POICalc: Function to generate coordinates points to plot using POIs

In fisheyeR: Fisheye and Hyperbolic-space-alike Interactive Visualization Tools in R

Description

POICalc Points Of Interest (POI) allows for the exploration of multidimensional data, by representing information according to its similarity with every POI defined for the set.

Usage

POICalc(objeto, NC, cx = 0, cy = 0, r = 1, ...)

Arguments

objeto	Object of class POI
NC	Number of POI (points of interest as proposed by Costa and Venturini. See references.
cx	x coordinates
cy	x coordinates
r	Plot Radius
...	further arguments

Details

POIs are located on a circle, and data are displayed within this circle according to their similarities to these POI. Interactive actions are possible: selection, zoom, dynamical change of POI.

Value

Pcoords	Matrix with POIs coordinates
PcoordsFI	Matrix with POIs coordinates with fisheye effect applied.
newPcoords	Matrix with coordinates for the lines joining POIs
objeto	Matrix with coordinates for elements in the main set.

Author(s)

Eduardo San Miguel Martin

References

Da Costa, David & Venturini, Gilles (2006). An Interactive Visualization Environment for Data Exploration Using Points of Interest. adma 2006: 416-423

Furnas, George (1986). Generalized Fisheye Views. Human Factors in computing systems, CHI '86 conference proceedings, ACM, New York, pp. 16-23.

Heidi Lam, Ronald A. Rensink, and Tamara Munzner (2006). Effects of 2D Geometric Transformations on Visual Memory. Proc. Applied Perception in Graphics and Visualization (APGV 2006), 119-126, 2006.

Keith Lau, Ron Rensink, and Tamara Munzner (2004). Perceptual Invariance of Nonlinear Focus+Context Transformations. Proc. First Symposium on Applied Perception in Graphics and Visualization (APGV 04) 2004, pp 65-72.

Lamping, J., Rao, R., Pirolli, P. (1995) A Focus+Context Technique Based on Hyperbolic Geometry for Visualizing Large Hierarchies. Proc. ACM Conf. Human Factors in Computing Systems, CHI. ACM. pp, 401-408

See Also

POIPlot-methods,POI-class,plotPOI

Examples

## Not run: 
   ## IRIS Example
   data(iris)
   
   # distance of each element to each dimension max and min
   matrizSim = cbind(
               1 - (max(iris[,1]) - iris[,1]) / (max(max(iris[,1]) - iris[,1])),
               1 - (max(iris[,2]) - iris[,2]) / (max(max(iris[,2]) - iris[,2])),
               1 - (max(iris[,3]) - iris[,3]) / (max(max(iris[,3]) - iris[,3])),
               1 - (max(iris[,4]) - iris[,4]) / (max(max(iris[,4]) - iris[,4])),

               1 - (min(iris[,1]) - iris[,1]) / (min(min(iris[,1]) - iris[,1])),
               1 - (min(iris[,2]) - iris[,2]) / (min(min(iris[,2]) - iris[,2])),
               1 - (min(iris[,3]) - iris[,3]) / (min(min(iris[,3]) - iris[,3])),
               1 - (min(iris[,4]) - iris[,4]) / (min(min(iris[,4]) - iris[,4])))

   # exaggerate diffs
   matrizSim  = matrizSim^3 
   
   # Create POI plot
   irisPOI = POICreate('POI')
   irisPOI@matrizSim <- matrizSim
   irisPOI@wordsInQuery <- c('high.Sepal.Length', 'high.Sepal.Width', 'high.Petal.Length', 'high.Petal.Width',
                             'low.Sepal.Length', 'low.Sepal.Width', 'low.Petal.Length', 'low.Petal.Width')
   POIcoords(irisPOI) <- POICalc(irisPOI ,length(irisPOI@wordsInQuery))
   irisPOI@docs <- cbind(matrix(seq(1:nrow(irisPOI@objeto))),matrix(seq(1:nrow(irisPOI@objeto))))
   irisPOI@colores <- c(rep(2,50),rep(3,50),rep(4,50))
   try(rm('POI.env'), silent = T)
   plotPOI(irisPOI)

## End(Not run)

fisheyeR documentation built on May 2, 2019, 12:47 a.m.