Comparing (Business Analytics)

Load the .csv data from the web

seg.df = read.csv("http://goo.gl/qw303p")
summary(seg.df)

##       age           gender        income            kids        ownHome   
##  Min.   :19.26   Female:157   Min.   : -5183   Min.   :0.00   ownNo :159  
##  1st Qu.:33.01   Male  :143   1st Qu.: 39656   1st Qu.:0.00   ownYes:141  
##  Median :39.49                Median : 52014   Median :1.00               
##  Mean   :41.20                Mean   : 50937   Mean   :1.27               
##  3rd Qu.:47.90                3rd Qu.: 61403   3rd Qu.:2.00               
##  Max.   :80.49                Max.   :114278   Max.   :7.00               
##   subscribe         Segment   
##  subNo :260   Moving up : 70  
##  subYes: 40   Suburb mix:100  
##               Travelers : 80  
##               Urban hip : 50  
##                               
## 

Explore the data structure

str(seg.df)

## 'data.frame':    300 obs. of  7 variables:
##  $ age      : num  47.3 31.4 43.2 37.3 41 ...
##  $ gender   : Factor w/ 2 levels "Female","Male": 2 2 2 1 1 2 2 2 1 1 ...
##  $ income   : num  49483 35546 44169 81042 79353 ...
##  $ kids     : int  2 1 0 1 3 4 3 0 1 0 ...
##  $ ownHome  : Factor w/ 2 levels "ownNo","ownYes": 1 2 2 1 2 2 1 1 1 2 ...
##  $ subscribe: Factor w/ 2 levels "subNo","subYes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Segment  : Factor w/ 4 levels "Moving up","Suburb mix",..: 2 2 2 2 2 2 2 2 2 2 ...

head(seg.df)

##        age gender   income kids ownHome subscribe    Segment
## 1 47.31613   Male 49482.81    2   ownNo     subNo Suburb mix
## 2 31.38684   Male 35546.29    1  ownYes     subNo Suburb mix
## 3 43.20034   Male 44169.19    0  ownYes     subNo Suburb mix
## 4 37.31700 Female 81041.99    1   ownNo     subNo Suburb mix
## 5 40.95439 Female 79353.01    3  ownYes     subNo Suburb mix
## 6 43.03387   Male 58143.36    4  ownYes     subNo Suburb mix

summary(seg.df)

##       age           gender        income            kids        ownHome   
##  Min.   :19.26   Female:157   Min.   : -5183   Min.   :0.00   ownNo :159  
##  1st Qu.:33.01   Male  :143   1st Qu.: 39656   1st Qu.:0.00   ownYes:141  
##  Median :39.49                Median : 52014   Median :1.00               
##  Mean   :41.20                Mean   : 50937   Mean   :1.27               
##  3rd Qu.:47.90                3rd Qu.: 61403   3rd Qu.:2.00               
##  Max.   :80.49                Max.   :114278   Max.   :7.00               
##   subscribe         Segment   
##  subNo :260   Moving up : 70  
##  subYes: 40   Suburb mix:100  
##               Travelers : 80  
##               Urban hip : 50  
##                               
## 

# loading the lattice package
library(lattice)
library(ggplot2)

ggplot(seg.df, aes(x = Segment, fill = subscribe))+
  geom_bar()+
  labs(
    y= ""
  )

Figure 1: Figure 1.1 proportions by subscribe across segment