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 'Income' 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_transactions' data set contains the data already prepared
     and coerced to 'transactions'.

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

     data("Income")
     data("Income_transactions")

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

     'Adult' 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_transactions', the original data frame in
     'Income' 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("Income")
     Income[1:3, ]

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

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

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

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

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

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

