Data Visualization with R Models and relationship between ...€¦ · Outline 2 Qualitative...
Transcript of Data Visualization with R Models and relationship between ...€¦ · Outline 2 Qualitative...
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Data Visualization with R, Models andrelationship between variables
Dhafer Malouche
http://dhafermalouche.net
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Outline
2 Qualitative variables
2 Quantitative variables, corrplot package
ggfortify package, visualizing models
tabplot package
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2 Qualitative variables
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Contingency table> require(graphics)
> M <- as.table( cbind( c( 18,28,14), c( 20,51,28) , c( 12,25,9)))
> dimnames( M) <- list( Husband = c(" Tall", "Medium", "Short"), Wife = c(" Tall"," Medium", "Short"))
> M
Wife
Husband Tall Medium Short
Tall 18 20 12
Medium 28 51 25
Short 14 28 9
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Mosaic plot> mosaicplot( M, col = c(" green", "red"),main = "Husband x Wife")
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Mosaic plot
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Mosaic plot> library(vcd)
> mosaic(M, shade=T,main = "Husband x Wife")
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Mosaic plot
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Radial or Radar chartsRadial or Radar charts are
called Spider or Web or Polar charts.
a way of comparing multiple quantitative variables.
are also useful for seeing which variables are scoring high or low within a dataset.
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Method 1We use the following R function
> source('CreatRadialPlot.R') > CreateRadialPlot function (plot.data, axis.labels = colnames(plot.data)[-1], grid.min = -0.5, grid.mid = 0, grid.max = 0.5, centre.y = grid.min - ((1/9) * (grid.max - grid.min)), plot.extent.x.sf = 1.2, plot.extent.y.sf = 1.2, x.centre.range = 0.02 * (grid.max - centre.y), label.centre.y = FALSE, grid.line.width = 0.5, gridline.min.linetype = "longdash", gridline.mid.linetype = "longdash", gridline.max.linetype = "longdash", gridline.min.colour = "grey", gridline.mid.colour = "blue", gridline.max.colour = "grey", grid.label.size = 4, gridline.label.offset = -0.02 * (grid.max - centre.y), label.gridline.min = TRUE, axis.label.offset = 1.15, axis.label.size = 3, axis.line.colour = "grey", group.line.width = 1, group.point.size = 4, background.circle.colour = "yellow", background.circle.transparency = 0.2, plot.legend = if (nrow(plot.data) > 1) TRUE else FALSE, legend.title = "Cluster", legend.text.size = grid.label.size) { var.names <- colnames(plot.data)[-1] plot.extent.x = (grid.max + abs(centre.y)) * plot.extent.x.sf plot.extent.y = (grid.max + abs(centre.y)) * plot.extent.y.sf if (length(axis.labels) != ncol(plot.data) - 1) return("Error: 'axis.labels' contains the wrong number of axis labels") if (min(plot.data[, -1]) < centre.y) return("Error: plot.data' contains value(s) < centre.y") if (max(plot.data[, -1]) > grid.max) return("Error: 'plot.data' contains value(s) > grid.max") CalculateGroupPath <- function(df) { path <- as.factor(as.character(df[, 1])) angles = seq(from = 0, to = 2 * pi, by = (2 * pi)/(ncol(df) - 1)) graphData = data.frame(seg = "", x = 0, y = 0) graphData = graphData[-1, ] for (i in levels(path)) { pathData = subset(df, df[, 1] == i) for (j in c(2:ncol(df))) { graphData = rbind(graphData, data.frame(group = i, x = pathData[, j] * sin(angles[j - 1]), y = pathData[, j] * cos(angles[j - 1]))) } graphData = rbind(graphData, data.frame(group = i, x = pathData[, 2] * sin(angles[1]), y = pathData[, 2] * cos(angles[1]))) } colnames(graphData)[1] <- colnames(df)[1] graphData } CaclulateAxisPath = function(var.names, min, max) { n.vars <- length(var.names) angles <- seq(from = 0, to = 2 * pi, by = (2 * pi)/n.vars) min.x <- min * sin(angles) min.y <- min * cos(angles) max.x <- max * sin(angles)
max.y <- max * cos(angles) axisData <- NULL for (i in 1:n.vars) { a <- c(i, min.x[i], min.y[i]) b <- c(i, max.x[i], max.y[i]) axisData <- rbind(axisData, a, b) }
i " i " " " " "
