plotPOI: Functions for POI plotting
In fisheyeR: Fisheye and Hyperbolic-space-alike Interactive Visualization Tools in R
Description
Usage
Arguments
Details
See Also
Examples
View source: R/Fisheyer_Functions.r
Description
Function for plotting objects of class POI.
Usage
1
2
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
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## 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))))
usaPOI@cos.query.docs <- 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))))
varPOI@cos.query.docs <- rep(1,nrow(matrizSim))
varPOI@colores <- cl$cluster + 1
try(rm('POI.env'), silent = T)
plotPOI(varPOI)
## End(Not run)
fisheyeR documentation built on May 2, 2019, 12:47 a.m.
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Description Usage Arguments Details See Also Examples
View source: R/Fisheyer_Functions.r
Description
Function for plotting objects of class POI.
Usage
1 2 | 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | ## 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))))
usaPOI@cos.query.docs <- 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))))
varPOI@cos.query.docs <- rep(1,nrow(matrizSim))
varPOI@colores <- cl$cluster + 1
try(rm('POI.env'), silent = T)
plotPOI(varPOI)
## End(Not run)
|
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