fisheyeR (version 0.9)

plotPOI: Functions for POI plotting

Description

Function for plotting objects of class POI.

Usage

plotPOI(POI) plotPOIGraph(POI)

Arguments

POI

Object of class POI

Details

plotPOI and plotPOIGraph will try to load tkrplot in order to allow graphical user interaction. Otherwise POIPlot will be used.

Graphical User Interaction defaults:

Mouse Click Motion

Drag points over. Basic interaction which allows you to move any point to the center of the disk, where detail is augmented.

'+' Increments fisheye distortion factor. Same functionality using mousewheel.

'-' Decrements fisheye distortion factor. Same functionality using mousewheel.

'0' Increments animation smoothness.

'.' Decrements animation smoothness.

Mouse Double Clicking Retunrs closest point.

Mouse Middle Clicking Draw a circle and returns and object of class vector called 'insiders' with every element inside. "insiders" object will be created in POI.env. (POI.env$insiders)

See Also

POI-class,POIPlot-methods, POIPlot

Examples

## Not run:    
#    ## rgb colors 
#    rgbPOI = POICreate(type = 'POI', wordsInQuery = c('red','green','blue'),
#                        colores = colors(), itemsCol = colors(),
#                        docs = cbind(colors(), 1:length(colors())),
#                        cos.query.docs = rep(1,length(colors())),
#                        matrizSim = t(col2rgb(colors())) / max(t(col2rgb(colors())))
#    )
#    POIcoords(rgbPOI) <- POICalc(rgbPOI ,length(rgbPOI@wordsInQuery))
#    try(rm('POI.env'), silent = T)
#    plotPOI(rgbPOI)
#    
#    ## graph example
#    # igraph package  -- graph.tree example looks great!
#    if (require(igraph)) {
#      GRAPH <- graph.tree(500, children = 10, mode = 'in')    
#      fCompress <- 350 # compress factor
#      graphPOI <- POICreate(type = 'POIGraph')
#      graphPOI@objeto <- layout.fruchterman.reingold(GRAPH,dim = 2) / fCompress
#      graphPOI@EDGES <- cbind(GRAPH[[3]],GRAPH[[4]]) + 1 
#      graphPOI@docs <- matrix(c(seq(1:nrow(graphPOI@objeto)), seq(1:nrow(graphPOI@objeto))), ncol = 2)
#      try(rm('POI.env'), silent = T)
#      plotPOIGraph(graphPOI)
#     } 
#    # manually made  -- but igraph example looks great!!
#    graphPOI <- POICreate(type = 'POIGraph')
#    graphPOI@objeto <- graphPOI@objeto <- rbind(c(0,.05), c(.05,0), c(0,-.05), c(-.05,0) ,round(circulo(0,0,.3,PLOT = FALSE),2))
#    graphPOI@EDGES <-  matrix(c(rep(1,25), rep(2,25), rep(3,25), rep(4,25), seq(1,100)), ncol = 2)
#    graphPOI@docs <- matrix(c(seq(1:nrow(graphPOI@objeto)), seq(1:nrow(graphPOI@objeto))), ncol = 2)
#    graphPOI@colores <- c(rep(2,25), rep(3,25), rep(4,25), rep(5,25))
#    try(rm('POI.env'), silent = T)
#    plotPOIGraph(graphPOI)
# 
#    ## 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])))
# 
#    matrizSim  = matrizSim^3 
#    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)
# 
#    ## USArrest Example
#    # POIS = (high - low) murder, assault and rape rates
#    # colors = Population
#    data(USArrests)
#    matrizSim = cbind(
#     1 - (max(USArrests[,1]) - USArrests[,1]) / (max(max(USArrests[,1]) - USArrests[,1])),
#     1 - (max(USArrests[,2]) - USArrests[,2]) / (max(max(USArrests[,2]) - USArrests[,2])),
#     1 - (max(USArrests[,4]) - USArrests[,4]) / (max(max(USArrests[,4]) - USArrests[,4])),                                                                                    
#     1 - (min(USArrests[,1]) - USArrests[,1]) / (min(min(USArrests[,1]) - USArrests[,1])),
#     1 - (min(USArrests[,2]) - USArrests[,2]) / (min(min(USArrests[,2]) - USArrests[,2])),
#     1 - (min(USArrests[,4]) - USArrests[,4]) / (min(min(USArrests[,4]) - USArrests[,4])))
# 
#    usaPOI = POICreate('POI')
#    usaPOI@matrizSim <- matrizSim
#    usaPOI@wordsInQuery <- c(paste('High', names(USArrests[,c(1,2,4)])), paste('Low', names(USArrests[,c(1,2,4)])))
#    POIcoords(usaPOI) <- POICalc(usaPOI ,length(usaPOI@wordsInQuery))
#    usaPOI@docs <- cbind(matrix(rownames(USArrests)),matrix(seq(1:nrow(usaPOI@objeto))))
#    [e-mail] <-  USArrests[,3] / max(USArrests[,3])
#    POIcolors(usaPOI)<- query2Cols(usaPOI, 'terrain')
#    try(rm('POI.env'), silent = T)
#    plotPOI(usaPOI)
# 
#    ## clusters  EXAMPLE
#    x <- matrix(rnorm(1500, mean = 0, sd = .5), ncol = 5) 
#    atipV1 = sample(nrow(x), as.integer(nrow(x)/3)) # outliers in V1
#    atipV2 = sample(nrow(x), as.integer(nrow(x)/3)) # outliers in V2
#    x[atipV1, 1] <- rnorm(100, mean = 2, sd = .5)  
#    x[atipV2, 2] <- rnorm(100, mean = 2, sd = .5)  
#    cl <- kmeans(x, 3, iter.max = 100 ,nstart = 25)
#    matrizSim =  sqrt(round((x - colMeans(x))^2,1 )/nrow(x))    # simmilarity within outliers
#    # OR (uncomment one)
#    # matrizSim =  1 - sqrt(round((x - colMeans(x))^2,1 )/nrow(x))    # simmilarity within mean
#    varPOI = POICreate('POI')
#    varPOI@matrizSim <- matrizSim
#    varPOI@wordsInQuery <- 1:ncol(matrizSim)
#    POIcoords(varPOI) <- POICalc(varPOI ,length(varPOI@wordsInQuery))
#    # if elements labels bother 
#    varPOI@docs  <- cbind(rep(' ',nrow(varPOI@objeto)),matrix(seq(1:nrow(varPOI@objeto)))) 
#    [e-mail] <-  rep(1,nrow(matrizSim))
#    varPOI@colores <- cl$cluster  + 1
#    try(rm('POI.env'), silent = T)
#    plotPOI(varPOI)
#  
# ## End(Not run)