Bertin display

Description
One of the most popular ways of displaying grid data has been adopted from Bertin's (1966) graphical proposals, which have had an immense influence onto data visualization. One of the most appealing ideas presented by Bertin is the concept of the reordable matrix. It is comprised of graphical displays for each cell of a matrix, allowing to identify structures by eye-balling reordered versions of the data matrix (see Bertin, 1966). In the context of repertory grids, the Bertin display is made up of a simple colored rectangle where the color denotes the corresponding score. Bright values correspond to low, dark to high scores. For an example of how to analyze a Bertin display see e. g. Dick (2000) and Raeithel (1998).

Bertin Default
bertin(boeker)



Various settings can be modified in the bertin function. To see the whole set of options type ?bertin to the R console.

Color
E.g. to change the color of the display use the argument color.

bertin(boeker, color=c("white", "darkred"))



You can also assign three colors to the color ramp that is used. bertin(feixas2004, color=c("darkblue", "white", "darkred"))



Printing of Scores
To suppress thr printing of the scores and only plot the colors use

bertin(boeker, showvalues=FALSE)



Construct / Element Index
The argument id allows to manage the printing of an index number of the elements and constructs.

bertin(boeker, id=c(T, F))         # only index numbers for constructs bertin(boeker, id=c(F, T))         # only index numbers for elements bertin(boeker, id=c(F, F))         # no index numbers

Clustered Bertin
Beside the standard Bertin display also a clustered version is available. It contains a standard Bertin display in its center and dendrograms at the sides. How to cluster a grid is described here. You should read the cluster section first to understand the following code.

As a default Euclidean distance and ward clustering is applied to the grid.

bertinCluster(feixas2004)



Like in the standard Bertin the colors can be set.

bertinCluster(feixas2004, color=c("white", "darkred"))



To apply different distance and cluster methods use the arguments dmethod (or equivalently: dm) and cmethod (or equivalently: cm). For more information on clustering go to here. The following code uses manhattan distance and single linkage clustering.

bertinCluster(feixas2004, dmethod="manhattan", cm="single")



Sometimes it is desirable to spot structures by eye-balling the colors. For this purpose the printing of the scores can be surpressed.

bertinCluster(feixas2004, showvalues=FALSE)



Other options include to suppress the axis of the dendrogram or to chose a rectangular type of dendrogram.

bertinCluster(feixas2004, draw.axis=F)       # plot 1: no axis drawn for dendrogram bertinCluster(feixas2004, type="rectangle")  # plot 2: rectangle type dendrogram

Literature

 * Bertin, J. (1966). Sémiologie graphique: Diagrammes, réseaux, cartographie. Paris: Mouton.
 * Dick, M. (2000). The Use of Narrative Grid Interviews in Psychological Mobility Research. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 1(2).
 * Raeithel, A. (1998). Kooperative Modellproduktion von Professionellen und Klienten - erlauetert am Beispiel des Repertory Grid. Selbstorganisation, Kooperation, Zeichenprozess: Arbeiten zu einer kulturwissenschaftlichen, anwendungsbezogenen Psychologie (pp. 209-254). Opladen: Westdeutscher Verlag.