R

x = Data$Price

b= ifelse((x<8000),5/100,
          ifelse((x>=8000 & x< 9000),6/100,
          ifelse((x>=9000 & x< 10000),7/100,
          ifelse((x>=10000 & x< 11000),8/100,
          ifelse((x>=11000 & x< 13000),9/100,10/100
                    )))))
Data$Booking_Fee = b
datatable(Data)

R

# Contoh mengumpulkan data penjualan Lala

library(DT)

penjual = "Lala"

for (x in penjual){
  print(subset(Data, subset=(Marketing_Name == x)))
}

##        Id Marketing_Name Work_Exp      City   Cluster Price Date_Sales
## 10     10           Lala      7.3 Tengerang   Albasia 14667 2018-09-07
## 60     60           Lala      7.3     Bogor    Lavesh 12834 2018-01-26
## 110   110           Lala      7.3    Bekasi     Arana 14483 2020-02-29
## 160   160           Lala      7.3   Jakarta   Palmyra  7171 2020-02-04
## 210   210           Lala      7.3    Bekasi   Permata 10519 2019-08-01
## 260   260           Lala      7.3   Jakarta     Asera  8065 2020-07-31
## 310   310           Lala      7.3     Bogor   Peronia 14324 2018-06-04
## 360   360           Lala      7.3 Tengerang Primadona  8770 2019-10-20
## 410   410           Lala      7.3 Tengerang  Alamanda 11020 2018-12-02
## 460   460           Lala      7.3     Depok    Winona 10560 2018-12-22
## 510   510           Lala      7.3   Jakarta     Asoka 12615 2020-04-11
## 560   560           Lala      7.3   Jakarta   Mutiara 13936 2018-10-11
## 610   610           Lala      7.3     Bogor  Victoria 12689 2019-09-06
## 660   660           Lala      7.3    Bekasi   Mutiara  7727 2020-07-11
## 710   710           Lala      7.3    Bekasi   Alindra  8257 2018-03-13
## 760   760           Lala      7.3     Depok Primadona  9170 2018-09-24
## 810   810           Lala      7.3     Bogor   Palmyra 14452 2019-11-16
## 860   860           Lala      7.3     Depok    Narada  7298 2019-05-11
## 910   910           Lala      7.3   Jakarta   Permata  9318 2020-01-14
## 960   960           Lala      7.3     Depok  Victoria  7632 2018-12-06
## 1010 1010           Lala      7.3   Jakarta     Adara  8882 2018-10-01
## 1060 1060           Lala      7.3     Bogor   Peronia 12757 2018-09-20
## 1110 1110           Lala      7.3 Tengerang     Adara 11064 2020-05-19
## 1160 1160           Lala      7.3     Bogor   Permata 12819 2019-09-13
## 1210 1210           Lala      7.3     Depok     Tiara 14475 2020-03-16
## 1260 1260           Lala      7.3     Depok   Mutiara 13854 2018-01-08
## 1310 1310           Lala      7.3 Tengerang   Permata  9316 2018-09-20
## 1360 1360           Lala      7.3 Tengerang     Asoka 13901 2019-06-10
## 1410 1410           Lala      7.3 Tengerang Primadona 10574 2020-03-11
## 1460 1460           Lala      7.3   Jakarta   Palmyra  8846 2019-03-01
## 1510 1510           Lala      7.3     Depok   Palmyra  8838 2019-06-29
## 1560 1560           Lala      7.3    Bekasi  Victoria 10617 2018-06-18
## 1610 1610           Lala      7.3    Bekasi   Permata  9192 2019-01-03
## 1660 1660           Lala      7.3    Bekasi     Tiara 11817 2019-01-02
## 1710 1710           Lala      7.3   Jakarta      Neon 14608 2020-01-12
## 1760 1760           Lala      7.3     Bogor   Albasia 14146 2018-01-28
## 1810 1810           Lala      7.3    Bekasi    Winona  8678 2019-07-01
## 1860 1860           Lala      7.3     Depok   Mutiara 10003 2018-05-23
## 1910 1910           Lala      7.3 Tengerang    Lavesh  7066 2018-07-29
## 1960 1960           Lala      7.3     Depok   Palmyra  7598 2019-08-28
## 2010 2010           Lala      7.3    Bekasi   Permata  8568 2019-09-10
## 2060 2060           Lala      7.3     Depok   Permata  7613 2018-03-06
## 2110 2110           Lala      7.3 Tengerang   Alindra 12399 2018-07-30
## 2160 2160           Lala      7.3     Depok     Arana 13349 2018-05-16
## 2210 2210           Lala      7.3     Bogor     Adara 10914 2018-05-30
## 2260 2260           Lala      7.3   Jakarta  Alamanda  7218 2018-07-14
## 2310 2310           Lala      7.3 Tengerang  Alamanda  8431 2018-09-09
## 2360 2360           Lala      7.3 Tengerang   Alindra 10065 2020-01-10
## 2410 2410           Lala      7.3 Tengerang     Tiara  7283 2018-09-12
## 2460 2460           Lala      7.3     Depok     Tiara 12151 2020-04-23
## 2510 2510           Lala      7.3     Bogor   Albasia  8837 2019-05-25
## 2560 2560           Lala      7.3 Tengerang Primadona  7015 2018-07-30
## 2610 2610           Lala      7.3     Bogor    Winona 13383 2019-04-23
## 2660 2660           Lala      7.3   Jakarta   Peronia  8781 2020-02-27
## 2710 2710           Lala      7.3   Jakarta   Palmyra  9043 2020-01-12
## 2760 2760           Lala      7.3     Depok    Winona  9833 2018-05-06
## 2810 2810           Lala      7.3     Bogor     Asera  7487 2018-06-11
## 2860 2860           Lala      7.3    Bekasi    Lavesh 13315 2019-08-28
## 2910 2910           Lala      7.3    Bekasi  Victoria 11862 2018-11-29
## 2960 2960           Lala      7.3    Bekasi   Peronia 14292 2020-04-08
## 3010 3010           Lala      7.3     Depok     Asera  8696 2019-01-18
## 3060 3060           Lala      7.3 Tengerang     Tiara 13862 2020-07-06
## 3110 3110           Lala      7.3    Bekasi   Albasia 14920 2019-05-25
## 3160 3160           Lala      7.3    Bekasi     Arana 13962 2019-06-21
## 3210 3210           Lala      7.3 Tengerang Primadona 10115 2020-08-19
## 3260 3260           Lala      7.3 Tengerang     Adara  9634 2020-05-09
## 3310 3310           Lala      7.3   Jakarta     Asoka 13041 2019-04-08
## 3360 3360           Lala      7.3    Bekasi  Alamanda 14473 2018-08-08
## 3410 3410           Lala      7.3     Depok   Peronia 12881 2018-02-17
## 3460 3460           Lala      7.3     Bogor   Peronia 13122 2019-09-05
## 3510 3510           Lala      7.3     Depok     Asera 12985 2018-04-01
## 3560 3560           Lala      7.3 Tengerang Teradamai  9267 2020-02-27
## 3610 3610           Lala      7.3   Jakarta     Adara 11237 2020-08-25
## 3660 3660           Lala      7.3 Tengerang    Winona  7324 2020-03-05
## 3710 3710           Lala      7.3     Bogor     Tiara 14657 2019-06-13
## 3760 3760           Lala      7.3   Jakarta     Asera 12701 2018-08-30
## 3810 3810           Lala      7.3    Bekasi    Winona 11141 2020-07-30
## 3860 3860           Lala      7.3   Jakarta   Albasia 12348 2019-08-13
## 3910 3910           Lala      7.3    Bekasi   Permata  9343 2019-08-10
## 3960 3960           Lala      7.3     Bogor   Alindra  8577 2018-06-18
## 4010 4010           Lala      7.3    Bekasi     Arana 11562 2019-08-04
## 4060 4060           Lala      7.3    Bekasi      Neon 13368 2018-02-02
## 4110 4110           Lala      7.3    Bekasi     Arana 13116 2018-01-01
## 4160 4160           Lala      7.3     Depok Teradamai 10563 2018-05-12
## 4210 4210           Lala      7.3     Bogor      Neon  9992 2020-04-08
## 4260 4260           Lala      7.3 Tengerang     Asera 12172 2018-08-01
## 4310 4310           Lala      7.3    Bekasi      Neon 10554 2019-06-09
## 4360 4360           Lala      7.3     Depok Sweethome  9495 2019-08-07
## 4410 4410           Lala      7.3   Jakarta   Peronia  8445 2019-05-02
## 4460 4460           Lala      7.3 Tengerang Sweethome  7289 2020-05-02
## 4510 4510           Lala      7.3   Jakarta Teradamai 13575 2020-01-20
## 4560 4560           Lala      7.3     Depok  Victoria 10819 2019-06-25
## 4610 4610           Lala      7.3   Jakarta   Permata 13434 2020-06-24
## 4660 4660           Lala      7.3     Depok   Alindra  7436 2019-12-13
## 4710 4710           Lala      7.3     Depok  Alamanda  7500 2020-01-28
## 4760 4760           Lala      7.3 Tengerang    Winona 12492 2020-02-21
## 4810 4810           Lala      7.3     Bogor   Mutiara 10528 2019-05-26
## 4860 4860           Lala      7.3   Jakarta      Neon 10714 2019-01-17
## 4910 4910           Lala      7.3   Jakarta Sweethome 10126 2019-05-26
## 4960 