Package 'cABCanalysis'

ABC Classification

Description

Divides a numeric dataset into three classes (A, B, and C) using ABC analysis. The classification is based on geometric properties of the ABC curve and identifies regions of high, balanced, and low efficiency. Class interpretation:

A:	Low effort, high yield (Pareto items)
B:	Balanced effort and yield
C:	High effort, low yield (submarginal items)

Usage

cABC_analysis(Data, PlotIt = FALSE, useGGPlot = TRUE, plotArgs = NULL)

Arguments

Data	Positive numeric vector which is not uniformly distributed. If matrix or dataframe then the first column will be used.
PlotIt	Logical. If TRUE, an ABC plot is generated.
useGGPlot	Logical, default TRUE. If TRUE a ggplot2 plot is produced; if FALSE a base-R plot is produced. Only relevant when PlotIt = TRUE.
plotArgs	NULL (default), or a named list of cosmetic plot options controlling colors, line width, title, and more. See cABC_plot_style for the full list of supported options. Only used when PlotIt = TRUE and useGGPlot = TRUE. Can be build with cABC_plot_style for autocomplete and validation, e.g. cABC_plot_style(LineWidth = 2, CurveColor = "darkred"), or passed as a raw named list with the same element names. Any option not specified falls back to the package default.

Details

Calculation of Boundaries is done on the ABC Curve (see cABC_curve) with:

Pareto Point:	The point with minimal distance to (0,1) -> A|B Boundary
Breakeven Point:	The point where slope equals to 1
Juren Point:	The point with minimal distance to (BreakevenPoint_x,1) -> B|C Boundary

For more calculation details see: Ultsch A, Lotsch J (2015) "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data". PLoS ONE 10(6): e0129767. <doi:10.1371/journal.pone.0129767>.

Data cleaning: Before classification, non-numeric values and NAs are coerced to 0, negative values are set to 0. A warning is issued when items are converted. If a matrix or data frame is supplied, only the first column is used.

Degenerate inputs (single point, all-identical values, very small datasets) are caught before curve fitting, see cABC_handle_specials for the full behavior. Boundary duplicate values that span two classes after classification are resolved by cABC_postprocess_classes. In both cases a warning is issued when a special case is triggered.

Value

A list containing:

Aind, Bind, Cind

Integer vectors of indices (into the original Data) for items assigned to classes A, B, and C respectively. In special-case returns (single point or all-identical), only Aind is populated; Bind and Cind are integer(0).

ABexchanged

Logical; TRUE if the Pareto point and Break-even point were swapped to maintain coordinate logic (i.e. the Break-even point was to the left of the Pareto point on the curve).

A, B, C

c(x, y) coordinates for the Pareto point (A), the Break-even point (B), and the Submarginal point (C). NULL in special-case returns.

smallestAData

Cumulative yield at the boundary of Class A. NULL in special-case returns.

smallestBData

Cumulative yield at the boundary of Class B. NULL in special-case returns.

AlimitIndInInterpolation

Index of the A boundary in the interpolated [p, ABC] curve. NULL in special-case returns.

BlimitIndInInterpolation

Index of the C boundary in the interpolated [p, ABC] curve. NULL in special-case returns.

Plot

Generated plot object

p

Numeric vector of effort values (x-axis) of the interpolation curve. NULL in special-case returns.

ABC

Numeric vector of yield values (y-axis) of the interpolation curve. NULL in special-case returns.

ABLimit

Data value closest to the threshold separating Class A from Class B. NULL in special-case returns.

BCLimit

Data value closest to the threshold separating Class B from Class C. NULL in special-case returns.

Author(s)

André Himmelspach (01/2026)

Examples

data("SwissInhabitants")
# Set plotting options
opts <- cABC_plot_style(LineWidth = 1.5, CurveColor = "darkblue")

abc <- cABC_analysis(SwissInhabitants, PlotIt = TRUE, plotArgs = opts)

# Extract the data belonging to each class
A <- abc$Aind; B <- abc$Bind; C <- abc$Cind
Agroup <- SwissInhabitants[A]
Bgroup <- SwissInhabitants[B]
Cgroup <- SwissInhabitants[C]

---

ABC Plot style

Description

Constructs a validated list of cosmetic options for customizing ABC plots produced by cABC_analysis. Pass the result to cABC_analysis(..., plot_args = cABC_plot_style(...)).

