Startup

First we need to find, install and load various packages. These packages will be available on CRAN and can be accessed and installed in the usual way.

knitr::opts_chunk$set(
  comment = "R>",
  warning = FALSE,
  message = FALSE
)

library(tidyverse)
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library(ggpubr)
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library(zoo)
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library(lubridate)
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library(ggrepel)
library(FNN)
library(stringr)
library(viridis)
## Loading required package: viridisLite
library(gridExtra)
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library(dplyr)
source("functions/scale.bar.func.R")
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## Loading required package: sp
## Checking rgeos availability: TRUE

Load data

Now to get to the data. The first step involves the loading of the site list. This data represents the statistical properties of the seawater temperature representing the South African coastline, such as the mean, minimum and maximum temperatures. These values vary among coastal sections due to the influence of the cold Benguala and warm Agulhas currents.

load("data/site_list_v4.2.RData")

Clustering

Thereafter we performing the k-means clustering on a data matrix using the kmeans() function, which uses multiple random seeds to find a number of clusting solutions; it selects as the final solution the one that has the minimum total within-cluster sum of squared distances. These variables include the mean, min and max temperature values. This allows me to group sites based on similar temperature variations.

set.seed(10)
kmeans(site_list[,c(15, 18, 19)], 6)$cluster
R>   [1] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 5 3 3 5 3 3
R>  [38] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1
R>  [75] 2 2 1 2 1 1 1 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 4 1 1
R> [112] 2 1 1 4 2 4 1 4 2 4 4 4 2 2 2 2 1 2 2 6 6 6 6 6
clust_columns <- site_list[,c(15, 18:19)]

The coastal region is divided into three sections, namely the cold west coast, warm east coast and the subtropical south coast. The Benguela Marine Province (BMP) is located to the west of Cape Point and is characterized by the movement of cold water from the Southern Ocean moving north. The cold temperate west coast mentioned above is greatly affected by the upwelling caused by offshore winds, generated by the cold Benguela current. Thus the seawater temperatures found around South Africa’s coastline exhibits a large variational range.

Plotting the cluster analyses

clust_i <- function(i) {
  set.seed(10)
  ggplot(data = site_list,
         aes(x = lon, y = lat,
             colour = as.factor(kmeans(clust_columns, i)$cluster))) +
    borders() +
    geom_point() +
    labs(colour = "cluster") +
    coord_equal(xlim = c(15, 35), ylim = c(-37, -27)) +
    ggtitle(paste0("clust = ", i))
}

clust_8 <- clust_i(8)
clust_7 <- clust_i(7)
clust_6 <- clust_i(6)
clust_5 <- clust_i(5)
clust_4 <- clust_i(4)
clust_3 <- clust_i(3)

clusters <- ggarrange(clust_8, clust_7, clust_6, clust_5, clust_4, clust_3, common.legend = T)
clusters

The plotting functions partition the data into the clusters and colour code each point accordingly so that we are able to see patterns that exist and distictly group the different sites with similar temperatures along the three distinct coasts. Cluster six was selected as this represented the most accurate and distinct site groupings based on this temperature distribution. Showing a distinct east, south and west coast based on the mean, min and max temperature values.

To wrap up this blog, the next bit of code we are loading in the datasets in order to better map our cluster analyses in order to show variation in temperature as mentioned in the literature.

load("data/sa_provinces.RData")
load("data/south_africa_coast.RData")
load("data/southern_africa_coast.Rdata")
load("data/africa_coast.RData")
load("data/sa_provinces_new.RData")
load("data/sa_coast.Rdata")

africa_map <- ggplot(africa_coast, aes(x = lon, y = lat)) + 
  theme_bw() + 
  coord_equal() + 
  geom_polygon(aes(group = group), colour = "black", fill = "grey80") + 
  geom_polygon(data = sa_provinces_new, (aes(group = group))) +
  annotate("text", label = "Africa", x = 16.0, y = 15.0, size = 3) + 
  theme(panel.border = element_rect(colour = "black", size = 0.4), 
        plot.background = element_blank(), 
        axis.ticks = element_blank(), 
        axis.text = element_blank(), 
        axis.title = element_blank(), 
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank()) +
  coord_map(xlim = c(-20, 53), ylim = c(-36, 38), projection = "mercator") 
africa_map

clust_i <- function(i) {
  set.seed(10)
  ggplot(data = south_africa_coast, aes(x = lon, y = lat)) +
    geom_polygon(aes(group = group), fill = "white") +
    borders() +
    geom_point(data = site_list,
               aes(x = lon, y = lat,
                   colour = as.factor(kmeans(clust_columns, i)$cluster), size = 2)) +
    labs(colour = "cluster")  +
    annotate("text", label = "INDIAN\nOCEAN", x = 34.00, y = -35.0,
             size = 4.0, angle = 0, colour = "black") +
    annotate("text", label = "ATLANTIC\nOCEAN", x = 14.00, y = -35.0,
             size = 4.0, angle = 0, colour = "black") +
    geom_segment(aes(x = 17.2, y = -32.6, xend = 15.2, yend = -29.5),
                 arrow = arrow(length = unit(0.3, "cm")),
                 size = 0.1, colour = "black") +
    annotate("text", label = "Benguela", x = 16.0, y = -31.7,
             size = 4, angle = 302, colour = "black") +
    geom_segment(aes(x = 33, y = -29.5, xend = 29.8, yend = -33.0),
                arrow = arrow(length = unit(0.3, "cm")),
                size = 0.1, colour = "black") +
    annotate("text", label = "Agulhas", x = 31.7, y = -31.7,
             size = 4, angle = 50, colour = "black") +
    # Improve on the x and y axis labels
    coord_fixed(ratio = 1, xlim = c(10.5, 39.5), ylim = c(-39.5, -25.5),
                expand = TRUE) +
    scale_x_continuous(expand = c(0, 0),
                       labels = scales::unit_format(unit = "°E", sep = "")) +
    scale_y_continuous(expand = c(0, 0),
                       labels = scales::unit_format(unit = "°S", sep = "")) +
    labs(x = NULL, y = NULL) +
    scaleBar(lon = 32.0, lat = -38.7, distanceLon = 200, distanceLat = 50, 
             distanceLegend = 90, dist.unit = "km", arrow.length = 100,
             arrow.distance = 130, arrow.North.size = 3) +
    theme_bw() +
    theme(panel.border = element_rect(fill = NA, colour = "black", size = 1),
          axis.text = element_text(colour = "black", size = 18),
          axis.title = element_text(colour = "black", size = 18),
          axis.ticks = element_line(colour = "black"))
}

clust_6 <- clust_i(8)
clust_6