Summarize variable variations in clusters (k-means) using R

Question

I have a df that I got after implementing k-means clustering on my original dataset. I have 4 different clusters here and what I would like to know is how much is the variation of the 4 variables (V1 to V4) in each cluster. In other words, what variation in those 4 variables is causing the clusters being separated.

fit <- kmeans(df, 4, iter.max=1000, nstart=25)
palette(alpha(brewer.pal(9,'Set1'), 0.5))
plot(df, col=fit$clust, pch=16)
aggregate(df, by=list(fit$cluster), FUN=mean)
clust.out <- fit$cluster
df1 <- data.frame(df, fit$cluster)

Here is my df1 after k-means

+-------+-------+-------+--------+--------+-------------+
|  ID   |  V1   |  V2   |   V3   |   V4   | fit.cluster |
+-------+-------+-------+--------+--------+-------------+
| DJ123 | 0.5   | 0.7   | -0.4   | -0.1   |           1 |
| DJ123 | 0.46  | 0.68  | -0.39  | -0.09  |           1 |
| DJ123 | 0.77  | 0.9   | -0.4   | -0.4   |           2 |
| DJ123 | 11.23 | 11.11 | -11.21 | -11.21 |           4 |
| DJ123 | 1.5   | 1.7   | -1.4   | -5.1   |           3 |
| DJ123 | 0.76  | 0.9   | -0.4   | -0.4   |           2 |
| DJ123 | 1.5   | 2.7   | -1.4   | -4.1   |           3 |
+-------+-------+-------+--------+--------+-------------+

Could you please provide a sample code to get the summary statistics within clusters? I hope my question was clear.

Answer 1

You can use ddply from plyr to do this easily.

library(plyr)
ddply(df,.(cluster),summarise,variance1 = var(V1),variance2 = var(V2),mean1 = mean(V1),...)

You can also do it this way,

ddply(df,.(cluster),function(x){
  res = c(as.numeric(colwise(var)(x)),as.numeric(colwise(mean)(x)))
  names(res) = paste0(rep(c('Var','Mean'),each = 4),rep(1:4,2))
  res
})