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colnames(axisData) <- c("axis.no", "x", "y") rownames(axisData) <- seq(1:nrow(axisData)) as.data.frame(axisData) } funcCircleCoords <- function(center = c(0, 0), r = 1, npoints = 100) { tt <- seq(0, 2 * pi, length.out = npoints) xx <- center[1] + r * cos(tt) yy <- center[2] + r * sin(tt) return(data.frame(x = xx, y = yy)) } plot.data.offset <- plot.data plot.data.offset[, 2:ncol(plot.data)] <- plot.data[, 2:ncol(plot.data)] + abs(centre.y) group <- NULL group$path <- CalculateGroupPath(plot.data.offset) axis <- NULL axis$path <- CaclulateAxisPath(var.names, grid.min + abs(centre.y), grid.max + abs(centre.y)) axis$label <- data.frame(text = axis.labels, x = NA, y = NA) n.vars <- length(var.names) angles = seq(from = 0, to = 2 * pi, by = (2 * pi)/n.vars) axis$label$x <- sapply(1:n.vars, function(i, x) { ((grid.max + abs(centre.y)) * axis.label.offset) * sin(angles[i]) }) axis$label$y <- sapply(1:n.vars, function(i, x) { ((grid.max + abs(centre.y)) * axis.label.offset) * cos(angles[i]) }) gridline <- NULL gridline$min$path <- funcCircleCoords(c(0, 0), grid.min + abs(centre.y), npoints = 360) gridline$mid$path <- funcCircleCoords(c(0, 0), grid.mid + abs(centre.y), npoints = 360) gridline$max$path <- funcCircleCoords(c(0, 0), grid.max + abs(centre.y), npoints = 360) gridline$min$label <- data.frame(x = gridline.label.offset, y = grid.min + abs(centre.y), text = as.character(grid.min)) gridline$max$label <- data.frame(x = gridline.label.offset, y = grid.max + abs(centre.y), text = as.character(grid.max)) gridline$mid$label <- data.frame(x = gridline.label.offset, y = grid.mid + abs(centre.y), text = as.character(grid.mid)) theme_clear <- theme_bw() + theme(legend.position = "bottom", axis.text.y = element_blank(), axis.text.x = element_blank(), axis.ticks = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_blank(), legend.key = element_rect(linetype = "blank")) if (plot.legend == FALSE) theme_clear <- theme_clear + theme(legend.position = "none") base <- ggplot(axis$label) + xlab(NULL) + ylab(NULL) + coord_equal() + geom_text(data = subset(axis$label, axis$label$x < (-x.centre.range)), aes(x = x, y = y, label = text), size = axis.label.size, hjust = 1) + scale_x_continuous(limits = c(-plot.extent.x, plot.extent.x)) + scale_y_continuous(limits = c(-plot.extent.y, plot.extent.y)) base <- base + geom_text(data = subset(axis$label, abs(axis$label$x) <= x.centre.range), aes(x = x, y = y, label = text), size = axis.label.size, hjust = 0.5) base <- base + geom_text(data = subset(axis$label, axis$label$x > x.centre.range), aes(x = x, y = y, label = text), size = axis.label.size, hjust = 0)
base <- base + theme_clear base <- base + geom_polygon(data = gridline$max$path, aes(x, y), fill = background.circle.colour, alpha = background.circle.transparency) base <- base + geom_path(data = axis$path, aes(x = x, y = y, group = axis.no), colour = axis.line.colour) base <- base + geom_path(data = group$path, aes(x = x, y = y, group = group, colour = group), size = group.line.width)
i $
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base <- base + geom_point(data = group$path, aes(x = x, y = y, group = group, colour = group), size = group.point.size) if (plot.legend == TRUE) base <- base + labs(colour = legend.title, size = legend.text.size) base <- base + geom_path(data = gridline$min$path, aes(x = x, y = y), lty = gridline.min.linetype, colour = gridline.min.colour, size = grid.line.width) base <- base + geom_path(data = gridline$mid$path, aes(x = x, y = y), lty = gridline.mid.linetype, colour = gridline.mid.colour, size = grid.line.width) base <- base + geom_path(data = gridline$max$path, aes(x = x, y = y), lty = gridline.max.linetype, colour = gridline.max.colour, size = grid.line.width) if (label.gridline.min == TRUE) { base <- base + geom_text(aes(x = x, y = y, label = text), data = gridline$min$label, fontface = "bold", size = grid.label.size, hjust = 1) } base <- base + geom_text(aes(x = x, y = y, label = text), data = gridline$mid$label, fontface = "bold", size = grid.label.size, hjust = 1) base <- base + geom_text(aes(x = x, y = y, label = text), data = gridline$max$label, fontface = "bold", size = grid.label.size, hjust = 1) if (label.centre.y == TRUE) { centre.y.label <- data.frame(x = 0, y = 0, text = as.character(centre.y)) base <- base + geom_text(aes(x = x, y = y, label = text), data = centre.y.label, fontface = "bold", size = grid.label.size, hjust = 0.5) } return(base) }
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ExampleSchool dropout in Tunisia
> library(ggplot2) > source('CreatRadialPlot.R') > df <- read.csv("drop_out_school_tunisia.csv") > df X group bizerte siliana monastir mahdia tunis sfax National 1 xFemale Female 7.575758 5.952381 11.83432 6.569343 12.328767 3.960396 8.253968 2 xmale Male 14.285714 6.363636 23.00000 6.206897 6.024096 5.343511 11.473272 3 xAll All 11.585366 6.185567 17.88618 6.382979 8.974359 4.741379 10.021475 > df <- df[,-1]
