVar3_mean, tmpVar4_mean)
Class <- rep(1:3, 4)
Deviation <- c(tmpVar5$Sepal.Length, tmpVar6$Sepal.Width, tmpVar7$Petal.Length, tmpVar8$Petal.Width)
  
  if(length(Values) == length(Class)){
    ClassBarPlot(Values = Values, Cls = Class, Deviation = Deviation)
  }
  

DataVisualizations documentation built on April 3, 2025, 8:24 p.m.