Basic Plots
Time Series
r.plot(matrixTimeSeries100)
r.plot(x=seq(0,1,1/199), y=matrixTimeSeries100)
r.plot(matrixTimeSeries5)
r.plot.add(matrixTimeSeries5, type='p')
Lines I
r.plot(x_seq, y_sin, type='l')
Lines II
r.plot.new(xlim=c(0,10), ylim=c(-1,1),xlab="x",ylab="y")
r.plot.add(x_seq, y_sin, type='l', icol=1)
r.plot.add(x_seq, y_cos, type='l', icol=2)
r.plot.new(xlim=c(0,10), ylim=c(-1,1))
r.plot.add(x_seq, y_sin, type='l', col=1)
r.plot.add(x_seq, y_cos, type='l', col=2)
r.plot.new(xlim=c(0,10), ylim=c(-1,1))
r.plot.add(x_seq, y_sin, type='l', col=rgb(0.8,0.8,0.0))
r.plot.add(x_seq, y_cos, type='l', col=rgb(0.0,0.8,0.8))
Points I
r.plot(y_norm[1:100], type='p')
r.plot.add(y_norm[1:100],type='l', col=rgb(0,0,0,0.1))
r.plot(x_norm[1:10], y_norm[1:10])
r.plot.add(x_norm[1:10], y_norm[1:10],type='l', col=rgb(0,0,0,0.1))
r.plot(x_unif, y_unif)
r.plot.new(x_unif, y_unif)
r.plot.add(x_unif, y_unif)
r.plot.new(xlim=c(-0.5,0.5), ylim=c(-0.5,0.5))
r.plot.add(x_unif, y_unif)
r.plot.add(x_unif, z_unif, icol=2)
Points II
r.plot.new(c(x_unif,x_unif), c(y_unif,z_unif))
r.plot.add(x_unif, y_unif, icol=1, alpha=0.3)
r.plot.add(x_unif, z_unif, icol=2, alpha=0.3)
r.plot.new(c(x_unif,x_unif), c(y_unif,z_unif))
r.plot.add(x_unif, y_unif, col=1, alpha=0.3)
r.plot.add(x_unif, z_unif, col=2, alpha=0.3)
r.plot.new(c(x_unif,x_unif), c(y_unif,z_unif))
r.plot.add(x_unif, y_unif, col=rgb(1,1,0), alpha=0.3)
r.plot.add(x_unif, z_unif, col=rgb(1,0,1), alpha=0.3)
r.plot(x_unif, y_unif, col=heat.colors(5))
r.plot(1:4, 1:4, col=terrain.colors(5), cex=20, alpha=0.6)
Other Plots
Third Axis
r.plot.new(x_seq, y_sin, thirdAxis=TRUE, main="Plot with 3rd Axis using new")
r.plot.add(x_seq, y_sin, type='l')
r.plot.coord(x_seq, y_exp)
r.plot.coord.axis(y_exp)
r.plot.add(x_seq, y_exp, col=2, type='l')
r.plot(x_seq, y_sin, type='l', thirdAxis=TRUE, main="Plot with 3rd Axis")
r.plot.add(x_seq, y_sin, type='p')
r.plot.coord(x_seq, y_exp)
r.plot.coord.axis(y_exp)
r.plot.add(x_seq, y_exp, col=2, type='l')
r.plot.add(x_seq, y_exp, col=2, type='p')
Bar plot
r.plot.bar(vecNum, main="numeric vector")
r.plot.bar(vecChar, main="character vector")
r.plot.bar(iris[c(1,51,101),c(5,1)], main="data.frame", sub="\ntwo columns (label, value)")
r.plot.bar(iris[c(1,51,101),1], main="vector", sub="\nautomatic useVector")
r.plot.bar(iris[c(1,51,51,101),1], main="vector", sub="\nautomatic useVector")
r.plot.bar(c(0.8,0.8,0.5,0.2), useVector=FALSE)
r.plot.bar(c(0.8,0.8,0.5,0.2), useVector=TRUE)
r.plot.bar(table_2, main="table")
r.plot.bar(table_3, main="table")
r.plot.bar(table_3, beside=TRUE)
r.plot.bar(table_3, horizontal=TRUE)
r.plot.bar(table_3, beside=TRUE, horizontal=TRUE)
r.plot.bar(table_3, beside=TRUE, horizontal=TRUE, background=TRUE, box=FALSE)