4960           Lala      7.3   Jakarta   Albasia 11910 2018-09-03
## 5010 5010           Lala      7.3 Tengerang    Lavesh 11865 2019-01-03
## 5060 5060           Lala      7.3   Jakarta  Alamanda 10555 2018-08-18
## 5110 5110           Lala      7.3   Jakarta  Victoria 10823 2018-12-06
## 5160 5160           Lala      7.3   Jakarta   Albasia  9187 2018-05-08
## 5210 5210           Lala      7.3   Jakarta   Mutiara 14768 2019-11-14
## 5260 5260           Lala      7.3   Jakarta   Albasia 11928 2020-01-07
## 5310 5310           Lala      7.3   Jakarta     Adara  9990 2020-06-06
## 5360 5360           Lala      7.3   Jakarta    Narada  8340 2019-04-05
## 5410 5410           Lala      7.3   Jakarta     Asoka  7760 2018-04-06
## 5460 5460           Lala      7.3     Bogor Teradamai 13703 2019-03-27
## 5510 5510           Lala      7.3     Bogor     Asoka 10654 2018-06-24
## 5560 5560           Lala      7.3     Bogor    Narada  9051 2018-04-11
## 5610 5610           Lala      7.3    Bekasi   Alindra 14662 2018-03-21
## 5660 5660           Lala      7.3     Bogor   Permata  8293 2020-01-20
## 5710 5710           Lala      7.3 Tengerang   Peronia  8566 2018-09-03
## 5760 5760           Lala      7.3     Depok  Alamanda  7782 2019-05-30
## 5810 5810           Lala      7.3     Bogor     Adara 14502 2018-04-16
## 5860 5860           Lala      7.3   Jakarta     Tiara  7766 2018-08-18
## 5910 5910           Lala      7.3    Bekasi     Tiara 14108 2019-01-12
## 5960 5960           Lala      7.3   Jakarta     Tiara 12713 2018-09-11
## 6010 6010           Lala      7.3     Depok    Narada  9520 2019-10-19
## 6060 6060           Lala      7.3 Tengerang Sweethome 11188 2020-01-26
## 6110 6110           Lala      7.3    Bekasi   Peronia 12356 2020-08-22
## 6160 6160           Lala      7.3   Jakarta   Alindra  7438 2018-10-15
## 6210 6210           Lala      7.3   Jakarta      Neon 10293 2018-12-16
## 6260 6260           Lala      7.3    Bekasi    Narada 14942 2019-09-26
## 6310 6310           Lala      7.3     Bogor     Asoka  9448 2020-08-22
## 6360 6360           Lala      7.3     Bogor   Palmyra 11207 2019-11-29
## 6410 6410           Lala      7.3    Bekasi     Asera  8608 2019-07-14
## 6460 6460           Lala      7.3     Depok   Albasia 11832 2020-07-22
## 6510 6510           Lala      7.3     Depok    Narada 14956 2019-04-15
## 6560 6560           Lala      7.3     Depok   Peronia 10613 2020-05-24
## 6610 6610           Lala      7.3   Jakarta Teradamai  8343 2018-07-12
## 6660 6660           Lala      7.3     Depok      Neon 14938 2018-02-24
## 6710 6710           Lala      7.3   Jakarta Teradamai 13054 2019-05-01
## 6760 6760           Lala      7.3 Tengerang      Neon 10371 2018-02-28
## 6810 6810           Lala      7.3 Tengerang    Winona  8502 2019-06-16
## 6860 6860           Lala      7.3 Tengerang     Adara 10789 2019-04-26
## 6910 6910           Lala      7.3    Bekasi    Narada 12742 2019-03-26
## 6960 6960           Lala      7.3     Bogor  Alamanda  7308 2019-08-21
## 7010 7010           Lala      7.3   Jakarta    Lavesh 10189 2019-07-07
## 7060 7060           Lala      7.3   Jakarta   Albasia  8593 2019-04-16
## 7110 7110           Lala      7.3   Jakarta Primadona  8932 2019-10-24
## 7160 7160           Lala      7.3   Jakarta    Narada 11863 2020-04-29
## 7210 7210           Lala      7.3 Tengerang   Mutiara 11476 2018-05-26
## 7260 7260           Lala      7.3 Tengerang   Permata 11097 2020-07-16
## 7310 7310           Lala      7.3    Bekasi    Narada 14396 2018-12-21
## 7360 7360           Lala      7.3   Jakarta   Peronia  7417 2020-09-14
## 7410 7410           Lala      7.3   Jakarta   Albasia  8152 2018-02-21
## 7460 7460           Lala      7.3    Bekasi Sweethome  8794 2019-07-06
## 7510 7510           Lala      7.3     Bogor   Alindra  7047 2018-08-20
## 7560 7560           Lala      7.3   Jakarta Teradamai 12712 2018-12-22
## 7610 7610           Lala      7.3    Bekasi     Asera 10994 2020-06-27
## 7660 7660           Lala      7.3   Jakarta   Permata  9448 2019-05-21
## 7710 7710           Lala      7.3 Tengerang   Albasia 14119 2018-04-25
## 7760 7760           Lala      7.3    Bekasi  Alamanda  8977 2020-06-25
## 7810 7810           Lala      7.3 Tengerang     Arana 12157 2019-08-30
## 7860 7860           Lala      7.3   Jakarta   Palmyra  8212 2020-06-03
## 7910 7910           Lala      7.3     Depok     Tiara 14692 2020-07-12
## 7960 7960           Lala      7.3     Bogor   Permata  7490 2019-05-24
## 8010 8010           Lala      7.3   Jakarta  Victoria 11113 2019-04-16
## 8060 8060           Lala      7.3   Jakarta   Permata 11984 2019-02-03
## 8110 8110           Lala      7.3     Depok    Winona 13866 2018-08-19
## 8160 8160           Lala      7.3     Depok Primadona  8621 2018-01-30
## 8210 8210           Lala      7.3     Bogor Teradamai 12270 2018-07-03
## 8260 8260           Lala      7.3     Bogor Sweethome  9260 2018-11-24
## 8310 8310           Lala      7.3     Depok   Palmyra  9437 2019-12-14
## 8360 8360           Lala      7.3     Bogor  Alamanda 14665 2018-09-08
## 8410 8410           Lala      7.3   Jakarta     Asera 11419 2018-07-09
## 8460 8460           Lala      7.3     Depok   Peronia 14831 2018-10-01
## 8510 8510           Lala      7.3   Jakarta    Narada 13735 2020-04-30
## 8560 8560           Lala      7.3   Jakarta Sweethome 11793 2020-03-29
## 8610 8610           Lala      7.3     Depok  Victoria 12082 2019-07-25
## 8660 8660           Lala      7.3    Bekasi   Alindra  9739 2018-04-13
## 8710 8710           Lala      7.3 Tengerang   Peronia 12553 2018-05-24
## 8760 8760           Lala      7.3     Bogor   Alindra 11505 2019-08-12
## 8810 8810           Lala      7.3    Bekasi   Alindra  8785 2018-08-08
## 8860 8860           Lala      7.3    Bekasi      Neon  7896 2018-06-08
## 8910 8910           Lala      7.3     Depok   Mutiara  9219 2018-05-03
## 8960 8960           Lala      7.3 Tengerang   Peronia 12213 2018-12-11
## 9010 9010           Lala      7.3     Bogor   Permata 14582 2018-09-26
## 9060 9060           Lala      7.3   Jakarta Teradamai 11685 2018-11-20
## 9110 9110           Lala      7.3   Jakarta      Neon 14282 2019-06-18
## 9160 9160           Lala      7.3     Depok   Mutiara  8316 2019-06-08
## 9210 9210           Lala      7.3    Bekasi     Arana  7576 2019-05-22
## 9260 9260           Lala      7.3     Depok Primadona 13372 2019-06-03
## 9310 9310           Lala      7.3     Depok   Palmyra 13099 2018-04-28
## 9360 9360           Lala      7.3   Jakarta      Neon  8824 2020-02-10
## 9410 9410           Lala      7.3     Bogor   Palmyra 13166 2019-09-05
## 9460 9460           Lala      7.3   Jakarta    Narada 11265 2019-11-04
## 9510 9510           Lala      7.3 Tengerang  Victoria  8865 2018-10-25
## 9560 9560           Lala      7.3   Jakarta  Alamanda 10588 2018-05-12
## 9610 9610           Lala      7.3     Depok  Alamanda  8230 2019-09-28
## 9660 9660           Lala      7.3 Tengerang   Mutiara 12355 2019-05-20
## 9710 9710           Lala      7.3    Bekasi     Adara  8009 2019-02-19
## 9760 9760           Lala      7.3     Bogor     Tiara 14258 2020-05-03
## 9810 9810           Lala      7.3     Depok Teradamai 13860 2019-09-27
## 9860 9860           Lala      7.3     Bogor     Asera 10900 2019-11-17
## 9910 9910           Lala      7.3 Tengerang Teradamai 11355 2018-01-25
## 9960 9960           Lala      7.3 Tengerang     Asera  9298 2018-04-20
##      Advertisement  Class Booking_Fee
## 10               4   High        0.10
## 60              19   High        0.09
## 110              3   High        0.10
## 160             19    Low        0.05
## 210             19 Medium        0.08
## 260             18    Low        0.06
## 310             14   High        0.10
## 360             13    Low        0.06
## 410             