Income Data Set

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Description

The IncomeESL data set originates from an example in the book ‘The Elements of Statistical Learning’ (see Section source). The data set is an extract from this survey. It consists of 8993 instances (obtained from the original data set with 9409 instances, by removing those observations with the annual income missing) with 14 demographic attributes. The data set is a good mixture of categorical and continuous variables with a lot of missing data. This is characteristic of data mining applications. The Income data set contains the data already prepared and coerced to transactions.

Usage

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data("Income")
data("IncomeESL")

Format

IncomeESL is a data frame with 8993 observations on the following 14 variables.

income

an ordered factor with levels [0,10) < [10,15) < [15,20) < [20,25) < [25,30) < [30,40) < [40,50) < [50,75) < 75+

sex

a factor with levels male female

marital status

a factor with levels married cohabitation divorced widowed single

age

an ordered factor with levels 14-17 < 18-24 < 25-34 < 35-44 < 45-54 < 55-64 < 65+

education

an ordered factor with levels grade <9 < grades 9-11 < high school graduate < college (1-3 years) < college graduate < graduate study

occupation

a factor with levels professional/managerial sales laborer clerical/service homemaker student military retired unemployed

years in bay area

an ordered factor with levels <1 < 1-3 < 4-6 < 7-10 < >10

dual incomes

a factor with levels not married yes no

number in household

an ordered factor with levels 1 < 2 < 3 < 4 < 5 < 6 < 7 < 8 < 9+

number of children

an ordered factor with levels 0 < 1 < 2 < 3 < 4 < 5 < 6 < 7 < 8 < 9+

householder status

a factor with levels own rent live with parents/family

type of home

a factor with levels house condominium apartment mobile Home other

ethnic classification

a factor with levels american indian asian black east indian hispanic pacific islander white other

language in home

a factor with levels english spanish other

Details

To create Income (the transactions object), the original data frame in IncomeESL is prepared in a similar way as described in ‘The Elements of Statistical Learning.’ We removed cases with missing values and cut each ordinal variable (age, education, income, years in bay area, number in household, and number of children) at its median into two values (see Section examples).

Author(s)

Michael Hahsler

Source

Impact Resources, Inc., Columbus, OH (1987).

Obtained from the web site of the book: Hastie, T., Tibshirani, R. \& Friedman, J. (2001) The Elements of Statistical Learning. Springer-Verlag. (http://www-stat.stanford.edu/~tibs/ElemStatLearn/; called ‘Marketing’)

Examples

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data("IncomeESL")
IncomeESL[1:3, ]

## remove incomplete cases
IncomeESL <- IncomeESL[complete.cases(IncomeESL), ]

## preparing the data set
IncomeESL[["income"]] <- factor((as.numeric(IncomeESL[["income"]]) > 6) +1,
  levels = 1 : 2 , labels = c("$0-$40,000", "$40,000+"))
	  
IncomeESL[["age"]] <- factor((as.numeric(IncomeESL[["age"]]) > 3) +1,
  levels = 1 : 2 , labels = c("14-34", "35+"))

IncomeESL[["education"]] <- factor((as.numeric(IncomeESL[["education"]]) > 4) +1,
  levels = 1 : 2 , labels = c("no college graduate", "college graduate"))

IncomeESL[["years in bay area"]] <- factor(
  (as.numeric(IncomeESL[["years in bay area"]]) > 4) +1,
  levels = 1 : 2 , labels = c("1-9", "10+"))

IncomeESL[["number in household"]] <- factor(
  (as.numeric(IncomeESL[["number in household"]]) > 3) +1,
  levels = 1 : 2 , labels = c("1", "2+"))

IncomeESL[["number of children"]] <- factor(
  (as.numeric(IncomeESL[["number of children"]]) > 1) +0,
  levels = 0 : 1 , labels = c("0", "1+"))
	
##  creating transactions
Income <- as(IncomeESL, "transactions")
Income

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