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# S Analysis --------------------------------------------------------------
plotData <- ggplot_build(plot = p)
plotData <- plotData$data
plotData <- plotData[[3]] # Extract the frecuency matrix
head(plotData)
summary(plotData)
plotData[ plotData$count > 500,]
#       fill      xbin      ybin count    density ymax ymin yint xmax xmin xint PANEL group
# 69 #A0FF00 (200,250] (350,400]   617 0.05875071  400  350    8  250  200    5     1     1
# 70 #E5ED00 (250,300] (350,400]   697 0.06636831  400  350    8  300  250    6     1     1
# 79 #FF0000 (700,750] (350,400]   942 0.08969720  400  350    8  750  700   15     1     1
# 80 #50FF00 (750,800] (350,400]   537 0.05113312  400  350    8  800  750   16     1     1
# 88 #C5F800 (250,300] (400,450]   658 0.06265473  450  400    9  300  250    6     1     1
plotDataDensity <- data.frame(Square = paste0(plotData$xbin ,"-" , plotData$ybin), Count = plotData$count)
plotDataDensity$Density <- plotDataDensity$Count / sum(plotDataDensity$Count) *100
head(plotDataDensity)
summary(plotDataDensity)
plotDataDensity[ plotDataDensity$Density > 1,]
prop.test(x = c(plotDataDensity$Count[70], plotDataDensity$Count[79]), 
          n = rep(sum(plotDataDensity$Count),2) )
# 2-sample test for equality of proportions with continuity correction
# 
# data:  c(plotDataDensity$Count[70], plotDataDensity$Count[79]) out of rep(sum(plotDataDensity$Count), 2)
# X-squared = 39.399, df = 1, p-value = 3.455e-10
# alternative hypothesis: two.sided
# 95 percent confidence interval:
#   -0.03067201 -0.01598577
# sample estimates:
#   prop 1     prop 2 
# 0.06636831 0.08969720 
# E Analysis --------------------------------------------------------------