10 Medium        0.09
## 460              3 Medium        0.08
## 510              1   High        0.09
## 560             11   High        0.10
## 610             11   High        0.09
## 660              9    Low        0.05
## 710              3    Low        0.06
## 760             12    Low        0.07
## 810              1   High        0.10
## 860             18    Low        0.05
## 910             10    Low        0.07
## 960              2    Low        0.05
## 1010             2    Low        0.06
## 1060            17   High        0.09
## 1110            20 Medium        0.09
## 1160            10   High        0.09
## 1210             6   High        0.10
## 1260             3   High        0.10
## 1310            14    Low        0.07
## 1360            10   High        0.10
## 1410             7 Medium        0.08
## 1460            12    Low        0.06
## 1510             8    Low        0.06
## 1560            14 Medium        0.08
## 1610            13    Low        0.07
## 1660            16 Medium        0.09
## 1710             7   High        0.10
## 1760             8   High        0.10
## 1810            19    Low        0.06
## 1860            20 Medium        0.08
## 1910            15    Low        0.05
## 1960            12    Low        0.05
## 2010            20    Low        0.06
## 2060             5    Low        0.05
## 2110            14   High        0.09
## 2160            10   High        0.10
## 2210             7 Medium        0.08
## 2260             4    Low        0.05
## 2310            11    Low        0.06
## 2360            17 Medium        0.08
## 2410             1    Low        0.05
## 2460             6   High        0.09
## 2510             5    Low        0.06
## 2560             2    Low        0.05
## 2610            15   High        0.10
## 2660             7    Low        0.06
## 2710            11    Low        0.07
## 2760             8    Low        0.07
## 2810            13    Low        0.05
## 2860             3   High        0.10
## 2910            15 Medium        0.09
## 2960             5   High        0.10
## 3010             2    Low        0.06
## 3060            20   High        0.10
## 3110             9   High        0.10
## 3160            15   High        0.10
## 3210             1 Medium        0.08
## 3260            13    Low        0.07
## 3310            20   High        0.10
## 3360             1   High        0.10
## 3410            12   High        0.09
## 3460            18   High        0.10
## 3510             5   High        0.09
## 3560            12    Low        0.07
## 3610            11 Medium        0.09
## 3660             8    Low        0.05
## 3710            12   High        0.10
## 3760             1   High        0.09
## 3810             6 Medium        0.09
## 3860            19   High        0.09
## 3910             5    Low        0.07
## 3960             6    Low        0.06
## 4010             3 Medium        0.09
## 4060            18   High        0.10
## 4110            17   High        0.10
## 4160            17 Medium        0.08
## 4210             9    Low        0.07
## 4260             6   High        0.09
## 4310             2 Medium        0.08
## 4360            20    Low        0.07
## 4410            16    Low        0.06
## 4460            16    Low        0.05
## 4510            20   High        0.10
## 4560            14 Medium        0.08
## 4610             5   High        0.10
## 4660            13    Low        0.05
## 4710             8    Low        0.05
## 4760             3   High        0.09
## 4810            20 Medium        0.08
## 4860             3 Medium        0.08
## 4910            16 Medium        0.08
## 4960             6 Medium        0.09
## 5010            19 Medium        0.09
## 5060             4 Medium        0.08
## 5110             9 Medium        0.08
## 5160             1    Low        0.07
## 5210            15   High        0.10
## 5260             7 Medium        0.09
## 5310             1    Low        0.07
## 5360            15    Low        0.06
## 5410             2    Low        0.05
## 5460             9   High        0.10
## 5510             7 Medium        0.08
## 5560             6    Low        0.07
## 5610            16   High        0.10
## 5660            13    Low        0.06
## 5710            13    Low        0.06
## 5760            12    Low        0.05
## 5810            19   High        0.10
## 5860            12    Low        0.05
## 5910            14   High        0.10
## 5960             3   High        0.09
## 6010             3    Low        0.07
## 6060            13 Medium        0.09
## 6110            14   High        0.09
## 6160             8    Low        0.05
## 6210            16 Medium        0.08
## 6260             7   High        0.10
## 6310            20    Low        0.07
## 6360             7 Medium        0.09
## 6410            13    Low        0.06
## 6460             5 Medium        0.09
## 6510            10   High        0.10
## 6560            14 Medium        0.08
## 6610            18    Low        0.06
## 6660             6   High        0.10
## 6710             6   High        0.10
## 6760            10 Medium        0.08
## 6810            11    Low        0.06
## 6860            12 Medium        0.08
## 6910             6   High        0.09
## 6960             1    Low        0.05
## 7010             8 Medium        0.08
## 7060            11    Low        0.06
## 7110             1    Low        0.06
## 7160             6 Medium        0.09
## 7210            18 Medium        0.09
## 7260             7 Medium        0.09
## 7310             7   High        0.10
## 7360            11    Low        0.05
## 7410            17    Low        0.06
## 7460            16    Low        0.06
## 7510            19    Low        0.05
## 7560             8   High        0.09
## 7610             4 Medium        0.08
## 7660             7    Low        0.07
## 7710            10   High        0.10
## 7760            19    Low        0.06
## 7810            17   High        0.09
## 7860             1    Low        0.06
## 7910             7   High        0.10
## 7960             9    Low        0.05
## 8010             9 Medium        0.09
## 8060             7 Medium        0.09
## 8110             3   High        0.10
## 8160             4    Low        0.06
## 8210            14   High        0.09
## 8260            14    Low        0.07
## 8310            17    Low        0.07
## 8360            20   High        0.10
## 8410            17 Medium        0.09
## 8460            19   High        0.10
## 8510            10   High        0.10
## 8560            12 Medium        0.09
## 8610            12   High        0.09
## 8660             8    Low        0.07
## 8710            19   High        0.09
## 8760             4 Medium        0.09
## 8810             3    Low        0.06
## 8860             3    Low        0.05
## 8910            10    Low        0.07
## 8960             3   High        0.09
## 9010            13   High        0.10
## 9060             7 Medium        0.09
## 9110            11   High        0.10
## 9160             4    Low        0.06
## 9210            10    Low        0.05
## 9260            10   High        0.10
## 9310             5   High        0.10
## 9360            18    Low        0.06
## 9410             2   High        0.10
## 9460             3 Medium        0.09
## 9510             3    Low        0.06
## 9560            14 Medium        0.08
## 9610            13    Low        0.06
## 9660            13   High        0.09
## 9710             7    Low        0.06
## 9760            20   High        0.10
## 9810            11   High        0.10
## 9860            10 Medium        0.08
## 9910             3 Medium        0.09
## 9960            14    Low        0.07

