r：根据使用 cutree() 进行分层聚类的等值线图

Question

I am doing a hierarchial clustering on two parameters (X1, X2) for 50 different African countries. Thereby I want to identify for 5 different groups/clusters within the continent of Africa. I am using the following code to do so:

hc <- hclust(dist(df), method = "complete")
member <- cutree(hc, 5)

Now, I would like to use the information stored in member (ie the cluster-ID each country belongs to) in order to color a map of Africa so that each cluster is represented by another color. I know that there are many tutorials on coloring maps, like this . But I am wondering wether there is a specific map-coloring-approach tailored to the results of a hierarchial cluster analysis?

Has anybody ever done that? I am happy for any advice or tipp on how to achieve this in a most efficient way!

(Scaled) data looks as follows:

df <- structure(list(Country = structure(1:50, .Label = c("Angola", 
"Benin", "Botswana", "Burkina Faso", "Burundi", "Cabo Verde", 
"Cameroon", "Central African Republic", "Chad", "Comoros", "Congo", 
"Cote d'Ivoire", "Democratic Republic of Congo", "Djibouti", 
"Equatorial Guinea", "Eritrea", "Ethiopia", "Gabon", "Gambia", 
"Ghana", "Guinea", "Guinea-Bissau", "Kenya", "Lesotho", "Liberia", 
"Madagascar", "Malawi", "Mali", "Mauritania", "Mauritius", "Mozambique", 
"Namibia", "Niger", "Nigeria", "Reunion", "Rwanda", "Sao Tome and Principe", 
"Senegal", "Seychelles", "Sierra Leone", "Somalia", "South Africa", 
"South Sudan", "Sudan", "Swaziland", "Tanzania", "Togo", "Uganda", 
"Zambia", "Zimbabwe"), class = "factor"), X1 = c(-0.18, -1.03, 
0.6, 1.55, 0.22, 0.26, 0.76, 2.15, -1.43, 0.99, 1.79, -0.39, 
1.73, 1.57, 1.11, -0.09, -1.49, -0.46, -0.48, -1.22, -0.78, -1.46, 
-1.22, 0.35, 0.45, 1.29, -1.37, -0.61, 0.92, -1.3, 0.42, -1.18, 
1.4, -0.83, 0.06, -0.76, -0.19, -0.37, -0.63, 0.64, 0.93, 0.33, 
-0.76, -0.21, -0.59, -0.41, -0.74, 0.39, -1.1, 1.35), X2 = c(-0.22, 
-0.42, 0.72, -0.59, -1.27, 0.64, -1.35, -1.4, -0.35, -1.43, 1.07, 
-0.01, -0.51, 0.11, 1.14, -0.89, 0.77, 1.45, -1.67, -0.83, 0.71, 
0.92, 1.63, 1.68, 0.23, -0.18, 0.07, 0.8, -0.02, 0.82, -0.72, 
-0.41, -0.26, 0.02, -1.68, 1.67, 0.18, 0.98, 1.45, 0.31, -1.23, 
-1.38, -0.63, 1.41, -0.12, 0, -1.3, -1.64, 0.21, 1.52)), .Names = c("Country", 
"X1", "X2"), row.names = c(NA, -50L), class = "data.frame")

Answer 1

Using your data and plotly:

library(plotly)


hc <- hclust(dist(df), method = "complete")
df$member <- cutree(hc, 5)


#Grabbing the Africa Geo from a plotly example
g <- list(
  scope = 'africa',
  showframe = F,
  showland = T,
  landcolor = toRGB("grey90")
)

plot_ly(df, z = member, type = 'choropleth', mode = 'markers', locations = Country,
    locationmode = 'country names') %>% layout(geo = g)

You can use the plotly settings found here: https://plot.ly/r/reference/

to switch to a non-continuous scale, but it so happens that the continuous scale works fine for plotting only 5. For more you'd want a diverging color scale.