Hands-on_EX05b

Author

Edward

6 Visual Correlation Analysis

6.2 Installing and Launching R Packages

pacman::p_load(corrplot, ggstatsplot, tidyverse)

6.3 Importing and Preparing The Data Set

6.3.1 Importing Data

wine <- read_csv("data/wine_quality.csv")

**6.4 Building Correlation Matrix: pairs() method**

6.4.1 Building a basic correlation matrix

pairs(wine[,1:11])

pairs(wine[,2:12])

6.4.2 Drawing the lower corner

To show the lower half of the correlation matrix, the upper.panel argument will be used as shown in the code chunk below.

pairs(wine[,2:12], upper.panel = NULL)

Similarly, you can display the upper half of the correlation matrix by using the code chun below.

pairs(wine[,2:12], lower.panel = NULL)

6.4.3 Including with correlation coefficients

To show the correlation coefficient of each pair of variables instead of a scatter plot, panel.cor function will be used.

panel.cor <- function(x, y, digits=2, prefix="", cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr=usr))
par(usr = c(0, 1, 0, 1))
r <- abs(cor(x, y, use="complete.obs"))
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep="")
if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex = cex.cor * (1 + r) / 2)
}

pairs(wine[,2:12], 
      upper.panel = panel.cor)

6.5 Visualising Correlation Matrix: ggcormat()

One of the major limitation of the correlation matrix is that the scatter plots appear very cluttered when the number of observations is relatively large (i.e. more than 500 observations). To over come this problem, Corrgram data visualisation technique suggested by D. J. Murdoch and E. D. Chow (1996) and Friendly, M (2002) and will be used.

The are at least three R packages provide function to plot corrgram, they are:

6.5.1 The basic plot

ggstatsplot::ggcorrmat(
  data = wine, 
  cor.vars = 1:11,
  lab_size = 2.5, 
  sig_lab_size = 2,
  number.cex = 0.6,
  ggplot.component = list(
    theme(text=element_text(size=10),
      axis.text.x = element_text(size = 10),
      axis.text.y = element_text(size = 10))))

ggstatsplot::ggcorrmat(
  data = wine, 
  cor.vars = 1:11,
  lab_size = 2.5, 
  sig_lab_size = 2,
  number.cex = 0.7,
  ggcorrplot.args = list(outline.color = "black", 
                         hc.order = TRUE,
                         tl.cex = 6),
  title    = "Correlogram for wine dataset",
  subtitle = "Four pairs are no significant at p < 0.05",
    ggplot.component = list(
    theme(text=element_text(size=10),
      axis.text.x = element_text(size = 10),
      axis.text.y = element_text(size = 10)))
)

Things to learn from the code chunk above:

cor.vars argument is used to compute the correlation matrix needed to build the corrgram.
ggcorrplot.args argument provide additional (mostly aesthetic) arguments that will be passed to ggcorrplot::ggcorrplot function. The list should avoid any of the following arguments since they are already internally being used: corr, method, p.mat, sig.level, ggtheme, colors, lab, pch, legend.title, digits.

The sample sub-code chunk can be used to control specific component of the plot such as the font size of the x-axis, y-axis, and the statistical report.

lab_size controls correlation numbers

sig_lab_size controls significance markers

tl.cex controls variable names

ggplot.component controls only theme elements, not matrix values

ggplot.component = list(
    theme(text=element_text(size=5),
      axis.text.x = element_text(size = 8),
      axis.text.y = element_text(size = 8)))

6.6 Building multiple plots

grouped_ggcorrmat(
  data = wine,
  cor.vars = 1:11,
  lab_size = 3, 
  sig_lab_size = 2.5,
  grouping.var = type,
  type = "robust",
  p.adjust.method = "holm",
  plotgrid.args = list(ncol = 2),
  number.cex = 0.5,
  ggcorrplot.args = list(outline.color = "black", 
                         hc.order = TRUE,
                         tl.cex = 2),
  
    ggplot.component = list(
    theme(text=element_text(size=8),
      axis.text.x = element_text(size = 5),
      axis.text.y = element_text(size = 5))),
  
  
  annotation.args = list(
    tag_levels = "a",
    title = "Correlogram for wine dataset",
    subtitle = "The measures are: alcohol, sulphates, fixed acidity, citric acid, chlorides, residual sugar, density, free sulfur dioxide and volatile acidity",
    caption = "Dataset: UCI Machine Learning Repository"
  )
)

6.7 Visualising Correlation Matrix using corrplot Package

6.7.1 Getting started with corrplot

The intensity of the colour or also know as saturation is used to represent the strength of the correlation coefficient. Darker colours indicate relatively stronger linear relationship between the paired variables. On the other hand, lighter colours indicates relatively weaker linear relationship.

wine.cor <- cor(wine[, 1:11])

corrplot(wine.cor)

6.7.2 Working with visual geometrics

corrplot(wine.cor, 
         method = "ellipse")

6.7.3 Working with layout

corrplot(wine.cor, 
         method = "ellipse", 
         type="lower")

corrplot(wine.cor, 
         method = "ellipse", 
         type="lower",
         diag = FALSE,
         tl.col = "black")

6.7.4 Working with mixed layout

With corrplot package, it is possible to design corrgram with mixed visual matrix of one half and numerical matrix on the other half.

corrplot.mixed(wine.cor, 
               lower = "ellipse", 
               upper = "number",
               tl.pos = "lt",
               diag = "l",
               tl.col = "black")

6.7.5 Combining corrgram with the significant test

wine.sig = cor.mtest(wine.cor, conf.level= .95)

corrplot(wine.cor,
         method = "number",
         type = "lower",
         diag = FALSE,
         tl.col = "black",
         tl.srt = 45,
         number.cex = 0.7,
         p.mat = wine.sig$p,
         sig.level = .05)

6.7.6 Reorder a corrgram

By default, the order of attributes of a corrgram is sorted according to the correlation matrix (i.e. “original”). The default setting can be over-write by using the order argument of corrplot(). Currently, corrplot package support four sorting methods, they are:

“AOE” is for the angular order of the eigenvectors. See Michael Friendly (2002) for details.
“FPC” for the first principal component order.
“hclust” for hierarchical clustering order, and “hclust.method” for the agglomeration method to be used.
- “hclust.method” should be one of “ward”, “single”, “complete”, “average”, “mcquitty”, “median” or “centroid”.
“alphabet” for alphabetical order.

corrplot.mixed(wine.cor, 
               lower = "ellipse", 
               upper = "number",
               tl.pos = "lt",
               diag = "l",
               order="AOE",
               tl.col = "black",
               number.cex = 0.7)

6.7.7 Reordering a correlation matrix using hclust

corrplot(wine.cor, 
         method = "ellipse", 
         tl.pos = "lt",
         tl.col = "black",
         order="hclust",
         hclust.method = "ward.D",
         addrect = 3)