# Contoh mengumpulkan data penjualan Jeffry

library(DT)

penjual = "Jeffry"

for (x in penjual){
  print(subset(Data, subset=(Marketing_Name == x)))
}

##        Id Marketing_Name Work_Exp      City   Cluster Price Date_Sales
## 6       6         Jeffry      4.7     Depok  Alamanda  7856 2018-02-17
## 56     56         Jeffry      4.7 Tengerang   Alindra 12960 2018-01-20
## 106   106         Jeffry      4.7 Tengerang   Permata 13052 2018-11-06
## 156   156         Jeffry      4.7     Bogor Sweethome 10238 2018-08-05
## 206   206         Jeffry      4.7 Tengerang     Tiara  8553 2020-06-08
## 256   256         Jeffry      4.7   Jakarta     Asera 13589 2019-09-03
## 306   306         Jeffry      4.7     Depok Sweethome 12918 2018-04-21
## 356   356         Jeffry      4.7    Bekasi Teradamai 13269 2020-04-28
## 406   406         Jeffry      4.7    Bekasi  Victoria 10587 2020-01-19
## 456   456         Jeffry      4.7    Bekasi   Mutiara 14217 2018-04-22
## 506   506         Jeffry      4.7 Tengerang     Tiara  9274 2019-11-18
## 556   556         Jeffry      4.7     Depok    Winona  8095 2018-01-10
## 606   606         Jeffry      4.7   Jakarta   Mutiara 11740 2019-11-10
## 656   656         Jeffry      4.7    Bekasi Primadona 13816 2019-08-02
## 706   706         Jeffry      4.7     Bogor   Permata 13740 2019-03-27
## 756   756         Jeffry      4.7     Bogor     Arana  8104 2019-04-28
## 806   806         Jeffry      4.7   Jakarta  Alamanda 11770 2020-07-26
## 856   856         Jeffry      4.7     Bogor     Adara 12495 2018-10-16
## 906   906         Jeffry      4.7 Tengerang   Permata 12402 2018-05-15
## 956   956         Jeffry      4.7     Depok    Lavesh  9454 2018-09-05
## 1006 1006         Jeffry      4.7    Bekasi     Tiara 11718 2019-01-25
## 1056 1056         Jeffry      4.7   Jakarta      Neon 14104 2019-09-17
## 1106 1106         Jeffry      4.7     Depok   Alindra  7397 2018-02-03
## 1156 1156         Jeffry      4.7 Tengerang     Adara  9747 2019-12-27
## 1206 1206         Jeffry      4.7 Tengerang   Peronia 10847 2019-06-17
## 1256 1256         Jeffry      4.7     Depok Sweethome 10055 2019-12-23
## 1306 1306         Jeffry      4.7    Bekasi    Winona 11449 2019-07-07
## 1356 1356         Jeffry      4.7   Jakarta   Alindra 10516 2019-11-24
## 1406 1406         Jeffry      4.7   Jakarta     Adara  8956 2018-07-09
## 1456 1456         Jeffry      4.7     Depok   Permata 10168 2019-04-15
## 1506 1506         Jeffry      4.7 Tengerang     Tiara 13466 2019-04-04
## 1556 1556         Jeffry      4.7    Bekasi   Palmyra 13076 2019-04-30
## 1606 1606         Jeffry      4.7   Jakarta     Asera  8705 2019-08-16
## 1656 1656         Jeffry      4.7     Depok Primadona 12248 2018-06-12
## 1706 1706         Jeffry      4.7 Tengerang     Asoka 10750 2018-04-17
## 1756 1756         Jeffry      4.7     Depok Sweethome  9640 2020-01-20
## 1806 1806         Jeffry      4.7   Jakarta Teradamai  9593 2019-07-12
## 1856 1856         Jeffry      4.7 Tengerang   Peronia 12893 2019-12-08
## 1906 1906         Jeffry      4.7     Bogor   Palmyra  8614 2019-01-26
## 1956 1956         Jeffry      4.7   Jakarta   Permata 13894 2018-05-04
## 2006 2006         Jeffry      4.7    Bekasi    Lavesh 13540 2019-10-04
## 2056 2056         Jeffry      4.7   Jakarta   Mutiara  8097 2019-09-25
## 2106 2106         Jeffry      4.7     Depok     Tiara 13548 2018-09-19
## 2156 2156         Jeffry      4.7 Tengerang   Alindra 10106 2019-12-16
## 2206 2206         Jeffry      4.7   Jakarta  Alamanda 10359 2018-02-07
## 2256 2256         Jeffry      4.7     Depok  Victoria 12682 2020-09-06
## 2306 2306         Jeffry      4.7     Depok     Arana  9676 2019-12-04
## 2356 2356         Jeffry      4.7   Jakarta   Albasia  9455 2020-04-15
## 2406 2406         Jeffry      4.7     Bogor Teradamai 12724 2018-06-15
## 2456 2456         Jeffry      4.7   Jakarta    Lavesh 12479 2020-03-05
## 2506 2506         Jeffry      4.7 Tengerang     Adara 14439 2020-05-11
## 2556 2556         Jeffry      4.7    Bekasi   Permata  7227 2018-08-19
## 2606 2606         Jeffry      4.7    Bekasi  Alamanda 13070 2019-09-29
## 2656 2656         Jeffry      4.7     Bogor Sweethome  8366 2019-01-04
## 2706 2706         Jeffry      4.7    Bekasi    Lavesh  7388 2020-01-09
## 2756 2756         Jeffry      4.7    Bekasi Teradamai 11183 2018-04-07
## 2806 2806         Jeffry      4.7    Bekasi   Mutiara 10983 2019-06-19
## 2856 2856         Jeffry      4.7     Bogor Primadona 12133 2019-11-26
## 2906 2906         Jeffry      4.7 Tengerang    Narada  8779 2020-01-06
## 2956 2956         Jeffry      4.7     Bogor  Alamanda 13234 2018-10-05
## 3006 3006         Jeffry      4.7   Jakarta Teradamai 12579 2018-12-03
## 3056 3056         Jeffry      4.7 Tengerang      Neon  9745 2018-12-27
## 3106 3106         Jeffry      4.7    Bekasi  Alamanda 14293 2019-01-04
## 3156 3156         Jeffry      4.7     Bogor Teradamai  7751 2019-01-05
## 3206 3206         Jeffry      4.7 Tengerang      Neon 13012 2019-05-06
## 3256 3256         Jeffry      4.7 Tengerang    Narada  8555 2018-03-07
## 3306 3306         Jeffry      4.7    Bekasi  Alamanda 13324 2020-07-19
## 3356 3356         Jeffry      4.7    Bekasi    Winona 14760 2018-11-16
## 3406 3406         Jeffry      4.7    Bekasi Primadona  7515 2020-05-29
## 3456 3456         Jeffry      4.7   Jakarta     Asoka  8277 2019-02-01
## 3506 3506         Jeffry      4.7 Tengerang   Palmyra  8818 2019-08-18
## 3556 3556         Jeffry      4.7 Tengerang     Tiara 14636 2019-07-08
## 3606 3606         Jeffry      4.7     Depok     Adara 14665 2019-12-29
## 3656 3656         Jeffry      4.7   Jakarta Primadona  7110 2018-07-23
## 3706 3706         Jeffry      4.7    Bekasi     Adara 11376 2019-08-11
## 3756 3756         Jeffry      4.7    Bekasi      Neon 13560 2019-03-11
## 3806 3806         Jeffry      4.7     Bogor     Asoka 11773 2018-07-09
## 3856 3856         Jeffry      4.7     Depok   Palmyra 14764 2020-06-21
## 3906 3906         Jeffry      4.7     Depok     Tiara  8523 2019-06-29
## 3956 3956         Jeffry      4.7   Jakarta   Mutiara 14326 2019-05-07
## 4006 4006         Jeffry      4.7     Depok     Asoka 11348 2020-01-20
## 4056 4056         Jeffry      4.7     Bogor    Winona 13383 2018-09-21
## 4106 4106         Jeffry      4.7     Bogor Sweethome 13653 2020-04-13
## 4156 4156         Jeffry      4.7 Tengerang   Albasia 12536 2018-07-18
## 4206 4206         Jeffry      4.7   Jakarta   Albasia  9591 2018-01-10
## 4256 4256         Jeffry      4.7    Bekasi   Alindra  8844 2019-03-13
## 4306 4306         Jeffry      4.7    Bekasi    Lavesh  7793 2019-12-28
## 4356 4356         Jeffry      4.7     Depok Sweethome 11668 2020-05-08
## 4406 4406         Jeffry      4.7 Tengerang    Lavesh  8874 2020-03-11
## 4456 4456         Jeffry      4.7   Jakarta  Alamanda 14523 2019-03-28
## 4506 4506         Jeffry      4.7     Bogor Teradamai 14326 2020-03-05
## 4556 4556         Jeffry      4.7     Depok   Albasia 10314 2019-06-04
## 4606 4606         Jeffry      4.7     Bogor Teradamai 12509 2019-12-24
## 4656 4656         Jeffry      4.7 Tengerang Primadona  9597 2019-11-20
## 4706 4706         Jeffry      4.7 Tengerang   Permata 11012 2018-06-24
## 4756 4756         Jeffry      4.7   Jakarta     Arana 13527 2019-01-26
## 4806 4806         Jeffry      4.7   Jakarta     Asera  9108 2019-08-16
## 4856 4856         Jeffry      4.7   Jakarta Primadona  9911 2018-09-21
## 4906 4906         Jeffry      4.7    Bekasi     Arana  8222 2019-06-27
## 4956 4956         Jeffry      4.7     Depok   Albasia 14580 2019-10-23
## 5006 5006         Jeffry      4.7   Jakarta     Arana 12875 2018-06-16
## 5056 5056         Jeffry      4.7 Tengerang   Mutiara 