Usage

cABC_plot_style(
  LineWidth = NULL,
  ShowUniform = NULL,
  ShowBoundary = NULL,
  Plot_title = NULL,
  UniformColor = NULL,
  IdentityColor = NULL,
  EquilibriumColor = NULL,
  CurveColor = NULL,
  PointColor = NULL,
  ABoundaryColor = NULL,
  BBoundaryColor = NULL,
  CBoundaryColor = NULL,
  BoundaryLineColor = NULL,
  LabelColor = NULL,
  LegendTextSize = NULL,
  LegendX = NULL,
  LegendY = NULL,
  LegendSpacing = NULL,
  Theme = NULL
)

Arguments

LineWidth	Numeric. Line width of the main ABC curve. Default is 1.25.
ShowUniform	Logical. Whether to draw the uniform reference curve. Default is TRUE.
ShowBoundary	Logical. Whether to draw lines indicating boundaries. Default is TRUE.
Plot_title	Character. Title of the plot. Default is "ABC plot".
UniformColor	Color of the uniform reference curve. Any valid R color specification, e.g. a color name ("green"), a hex code ("#00FF00"), or a palette index (colors()[26]). Default is "green".
IdentityColor	Color of the identity line (y = x). Any valid R color specification (see UniformColor). Default is grDevices::colors()[452].
EquilibriumColor	Color of the equilibrium diagonal (y = 1 - x). Any valid R color specification (see UniformColor). Default is grDevices::colors()[175].
CurveColor	Color of the ABC curve itself. Any valid R color specification (see UniformColor). Default is "blue".
PointColor	Color of individual data points (shown when n < 20). Any valid R color specification (see UniformColor). Default is "blue".
ABoundaryColor	Color of the A|B boundary star and label. Any valid R color specification (see UniformColor). Default is "red".
BBoundaryColor	Color of the B boundary star. Any valid R color specification (see UniformColor). Default is "green".
CBoundaryColor	Color of the B|C boundary star and label. Any valid R color specification (see UniformColor). Default is "blue".
BoundaryLineColor	Color of the orthogonal boundary guide lines. Any valid R color specification (see UniformColor). Default is "red".
LabelColor	Color of the A/B/C set labels and counts. Any valid R color specification (see UniformColor). Default is "red".
LegendTextSize	Numeric. Font size of the legend text. Default is 4.
LegendX	Numeric. X-position of the legend. Default is 0.80.
LegendY	Numeric, or NULL (default). Y-position where the legend starts. If NULL, this is calculated automatically based on the A/C boundary positions to avoid overlapping the plot.
LegendSpacing	Numeric. Vertical spacing between legend entries. Default is 0.05.
Theme	A ggplot2 theme object (e.g. ggplot2::theme_minimal()). Default is ggplot2::theme_light() with a few structural tweaks applied on top (no panel grid, a bordered panel, and a centered plot title).

Details

Any option left at NULL falls back to the package default cABC_default_plot_args.

Value

A named list of plot options, suitable for the plot_args argument of cABC_analysis.

See Also

cABC_analysis

Examples

opts <- cABC_plot_style(LineWidth = 2, CurveColor = "darkred")
data("SwissInhabitants")
abc <- cABC_analysis(SwissInhabitants, PlotIt = TRUE, plotArgs = opts)

---

SwissInhabitants in 1900

Description

Number of inhabitants in the 2896 villages of Switzerland in the year 1900.

Usage

data("SwissInhabitants")

Format

A numeric vector of length 2896 containing the population counts.

Details

This data set consists of the number of inhabitants in the 2896 communes (cities and villages) in Switzerland in 1900. The data is unordered for anonymity reasons.

Source

Schuler, M., Ullmann, D. (2002). Eidgenossische Volkszahlung: Bevoelkerungsentwicklung der Gemeinden. Bundesamt fur Statistik, Neuchatel, Switzerland.

References

Behnisch, M., Ultsch, A. (2010). Population Patterns in Switzerland 1850-2000. In: Gaul, W. et al (Eds), Advances in Data Analysis, Data Handling and Business Intelligence, Springer, Heidelberg, pp. 163-173.

Examples

data(SwissInhabitants)
summary(SwissInhabitants)

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