YaleEnergy            package:YaleToolkit            R Documentation

_M_o_n_t_h_l_y _e_n_e_r_g_y _c_o_n_s_u_m_p_t_i_o_n _o_f _Y_a_l_e _r_e_s_i_d_e_n_t_i_a_l _c_o_l_l_e_g_e_s.

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

     The data set contains monthly energy time series for Yale
     residential college, from July 1999 through July 2006

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

     data(YaleEnergy)

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

     A data frame with 1020 observations on the following 18 variables.

     '_n_a_m_e' a factor with levels 'BERKELEY' 'BRANFORD' 'CALHOUN'
          'DAVENPORT' 'EZRA STILES' 'JONATHAN EDWARDS' 'MORSE'
          'PIERSON' 'SAYBROOK' 'SILLIMAN' 'TIMOTHY DWIGHT' 'TRUMBULL'

     '_a_d_d_r_e_s_s' a factor with levels '189 ELM ST.' '205 ELM ST.' '241
          ELM ST.' '242 ELM ST.' '248 YORK ST.' '261 PARK ST.' '302
          YORK ST.' '345 TEMPLE ST.' '505 COLLEGE ST.' '70 HIGH ST.'
          '74 HIGH ST.'

     '_g_s_f' gross square footage of the college

     '_E_L' electrical consumption in kilowatt hours

     '_E_L_S_Q_F_T' electrical consumption per square foot

     '_C_H_W' chilled water consumption in tons

     '_S_Q_F_T_C_H_W' square feet per ton of chilled water

     '_S_T_E_A_M' steam consumption in pounds

     '_S_T_E_A_M_S_Q_F_T' steam per square foot

     '_M_B_T_U' million British Thermal Units (BTU) from chilled water and
          steam

     '_M_B_T_U_S_Q_F_T' million BTUs per square foot

     '_y_e_a_r' year of the record

     '_m_o_n_t_h' month of the record

     '_l_o_n' degrees longitude of the college

     '_l_a_t' degrees latitude

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

     John W. Emerson, Yale University

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

     data(YaleEnergy)
     whatis(YaleEnergy)

     y <- YaleEnergy             # This is just for convenience.
     esqft <- list(data.frame(y[y$name==y$name[1],"ELSQFT"]),
                   data.frame(y[y$name==y$name[2],"ELSQFT"]),
                   data.frame(y[y$name==y$name[3],"ELSQFT"]),
                   data.frame(y[y$name==y$name[4],"ELSQFT"]),
                   data.frame(y[y$name==y$name[5],"ELSQFT"]),
                   data.frame(y[y$name==y$name[6],"ELSQFT"]),
                   data.frame(y[y$name==y$name[7],"ELSQFT"]),
                   data.frame(y[y$name==y$name[8],"ELSQFT"]),
                   data.frame(y[y$name==y$name[9],"ELSQFT"]),
                   data.frame(y[y$name==y$name[10],"ELSQFT"]),
                   data.frame(y[y$name==y$name[11],"ELSQFT"]),
                   data.frame(y[y$name==y$name[12],"ELSQFT"]))

     # The sparkmat() command does most of the work:
     sparkmat(esqft, locs=data.frame(y$lon, y$lat), new=TRUE,
              w=0.002, h=0.0002, just=c("left", "top"))

     # We'll add some text for a nice finished product:
     grid.text(y[1:12,1], unit(y$lon[1:12]+0.001, "native"),
               unit(y$lat[1:12]+0.00003, "native"),
               just=c("center", "bottom"), gp=gpar(cex=0.7))
     grid.text("Degrees Longitude", 0.5, unit(-2.5, "lines"))
     grid.text("Degrees Latitude", unit(-4.5, "lines"), 0.5, rot=90)
     grid.text("Monthly Electrical Consumption (KwH/SqFt) of Yale Colleges",
               0.5, 0.8, gp=gpar(cex=1, font=2))
     grid.text("July 1999 - July 2006",
               0.5, 0.74, gp=gpar(cex=1, font=2))