10357 2019-02-06
## 5106 5106         Jeffry      4.7 Tengerang    Lavesh 13034 2018-06-02
## 5156 5156         Jeffry      4.7     Depok Teradamai 13814 2019-11-13
## 5206 5206         Jeffry      4.7     Depok Sweethome 11495 2018-04-10
## 5256 5256         Jeffry      4.7     Bogor      Neon 10621 2019-03-18
## 5306 5306         Jeffry      4.7 Tengerang      Neon 14132 2019-12-18
## 5356 5356         Jeffry      4.7    Bekasi    Lavesh 14417 2018-09-05
## 5406 5406         Jeffry      4.7     Bogor     Asoka 11941 2020-08-26
## 5456 5456         Jeffry      4.7    Bekasi   Peronia 14437 2018-07-05
## 5506 5506         Jeffry      4.7     Bogor     Asoka 10822 2020-01-18
## 5556 5556         Jeffry      4.7     Depok     Asera 10223 2018-09-18
## 5606 5606         Jeffry      4.7     Depok   Mutiara 11924 2019-10-22
## 5656 5656         Jeffry      4.7     Depok     Adara 13163 2020-07-25
## 5706 5706         Jeffry      4.7     Bogor   Alindra  9360 2019-03-03
## 5756 5756         Jeffry      4.7    Bekasi   Permata 11429 2018-08-10
## 5806 5806         Jeffry      4.7 Tengerang     Adara 14130 2019-07-15
## 5856 5856         Jeffry      4.7     Bogor     Tiara 10551 2020-06-08
## 5906 5906         Jeffry      4.7    Bekasi     Arana 13128 2019-05-07
## 5956 5956         Jeffry      4.7    Bekasi     Arana 12667 2019-08-11
## 6006 6006         Jeffry      4.7     Depok    Narada 13578 2018-01-14
## 6056 6056         Jeffry      4.7     Bogor   Mutiara  7363 2019-11-28
## 6106 6106         Jeffry      4.7     Depok   Albasia  7200 2019-12-14
## 6156 6156         Jeffry      4.7    Bekasi      Neon 11609 2019-06-01
## 6206 6206         Jeffry      4.7     Bogor   Palmyra 12108 2019-06-02
## 6256 6256         Jeffry      4.7    Bekasi Teradamai  7228 2019-12-20
## 6306 6306         Jeffry      4.7     Depok  Alamanda 10433 2018-12-28
## 6356 6356         Jeffry      4.7   Jakarta  Victoria 14514 2019-05-15
## 6406 6406         Jeffry      4.7   Jakarta     Asera 14956 2018-08-02
## 6456 6456         Jeffry      4.7    Bekasi     Tiara 14376 2020-09-18
## 6506 6506         Jeffry      4.7     Bogor     Tiara  9052 2018-01-29
## 6556 6556         Jeffry      4.7 Tengerang Sweethome  7488 2018-05-24
## 6606 6606         Jeffry      4.7     Depok Teradamai  7358 2019-01-19
## 6656 6656         Jeffry      4.7 Tengerang   Alindra  8056 2019-08-15
## 6706 6706         Jeffry      4.7   Jakarta    Narada  7449 2020-07-02
## 6756 6756         Jeffry      4.7     Bogor Teradamai 10044 2020-03-18
## 6806 6806         Jeffry      4.7    Bekasi Sweethome 11241 2020-08-05
## 6856 6856         Jeffry      4.7   Jakarta     Adara 12449 2019-08-08
## 6906 6906         Jeffry      4.7    Bekasi   Peronia  7244 2018-11-22
## 6956 6956         Jeffry      4.7   Jakarta    Lavesh 11169 2020-02-04
## 7006 7006         Jeffry      4.7     Depok  Victoria 12826 2020-06-14
## 7056 7056         Jeffry      4.7 Tengerang   Alindra 11604 2018-09-17
## 7106 7106         Jeffry      4.7 Tengerang    Narada 11800 2019-11-17
## 7156 7156         Jeffry      4.7     Bogor     Adara 10414 2018-06-28
## 7206 7206         Jeffry      4.7     Bogor     Tiara  7618 2020-04-09
## 7256 7256         Jeffry      4.7     Bogor  Alamanda 11830 2018-12-09
## 7306 7306         Jeffry      4.7 Tengerang   Permata 12145 2018-08-28
## 7356 7356         Jeffry      4.7     Bogor  Alamanda  8349 2018-11-15
## 7406 7406         Jeffry      4.7     Depok   Peronia 14714 2018-06-25
## 7456 7456         Jeffry      4.7   Jakarta Primadona 13763 2019-09-27
## 7506 7506         Jeffry      4.7   Jakarta   Palmyra  7771 2018-02-24
## 7556 7556         Jeffry      4.7     Bogor    Lavesh  7817 2020-07-15
## 7606 7606         Jeffry      4.7     Bogor     Asoka  7759 2019-01-08
## 7656 7656         Jeffry      4.7   Jakarta      Neon 13812 2019-08-01
## 7706 7706         Jeffry      4.7     Depok Teradamai 14727 2019-01-16
## 7756 7756         Jeffry      4.7   Jakarta     Adara 14396 2020-01-19
## 7806 7806         Jeffry      4.7   Jakarta  Victoria  7275 2019-01-13
## 7856 7856         Jeffry      4.7    Bekasi     Arana 11698 2018-12-14
## 7906 7906         Jeffry      4.7   Jakarta     Asoka 10012 2019-02-19
## 7956 7956         Jeffry      4.7 Tengerang Teradamai  8803 2020-07-18
## 8006 8006         Jeffry      4.7     Bogor     Asera  7212 2020-04-01
## 8056 8056         Jeffry      4.7    Bekasi Primadona  7911 2018-07-14
## 8106 8106         Jeffry      4.7     Depok    Winona 11325 2018-07-02
## 8156 8156         Jeffry      4.7   Jakarta   Mutiara 10797 2018-01-17
## 8206 8206         Jeffry      4.7     Bogor Teradamai  8887 2020-09-23
## 8256 8256         Jeffry      4.7     Depok   Palmyra 13998 2019-06-26
## 8306 8306         Jeffry      4.7     Bogor   Alindra 10856 2018-06-16
## 8356 8356         Jeffry      4.7    Bekasi    Lavesh 11962 2018-12-13
## 8406 8406         Jeffry      4.7     Depok   Mutiara 11578 2019-09-22
## 8456 8456         Jeffry      4.7     Bogor    Lavesh  7612 2019-04-23
## 8506 8506         Jeffry      4.7    Bekasi  Victoria 11769 2018-03-17
## 8556 8556         Jeffry      4.7    Bekasi  Alamanda 14512 2020-05-01
## 8606 8606         Jeffry      4.7    Bekasi  Alamanda 14323 2018-11-27
## 8656 8656         Jeffry      4.7 Tengerang     Asera 13543 2019-03-22
## 8706 8706         Jeffry      4.7    Bekasi    Winona 10802 2018-06-03
## 8756 8756         Jeffry      4.7     Depok      Neon 12754 2018-05-17
## 8806 8806         Jeffry      4.7     Depok   Mutiara  8091 2018-05-14
## 8856 8856         Jeffry      4.7 Tengerang Sweethome 11322 2019-06-09
## 8906 8906         Jeffry      4.7     Depok   Peronia 12487 2020-01-09
## 8956 8956         Jeffry      4.7 Tengerang Teradamai  7858 2019-07-26
## 9006 9006         Jeffry      4.7     Bogor   Mutiara 10753 2019-02-11
## 9056 9056         Jeffry      4.7    Bekasi Primadona  9193 2020-09-15
## 9106 9106         Jeffry      4.7 Tengerang    Winona  9555 2020-07-27
## 9156 9156         Jeffry      4.7     Depok     Asoka 12705 2019-10-24
## 9206 9206         Jeffry      4.7     Depok   Alindra  9667 2018-09-08
## 9256 9256         Jeffry      4.7 Tengerang     Asera 13717 2018-01-30
## 9306 9306         Jeffry      4.7     Depok    Lavesh 10608 2020-08-23
## 9356 9356         Jeffry      4.7    Bekasi    Narada 11169 2018-05-07
## 9406 9406         Jeffry      4.7 Tengerang      Neon 10003 2019-10-03
## 9456 9456         Jeffry      4.7   Jakarta     Arana 14217 2020-03-02
## 9506 9506         Jeffry      4.7 Tengerang      Neon 14062 2020-08-02
## 9556 9556         Jeffry      4.7     Depok   Peronia 10845 2020-07-16
## 9606 9606         Jeffry      4.7     Bogor     Asera 11142 2019-07-19
## 9656 9656         Jeffry      4.7    Bekasi   Permata 10720 2018-04-29
## 9706 9706         Jeffry      4.7     Depok     Asoka 10234 2018-02-26
## 9756 9756         Jeffry      4.7     Bogor   Peronia 14886 2020-03-13
## 9806 9806         Jeffry      4.7     Bogor  Alamanda 13385 2018-07-18
## 9856 9856         Jeffry      4.7   Jakarta Sweethome 11345 2019-11-02
## 9906 9906         Jeffry      4.7     Depok   Mutiara 14814 2018-10-12
## 9956 9956         Jeffry      4.7   Jakarta Teradamai 11948 2018-05-07
##      Advertisement  Class Booking_Fee
## 6                3    Low        0.05
## 56               9   High        0.09
## 106              2   High        0.10
## 156              9 Medium        0.08
## 206              9    Low        0.06
## 256             16   High        0.10
## 306             13   High        0.09
## 356              8   High        0.10
## 406              7 Medium        0.08
## 456              2   High        0.10
## 506              1    Low        