> 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

> mosaicplot( M, col = c(" green", "red"),main = "Husband x Wife")

> library(vcd)
> mosaic(M, shade=T,main = "Husband x Wife")

> 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)
        }
        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$path, aes(x = x, y = y, 
        group = group, colour = group), size = group.line.width)
    base <- base + geom_point(data = group$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)
}

> 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]

> 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 = '')

> 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

> 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)

> 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

> 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)

> 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)

> library(corrplot)
> data(mtcars)
> head(mtcars)
> M <- cor(mtcars)
> corrplot(M, method = "circle")

> corrplot(M, method = "square")

> corrplot(M, method = "ellipse")

> corrplot(M, method = "number")

> corrplot(M, method = "pie")

> corrplot(M, type = "upper")

> corrplot.mixed(M, lower = "ellipse", upper = "number")

> corrplot(M, order = "hclust")

> corrplot(M, order = "hclust",addrect = 3)

> mycol <- colorRampPalette(c("red", "white", "blue"))
> corrplot(M, order = "hclust",addrect = 2,col=mycol(50))

> wb <- c("white", "black")
> corrplot(M, order = "hclust",
+          addrect = 2, 
+          col = wb, bg = "gold2")

> 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))
+ }

> 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

> corrplot(M, p.mat = res1[[1]], sig.level = 0.1)

> corrplot(M, p.mat = res1[[1]], sig.level = 0.01)

> corrplot(M, p.mat = res1[[1]], sig.level = 0.01,insig = "p-value")

> corrplot(M, p.mat = res1[[1]], sig.level = 0.01,insig = "blank")

> library(xtable)
> mcor<-round(cor(mtcars),2)
> upper<-mcor
> upper[upper.tri(mcor)]<-""
> upper<-as.data.frame(upper)

> print(xtable(upper), type="html")

> # 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") 
+     }
+ } 
> 

> corstars(mtcars[,1:7], 
+          result="html")

> library(ggfortify)
> head(AirPassengers)
[1] 112 118 132 129 121 135
> class(AirPassengers)
[1] "ts"

> autoplot(AirPassengers)

> p <- autoplot(AirPassengers)
> p + ggtitle('AirPassengers') + xlab('Year') + ylab('Passengers')

> 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

> df <- iris[c(1, 2, 3, 4)]
> autoplot(prcomp(df))

> autoplot(prcomp(df), 
+          data = iris, 
+          colour = 'Species', 
+          shape = FALSE, 
+          label.size = 3, frame=T)

> autoplot(prcomp(df), data = iris, colour = 'Species',
+          loadings = TRUE, loadings.colour = 'blue',
+          loadings.label = TRUE, loadings.label.size = 3)

> 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)

> library(lfda)
> model <- lfda(x = iris[-5], y = iris[, 5], r = 3, metric="plain")
> autoplot(model, 
+          data = iris, 
+          frame = TRUE, 
+          frame.colour = 'Species')

> 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                y                z         
 Min.   :0.2000   Fair     : 1610   D: 6775   SI1    :13065   Min.   :43.00   Min.   :43.00   Min.   :  326   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
 1st Qu.:0.4000   Good     : 4906   E: 9797   VS2    :12258   1st Qu.:61.00   1st Qu.:56.00   1st Qu.:  950   1st Qu.: 4.710   1st Qu.: 4.720   1st Qu.: 2.910  
 Median :0.7000   Very Good:12082   F: 9542   SI2    : 9194   Median :61.80   Median :57.00   Median : 2401   Median : 5.700   Median : 5.710   Median : 3.530  
 Mean   :0.7979   Premium  :13791   G:11292   VS1    : 8171   Mean   :61.75   Mean   :57.46   Mean   : 3933   Mean   : 5.731   Mean   : 5.735   Mean   : 3.539  
 3rd Qu.:1.0400   Ideal    :21551   H: 8304   VVS2   : 5066   3rd Qu.:62.50   3rd Qu.:59.00   3rd Qu.: 5324   3rd Qu.: 6.540   3rd Qu.: 6.540   3rd Qu.: 4.040  
 Max.   :5.0100                     I: 5422   VVS1   : 3655   Max.   :79.00   Max.   :95.00   Max.   :18823   Max.   :10.740   Max.   :58.900   Max.   :31.800  
                                    J: 2808   (Other): 2531                                                                                                     

> require(tabplot)
> tableplot(diamonds)

> tableplot(diamonds, nBins=2,select =c(carat,color))

> 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]))

> # add some NA's
> diamonds2=diamonds
> diamonds2$price[which(diamonds2$cut == "Ideal")]<-NA
> diamonds2$cut[diamonds2$depth>65]=NA
> tableplot(diamonds2,colorNA = "black")

> tableplot(diamonds, nBins=5, select = c(carat, price, cut, color, clarity), sortCol = price, 
+     from = 0, to = 5)

> tableplot(diamonds, subset = price < 5000 & cut == "Premium")

> tableplot(diamonds, pals = list(cut="Set1(6)", color="Set5", clarity=rainbow(8)))

> # 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