r.plot.bar(table_3, beside=TRUE, horizontal=TRUE, background=FALSE, box=FALSE)
Histogram plot
r.plot.histogram(x_gaussian)
r.plot.histogram(x_gaussian, main="Density", freq=FALSE)
r.plot.histogram(x_gaussian, breaks = 20)
Distribution plot
r.plot.distribution(x_gaussian)
Heatmap
x = runif(5000)
y = runif(5000)
z = (x-0.7)^2+(y-0.6)^2
r.plot.heatmap(x,y,z)
r.plot.heatmap(x,y,z,mean)
data(volcano)
r.plot.heatmap(matrixData=volcano)
r.plot.heatmap(matrixData=volcano, palette=terrain.colors(12))
r.plot.heatmap(matrixData=volcano, contour=FALSE, palette=terrain.colors(12))
r.plot.heatmap(rnorm(10000), rnorm(10000),
xbreaks=30, ybreaks=30,
contour=FALSE,
palette=r.color.gradient.palette(c("white", r.color(1))))
r.plot.heatmap(rnorm(10000), rnorm(10000),
xbreaks=30, ybreaks=30,
contour=FALSE,
smooth=0.8,
palette=r.color.gradient.palette(c("white", r.color(1))))
Treemap
x = runif(1000)
f = cut(x, breaks = 10)
t = table(f)
segmentosNames <- names(t)
segmentosGroup <- names(t)
segmentosArea <- as.numeric(t)
segmentosColor <- runif(length(segmentosNames))
r.plot.treemap(
segment=segmentosNames,
segmentgroup=segmentosGroup,
area=segmentosArea,
color=segmentosColor,
colorScaleLeft=rgb(0.8,0.2,0.8), colorScaleCenter="White", colorScaleRight=rgb(0.2,0.2,0.8),
main="Tree Map")
Radial plot
x1 <- runif(5)
x2 <- 0.2+0.6*x1+0.2*runif(5)
df <- data.frame(group = c("Blue Collar Communities", "Prospering Suburbs"), matrix(c(x1,x2), nrow = 2, byrow = TRUE))
colnames(df)[2:ncol(df)] <- c("A", "B", "C", "D", "E")
r.plot.radial(df, legend=FALSE)
r.plot.radial(df)
r.ggplot.radial(df)
Correlation plots
Mcorr = cor(iris[,-5])
r.ggplot.corr(Mcorr)
Mcorr = cor(iris[,rep(1:4,10)])
r.ggplot.corr(Mcorr)
Mcorr = r.plot.matrix.communities(Mcorr)
r.ggplot.corr(Mcorr)
Matrix & Graph plots
data(UKfaculty, package = "igraphdata")
x = as.matrix(igraph::get.adjacency(UKfaculty))
mat = matrix(runif(400), 20)
r.plot.heatmap(matrixData=mat, contour=FALSE)
r.plot.matrix(mat)
r.plot.matrix(-mat)
r.plot.matrix(mat-0.5)
r.plot.matrix(x)
r.plot.matrix.communities(x)
r.plot.matrix(r.plot.matrix.communities(x))
r.plot.heatmap(matrixData=r.plot.matrix.communities(x), contour=FALSE)
r.plot.graph.text(x, vertexLabelCex=0.5, edgeWidthMax=1.5)
Palette
Gradient Colors
x = runif(10000)-0.5
y = runif(10000)-0.5
r.plot(x, y, col=r.color.gradient(x^2+y^2), alpha=0.4)
r.plot(x, y, col=r.color.gradient(x^2+y^2, levels=3), cex=2)
Show and Set Palette
r.palette.get()
## [1] "#6673CCA6" "#CC3333A6" "#1AE61AA6" "#E68000A6" "#CC00CCA6"
## [6] "#E0DB00A6" "#E60080A6" "#8000FFA6" "#80D900A6" "#0099E6A6"
## [11] "#666666A6" "#1A1A80A6" "#801A1AA6" "#006633A6" "#994D1AA6"
## [16] "#801A80A6" "#80801AA6" "#1A8080A6" "#00E680A6" "#333333A6"
r.palette.show()
r.palette.show(heat.colors(12))
r.palette.show(rainbow(12))
