tabula (version 1.6.1)

plot_bar: Bar Plot

Description

Plots a Bertin, Ford (battleship curve) or Dice-Leraas diagram.

Usage

plot_bertin(object, ...)
plot_ford(object, ...)
# S4 method for matrix
plot_bertin(object, threshold = NULL, scale = NULL)
# S4 method for matrix
plot_ford(object)
# S4 method for CountMatrix
plot_ford(object, EPPM = FALSE)

Arguments

object

An abundance matrix to be plotted.

...

Currently not used.

threshold

A function that takes a numeric vector as argument and returns a numeric threshold value (see below). If NULL (the default), no threshold is computed.

scale

A function used to scale each variable, that takes a numeric vector as argument and returns a numeric vector. If NULL (the default), no scaling is performed.

EPPM

A logical scalar: should the EPPM be drawn (see below)?

Value

A ggplot2::ggplot object.

Bertin Matrix

As de Falguerolles et al. (1997) points out: "In abstract terms, a Bertin matrix is a matrix of displays. ... To fix ideas, think of a data matrix, variable by case, with real valued variables. For each variable, draw a bar chart of variable value by case. High-light all bars representing a value above some sample threshold for that variable."

EPPM

This positive difference from the column mean percentage (in french "<U+00E9>cart positif au pourcentage moyen", EPPM) represents a deviation from the situation of statistical independence. As independence can be interpreted as the absence of relationships between types and the chronological order of the assemblages, EPPM is a useful graphical tool to explore significance of relationship between rows and columns related to seriation (Desachy 2004).

Details

If EPPM is TRUE and if a relative abundance is greater than the mean percentage of the type, the exceeding part is highlighted.

References

Bertin, J. (1977). La graphique et le traitement graphique de l'information. Paris: Flammarion. Nouvelle Biblioth<U+00E8>que Scientifique.

de Falguerolles, A., Friedrich, F. & Sawitzki, G. (1997). A Tribute to J. Bertin's Graphical Data Analysis. In W. Badilla & F. Faulbaum (eds.), SoftStat '97: Advances in Statistical Software 6. Stuttgart: Lucius & Lucius, p. 11-20.

Desachy, B. (2004). Le s<U+00E9>riographe EPPM: un outil informatis<U+00E9> de s<U+00E9>riation graphique pour tableaux de comptages. Revue arch<U+00E9>ologique de Picardie, 3(1), 39-56. 10.3406/pica.2004.2396.

Ford, J. A. (1962). A quantitative method for deriving cultural chronology. Washington, DC: Pan American Union. Technical manual 1.

See Also

eppm()

Other plot: plot_diversity, plot_line, plot_matrix, plot_spot()

Examples

# NOT RUN {
## Abundance data
## Coerce dataset to a count matrix
data("mississippi", package = "folio")
counts1 <- as_count(mississippi)

## Plot a Bertin diagram...
## ...without threshold
plot_bertin(counts1)
## ...with variables scaled to 0-1 and the variable mean as threshold
scale_01 <- function(x) (x - min(x)) / (max(x) - min(x))
plot_bertin(counts1, threshold = mean, scale = scale_01)

## Abundance data
## Coerce dataset to a count matrix (data from Desachy 2004)
data("compiegne", package = "folio")
counts2 <- as_count(compiegne)

## Plot a Ford diagram...
## ...without threshold
plot_ford(counts2)
## ...with EPPM
plot_ford(counts2, EPPM = TRUE)
# }