In-Class Excerise 5

Published

February 11, 2023

Modified

March 11, 2023

Installing and Launching R Packages

pacman::p_load(seriation, dendextend, heatmaply, GGally, parallelPlot, tidyverse)

Importing and Preparing The Data

Importing data set

wh <- read_csv("data/WHData-2018.csv")

Preparing the data

row.names(wh) <- wh$Country

Transforming the data frame into matrix

wh1 <- dplyr::select(wh, c(3, 7:12))
wh_matrix <- data.matrix(wh)

Heatmap

Plotting static heatmap

wh_heatmap <- heatmap(wh_matrix,
                      scale="column",
                      cexRow = 0.6, 
                      cexCol = 0.8,
                      margins = c(10, 4))

Creating interactive heatmap

heatmaply(wh_matrix[, -c(1, 2, 4, 5)],
          scale = "column")

Clustering

Plotting heatmap by using hierachical clustering algorithm with “Euclidean distance” and “ward.D” method.

heatmaply(normalize(wh_matrix[, -c(1, 2, 4, 5)]),
          dist_method = "euclidean",
          hclust_method = "ward.D")

dend_expend() will be used to determine the recommended clustering method to be used.

wh_d <- dist(normalize(wh_matrix[, -c(1, 2, 4, 5)]), method = "euclidean")
dend_expend(wh_d)[[3]]
  dist_methods hclust_methods     optim
1      unknown         ward.D 0.6137851
2      unknown        ward.D2 0.6289186
3      unknown         single 0.4774362
4      unknown       complete 0.6434009
5      unknown        average 0.6701688
6      unknown       mcquitty 0.5020102
7      unknown         median 0.5901833
8      unknown       centroid 0.6338734

find_k() is used to determine the optimal number of cluster

wh_clust <- hclust(wh_d, method = "average")
num_k <- find_k(wh_clust)
plot(num_k)

Visualising the clustering

heatmaply(normalize(wh_matrix[, -c(1, 2, 4, 5)]),
          dist_method = "euclidean",
          hclust_method = "average",
          k_row = 3)

Using Blue color paletter of rColorBrewer

heatmaply(normalize(wh_matrix[, -c(1, 2, 4, 5)]),
          seriate = "none",
          colors = Blues)

Adding plotting features to ensure cartographic quality:

  • k_row is used to produce 5 groups.

  • margins is used to change the top margin to 60 and row margin to 200.

  • fontsizw_row and fontsize_col are used to change the font size for row and column labels to 4.

  • main is used to write the main title of the plot.

  • xlab and ylab are used to write the x-axis and y-axis labels respectively.

heatmaply(normalize(wh_matrix[, -c(1, 2, 4, 5)]),
          Colv=NA,
          seriate = "none",
          colors = Blues,
          k_row = 5,
          margins = c(NA,200,60,NA),
          fontsize_row = 4,
          fontsize_col = 5,
          main="World Happiness Score and Variables by Country, 2018 \nDataTransformation using Normalise Method",
          xlab = "World Happiness Indicators",
          ylab = "World Countries"
          )

Parallel Coordinates Plot

Plotting basic static parallel coordinates

ggparcoord(data = wh, 
           columns = c(7:12))

Adding boxplot

ggparcoord(data = wh, 
           columns = c(7:12), 
           groupColumn = 2,
           scale = "uniminmax",
           alphaLines = 0.2,
           boxplot = TRUE, 
           title = "Parallel Coordinates Plot of World Happines Variables")

Learning points:

  • groupColumn argument is used to group the observations (i.e. parallel lines) by using a single variable (i.e. Region) and colour the parallel coordinates lines by region name.

  • scale argument is used to scale the variables in the parallel coordinate plot by using uniminmax method. The method univariately scale each variable so the minimum of the variable is zero and the maximum is one.

  • alphaLines argument is used to reduce the intensity of the line colour to 0.2. The permissible value range is between 0 to 1.

  • boxplot argument is used to turn on the boxplot by using logical TRUE. The default is FALSE.

Parallel coordinates with facet

ggparcoord(data = wh, 
           columns = c(7:12), 
           groupColumn = 2,
           scale = "uniminmax",
           alphaLines = 0.2,
           boxplot = TRUE, 
           title = "Multiple Parallel Coordinates Plots of World Happines Variables by Region") +
  facet_wrap(~ Region) + 
  theme(axis.text.x = element_text(angle = 30, hjust=1))

Plotting Interactive Parallel Coordinates Plot

Basic plot

parallelPlot(wh,
             continuousCS = "YlOrRd",
             rotateTitle = TRUE)

Adding histogram

histoVisibility <- rep(TRUE, ncol(wh))
parallelPlot(wh,
             rotateTitle = TRUE,
             histoVisibility = histoVisibility)