r.palette.show(r.color.gradient.palette(c("red", "blue", "green"), levels=20))
r.plot(1,1,type='p', cex=20)
r.setPalette(rev(rainbow(8)))
r.setColorAlpha(0.4)
r.plot(1,1,type='p', cex=20)
r.palette.restore()
r.plot(1,1,type='p', cex=20)
Data Analysis
Dimensionality Redution
r.plot2D.data(iris[,-5])
r.plot2D.pca(iris[,-5])
theta = runif(100)
data = rbind(
cbind(0, cos(6*pi*theta), sin(6*pi*theta)),
cbind(cos(6*pi*theta), 0, sin(6*pi*theta)),
cbind(cos(6*pi*theta), sin(6*pi*theta), 0))
r.plot2D.data(data)
r.plot2D.pca(data)
r.plot2D.nn(data)
km = kmeans(iris[,-5],3)
r.plot2D.data(iris[,-5], clustModel=km$cluster)
r.plot2D.pca(iris[,-5], clustModel=km$cluster)
r.plot2D.data(iris[,-5], clustModel=km$cluster, clustReal=iris[,5])
r.plot2D.pca(iris[,-5], clustModel=km$cluster, clustReal=iris[,5])
r.plot2D.nn(iris[,-5])
r.plot2D.nn(iris[,-5], clustModel=km$cluster)
r.plot2D.nn(iris[,-5], clustModel=km$cluster, clustReal=iris[,5])
Binning
r.plot.burbujas
rmodel::r.segment
K-means
km = kmeans(iris[,-5],3)
r.plot.kmeans.shapes(iris[,-5], km)
## K-means clustering with 3 clusters of sizes 62, 38, 50
##
## Cluster means:
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.901612903 2.748387097 4.393548387 1.433870968
## 2 6.850000000 3.073684211 5.742105263 2.071052632
## 3 5.006000000 3.428000000 1.462000000 0.246000000
##
## Clustering vector:
## [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [36] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [71] 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 2
## [106] 2 1 2 2 2 2 2 2 1 1 2 2 2 2 1 2 1 2 1 2 2 1 1 2 2 2 2 2 1 2 2 2 2 1 2
## [141] 2 2 1 2 2 2 1 2 2 1
##
## Within cluster sum of squares by cluster:
## [1] 39.82096774 23.87947368 15.15100000
## (between_SS / total_SS = 88.4 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
r.plot.kmeans.shapes(iris[,-5], km, paintCentroids=TRUE)
## K-means clustering with 3 clusters of sizes 62, 38, 50
##
## Cluster means:
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.901612903 2.748387097 4.393548387 1.433870968
## 2 6.850000000 3.073684211 5.742105263 2.071052632
## 3 5.006000000 3.428000000 1.462000000 0.246000000
##
## Clustering vector:
## [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [36] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [71] 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 2
## [106] 2 1 2 2 2 2 2 2 1 1 2 2 2 2 1 2 1 2 1 2 2 1 1 2 2 2 2 2 1 2 2 2 2 1 2
## [141] 2 2 1 2 2 2 1 2 2 1
##
## Within cluster sum of squares by cluster:
## [1] 39.82096774 23.87947368 15.15100000
## (between_SS / total_SS = 88.4 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
r.plot.kmeans.smoothshapes(iris[,-5], km)
Model performance
x=runif(1000)
