| Income {arules} | R Documentation |
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.
data("Income")
data("IncomeESL")
IncomeESL is a data frame with 8993 observations on the
following 14 variables.
[0,10) < [10,15) < [15,20) < [20,25) < [25,30) < [30,40) < [40,50) < [50,75) < 75+male femalemarried cohabitation divorced widowed single14-17 < 18-24 < 25-34 < 35-44 < 45-54 < 55-64 < 65+grade <9 < grades 9-11 < high school graduate < college (1-3 years) < college graduate < graduate studyprofessional/managerial sales laborer clerical/service homemaker student military retired unemployed<1 < 1-3 < 4-6 < 7-10 < >10not married yes no1 < 2 < 3 < 4 < 5 < 6 < 7 < 8 < 9+0 < 1 < 2 < 3 < 4 < 5 < 6 < 7 < 8 < 9+own rent live with parents/familyhouse condominium apartment mobile Home otheramerican indian asian black east indian hispanic pacific islander white otherenglish spanish other
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).
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’)
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