0.07
## 556             18    Low        0.06
## 606             16 Medium        0.09
## 656              1   High        0.10
## 706             16   High        0.10
## 756              8    Low        0.06
## 806              2 Medium        0.09
## 856              5   High        0.09
## 906              4   High        0.09
## 956              9    Low        0.07
## 1006             8 Medium        0.09
## 1056             3   High        0.10
## 1106             8    Low        0.05
## 1156            11    Low        0.07
## 1206            14 Medium        0.08
## 1256             4 Medium        0.08
## 1306            18 Medium        0.09
## 1356            12 Medium        0.08
## 1406             5    Low        0.06
## 1456             9 Medium        0.08
## 1506             1   High        0.10
## 1556            16   High        0.10
## 1606            20    Low        0.06
## 1656            11   High        0.09
## 1706             7 Medium        0.08
## 1756            19    Low        0.07
## 1806            12    Low        0.07
## 1856             8   High        0.09
## 1906            19    Low        0.06
## 1956             3   High        0.10
## 2006             8   High        0.10
## 2056            10    Low        0.06
## 2106            18   High        0.10
## 2156             8 Medium        0.08
## 2206             9 Medium        0.08
## 2256            10   High        0.09
## 2306             2    Low        0.07
## 2356            11    Low        0.07
## 2406             2   High        0.09
## 2456             8   High        0.09
## 2506            12   High        0.10
## 2556            17    Low        0.05
## 2606            12   High        0.10
## 2656            16    Low        0.06
## 2706            11    Low        0.05
## 2756            10 Medium        0.09
## 2806            14 Medium        0.08
## 2856             4   High        0.09
## 2906             6    Low        0.06
## 2956            19   High        0.10
## 3006            12   High        0.09
## 3056             5    Low        0.07
## 3106             5   High        0.10
## 3156             9    Low        0.05
## 3206            20   High        0.10
## 3256             6    Low        0.06
## 3306            17   High        0.10
## 3356            13   High        0.10
## 3406             1    Low        0.05
## 3456            12    Low        0.06
## 3506            14    Low        0.06
## 3556             2   High        0.10
## 3606            17   High        0.10
## 3656             3    Low        0.05
## 3706             6 Medium        0.09
## 3756            10   High        0.10
## 3806            19 Medium        0.09
## 3856             9   High        0.10
## 3906             1    Low        0.06
## 3956             5   High        0.10
## 4006            10 Medium        0.09
## 4056             4   High        0.10
## 4106             3   High        0.10
## 4156            16   High        0.09
## 4206             1    Low        0.07
## 4256            19    Low        0.06
## 4306             2    Low        0.05
## 4356             9 Medium        0.09
## 4406             5    Low        0.06
## 4456            19   High        0.10
## 4506            12   High        0.10
## 4556             7 Medium        0.08
## 4606            13   High        0.09
## 4656            14    Low        0.07
## 4706            17 Medium        0.09
## 4756            13   High        0.10
## 4806             7    Low        0.07
## 4856             8    Low        0.07
## 4906             9    Low        0.06
## 4956            11   High        0.10
## 5006            19   High        0.09
## 5056             4 Medium        0.08
## 5106            19   High        0.10
## 5156             3   High        0.10
## 5206             9 Medium        0.09
## 5256             9 Medium        0.08
## 5306             6   High        0.10
## 5356             3   High        0.10
## 5406             5 Medium        0.09
## 5456             5   High        0.10
## 5506            19 Medium        0.08
## 5556            15 Medium        0.08
## 5606            18 Medium        0.09
## 5656            14   High        0.10
## 5706             2    Low        0.07
## 5756            13 Medium        0.09
## 5806            10   High        0.10
## 5856            13 Medium        0.08
## 5906            19   High        0.10
## 5956            10   High        0.09
## 6006             6   High        0.10
## 6056            11    Low        0.05
## 6106             6    Low        0.05
## 6156            20 Medium        0.09
## 6206             5   High        0.09
## 6256            19    Low        0.05
## 6306            17 Medium        0.08
## 6356             8   High        0.10
## 6406             7   High        0.10
## 6456             1   High        0.10
## 6506            16    Low        0.07
## 6556             6    Low        0.05
## 6606             1    Low        0.05
## 6656            14    Low        0.06
## 6706             2    Low        0.05
## 6756            16 Medium        0.08
## 6806             8 Medium        0.09
## 6856             9   High        0.09
## 6906            10    Low        0.05
## 6956            14 Medium        0.09
## 7006            13   High        0.09
## 7056             8 Medium        0.09
## 7106            15 Medium        0.09
## 7156            19 Medium        0.08
## 7206            13    Low        0.05
## 7256            10 Medium        0.09
## 7306            10   High        0.09
## 7356            14    Low        0.06
## 7406            17   High        0.10
## 7456            10   High        0.10
## 7506            17    Low        0.05
## 7556            19    Low        0.05
## 7606            11    Low        0.05
## 7656            14   High        0.10
## 7706             2   High        0.10
## 7756            16   High        0.10
## 7806             4    Low        0.05
## 7856            16 Medium        0.09
## 7906            20 Medium        0.08
## 7956            17    Low        0.06
## 8006             7    Low        0.05
## 8056            20    Low        0.05
## 8106            10 Medium        0.09
## 8156            18 Medium        0.08
## 8206            15    Low        0.06
## 8256             3   High        0.10
## 8306             2 Medium        0.08
## 8356            18 Medium        0.09
## 8406             5 Medium        0.09
## 8456            13    Low        0.05
## 8506            15 Medium        0.09
## 8556             7   High        0.10
## 8606            13   High        0.10
## 8656            14   High        0.10
## 8706            10 Medium        0.08
## 8756            20   High        0.09
## 8806             7    Low        0.06
## 8856             9 Medium        0.09
## 8906             9   High        0.09
## 8956            11    Low        0.05
## 9006             8 Medium        0.08
## 9056            14    Low        0.07
## 9106             9    Low        0.07
## 9156             1   High        0.09
## 9206             9    Low        0.07
## 9256            19   High        0.10
## 9306            15 Medium        0.08
## 9356            11 Medium        0.09
## 9406             9 Medium        0.08
## 9456            12   High        0.10
## 9506             7   High        0.10
## 9556             3 Medium        0.08
## 9606             1 Medium        0.09
## 9656             6 Medium        0.08
## 9706             7 Medium        0.08
## 9756            13   High        0.10
## 9806            16   High        0.10
## 9856            15 Medium        0.09
## 9906             4   High        0.10
## 9956             5 Medium        0.09