y=c(round(0.8*x[1:200]+0.2*runif(200)),round(0.6*x[201:700]+0.4*runif(500)),round(runif(300)))
rmodel::r.performance.metrics(x, y)
## ACTUAL_0 ACTUAL_1 PREDICTED PRECISION
## PREDICTED_0 352 140 492 0,71545
## PREDICTED_1 128 380 508 0,74803
## ACTUAL 480 520 1.000 0,73174
## RECALL 0,73333 0,73077 0,73205 0,73200
##
## F1 phi
## 1 0,72428 0,46379
## 2 0,73930 0,46379
## 3 0,73179 0,46379
##
## score
## Accuracy 0,732
## Precision 0,74803
## Recall (sensitivity) 0,73077
## specificity 0,73333
## Score F1 0,7393
## Score G 0,73935
## Score Beta 0,7393
## Score Phi (MCC) 0,46379
r.plot.confusionmatrix(x, y)
## ACTUAL_0 ACTUAL_1 PREDICTED PRECISION
## PREDICTED_0 352 140 492 0,71545
## PREDICTED_1 128 380 508 0,74803
## ACTUAL 480 520 1.000 0,73174
## RECALL 0,73333 0,73077 0,73205 0,73200
r.plot.F1(x, y)
## F1 phi
## 1 0,72428 0,46379
## 2 0,73930 0,46379
## 3 0,73179 0,46379
r.plot.roc(x,y)
r.plot.gain(x,y)
r.plot.lift(x,y)
Interactive
r.plot(iris$Sepal.Length, iris$Sepal.Width, icol=iris[,5])
r.iplot(iris$Sepal.Length, iris$Sepal.Width, icol=iris[,5])
r.iplot.kmeans.shapes(iris[,-5])
r.iplot.smoothkmeans(iris[,-5])
r.iplot2D.data(iris[,-5], clustReal = iris[,5])
r.iplot2D.pca(iris[,-5], clustReal = iris[,5])
Data
randomTimeSeries <- function(n=20, amplitud=1, phase=2*pi*runif(1), velocity=rexp(1), noise=0.05) {
return(amplitud*sin(phase+velocity*seq(0,2*pi,length.out=n))+noise*amplitud*rnorm(n))
}
matrixTimeSeries100 = cbind(unlist(sapply(1:100, function (x) {randomTimeSeries(200,velocity=1,noise=0)})))
matrixTimeSeries5 = cbind(unlist(sapply(1:5, function (x) {randomTimeSeries(50,velocity=1,noise=0)})))
x_seq = seq(0,10,0.1)
y_sin = sin(x_seq)
y_cos = cos(x_seq)
y_exp = exp(x_seq)
x_unif = runif(500)-0.5
y_unif = runif(500)-0.5
z_unif = runif(500)-0.5
x_norm = rnorm(500)
y_norm = rnorm(500)
z_norm = rnorm(500)
vecNum = round(1+5*runif(80)^3)
vecChar = letters[sample(1:26, 100, replace = TRUE)]
table_2 = table(round(runif(100)^2), round(1-runif(100)^3))
table_3 = table(round(2*runif(120)^2), round(2-2*runif(120)^3))
rownames(table_2) = c("Tipo A", "Tipo B")
colnames(table_2) = c("Tipo A", "Tipo B")
rownames(table_3) = c("Tipo A", "Tipo B", "Tipo C")
colnames(table_3) = c("Tipo A", "Tipo B", "Tipo C")
x_gaussian = rnorm(2000)
x = runif(5000)
y = runif(5000)
z = (x-0.7)^2+(y-0.6)^2
data(volcano)
x = runif(1000)
f = cut(x, breaks = 10)
t = table(f)
segmentosNames <- names(t)
segmentosGroup <- names(t)
segmentosArea <- as.numeric(t)
segmentosColor <- runif(length(segmentosNames))
x1 <- runif(5)
x2 <- 0.2+0.6*x1+0.2*runif(5)
df <- data.frame(group = c("Blue Collar Communities", "Prospering Suburbs"), matrix(c(x1,x2), nrow = 2, byrow = TRUE))
colnames(df)[2:ncol(df)] <- c("A", "B", "C", "D", "E")
Mcorr = cor(iris[,-5])
Mcorr = cor(iris[,rep(1:4,10)])
