This project seeks to provide a brief introduction to the top 12 biggest cities in the lovely land of South Africa. Using a very small dataset the aim is to showcase some of the things leaflet can do in a fun and exciting manner.
I got the data from the geonames site you can find here. This contains a list of the largest cities in South Africa in descending order along with their populations, latitudes and longitudes.
SA_Cities <- data.frame(name = c("Cape Town", "Durban", "Johannesburg", "Soweto","Pretoria",
"Port Elizabeth", "Pietermaritzburg", "Benoni", "Tembisa",
"East London", "Vereneging", "Bloemfontein"),
pop = c(3433441, 3120282, 2026469, 1695047, 1619438,967677,
750845, 605344, 511655, 478676, 474681, 463064),
lat = c(-33.926, -29.858, -26.202, -26.268, -25.745, -33.961,
-29.617, -26.188, -25.996, -33.015, -26.673, -29.121),
lng = c(18.423, 31.029, 28.044, 27.858, 28.188, 25.615,
30.393, 28.321, 28.227, 27.912, 27.926, 26.214))For this assignment I want to be truly South African. So the icons needed for the assignment have to be in South African fashion.
springbok_marker <- makeIcon(
iconUrl <- "https://upload.wikimedia.org/wikipedia/en/8/83/South_Africa_national_rugby_union_team.svg",
iconWidth = 31*215/230, iconHeight = 31,
iconAnchorX = 31*215/230/2, iconAnchorY = 16
)We can thus apply the marker and see the first map.
## Assuming "lng" and "lat" are longitude and latitude, respectivelyTHe next iteration is where the website for each location is displayed.
## Assuming "lng" and "lat" are longitude and latitude, respectivelyMuch better! However, the cities look a little bunched when they are added together. Let’s use a cluster to help visualise this better.
SA_Cities %>%
leaflet() %>%
addTiles() %>%
addMarkers(icon = springbok_marker,
popup = city_sites,
clusterOptions = markerClusterOptions())## Assuming "lng" and "lat" are longitude and latitude, respectively