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Example> CreateRadialPlot(df,grid.label.size = 5, + axis.label.size = 4,group.line.width = 2, + plot.extent.x.sf = 1.5, + background.circle.colour = 'gray', + grid.max = 26, + grid.mid = round(df[3,8],1), + grid.min = 4.5, + axis.line.colour = 'black', + legend.title = '')
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Example
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Method 2 with fmsb package> library(fmsb) > > # Create data: note in High school for several students > set.seed(99) > data=as.data.frame(matrix( sample( 0:20 , 15 , replace=F) , ncol=5)) > colnames(data)=c("math" , "english" , "biology" , + "music" , "R-coding" ) > rownames(data)=paste("mister" , letters[1:3] , sep="-") > # We add 2 lines to the dataframe: the max and min of each > # topic to show on the plot! > data=rbind(rep(20,5) , rep(0,5) , data)
> data math english biology music R-coding 1 20 20 20 20 20 2 0 0 0 0 0 mister-a 12 17 10 19 1 mister-b 2 9 4 6 16 mister-c 13 15 18 5 20
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Radar> colors_border=c( rgb(0.2,0.5,0.5,0.9), + rgb(0.8,0.2,0.5,0.9) , + rgb(0.7,0.5,0.1,0.9) ) > colors_in=c( rgb(0.2,0.5,0.5,0.4), + rgb(0.8,0.2,0.5,0.4) , + rgb(0.7,0.5,0.1,0.4) ) > radarchart( data , axistype=1 , + #custom polygon + pcol=colors_border , pfcol=colors_in , plwd=4 , plty=1, + #custom the grid + cglcol="grey", cglty=1, axislabcol="grey", + caxislabels=seq(0,20,5), cglwd=0.8, + #custom labels + vlcex=0.8 + ) > legend(x=0.7, y=1, + legend = rownames(data[-c(1,2),]), + bty = "n", pch=20 , + col=colors_in , text.col = "grey", cex=1.2, pt.cex=3)
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Radar
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googleVis package
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Preparing data> library(googleVis) > op <- options(gvis.plot.tag="chart") > df1=t(df[,-1]) > df1=cbind.data.frame(colnames(df[,-1]),df1) > > colnames(df1)=c("Gouvernorat","Female","Male","All") > df1[,-1]=round(df1[,-1],2) > df1 Gouvernorat Female Male All bizerte bizerte 7.58 14.29 11.59 siliana siliana 5.95 6.36 6.19 monastir monastir 11.83 23.00 17.89 mahdia mahdia 6.57 6.21 6.38 tunis tunis 12.33 6.02 8.97 sfax sfax 3.96 5.34 4.74 National National 8.25 11.47 10.02
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Preparing data> Bar <- gvisBarChart(df1,xvar="Gouvernorat", + yvar=c("Female","Male","All"), + options=list(width=1250, height=700, + title="Drop out School Rate", + titleTextStyle="{color:'red',fontName:'Courier',fontSize:16}", + bar="{groupWidth:'100%'}", + hAxis="{format:'#,#%'}")) > plot(Bar)
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Preparing data
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With some options> Bar <- gvisLineChart(df1[,-4],xvar="Gouvernorat", + options=list(width=1250, height=700, + title="Drop out School Rate", + titleTextStyle="{color:'red',fontName:'Courier',fontSize:16}", + bar="{groupWidth:'100%'}", + vAxis="{format:'#,#%'}")) > plot(Bar)
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With some options
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2 Quantitative variables, corrplotpackage
Displaying correlation matrix
CI of correlations
Test of Independences between a set of variables
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With circles> library(corrplot) > data(mtcars) > head(mtcars) > M <- cor(mtcars) > corrplot(M, method = "circle")
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With circles
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With squares> corrplot(M, method = "square")
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With squares
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With ellipses> corrplot(M, method = "ellipse")
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With ellipses
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With numbers> corrplot(M, method = "number")
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With numbers
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With pies> corrplot(M, method = "pie")
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With pies
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Only upper matrix
> corrplot(M, type = "upper")
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Only upper matrix
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Ellipses and numbers> corrplot.mixed(M, lower = "ellipse", upper = "number")
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Ellipses and numbers
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Reordering variablesCharacter, the ordering method of the correlation matrix.
