Income | R Documentation |
Survey example data from the book The Elements of Statistical Learning.
The data is provided in two formats:
Income
is an object of class transactions
with 6876 transactions (complete cases)
and 50 items. See below for details.
IncomeESL
is a data frame with 8993 observations on the
following 14 variables:
an ordered factor with
levels [0,10)
< [10,15)
< [15,20)
< [20,25)
<
[25,30)
< [30,40)
< [40,50)
< [50,75)
<
75+
a factor with levels male
female
a factor with levels married
cohabitation
divorced
widowed
single
an ordered factor with levels 14-17
< 18-24
<
25-34
< 35-44
< 45-54
< 55-64
< 65+
an ordered factor with levels grade <9
<
grades 9-11
< high school graduate
< college (1-3
years)
< college graduate
< graduate study
a factor with levels professional/managerial
sales
laborer
clerical/service
homemaker
student
military
retired
unemployed
an ordered factor with levels <1
< 1-3
<
4-6
< 7-10
< >10
a factor with
levels not married
yes
no
an ordered factor with levels 1
< 2
< 3
<
4
< 5
< 6
< 7
< 8
< 9+
an ordered factor with levels 0
< 1
< 2
< 3
< 4
< 5
< 6
< 7
< 8
< 9+
a factor with levels own
rent
live with parents/family
a factor
with levels house
condominium
apartment
mobile
Home
other
a factor with levels
american indian
asian
black
east indian
hispanic
pacific islander
white
other
a factor with levels english
spanish
other
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.
To create transactions for Income, 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).
Michael Hahsler
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.
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 <- transactions(IncomeESL)
Income
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