R

penjual= "Lala"

r <- subset(Data, subset=(Marketing_Name == penjual))

p= ifelse((r$Work_Exp <3 ),
          (r$Price * r$Booking_Fee) *(2/100),
          (r$Price * r$Booking_Fee) *(3/100))

r$Bonus =p
r

Bonus = sum(r$Bonus)
Bonus

## [1] 5404.431

R

sales = c("Angel","Sherly","Vanessa","Irene","Julian",
           "Jeffry","Nikita","Kefas","Siana","Lala",
           "Fallen","Ardifo","Kevin","Juen","Jerrel",
           "Imelda","Widi","Theodora","Elvani","Jonathan",
          "Sofia","Abraham","Siti","Niko","Sefli",
            "Bene", "Diana", "Pupe", "Andi", "Tatha",
          "Endri", "Monika", "Hans", "Debora","Hanifa",
            "James", "Jihan", "Friska","Ardiwan", "Bakti",
            "Anthon","Amry", "Wiwik", "Bastian", "Budi",
            "Leo","Simon","Matius","Arry", "Eliando")

Angel = subset(Data, subset=(Marketing_Name == "Angel"))
Sherly = subset(Data, subset=(Marketing_Name == "Sherly"))
Vanessa = subset(Data, subset=(Marketing_Name == "Vanessa"))
Irene = subset(Data, subset=(Marketing_Name == "Irene"))
Julian = subset(Data, subset=(Marketing_Name == "Julian"))
Jeffry = subset(Data, subset=(Marketing_Name == "Jeffry"))
Nikita = subset(Data, subset=(Marketing_Name == "Nikita"))
Kefas = subset(Data, subset=(Marketing_Name == "Kefas"))
Siana = subset(Data, subset=(Marketing_Name == "Siana"))
Lala = subset(Data, subset=(Marketing_Name == "Lala"))
Fallen = subset(Data, subset=(Marketing_Name == "Fallen"))
Ardifo = subset(Data, subset=(Marketing_Name == "Ardifo"))
Kevin = subset(Data, subset=(Marketing_Name == "Kevin"))
Juen = subset(Data, subset=(Marketing_Name == "Juen"))
Jerrel = subset(Data, subset=(Marketing_Name == "Jurrel"))
Imelda = subset(Data, subset=(Marketing_Name == "Imelda"))
Widi = subset(Data, subset=(Marketing_Name == "Widi"))
Theodora = subset(Data, subset=(Marketing_Name == "Theodora"))
Elvani = subset(Data, subset=(Marketing_Name == "Elvani"))
Jonathan = subset(Data, subset=(Marketing_Name == "Jonathan"))
Sofia = subset(Data, subset=(Marketing_Name == "Sofial"))
Abraham = subset(Data, subset=(Marketing_Name == "Abraham"))
Siti = subset(Data, subset=(Marketing_Name == "Siti"))
Niko = subset(Data, subset=(Marketing_Name == "Niko"))
Sefli = subset(Data, subset=(Marketing_Name == "Sefli"))
Bene = subset(Data, subset=(Marketing_Name == "Bene"))
Diana = subset(Data, subset=(Marketing_Name == "Diana"))
Pupe = subset(Data, subset=(Marketing_Name == "Pupe"))
Andi = subset(Data, subset=(Marketing_Name == "Andi"))
Tatha = subset(Data, subset=(Marketing_Name == "Tatha"))
Endri = subset(Data, subset=(Marketing_Name == "Endri"))
Monika = subset(Data, subset=(Marketing_Name == "Monika"))
Hans = subset(Data, subset=(Marketing_Name == "Hans"))
Debora = subset(Data, subset=(Marketing_Name == "Debora"))
Hanifa = subset(Data, subset=(Marketing_Name == "hanifa"))
James = subset(Data, subset=(Marketing_Name == "James"))
Jihan = subset(Data, subset=(Marketing_Name == "Jihan"))
Friska = subset(Data, subset=(Marketing_Name == "Friska"))
Ardiwan = subset(Data, subset=(Marketing_Name == "Ardiwan"))
Bakti = subset(Data, subset=(Marketing_Name == "Bakti"))
Anthon = subset(Data, subset=(Marketing_Name == "Anthon"))
Amry = subset(Data, subset=(Marketing_Name == "Amry"))
Wiwik = subset(Data, subset=(Marketing_Name == "Wiwik"))
Bastian = subset(Data, subset=(Marketing_Name == "Bastian"))
Budi = subset(Data, subset=(Marketing_Name == "Budi"))
Leo = subset(Data, subset=(Marketing_Name == "Leo"))
Simon = subset(Data, subset=(Marketing_Name == "Simon"))
Matius = subset(Data, subset=(Marketing_Name == "Matius"))
Arry = subset(Data, subset=(Marketing_Name == "Arry"))
Eliando = subset(Data, subset=(Marketing_Name == "Eliando"))

total = 
  c(sum(Angel$Price),sum(Sherly$Price),sum(Vanessa$Price),sum(Irene$Price),
    sum(Julian$Price),sum(Jeffry$Price),sum(Nikita$Price),sum(Kefas$Price),sum(Siana$Price),
    sum(Lala$Price),sum(Fallen$Price),sum(Ardifo$Price),sum(Kevin$Price),sum(Juen$Price),
    sum(Jerrel$Price),sum(Imelda$Price),sum(Widi$Price),sum(Theodora$Price),sum(Elvani$Price),
    sum(Jonathan$Price),sum(Sofia$Price),sum(Abraham$Price),sum(Siti$Price),sum(Niko$Price),
    sum(Sefli$Price),sum(Bene$Price),sum(Diana$Price),sum(Pupe$Price),sum(Andi$Price),
    sum(Tatha$Price),sum(Endri$Price),sum(Monika$Price),sum(Hans$Price),sum(Debora$Price),
    sum(Hanifa),sum(James$Price),sum(Jihan$Price),sum(Friska$Price),sum(Ardiwan$Price),
    sum(Bakti$Price),sum(Anthon$Price),sum(Amry$Price),sum(Wiwik$Price),sum(Bastian$Price),
    sum(Budi$Price),sum(Leo$Price),sum(Simon$Price),sum(Matius$Price),sum(Arry$Price),
    sum(Eliando$Price))

marketing = data.frame(sales,total)

marketing

# Siapa nama marketing pemasaran terbaik?

terbaik = which.max(marketing$total)
paling_terbaik = marketing[terbaik,]

paling_terbaik

city = c("Jakarta","Bogor","Tangerang","Depok","Bekasi")

Jakarta = subset(Data, subset=(City == "Jakarta"))
Bogor = subset(Data, subset=(City == "Bogor"))
Tangerang = subset(Data, subset=(City == "Tangerang"))
Depok = subset(Data, subset=(City == "Depok"))
Bekasi = subset(Data, subset=(City == "Bekasi"))

rata_city = c(sum(Jakarta$Price)/length(Jakarta$Id),sum(Bogor$Price)/length(Bogor$Id),
              sum(Tangerang$Price)/length(Tangerang$Id),sum(Depok$Price)/length(Depok$Id),
              sum(Bekasi$Price)/length(Bekasi$Id))

kota= data.frame(city,rata_city)
kota

# Kota dan Cluster mana yang paling menguntungkan?

menguntungkan = which.max(kota$rata_city)
kota_menguntungkan = kota[menguntungkan,]
kota_menguntungkan

cluster = c("Victoria","Palmyra","Winona","Tiara", "Narada",
            "Peronia","Lavesh","Alindra","Sweethome", "Asera",
            "Teradamai","Albasia", "Adara","Neon","Arana",
            "Asoka", "Primadona", "Mutiara","Permata","Alamanda")

Victoria = subset(Data, subset=(Marketing_Name == "Victoria"))
Palmyra = subset(Data, subset=(Marketing_Name == "Palmyra"))
Winona = subset(Data, subset=(Marketing_Name == "Winora"))
Tiara = subset(Data, subset=(Marketing_Name == "Tiara"))
Narada = subset(Data, subset=(Marketing_Name == "Narada"))
Peronia = subset(Data, subset=(Marketing_Name == "Peronia"))
Lavesh = subset(Data, subset=(Marketing_Name == "Lavesh"))
Alinda = subset(Data, subset=(Marketing_Name == "Alinda"))
Sweethome = subset(Data, subset=(Marketing_Name == "Sweethome"))
Asera = subset(Data, subset=(Marketing_Name == "Asera"))
Teradamai = subset(Data, subset=(Marketing_Name == "Terdamai"))
Albastia = subset(Data, subset=(Marketing_Name == "Albastia"))
Adara = subset(Data, subset=(Marketing_Name == "Adara"))
Neon = subset(Data, subset=(Marketing_Name == "Neon"))
Arana = subset(Data, subset=(Marketing_Name == "Arana"))
Asoka = subset(Data, subset=(Marketing_Name == "Asoka"))
Primadona = subset(Data, subset=(Marketing_Name == "Primadona"))
Mutiara = subset(Data, subset=(Marketing_Name == "Mutiara"))
Permata = subset(Data, subset=(Marketing_Name == "Permata"))
Alamanda = subset(Data, subset=(Marketing_Name == "Alamanda"))

rata = c(sum(Victoria$Price) / length(Victoria$Id),sum(Palmyra$Price) / length(Palmyra$Id),
         sum(Winona$Price)/length(Winona$Id),sum(Tiara$Price)/length(Tiara$Id),
         sum(Narada$Price)/length(Narada$Id),sum(Peronia$Price)/length(Peronia$Id),
         sum(Lavesh$Price)/length(Lavesh$Id),sum(Alinda$Price)/length(Alinda$Id),
         sum(Sweethome$Price)/length(Sweethome$Id),sum(Asera$Price)/length(Asera$Id),
         sum(Teradamai$Price)/length(Teradamai$Id),sum(Albastia$Price)/length(Albastia$Id),
         sum(Adara$Price)/length(Adara$Id),sum(Neon$Price)/length(Neon$Id),
         sum(Arana$Price)/length(Arana$Id),sum(Asoka$Price)/length(Asoka$Id),
         sum(Primadona$Price)/length(Primadona$Id),sum(Mutiara$Price)/length(Mutiara$Id),
         sum(Permata$Price)/length(Permata$Id),sum(Alamanda$Price)/length(Alamanda$Id)
         )

clus_ter = data.frame(cluster,rata)

cluster_terbaik = clus_ter[which.max(clus_ter$rata),]

# biaya iklan
sales="Lala"

table_sales = subset(Data, subset = (Marketing_Name == sales))

iklan = (table_sales$Advertisement * 4)

total_iklan = print(sum(iklan))

## [1] 8156

R

ID                  <- (1:50000)
Marital_Status      <- sample(c("Yes","No"), 50000, replace= T)
Address             <- sample(c("Jakarta","Bogor","Depok","Tengerang","Bekasi"),50000, replace = T)
Work_Location       <- sample(c("Jakarta","Bogor","Depok","Tengerang","Bekasi"),50000, replace = T)
Age                 <- sample(c(19:60),50000, replace = T)
Academic            <- sample(c("J.School","H.School","Sarjana","Magister","Phd"),50000, replace = T)
Grade               = sample(c("A","B","C","D","E"),50000, replace = T)
Private_vehicle     = sample(c("Mobil", "Sepeda motor", "Umum"),50000, replace = T)
Home                = sample(c("Sewa", "Milik", "Kredit"),50000, replace = T)

Case <- data.frame(Id,
                   Marital_Status,
                   Address,
                   Work_Location,
                   Age,
                   Academic,
                   Grade,
                   Private_vehicle,
                   Home)
Case

