BCB744 Task C

Author

Affiliation

Smit, A. J.

University of the Western Cape

Assessment Sheet

9. Mapping with Style

Question 1

Aethetically improve the map you created in this lecture (above example) and add a title and subtitle. Also adjust it to show appropriately labelled axes. (/15)

Answer

# Load libraries
library(tidyverse) # ✓
library(scales) # ✓
library(ggsn) # replace this with ggspatial ✓

# Load Africa map
load("../data/africa_map.RData") # ✓

my_map <-ggplot() +
  borders(size = 0.2, fill = "cornsilk", colour = "black") + # ✓
  coord_equal(xlim = c(12, 37), ylim = c(-38, -22.1), expand = 0) + # ✓
  annotate("text", label = "Atlantic\nOcean", # ✓
           x = 15.1, y = -32.0, 
           size = 5.0, 
           angle = 30, 
           colour = "navy") +
  annotate("text", label = "Indian\nOcean", # ✓
           x = 33.2, y = -34.2, 
           size = 5.0, 
           angle = 330, 
           colour = "red4") +
  scalebar(x.min = 15, x.max = 26, y.min = -36, y.max = -35, # ✓
           dist = 400, dist_unit = "km", height = 0.3, st.dist = 0.8, st.size = 4,
           transform = TRUE, border.size = 0.2, model = "WGS84") +
  north(x.min = 12.5, x.max = 15.5, y.min = -37, y.max = -35, # ✓
        scale = 1.2, symbol = 16) +
  theme_minimal() + # ✓
  labs(title = "Southern Africa", # ✓
       subtitle = "An interesting subtitle", # ✓
       x = "Lon (°E)", y = "Lat (°S)") # ✓
my_map

Question 2

Add a the capital city/town of each province to the map using geom_point() and ensure the place name is correctly associated with its point. (/5)

Answer

# Load the data
caps <- read.csv("../data/south_africa_capitals.csv") # ✓ 

my_map +
  geom_point(data = caps, aes(x = Longitude, y = Latitude), size = 2, colour = "red") + # ✓ 
  geom_text(data = caps, aes(x = Longitude, y = Latitude, label = City), # ✓ 
            nudge_x = 1, nudge_y = 0.5, size = 2.4)

Note: The positioning of the city names isn’t optimal, but I won’t apply penalties to that.

Note: Use ChatGPT to generate the CSV file with city names and coordinates. This prompt worked for me:

Please create a CSV file with three columns: column 1 has the name of each of South Africa’s provincial capital cities; column 2 has its longitude; column 3 has the latitude. Coordinates in WGS84, please.

10. Mapping with Natural Earth and the sf Package

Questions 3–6 refer to the map above.

Question 3

Why does the map region extend so far south of the southern tip of Africa when we requested only the countries South Africa, Mozambique, Namibia, Zimbabwe, Botswana, Lesotho, and Eswatini? (/1)

Answer

• ✓ The map extends so far south because included in the map domain for which we downloaded the data is Prince Edward Island, a territory of South Africa located in the Southern Ocean.

Question 4

How do we fix this to plot a more sensible map of the region? (/1)

Answer

• ✓ To exclude this island, we need to crop the map to the region of interest.

Question 5

What does st_buffer(0.4) do? (/1)

Answer

• ✓ The st_buffer(0.4) function creates a buffer around the countries in the map. The buffer is 0.4 units wide.

Question 6

With the map that we created in the lecture with Natural Earth and sf (i.e., you can start with the script in the lecture), zoom into False Bay and the Cape Peninsula. Add the location of the Cape Town city centre to the map using geom_point(). Ensure the point is correctly associated with the city name. Ensure the map is correctly labelled and has a title, and is as close to publication quality as you can make it. Pay close attention to the axes (breaks and limits) and the map extent. Some marks are allocated to a pleasing and sensible aesthetic appearance. (/20)

Answer

# Load the packages
library(tidyverse)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)

# for the buffer to work as I expect, swith off
# the functions for spherical geometry:
sf_use_s2(FALSE)

# the full map extent:
xmin <- 12; ymin <- -36.5; xmax <- 40.5; ymax <- -10  # ✓
xlim <- c(xmin, xmax); ylim <- c(ymin, ymax)

# make a bounding box for cropping:
bbox <- st_bbox(c(xmin = xmin, ymin = ymin,  # ✓
  xmax = xmax, ymax = ymax))


# load the countries:
safrica_countries <- ne_countries(returnclass = 'sf',  # ✓
  continent = "Africa",
  country = c("South Africa", "Mozambique",
    "Namibia", "Zimbabwe", "Botswana",
    "Lesotho", "Eswatini"),
  scale = "large")

safrica_countries_new <- safrica_countries |> # ✓
  group_by(continent) |> 
  summarise() |> 
  st_crop(bbox) |>
  st_combine()

# For zooming into a smaller region (False Bay and 
# the Cape Peninsula):
xlim_zoom <- c(17.8, 19); ylim_zoom <- c(-34.5, -33.2) # ✓

# Cape Town lon and lat
ctown <- c(18.4241, -33.9249) # ✓

ggplot() +
  geom_sf(data = safrica_countries, colour = "indianred", fill = "beige") + # ✓
  geom_point(aes(x = ctown[1], y = ctown[2]), size = 3, colour = "blue") + # ✓
  geom_text(aes(x = ctown[1], y = ctown[2], label = "Cape Town"), # ✓
            nudge_x = 0.1, nudge_y = 0.08, size = 3) +
  coord_sf(xlim = xlim_zoom, ylim = ylim_zoom, expand = 0) + # ✓
  labs(title = "Cape Town", # ✓
       x = "Lon (°E)", y = "Lat (°S)") +
  scale_x_continuous(breaks = c(18, 19)) + # ✓
  scale_y_continuous(breaks = c(-34.2, -33.4)) # ✓