HCPC in FactomineR: How to count individuals in Clusters?

Question

the title says it all. I performed a multiple correspondence analysis (MCA) in FactomineR with factoshiny and did an HPCP afterwards. I now have 3 clusters on my 2 dimensions. While the factoshiny interface really helps visualize and navigate the analysis easily, I can't find a way to count the individuals in my clusters. Additionally, I would love to assign the clustervariables to the individuals on my dataset. Those operations are easily performed with hclust, but their algorithms don't work on categorical data.

##dummy dataset

x <- as.factor(c(1,1,2,1,3,4,3,2,1))
y <- as.factor(c(2,3,1,4,4,2,1,1,1))
z <- as.factor(c(1,2,1,1,3,4,2,1,1))

data <- data.frame(x,y,z)

# used packages
library(FactoMineR)
library(Factoshiny)
# the function used to open factoshiny in your browser
res.MCA <- Factoshiny(data)

# factoshiny code:
# res.MCA<-MCA(data,graph=FALSE)

# hcpc code in factoshiny
res.MCA<-MCA(data,ncp=8,graph=FALSE)
res.HCPC<-HCPC(res.MCA,nb.clust=3,consol=FALSE,graph=FALSE)
plot.HCPC(res.HCPC,choice='tree',title='Hierarchical tree')
plot.HCPC(res.HCPC,choice='map',draw.tree=FALSE,title='Factor map')
plot.HCPC(res.HCPC,choice='3D.map',ind.names=FALSE,centers.plot=FALSE,angle=60,title='Hierarchical tree on the factor map')

I now want a variable data$cluster with 3 levels so that I can count the individuals in the clusters.

Answer 1

To anyone encountering a similar problem, this helped:

res.HCPC$data.clust # returns all values and cluster membership for every individual
res.HCPC$data.clust[1,]$clust # for the first individual
table(res.HCPC$data.clust$clust) # gives table of frequencies per cluster