Articles - R Graphics Essentials

Prerequisites

Load required R packages and set the default theme:

library(ggplot2)
library(ggpubr)
theme_set(theme_pubr())

Bar plots of contingency tables

Demo data set: HairEyeColor (distribution of hair and eye color and sex in 592 statistics students)

• Prepare and inspect the data:

data("HairEyeColor")
df <- as.data.frame(HairEyeColor)
head(df)

##    Hair   Eye  Sex Freq
## 1 Black Brown Male   32
## 2 Brown Brown Male   53
## 3   Red Brown Male   10
## 4 Blond Brown Male    3
## 5 Black  Blue Male   11
## 6 Brown  Blue Male   50

• Create the bar graph:
  • Hair color on x-axis
  • Change bar fill by Eye color
  • Split the graph into multiple panel by Sex

ggplot(df, aes(x = Hair, y = Freq))+
  geom_bar(
    aes(fill = Eye), stat = "identity", color = "white",
    position = position_dodge(0.9)
    )+
  facet_wrap(~Sex) + 
  fill_palette("jco")

Balloon plot

Balloon plot is an alternative to bar plot for visualizing a large categorical data. We’ll use the function ggballoonplot() [in ggpubr], which draws a graphical matrix of a contingency table, where each cell contains a dot whose size reflects the relative magnitude of the corresponding component.

Demo data sets: Housetasks (a contingency table containing the frequency of execution of 13 house tasks in the couple.)

housetasks <- read.delim(
  system.file("demo-data/housetasks.txt", package = "ggpubr"),
  row.names = 1
  )
head(housetasks, 4)

##            Wife Alternating Husband Jointly
## Laundry     156          14       2       4
## Main_meal   124          20       5       4
## Dinner       77          11       7      13
## Breakfeast   82          36      15       7

• Create a simple balloon plot of a contingency table. Change the fill color by the values in the cells.

ggballoonplot(housetasks, fill = "value")+
  scale_fill_viridis_c(option = "C")

• Visualize a grouped frequency table. Demo data set: HairEyeColor. Create a multi-panel plot by Sex

df <- as.data.frame(HairEyeColor)
ggballoonplot(df, x = "Hair", y = "Eye", size = "Freq",
              fill = "Freq", facet.by = "Sex",
              ggtheme = theme_bw()) +
  scale_fill_viridis_c(option = "C")

Mosaic plot

A mosaic plot is basically an area-proportional visualization of observed frequencies, composed of tiles (corresponding to the cells) created by recursive vertical and horizontal splits of a rectangle. The area of each tile is proportional to the corresponding cell entry, given the dimensions of previous splits.

Mosaic graph can be created using either the function mosaicplot() [in graphics] or the function mosaic() [in vcd package]. Read more at: Visualizing Multi-way Contingency Tables with vcd.

Example of mosaic plot:

library(vcd)
mosaic(HairEyeColor, shade = TRUE, legend = TRUE) 

Correspondence analysis

Correspondence analysis can be used to summarize and visualize the information contained in a large contingency table formed by two categorical variables.

Required package: FactoMineR for the analysis and factoextra for the visualization

library(FactoMineR)
library(factoextra)
res.ca <- CA(housetasks, graph = FALSE)
fviz_ca_biplot(res.ca, repel = TRUE)

From the graphic above, it’s clear that:

• Housetasks such as dinner, breakfeast, laundry are done more often by the wife
• Driving and repairs are done more frequently by the husband

Read more at: Correspondence analysis in R