Income {arules} | R Documentation

## Income Data Set

### 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

    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.

### Examples

    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

[Package arules version 1.6-3 Index]