Income                package:arules                R Documentation

_I_n_c_o_m_e _D_a_t_a _S_e_t

_D_e_s_c_r_i_p_t_i_o_n:

     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'.

_U_s_a_g_e:

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

_F_o_r_m_a_t:

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

     _i_n_c_o_m_e an ordered factor with levels '[0,10)' < '[10,15)' <
          '[15,20)' < '[20,25)' < '[25,30)' < '[30,40)' < '[40,50)' <
          '[50,75)' < '75+'

     _s_e_x a factor with levels 'male' 'female'

     _m_a_r_i_t_a_l _s_t_a_t_u_s a factor with levels 'married' 'cohabitation'
          'divorced' 'widowed' 'single'

     _a_g_e an ordered factor with levels '14-17' < '18-24' < '25-34' <
          '35-44' < '45-54' < '55-64' < '65+'

     _e_d_u_c_a_t_i_o_n an ordered factor with levels 'grade <9' < 'grades 9-11'
          < 'high school graduate' < 'college (1-3 years)' < 'college
          graduate' < 'graduate study'

     _o_c_c_u_p_a_t_i_o_n a factor with levels 'professional/managerial' 'sales'
          'laborer' 'clerical/service' 'homemaker' 'student' 'military'
          'retired' 'unemployed'

     _y_e_a_r_s _i_n _b_a_y _a_r_e_a an ordered factor with levels '<1' < '1-3' <
          '4-6' < '7-10' < '>10'

     _d_u_a_l _i_n_c_o_m_e_s a factor with levels 'not married' 'yes' 'no'

     _n_u_m_b_e_r _i_n _h_o_u_s_e_h_o_l_d an ordered factor with levels '1' < '2' < '3'
          < '4' < '5' < '6' < '7' < '8' < '9+'

     _n_u_m_b_e_r _o_f _c_h_i_l_d_r_e_n an ordered factor with levels '0' < '1' < '2' <
          '3' < '4' < '5' < '6' < '7' < '8' < '9+'

     _h_o_u_s_e_h_o_l_d_e_r _s_t_a_t_u_s a factor with levels 'own' 'rent' 'live with
          parents/family'

     _t_y_p_e _o_f _h_o_m_e a factor with levels 'house' 'condominium'
          'apartment' 'mobile Home' 'other'

     _e_t_h_n_i_c _c_l_a_s_s_i_f_i_c_a_t_i_o_n a factor with levels 'american indian'
          'asian' 'black' 'east indian' 'hispanic' 'pacific islander'
          'white' 'other'

     _l_a_n_g_u_a_g_e _i_n _h_o_m_e a factor with levels 'english' 'spanish' 'other'

_D_e_t_a_i_l_s:

     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).

_S_o_u_r_c_e:

     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. (<URL:
     http://www-stat.stanford.edu/~tibs/ElemStatLearn/>; called
     'Marketing')

_E_x_a_m_p_l_e_s:

     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

