R for Marketing Research and Analytics

Chris Chapman and Elea McDonnell Feit
January 2016

Chapter 5: Differences Between Groups

Website for all data files:
http://r-marketing.r-forge.r-project.org/data.html

Load Segmentation/Subscription data

As usual, check the book for details on the data simulation. For now:

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

Descriptives: Selecting by group

mean(seg.df$income[seg.df$Segment == "Moving up"])

[1] 53090.97

mean(seg.df$income[seg.df$Segment == "Moving up" & 
                   seg.df$subscribe=="subNo"])

[1] 53633.73

This quickly gets tedious!

Descriptives: apply a function by group

by(VARIABLE of interest, GROUPING variable, FUNCTION)

by(seg.df$income, seg.df$Segment, mean)

seg.df$Segment: Moving up
[1] 53090.97
-------------------------------------------------------- 
seg.df$Segment: Suburb mix
[1] 55033.82
-------------------------------------------------------- 
seg.df$Segment: Travelers
[1] 62213.94
-------------------------------------------------------- 
seg.df$Segment: Urban hip
[1] 21681.93

Use list() to have more than one grouping variable:

by(seg.df$income, list(seg.df$Segment, seg.df$subscribe), mean)

: Moving up
: subNo
[1] 53633.73
-------------------------------------------------------- 
: Suburb mix
: subNo
[1] 54942.69
-------------------------------------------------------- 
: Travelers
: subNo
[1] 62746.11
-------------------------------------------------------- 
: Urban hip
: subNo
[1] 22082.11
-------------------------------------------------------- 
: Moving up
: subYes
[1] 50919.89
-------------------------------------------------------- 
: Suburb mix
: subYes
[1] 56461.41
-------------------------------------------------------- 
: Travelers
: subYes
[1] 58488.77
-------------------------------------------------------- 
: Urban hip
: subYes
[1] 20081.19

Aggregate: use a formula!

Break out income by segment, in data “seg.df”, computing the mean:

aggregate(income ~ Segment, data=seg.df, mean)

     Segment   income
1  Moving up 53090.97
2 Suburb mix 55033.82
3  Travelers 62213.94
4  Urban hip 21681.93

This extends easily to multiple dimensions:

aggregate(income ~ Segment + ownHome, data=seg.df, mean)

     Segment ownHome   income
1  Moving up   ownNo 54497.68
2 Suburb mix   ownNo 54932.83
3  Travelers   ownNo 63188.42
4  Urban hip   ownNo 21337.59
5  Moving up  ownYes 50216.37
6 Suburb mix  ownYes 55143.21
7  Travelers  ownYes 61889.12
8  Urban hip  ownYes 23059.27

Aggregate returns a data frame

agg.data <- aggregate(income ~ Segment + ownHome, 
                      data=seg.df, mean)
str(agg.data)

'data.frame':   8 obs. of  3 variables:
 $ Segment: Factor w/ 4 levels "Moving up","Suburb mix",..: 1 2 3 4 1 2 3 4
 $ ownHome: Factor w/ 2 levels "ownNo","ownYes": 1 1 1 1 2 2 2 2
 $ income : num  54498 54933 63188 21338 50216 ...

agg.data[2, ]

     Segment ownHome   income
2 Suburb mix   ownNo 54932.83

agg.data[2, 3]

[1] 54932.83

Tables

Reminder – a table counts occurrences of a single value, such as one level of a factor.

table(seg.df$Segment, seg.df$ownHome)


             ownNo ownYes
  Moving up     47     23
  Suburb mix    52     48
  Travelers     20     60
  Urban hip     40     10

Telling R to use seg.df for everything is easy with with():

with(seg.df, table(Segment, ownHome))

            ownHome
Segment      ownNo ownYes
  Moving up     47     23
  Suburb mix    52     48
  Travelers     20     60
  Urban hip     40     10

Note that table() uses R standard (X, Y) == Row, Column order.

prop.table()

Reminder – get proportions for a table by wrapping table() with prop.table():

with(seg.df, prop.table(table(Segment, ownHome)))

            ownHome
Segment           ownNo     ownYes
  Moving up  0.15666667 0.07666667
  Suburb mix 0.17333333 0.16000000
  Travelers  0.06666667 0.20000000
  Urban hip  0.13333333 0.03333333

The default computes full table proportions. Obtain marginal proportions by specifying rows (margin=1) or columns (margin=2):

with(seg.df, prop.table(table(Segment, ownHome), margin=1))

            ownHome
Segment          ownNo    ownYes
  Moving up  0.6714286 0.3285714
  Suburb mix 0.5200000 0.4800000
  Travelers  0.2500000 0.7500000
  Urban hip  0.8000000 0.2000000

Doing math in a table

Reminder – aggregate() can be used to apply a function to data, computing the result within with each group.

For instance, to add up the total number of kids in each segment, use sum:

aggregate(kids ~ Segment, data=seg.df, sum)

     Segment kids
1  Moving up  134
2 Suburb mix  192
3  Travelers    0
4  Urban hip   55

Visualization: Counts by Group

histogram() in the lattice package plots proportional frequency by group. This is an alternative to basic hist() that we saw in an earlier chapter.

To get subscribers (~subscribe) by segment (| Segment):

library(lattice)
histogram(~subscribe | Segment, data=seg.df)

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Histograms continued

You can plot counts instead of proportions with type=“count”. There are options for the layout (cols, rows in this case) and colors:

histogram(~subscribe | Segment, data=seg.df, type="count", 
          layout=c(4,1), col=c("burlywood", "darkolivegreen"))

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counts instead of proportions, and some visual options

Histograms by 2 factors

Break out by multiple factors using | var1 + var2 + …:

histogram(~subscribe | Segment + ownHome, data=seg.df)

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Continuous Data: "Spreadsheet" style

The general process is to aggregate() the data that you want, then plot that. For example: mean income by segment, using a barchart:

seg.mean <- aggregate(income ~ Segment, data=seg.df, mean)
library(lattice)
barchart(income ~ Segment, data=seg.mean, col="grey")

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Continuous data by two factors

Use aggregate with + to break out multiple factors:

seg.agg <- aggregate(income ~ Segment + ownHome, data=seg.df, mean)
barchart(income ~ Segment, data=seg.agg, 
         groups=ownHome, auto.key=TRUE,
         par.settings = simpleTheme(col=c("gray95", "gray50")) )

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Continuous Data: "Statistics" style

Boxplots show much more information about the data distribution (see book for details). **bwplot()** from **lattice** is an upgrade over boxplot() that we saw in earlier chapters:

library(lattice)
bwplot(Segment ~ income, data=seg.df, horizontal=TRUE, 
       xlab = "Income")