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Mosaic Chart Step by Step Tutorial

library("vcd")
library("scatterplot3d")
library(colorspace)
## random seed
myseed <- 1071
set.seed(myseed)

Data

data("Hospital")
data("Arthritis")
art <- xtabs(~ Treatment + Improved, data = Arthritis, subset = Sex == "Female")
names(dimnames(art))[2] <- "Improvement"
data("UCBAdmissions")
names(dimnames(UCBAdmissions)) <- c("Admission", "Gender", "Department")
data("Punishment")
punish <- xtabs(Freq ~ memory + attitude + age + education, data = Punishment)
dimnames(punish)[[3]][3] <- "40+"

Inference

set.seed(myseed)
art_max <- coindep_test(art, n = 5000)

set.seed(myseed)
ucb_max <- coindep_test(aperm(UCBAdmissions, c(3, 2, 1)), margin = "Department", n = 5000)
set.seed(myseed)
art_chisq <- coindep_test(art, n = 5000, indepfun = function(x) sum(x^2))
set.seed(myseed)
hos_chisq <- coindep_test(Hospital, n = 5000, indepfun = function(x) sum(x^2))

Figures

Figure 12.1: Grouped bar chart for the hospital data

barplot(Hospital,
  legend = rownames(Hospital), beside = TRUE,
  xlab = "Length of stay", ylab = "Number of patients"
)

###################################################
### Figure 12.2: 3D bar chart for the hospital data
###################################################
myHospital <- t(Hospital)[, 3:1]
mydat <- data.frame(
  "Length of stay" = as.vector(row(myHospital)),
  "Visit frequency" = as.vector(col(myHospital)),
  "Number of patients" = as.vector(myHospital)
)
scatterplot3d(mydat,
  type = "h", pch = " ", lwd = 10,
  x.ticklabs = c("2-9", "", "10-19", "", "20+"),
  y.ticklabs = c("Never", "", "Less than monthly", "", "Regular"),
  xlab = "Length of stay", ylab = "Visit frequency", zlab = "Number of patients",
  y.margin.add = 0.2,
  color = "black", box = FALSE
)

############################################################################
### Figure 12.3: Construction of a mosaic plot for the hospital data, step 1
############################################################################
mosaic(margin.table(Hospital, 2), split = TRUE)

Figure 12.4: Construction of a mosaic plot for the hospital data, step 2

mosaic(t(Hospital),
  split = TRUE, mar = c(left = 3.5),
  labeling_args = list(
    offset_labels = c(left = 0.5),
    offset_varnames = c(left = 1, top = 0.5), set_labels =
      list("Visit frequency" = c("Regular", "Less than\nmonthly", "Never"))
  )
)

###
### Figure 12.5: Mosaic plot for the Hospital data, alternative splitting

mosaic(Hospital)

Figure 12.6: Sieve plot for the hospital data

sieve(t(Hospital), split = TRUE, pop = FALSE, gp = gpar(lty = "dotted", col = "black"))
labeling_cells(text = t(Hospital), clip = FALSE, gp = gpar(fontface = 2, fontsize = 15))(t(Hospital))

Figure 12.7: Association plot for the hospital data

assoc(t(Hospital), split = TRUE)

Figure 12.8: Qualitative color palette for HSV and HCL space

par(mfrow = c(1, 2), mar = c(1, 1, 1, 1), oma = c(0, 0, 0, 0))
pie(rep(1, 9), radius = 1, col = rainbow(9), labels = 360 * 0:8 / 9)
pie(rep(1, 9), radius = 1, col = rainbow_hcl(9), labels = 360 * 0:8 / 9)

Figure 12.9: Diverging color palettes for HSV and HCL space

par(mfrow = c(1, 1), mar = c(1, 1, 1, 1), oma = c(0, 0, 0, 0))
plot.new()
rect(0:4 / 5, 0.2, 1:5 / 5, 0.5, border = 0, col = diverge_hcl(5))
rect(0, 0.2, 1, 0.5, border = 1, col = NULL)
rect(0:4 / 5, 0.55, 1:5 / 5, 0.85, border = 0, col = diverge_hsv(5))
rect(0, 0.55, 1, 0.85, border = 1, col = NULL)
text(c(1:5 / 5 - 0.1), 0.11, c("(260, 100, 50)", "(260, 50, 70)", "(H, 0, 90)", "(0, 50, 70)", "(0, 100, 50)"))
text(c(1:5 / 5 - 0.1), 0.91, c("(240, 100, 100)", "(240, 50, 100)", "(H, 0, 100)", "(0, 50, 100)", "(0, 100, 100)"))