Kuliah =c("Wirausaha","Guru","Programmer","Dokter","Chef","Polisi","Lawyer","Aktuaris")

Job = ifelse(Academic == "J.School","Student",
      ifelse(Academic == "H.School","Student",
             sample(Kuliah, replace = T)))

Case <- data.frame(Id,
                   Marital_Status,
                   Address,
                   Work_Location,
                   Age,
                   Academic,
                   Grade,
                   Private_vehicle,
                   Home,
                   Job)

Income = ifelse(Case$Job == "Wirausaha", 20000:25000,
         ifelse(Case$Job == "Guru", 5000,
         ifelse(Case$Job == "Programer", 15000,
         ifelse(Case$Job == "Dokter", 20000,
         ifelse(Case$Job == "Chef", 9000,
         ifelse(Case$Job == "Polisi", 14000,
         ifelse(Case$Job == "Lawyer", 16000,
         ifelse(Case$Job == "Aktuaris", 19000,5000
))))))))

Spending = ifelse(Case$Job == "Wirausaha", 10000,
         ifelse(Case$Job == "Guru", 3000,
         ifelse(Case$Job == "Programer", 10000,
         ifelse(Case$Job == "Dokter", 10000,
         ifelse(Case$Job == "Chef", 5000,
         ifelse(Case$Job == "Polisi", 4000,
         ifelse(Case$Job == "Lawyer", 6000,
         ifelse(Case$Job == "Aktuaris", 9000,1000
))))))))

Case$Income = Income
Case$Spending = Spending

Number_of_children = ifelse(Case$Marital_Status == "Yes", sample(c(0:10),50000,replace= T),0)
Case$Number_of_children = Number_of_children

Case

R

yes = subset(Case, subset=(Marital_Status == "Yes"))
no  = subset(Case, subset=(Marital_Status == "No"))

a = subset(Case, subset=(Grade == "A"))
b = subset(Case, subset=(Grade == "B"))
c = subset(Case, subset=(Grade == "C"))
d = subset(Case, subset=(Grade == "D"))
e = subset(Case, subset=(Grade == "E"))

aJa =subset(Case, subset=(Address == "Jakarta"))
aBo =subset(Case, subset=(Address == "Bogor"))
aDe =subset(Case, subset=(Address == "Depok"))
aTg =subset(Case, subset=(Address == "tangerang"))
aBe =subset(Case, subset=(Address == "Bekasi"))

kJa =subset(Case, subset=(Work_Location == "Jakarta"))
kBo =subset(Case, subset=(Work_Location == "Bogor"))
kDe =subset(Case, subset=(Work_Location == "Depok"))
kTg =subset(Case, subset=(Work_Location == "Tangerang"))
kBe =subset(Case, subset=(Work_Location == "Bekasi"))

J =subset(Case, subset=(Academic == "J.School"))
H =subset(Case, subset=(Academic == "H.School"))
S =subset(Case, subset=(Academic == "Sarjana"))
M =subset(Case, subset=(Academic == "Magister"))
P =subset(Case, subset=(Academic == "Phd"))

mobil = subset(Case, subset=(Private_vehicle == "Mobil"))
motor = subset(Case, subset=(Private_vehicle == "Sepeda motor"))
umum = subset(Case, subset=(Private_vehicle == "Umum"))

sewa = subset(Case, subset=(Home == "Sewa"))
milik = subset(Case, subset=(Home == "Milik"))
kredit = subset(Case, subset=(Home == "Kredit"))

wirausaha = subset(Case, subset=(Job == "Wirausaha"))
guru = subset(Case, subset=(Job == "Guru"))
programer = subset(Case, subset=(Job == "Programer"))
dokter = subset(Case, subset=(Job == "Dokter"))
chef = subset(Case, subset=(Job == "Chef"))
polisi = subset(Case, subset=(Job == "Polisi"))
lawyer = subset(Case, subset=(Job == "Lawyer"))
aktuaris = subset(Case, subset=(Job == "Aktuaris"))

Case_Word = function(x){
  mertial = function(x,y){
        if(x>y){
          print("Yes")}
        else{print("No")} }
marrige = mertial(length(yes$No), length(no$No))

alamat=
  function(a,b,c,d,e){
    if(a > max(c(b,c,d,e))){
      print("Jakarta")}
    else if(b > max(c(a,c,d,e))){ 
      print("Bogor")}
    else if(c > max(c(a,b,d,e))){ 
      print("Depok")}
    else if(c > max(c(a,b,c,e))){ 
      print("Tangerang")}
    else{print("Bekasi")}
  }
address = alamat(length(aJa$No), length(aBo$No), length(aDe$No), length(aTg$No), length(aBe$No))

Kerja=
  function(a,b,c,d,e){
    if(a > max(c(b,c,d,e))){
      print("Jakarta")}
    else if(b > max(c(a,c,d,e))){ 
      print("Bogor")}
    else if(c > max(c(a,b,d,e))){ 
      print("Depok")}
    else if(c > max(c(a,b,c,e))){ 
      print("Tangerang")}
    else{print("Bekasi")}
  }
work = Kerja(length(kJa$No), length(kBo$No), length(kDe$No), length(kTg$No), length(kBe$No))

akademis=
  function(a,b,c,d,e){
    if(a > max(c(b,c,d,e))){
      print("J.School")}
    else if(b > max(c(a,c,d,e))){ 
      print("H.School")}
    else if(c > max(c(a,b,d,e))){ 
      print("Sarjana")}
    else if(c > max(c(a,b,c,e))){ 
      print("Magister")}
    else{print("Phd")}
  }
academic = akademis(length(J$No), length(H$No), length(S$No), length(M$No), length(P$No))

nilai=
  function(a,b,c,d,e){
    if(a > max(c(b,c,d,e))){
      print("A")}
    else if(b > max(c(a,c,d,e))){ 
      print("B")}
    else if(c > max(c(a,b,d,e))){ 
      print("C")}
    else if(c > max(c(a,b,c,e))){ 
      print("D")}
    else{print("E")}
  }
ga = length(a$Address)
gb = length(b$No)
gc = length(c$No)
gd = length(d$No)
ge = length(e$No)
grade = nilai(ga,gb,gc,gd,ge)

vehicle=
  function(a,b,c){ 
    if(a > max(c(b,c))){ 
      print("mobil")}
    else if(b > max(c(a,c))){ 
      print("motor")}
    else{print("umum")}}
kendaraan = vehicle(length(mobil$No), length(motor$No), length(umum$No))

rumah=
  function(a,b,c){ 
    if(a > max(c(b,c))){ 
      print("sewa")}
    else if(b > max(c(a,c))){ 
      print("milik")}
    else{print("kredit")}}
home = rumah(length(sewa$No), length(milik$No), length(kredit$No))

pekerjaan = function(a,b,c,d,e,f,g,h){ 
  if(a > max(c(b,c,d,e,f,g,h,i))){ 
    print("wirausaha")}
  else if(a > max(c(a,c,d,e,f,g,h))){ 
    print("guru")}
  else if(a > max(c(a,b,d,e,f,g,h))){ 
    print("programer")}
  else if(a > max(c(a,b,c,e,f,g,h))){ 
    print("dokter")}
  else if(a > max(c(a,b,c,d,f,g,h))){ 
    print("chef")}
  else if(a > max(c(a,b,c,d,e,g,h))){ 
    print("polisi")}
  else if(a > max(c(a,b,c,d,e,f,h))){ 
    print("lawyer")}
  else{print("aktuaris")}}
 job = pekerjaan(length(wirausaha$No), length(guru$No), length(programer$No),
                length(dokter$No), length(chef$No), length(polisi$No),
                length(lawyer$No), length(aktuaris$No))
 
 return(cat(c("marrige", marrige,"\n",
              "alamat", address,"\n",
              "kerja", work,"\n",
              "akademis", academic,"\n",
              "grade", grade,"\n",
              "kendaraan", kendaraan,"\n",
              "home", home,"\n",
              "pekerjaan", job
              )))
}

CaseNumber(Case$Age)

## Minimum : 19 
##  Maksimum : 60 
##  Median : 47 
##  Modus : 60 
##  rata : 39.47 
##  varians : 147.333075061501 
##  Standard Deviasi : 12.138083665122

CaseNumber(Case$Income)

## Minimum : 5000 
##  Maksimum : 25000 
##  Median : 5000 
##  Modus : 25000 
##  rata : 10104.7 
##  varians : 52965396.300606 
##  Standard Deviasi : 7277.7329094029

CaseNumber(Case$Spending)

## Minimum : 1000 
##  Maksimum : 10000 
##  Median : 5000 
##  Modus : 10000 
##  rata : 4379.98 
##  varians : 13722029.6401928 
##  Standard Deviasi : 3704.32580103219

CaseNumber(Case$Number_of_children)

## Minimum : 0 
##  Maksimum : 10 
##  Median : 
##  Modus : 10 
##  rata : 2.54 
##  varians : 11.332607452149 
##  Standard Deviasi : 3.36639383497372