“original” for original order (default).
“AOE” for the angular order of the eigenvectors.
“FPC” for the first principal component order.
“hclust” for the hierarchical clustering order.
“alphabet” for alphabetical order.
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h-clust> corrplot(M, order = "hclust")
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h-clust
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Showing clusters with rectangles> corrplot(M, order = "hclust",addrect = 3)
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Showing clusters with rectangles
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Customizing the plot
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Colors> mycol <- colorRampPalette(c("red", "white", "blue")) > corrplot(M, order = "hclust",addrect = 2,col=mycol(50))
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Colors
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Background> wb <- c("white", "black") > corrplot(M, order = "hclust", + addrect = 2, + col = wb, bg = "gold2")
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Background
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An R code for Independence hypothesistesting
> cor.mtest <- function(mat, conf.level = 0.95) { + mat <- as.matrix(mat) + n <- ncol(mat) + p.mat <- lowCI.mat <- uppCI.mat <- matrix(NA, n, n) + diag(p.mat) <- 0 + diag(lowCI.mat) <- diag(uppCI.mat) <- 1 + for (i in 1:(n - 1)) { + for (j in (i + 1):n) { + tmp <- cor.test(mat[, i], mat[, j], conf.level = conf.level) + p.mat[i, j] <- p.mat[j, i] <- tmp$p.value + lowCI.mat[i, j] <- lowCI.mat[j, i] <- tmp$conf.int[1] + uppCI.mat[i, j] <- uppCI.mat[j, i] <- tmp$conf.int[2] + } + } + return(list(p.mat, lowCI.mat, uppCI.mat)) + }
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Independence hypothesis testing> res1 <- cor.mtest(mtcars, 0.95) > res1[[1]][1:3,1:3] [,1] [,2] [,3] [1,] 0.000000e+00 6.112687e-10 9.380327e-10 [2,] 6.112687e-10 0.000000e+00 1.802838e-12 [3,] 9.380327e-10 1.802838e-12 0.000000e+00 > res1[[2]][1:3,1:3] [,1] [,2] [,3] [1,] 1.0000000 -0.9257694 -0.9233594 [2,] -0.9257694 1.0000000 0.8072442 [3,] -0.9233594 0.8072442 1.0000000
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Adding p-values> corrplot(M, p.mat = res1[[1]], sig.level = 0.1)
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Non-significant independencecorrelations with a X
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Or
> corrplot(M, p.mat = res1[[1]], sig.level = 0.01)
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Or
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Writing p-values> corrplot(M, p.mat = res1[[1]], sig.level = 0.01,insig = "p-value")
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Writing p-values
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Displaying white squares instead of p-values
> corrplot(M, p.mat = res1[[1]], sig.level = 0.01,insig = "blank")
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Displaying squares instead of p-values
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Use xtable R package to display nicecorrelation table in html format
> library(xtable) > mcor<-round(cor(mtcars),2) > upper<-mcor > upper[upper.tri(mcor)]<-"" > upper<-as.data.frame(upper)
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Use xtable R package to display nicecorrelation table in html format
> print(xtable(upper), type="html")
