Extract cluster information and combine results - r

Question

I am attempting to run a clustering algorithm over a list of dissimilarity matrices for different numbers of clusters k and extract some information for each run.
This first block of code produces the list of dissimilarity matrices
library(tidyverse)
library(cluster)
library(rje)
dat=mtcars[,1:3]
v_names=names(dat)
combos=rje::powerSet(v_names)
combos=combos[lengths(combos)>1]
df_list=list()
for (i in seq_along(combos)){
df_list[[i]]=dat[combos[[i]]]
}
gower_ls=lapply(df_list,daisy,metric="gower")
Here is the section of code I am having a problem with
set.seed(4)
model_num <-c(NA)
sil_width <-c(NA)
min_sil<-c(NA)
mincluster<-c(NA)
k_clusters <-c(NA)
lowest_sil <-c(NA)
maxcluster <-c(NA)
model_vars <- c(NA)
clust_4=lapply(gower_ls,pam,diss=TRUE,k=4)
for(m in 1:length(clust_4)){
sil_width[m] <-clust_4[[m]][7]$silinfo$avg.width
min_sil[m] <- min(clust_4[[m]][7]$silinfo$clus.avg.widths)
mincluster[m] <-min(clust_4[[m]][6]$clusinfo[,1])
maxcluster[m] <-max(clust_4[[m]][6]$clusinfo[,1])
k_clusters[m]<- nrow(clust_4[[m]][6]$clusinfo)
lowest_sil[m]<-min(clust_4[[m]][7]$silinfo$widths)
model_num[m] <-m
}
colresults_4=as.data.frame(cbind( sil_width, min_sil,mincluster,maxcluster,k_clusters,model_num,lowest_sil))
How can I convert this piece of code to run for a given range of k? I've tried a nested loop but I was not able to code it correctly. Here are the desired results for k= 4:6, thanks.
structure(list(sil_width = c(0.766467312788453, 0.543226669407726,
0.765018469447229, 0.705326458357873, 0.698351173575526, 0.480565022092276,
0.753366365875066, 0.644345251543097, 0.699437672202048, 0.430310752506775,
0.678224885117295, 0.576411380463116), min_sil = c(0.539324315243191,
0.508330909368204, 0.637090842537915, 0.622120627356455, 0.539324315243191,
0.334047777245833, 0.430814518122641, 0.568591550281139, 0.539324315243191,
0.295113900268025, 0.430814518122641, 0.19040716086259), mincluster = c(5,
3, 4, 5, 2, 3, 3, 3, 2, 3, 3, 3), maxcluster = c(14, 12, 11,
14, 12, 10, 11, 11, 9, 6, 7, 7), k_clusters = c(4, 4, 4, 4, 5,
5, 5, 5, 6, 6, 6, 6), model_num = c(1, 2, 3, 4, 1, 2, 3, 4, 1,
2, 3, 4), lowest_sil = c(-0.0726256983240229, 0.0367238314801671,
0.308069836672298, 0.294247157041013, -0.0726256983240229, -0.122804288130541,
-0.317748917748917, 0.218164082936686, -0.0726256983240229, -0.224849074123824,
-0.317748917748917, -0.459909237820881)), row.names = c(NA, -12L
), class = "data.frame")

Answer 1

I was able to come up with a solution by writing a function clus_func that extracts the cluster information and then using cross2 and map2 from the purrr package:
library(tidyverse)
library(cluster)
library(rje)
dat=mtcars[,1:3]
v_names=names(dat)
combos=rje::powerSet(v_names)
combos=combos[lengths(combos)>1]
clus_func=function(x,k){
clust=pam(x,k,diss=TRUE)
clust_stats=as.data.frame(cbind(
avg_sil_width=clust$silinfo$avg.width,
min_clus_width=min(clust$silinfo$clus.avg.widths),
min_individual_sil=min(clust$silinfo$widths[,3]),
max_individual_sil=max(clust$silinfo$widths[,3]),
mincluster= min(clust$clusinfo[,1]),
maxcluster= max(clust$clusinfo[,1]),
num_k=max(clust$clustering) ))
}
df_list=list()
for (i in seq_along(combos)){
df_list[[i]]=dat[combos[[i]]]
}
gower_ls=lapply(df_list,daisy,metric="gower")
begin_k=4
end_k=6
cross_list=cross2(gower_ls,begin_k:end_k)
k=c(NA)
for(i in 1:length(cross_list)){ k[i]=cross_list[[i]][2]}
diss=c(NA)
for(i in 1:length(cross_list)){ diss[i]=cross_list[[i]][1]}
model_stats=map2(diss, k, clus_func)
model_stats=rbindlist(model_stats)