Figure 12.10: Spine plot with highlighting for the hospital data

mycol <- rep(grey.colors(2)[2:1], 1:2)
mosaic(t(Hospital),
  mar = c(left = 3.5),
  labeling_args = list(
    offset_labels = c(left = 0.5),
    offset_varnames = c(left = 1, top = 0.5), set_labels =
      list("Visit frequency" = c("Regular", "Less than\nmonthly", "Never"))
  ),
  split = TRUE, highlighting = 2, gp = gpar(fill = mycol, col = mycol)
)

Figure 12.11: Mosaic display of the hospital data with Friendly-like

### color coding of the residuals

mosaic(t(Hospital),
  split = TRUE, shade = TRUE,
  mar = c(left = 3.5),
  gp_args = list(p.value = hos_chisq$p.value),
  labeling_args = list(
    offset_labels = c(left = 0.5),
    offset_varnames = c(left = 1, top = 0.5), set_labels =
      list("Visit frequency" = c("Regular", "Less than\nmonthly", "Never"))
  )
)

Figure 12.12: Association plot of the hospital data with

### Friendly-like color coding of the residuals
##########################################################################
assoc(t(Hospital),
  split = TRUE, shade = TRUE, keep = TRUE,
  gp_args = list(p.value = hos_chisq$p.value)
)

Figure 12.13: Mosaic plot for the arthritis data, using the chi-squared test and fixed cut-off points for the shading

###########################################################################
mosaic(art, shade = TRUE, gp_args = list(p.value = art_chisq$p.value))

Figure 12.14: Mosaic plot for the arthritis data, using the maximum test and data-driven cut-off points for the residuals

set.seed(myseed)
mosaic(art, gp = shading_max, gp_args = list(n = 5000))

Figure 12.15: Pairs-plot for the UCB admissions data

pairs(UCBAdmissions)

Figure 12.16: Doubledecker plot for the UCB admissions data

doubledecker(aperm(aperm(UCBAdmissions, c(1, 3, 2))[2:1, , ], c(2, 3, 1)),
  margins = c(left = 0, right = 5), col = rev(grey.colors(2))
)

Figure 12.17: Mosic plot for the UCB admissions data

mosaic(aperm(UCBAdmissions, c(3, 2, 1)),
  data = UCBAdmissions, split = TRUE,
  shade = TRUE, keep = FALSE, residuals = ucb_max$residuals,
  gp_args = list(p.value = ucb_max$p.value), rep = c(Admission = FALSE)
)

Figure 12.18: Association plot for the UCB admissions data

assoc(aperm(UCBAdmissions, c(3, 2, 1)),
  split = c(TRUE, FALSE, TRUE), shade = TRUE,
  residuals_type = "Pearson", residuals = ucb_max$residuals,
  gp_args = list(p.value = ucb_max$p.value), rep = c(Admission = FALSE)
)

Figure 12.19: Conditional association plot for the UCB admissions data

cotabplot(aperm(UCBAdmissions, c(2, 1, 3)),
  panel = cotab_coindep, shade = TRUE,
  legend = FALSE,
  panel_args = list(type = "assoc", margins = c(2, 1, 1, 2), varnames = FALSE)
)

### Figure 12.20: Mosic plot with highlighting for the punishment data
mosaic(~ age + education + memory + attitude,
  data = punish, keep = FALSE,
  gp = gpar(fill = grey.colors(2)), spacing = spacing_highlighting,
  rep = c(attitude = FALSE)
)

Figure 12.21: Conditional mosaic plot for the punishment data

set.seed(123)
cotabplot(punish, panel = cotab_coindep, panel_args = list(varnames = FALSE, margins = c(2, 1, 1, 2)))

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