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Use xtable R package to display nicecorrelation table in html format
mpg cyl disp hp drat wt qsec vs am gear carbmpg 1
cyl -0.85 1
disp -0.85 0.9 1
hp -0.78 0.83 0.79 1
drat 0.68 -0.7 -0.71 -0.45 1
wt -0.87 0.78 0.89 0.66 -0.71 1
qsec 0.42 -0.59 -0.43 -0.71 0.09 -0.17 1
vs 0.66 -0.81 -0.71 -0.72 0.44 -0.55 0.74 1
am 0.6 -0.52 -0.59 -0.24 0.71 -0.69 -0.23 0.17 1
gear 0.48 -0.49 -0.56 -0.13 0.7 -0.58 -0.21 0.21 0.79 1
carb -0.55 0.53 0.39 0.75 -0.09 0.43 -0.66 -0.57 0.06 0.27 1
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Combine matrix of correlationcoefficients and significance levels
We use corstar function
> # x is a matrix containing the data > # method : correlation method. "pearson"" or "spearman"" is supported > # removeTriangle : remove upper or lower triangle > # results : if "html" or "latex" > # the results will be displayed in html or latex format > corstars <-function(x, method=c("pearson", "spearman"),
+ removeTriangle=c("upper", "lower"),
+ result=c("none", "html", "latex")){
+ #Compute correlation matrix + require(Hmisc)
+ x <- as.matrix(x)
+ correlation_matrix<-rcorr(x, type=method[1])
+ R <- correlation_matrix$r # Matrix of correlation coeficients + p <- correlation_matrix$P # Matrix of p-value +
+ # Define notions for significance levels; spacing is important. + mystars <- ifelse(p < .0001, "****", ifelse(p < .001, "*** ", ifelse(p < .01, "** ", ifelse(p < .05, "* ", " "
+
+ # trunctuate the correlation matrix to two decimal + R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1]
+
+ # build a new matrix that includes the correlations with their apropriate stars + Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x))
+ diag(Rnew) <- paste(diag(R), " ", sep="")
+ rownames(Rnew) <- colnames(x)
+ colnames(Rnew) <- paste(colnames(x), "", sep="")
+
+ # remove upper triangle of correlation matrix + if(removeTriangle[1]=="upper"){
+ Rnew <- as.matrix(Rnew)
+ Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
+ Rnew <- as.data.frame(Rnew)
+ }
+
+ # remove lower triangle of correlation matrix + else if(removeTriangle[1]=="lower"){
+ Rnew <- as.matrix(Rnew)
+ Rnew[lower.tri(Rnew, diag = TRUE)] <- ""
+ Rnew <- as.data.frame(Rnew)
+ }
+
+ # remove last column and return the correlation matrix + Rnew <- cbind(Rnew[1:length(Rnew)-1])
+ if (result[1]=="none") return(Rnew)
+ else{
+ if(result[1]=="html") print(xtable(Rnew), type="html")
+ else print(xtable(Rnew), type="latex")
+ }
+ }
>
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Combine matrix of correlationcoefficients and significance levels
> corstars(mtcars[,1:7], + result="html")
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Combine matrix of correlationcoefficients and significance levels
mpg cyl disp hp drat wtmpg
cyl -0.85****
disp -0.85**** 0.90****
hp -0.78**** 0.83**** 0.79****
drat 0.68**** -0.70**** -0.71**** -0.45**
wt -0.87**** 0.78**** 0.89**** 0.66**** -0.71****
qsec 0.42* -0.59*** -0.43* -0.71**** 0.09 -0.17
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My Shiny app: Visulazing Correlationmatrix
https://dhafer.shinyapps.io/CorrMatrixViz
Correlation Matrix: Visualization and Independence testsUpload your CSV File
Variables to use:
Browse...Visualization Pearson-Indepence Test
Active Data
Single represensation Mixed represensation
Customizingthe graphCorrelation type
Method
Type
Ordering
Position of text
labels.
