kmeans clustering, R

dta <- read.csv("file:///C:/Users/bk/Desktop/TEST_data.csv",header = T, row.names = 1)
head(dta)
cl <- kmeans(cntNorm,6)
cluster<-cl$cluster
table(cluster)

rNames <- rownames(cnrNorm)
df<-data.frame(cntNorm, rNames, cluster) # attach cluster info on the data frame
head(df)


library(reshape2)
mdata <- melt(df, id=c("rNames","cluster")) 
head(mdata)


# plotting using ggplot2
library(ggplot2)
jit <- position_jitter (width = .08, height = .08)
ggplot(mdata, aes(x=variable, y=log2(1+value), group = rNames)) + theme_bw() + 
  geom_line(position = jit, colour = alpha("black", 0.1)) + 
  stat_summary(aes(group = cluster), fun = mean, geom = "line", color="red", size = .5) +
  # theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  facet_wrap(~ cluster, ncol = 5, scales = "free")