How to Report the Distribution of Attributes per Cluster

Let’s say that you have applied your Clustering algorithm and you would like to report the distribution of the categorical variables per cluster in a “tidy” report. Below you can see a suggestion of how you can do it in R.

Generate the Data

Let’s assume that we came up with 3 clusters such as “C1, C2 and C3” and that we have 3 attributes such as:

• Gender: “M”, “F”
• Type: “A”, “B”, “C”, “D”
• Category: “High”, “Medium”, “Low”

library(tidyverse)

set.seed(5)

df1<-tibble(ID=seq_len(500))%>%
     mutate(Cluster = "C1",
            Gender=sample(c("M", "F"), n(), replace=TRUE, prob=c(0.6, 0.4)),
            Type=sample(c("A", "B", "C", "D"), n(), replace=TRUE, prob=c(0.20, 0.3, 0.4, 0.1)),
            Category=sample(c("High", "Medium", "Low"), n(), replace=TRUE, prob=c(0.1, 0.6, 0.3)))

df2<-tibble(ID=seq_len(300))%>%
  mutate(Cluster = "C2",
         Gender=sample(c("M", "F"), n(), replace=TRUE, prob=c(0.4, 0.6)),
         Type=sample(c("A", "B", "C", "D"), n(), replace=TRUE, prob=c(0.40, 0.1, 0.2, 0.3)),
         Category=sample(c("High", "Medium", "Low"), n(), replace=TRUE, prob=c(0.7, 0.2, 0.1)))

df3<-tibble(ID=seq_len(200))%>%
  mutate(Cluster = "C3",
         Gender=sample(c("M", "F"), n(), replace=TRUE, prob=c(0.2, 0.8)),
         Type=sample(c("A", "B", "C", "D"), n(), replace=TRUE, prob=c(0.5, 0.3, 0.1, 0.1)),
         Category=sample(c("High", "Medium", "Low"), n(), replace=TRUE, prob=c(0.1, 0.2, 0.7)))

df<-rbind.data.frame(df1, df2, df3)

df
 

# A tibble: 1,000 x 5
      ID Cluster Gender Type  Category
   <int> <chr>   <chr>  <chr> <chr>   
 1     1 C1      M      C     Medium  
 2     2 C1      F      C     Medium  
 3     3 C1      F      C     Medium  
 4     4 C1      M      B     Low     
 5     5 C1      M      B     Low     
 6     6 C1      F      C     Medium  
 7     7 C1      M      C     Medium  
 8     8 C1      F      B     High    
 9     9 C1      F      C     Medium  
10    10 C1      M      A     Medium  
# ... with 990 more rows

Report the Distribution of Attributes

attributes <- names(df[3:dim(df)[2]])


output<-NULL

for (a in attributes) {
  
  tmp<-df%>%group_by_(a, "Cluster")%>% summarise(n = n())%>%
    group_by(Cluster)%>%mutate(Prop=n/(sum(n)))%>%
    ungroup()%>%select(-n)%>%
    spread(Cluster, Prop)%>%mutate(Attribute = a)%>%select(Attribute, everything())
  colnames(tmp)[1:2]<-c("attribute", "values")
  
  output<-rbind(output, tmp)
  
}

output
 

# A tibble: 9 x 5
  attribute values    C1    C2    C3
  <chr>     <chr>  <dbl> <dbl> <dbl>
1 Gender    F      0.398 0.593 0.78 
2 Gender    M      0.602 0.407 0.22 
3 Type      A      0.188 0.413 0.425
4 Type      B      0.318 0.1   0.365
5 Type      C      0.39  0.193 0.105
6 Type      D      0.104 0.293 0.105
7 Category  High   0.114 0.683 0.065
8 Category  Low    0.312 0.103 0.75 
9 Category  Medium 0.574 0.213 0.185