pearson
circle
full
original
left and top
No file selected
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ggfortify package, visualizing models
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Time series> library(ggfortify) > head(AirPassengers) [1] 112 118 132 129 121 135 > class(AirPassengers) [1] "ts"
> autoplot(AirPassengers)
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Time serie
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Times series, customizing> p <- autoplot(AirPassengers) > p + ggtitle('AirPassengers') + xlab('Year') + ylab('Passengers')
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Times series, customizing
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Clustering> set.seed(1) > head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa
> p <- autoplot(kmeans(iris[-5], 3), data = iris) > p
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Clustering
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PCA> df <- iris[c(1, 2, 3, 4)] > autoplot(prcomp(df))
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PCA
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PCA, by showing groups! Convexes> autoplot(prcomp(df), + data = iris, + colour = 'Species', + shape = FALSE, + label.size = 3, frame=T)
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PCA, by showing groups! Convexes
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Biplot for a PCA> autoplot(prcomp(df), data = iris, colour = 'Species', + loadings = TRUE, loadings.colour = 'blue', + loadings.label = TRUE, loadings.label.size = 3)
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Biplot for a PCA
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Regression diagnostic> m <- lm(Petal.Width ~ Petal.Length, data = iris) > autoplot(m, which = 1:6, colour = 'dodgerblue3', + smooth.colour = 'black', + smooth.linetype = 'dashed', + ad.colour = 'blue', + label.size = 3, label.n = 5, label.colour = 'blue', + ncol = 3)
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Regression diagnostic
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Local Fisher Discriminant Analysis> library(lfda) > model <- lfda(x = iris[-5], y = iris[, 5], r = 3, metric="plain") > autoplot(model, + data = iris, + frame = TRUE, + frame.colour = 'Species')
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Local Fisher Discriminant Analysis
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tabplot package
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Data> require(ggplot2)
> data(diamonds)
> head(diamonds)
# A tibble: 6 x 10 carat cut color clarity depth table price x y z
<dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.230 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43
2 0.210 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31
3 0.230 Good E VS1 56.9 65.0 327 4.05 4.07 2.31
4 0.290 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63
5 0.310 Good J SI2 63.3 58.0 335 4.34 4.35 2.75
6 0.240 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48
> summary(diamonds)
carat cut color clarity depth table price x
Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065 Min. :43.00 Min. :43.00 Min. : 326 Min. : 0.0
1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258 1st Qu.:61.00 1st Qu.:56.00 1st Qu.: 950 1st Qu.: 4.7
Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194 Median :61.80 Median :57.00 Median : 2401 Median : 5.7
Mean :0.7979 Premium :13791 G:11292 VS1 : 8171 Mean :61.75 Mean :57.46 Mean : 3933 Mean : 5.7
3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066 3rd Qu.:62.50 3rd Qu.:59.00 3rd Qu.: 5324 3rd Qu.: 6.5
Max. :5.0100 I: 5422 VVS1 : 3655 Max. :79.00 Max. :95.00 Max. :18823 Max. :10.7
J: 2808 (Other): 2531
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Exploring Data> require(tabplot) > tableplot(diamonds)
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Exploring Data
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Exploring Data, how it works?> tableplot(diamonds, nBins=2,select =c(carat,color))
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Exploring Data, how it works?
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Exploring Data, how it works?> x=tableplot(diamonds, nBins=2,select =c(carat,color),decreasing = T) > names(x) > x$columns$carat$mean > z=sort(diamonds$carat,d=T) > dim(diamonds) > mean(z[1:26970]) > mean(z[26971:53940]) > x$columns$color$widths > y=diamonds$color[order(diamonds$carat,decreasing = T)] > prop.table(table(y[1:26940])) > prop.table(table(y[26971:53940]))
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Exploring Data, how it works?
[1] "dataset" "select" "subset" "nBins" "binSizes" "sortCol" "decreasing" "fro
[1] 1.1723063 0.4235732
[1] 53940 10
[1] 1.172306
[1] 0.4235732
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 0.09492028 0.1389692 0.1665925 0.2012236 0.1852799 0.13388951 0.07912495 0
[2,] 0.15628476 0.2242862 0.1872080 0.2174638 0.1226177 0.06714868 0.02499073 0
D E F G H I J
0.09561990 0.13938382 0.16674091 0.20100223 0.18511507 0.13333333 0.07880475
D E F G H I J
0.15550612 0.22376715 0.18694846 0.21757508 0.12298851 0.06781609 0.02539859
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Missing values> # add some NA's > diamonds2=diamonds > diamonds2$price[which(diamonds2$cut == "Ideal")]<-NA > diamonds2$cut[diamonds2$depth>65]=NA > tableplot(diamonds2,colorNA = "black")
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Missing values
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Zooming on data,> tableplot(diamonds, nBins=5, select = c(carat, price, cut, color, clarity), sortCol = price, + from = 0, to = 5)
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Zooming on data,
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Filtering data> tableplot(diamonds, subset = price < 5000 & cut == "Premium")
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Filtering data
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Change colors> tableplot(diamonds, pals = list(cut="Set1(6)", color="Set5", clarity=rainbow(8)))
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Change colors
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Preprocessing of Large data> # create large dataset > large_diamonds <- diamonds[rep(seq.int(nrow(diamonds)), 10),] > > system.time({ + p <- tablePrepare(large_diamonds) + }) user system elapsed 1.287 0.758 2.301