I'm using the following data set to perform a cluster analysis on categorical data - link to data set - using the following packages in R:
library(cluster)
library(dplyr)
library(ggplot2)
library(readr)
With the following code, I get to observe what the profile of clients is within 5 clusters (NB: I'm picking 5 clusters instead of 7 or 8 to keep things more or less simple):
df.torun <- subset(df.bank, select = -c(loan, contact, day, month, duration, campaign, pdays, previous, poutcome, y))
gower_dist <- daisy(df.torun, metric = "gower")
gower_mat <- as.matrix(gower_dist)
sil_width <- c(NA)
for(i in 2:8){
pam_fit <- pam(gower_dist, diss = TRUE, k = i)
sil_width[i] <- pam_fit$silinfo$avg.width
}
plot(1:8, sil_width,
xlab = "Number of clusters",
ylab = "Silhouette width")
lines(1:8, sil_width)
k <- 5
pam_fit <- pam(gower_dist, diss = TRUE, k)
pam_results <- df.torun %>%
mutate(cluster = pam_fit$clustering) %>%
group_by(cluster) %>%
do(the_summary = summary(.))
pam_results$the_summary
As you'll be able to see if you run this script using the data I shared, you'll get a lot of information about the profile o clients in the following categories: age, job, marital, education, default, balance and housing. Here's a screenshot of the results I get for cluster 1:
As can be seen in the image above, under the job column, some of the results are "hiding" under the category (Other).
My question: what code can I use to list all the words from the job column that are "hiding" under (Other)?
Thank you very much for your help!
You may use maxsum=. Example:
d <- data.frame(x=gl(10, 5), y=rnorm(50))
summary(d)
# x y
# 1 : 5 Min. :-1.7459
# 2 : 5 1st Qu.:-0.8480
# 3 : 5 Median :-0.2293
# 4 : 5 Mean :-0.1439
# 5 : 5 3rd Qu.: 0.4109
# 6 : 5 Max. : 2.5951
# (Other):20
summary(d, maxsum=11)
# x y
# 1 :5 Min. :-1.7459
# 2 :5 1st Qu.:-0.8480
# 3 :5 Median :-0.2293
# 4 :5 Mean :-0.1439
# 5 :5 3rd Qu.: 0.4109
# 6 :5 Max. : 2.5951
# 7 :5
# 8 :5
# 9 :5
# 10:5