DataVisualizations (version 1.3.3)

ClassBarPlot: 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)

Value

ggplot2 object

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).

Author

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)
  }