R

x = Data$Price

a= ifelse((x>12000),print('High'),
          ifelse((x>=10000 & x<= 12000), print("Medium"),print('Low')
                 ))

## [1] "High"
## [1] "Medium"
## [1] "Low"

Kelas = a
Data1 <- data.frame(Id, 
                             Marketing_Name,
                             Work_Exp,
                             City,
                             Cluster,
                    Price,
                    Kelas,
                    Date_Sales,
                             Advertisement)
datatable(Data1)

R

x = Data$Price

a= ifelse((x>=13000 & x<=15000),print('10%'),
          ifelse((x>=11000 & x<13000),print("9%"),
                 ifelse((x>=10000 & x<11000),print("8%"),
                        ifelse((x>=9000 & x<10000),print("7%"),
                               ifelse((x>=8000 & x<9000),print("6%"),print('5%')
                 )))))

## [1] "10%"
## [1] "9%"
## [1] "8%"
## [1] "7%"
## [1] "6%"
## [1] "5%"

Booking_Fee = a
Data1 <- data.frame(Id, 
                             Marketing_Name,
                             Work_Exp,
                             City,
                             Cluster,
                    Price,
                    Kelas,
                    Booking_Fee,
                    Date_Sales,
                             Advertisement)
datatable(Data1)

R

library(DT)

z=for(x  in "Lala" ){print(subset(Data,subset=(Marketing_Name== x)))}

##        Id Marketing_Name Work_Exp      City   Cluster Price Date_Sales
## 10     10           Lala      7.3    Bekasi  Victoria 14500 2020-07-29
## 60     60           Lala      7.3    Bekasi     Asera  7709 2019-11-12
## 110   110           Lala      7.3     Depok     Asoka  8455 2018-04-01
## 160   160           Lala      7.3 Tengerang Teradamai 14114 2020-06-22
## 210   210           Lala      7.3     Bogor     Asera 10330 2018-05-20
## 260   260           Lala      7.3    Bekasi    Winona  7901 2018-12-18
## 310   310           Lala      7.3     Bogor   Mutiara  7578 2018-10-04
## 360   360           Lala      7.3   Jakarta     Arana 13858 2018-06-17
## 410   410           Lala      7.3     Bogor   Mutiara  8719 2020-01-11
## 460   460           Lala      7.3   Jakarta   Albasia 13856 2020-07-21
## 510   510           Lala      7.3     Depok   Peronia  7975 2018-10-23
## 560   560           Lala      7.3    Bekasi Teradamai 13824 2018-05-25
## 610   610           Lala      7.3     Bogor      Neon  7150 2019-01-21
## 660   660           Lala      7.3   Jakarta   Palmyra  7464 2019-02-20
## 710   710           Lala      7.3     Bogor     Asera  7414 2020-01-04
## 760   760           Lala      7.3 Tengerang   Albasia 12674 2018-07-22
## 810   810           Lala      7.3   Jakarta Teradamai 12732 2018-10-14
## 860   860           Lala      7.3     Depok Sweethome 11691 2018-11-10
## 910   910           Lala      7.3   Jakarta   Permata  8818 2020-08-13
## 960   960           Lala      7.3     Bogor    Narada 14952 2018-12-17
## 1010 1010           Lala      7.3     Depok   Mutiara 10559 2020-05-22
## 1060 1060           Lala      7.3     Bogor     Asoka  8366 2019-10-29
## 1110 1110           Lala      7.3     Depok   Albasia 10046 2019-02-02
## 1160 1160           Lala      7.3   Jakarta  Victoria 14285 2020-05-04
## 1210 1210           Lala      7.3     Depok Teradamai  8764 2018-07-19
## 1260 1260           Lala      7.3     Depok    Narada  7444 2019-04-24
## 1310 1310           Lala      7.3     Depok   Albasia  8721 2018-05-03
## 1360 1360           Lala      7.3     Bogor     Arana  8295 2019-02-20
## 1410 1410           Lala      7.3 Tengerang Primadona 11995 2018-10-27
## 1460 1460           Lala      7.3 Tengerang    Narada 12948 2020-04-24
## 1510 1510           Lala      7.3    Bekasi   Alindra  9602 2019-05-24
## 1560 1560           Lala      7.3    Bekasi     Arana 14794 2020-03-03
## 1610 1610           Lala      7.3    Bekasi    Lavesh 12281 2020-06-18
## 1660 1660           Lala      7.3   Jakarta    Winona  7952 2019-12-21
## 1710 1710           Lala      7.3   Jakarta     Adara 12133 2018-06-13
## 1760 1760           Lala      7.3    Bekasi Teradamai  9523 2018-04-02
## 1810 1810           Lala      7.3     Bogor   Permata  7632 2019-08-03
## 1860 1860           Lala      7.3    Bekasi      Neon 14921 2018-08-13
## 1910 1910           Lala      7.3 Tengerang Teradamai 11724 2019-09-23
## 1960 1960           Lala      7.3     Depok   Palmyra 11093 2020-09-02
## 2010 2010           Lala      7.3 Tengerang Primadona 11959 2018-10-10
## 2060 2060           Lala      7.3   Jakarta      Neon 13503 2019-04-26
## 2110 2110           Lala      7.3     Bogor   Albasia 12862 2019-10-30
## 2160 2160           Lala      7.3     Depok   Peronia 14610 2018-12-17
## 2210 2210           Lala      7.3    Bekasi     Tiara  7362 2019-10-10
## 2260 2260           Lala      7.3 Tengerang   Albasia 14627 2019-03-13
## 2310 2310           Lala      7.3   Jakarta    Winona 13076 2019-04-12
## 2360 2360           Lala      7.3   Jakarta    Winona 13347 2020-06-12
## 2410 2410           Lala      7.3     Depok    Lavesh 13211 2019-09-13
## 2460 2460           Lala      7.3    Bekasi    Narada 10676 2019-05-16
## 2510 2510           Lala      7.3   Jakarta   Palmyra 10889 2018-10-03
## 2560 2560           Lala      7.3    Bekasi    Lavesh 10351 2020-07-17
## 2610 2610           Lala      7.3   Jakarta  Alamanda 13628 2018-09-03
## 2660 2660           Lala      7.3     Bogor   Mutiara 11092 2020-03-21
## 2710 2710           Lala      7.3     Bogor   Albasia 14781 2018-07-17
## 2760 2760           Lala      7.3   Jakarta   Mutiara 12271 2018-04-18
## 2810 2810           Lala      7.3     Depok Teradamai 11872 2019-09-12
## 2860 2860           Lala      7.3     Depok     Adara 10033 2018-07-10
## 2910 2910           Lala      7.3    Bekasi  Victoria 12298 2020-06-27
## 2960 2960           Lala      7.3 Tengerang   Palmyra  9763 2018-09-18
## 3010 3010           Lala      7.3   Jakarta   Peronia  8181 2018-12-29
## 3060 3060           Lala      7.3    Bekasi  Victoria 14682 2018-05-31
## 3110 3110           Lala      7.3     Bogor  Alamanda 12421 2018-07-03
## 3160 3160           Lala      7.3 Tengerang     Asoka 13052 2020-05-27
## 3210 3210           Lala      7.3     Bogor   Palmyra  7568 2018-10-16
## 3260 3260           Lala      7.3 Tengerang  Alamanda 11526 2018-06-13
## 3310 3310           Lala      7.3   Jakarta    Lavesh 12292 2019-01-08
## 3360 3360           Lala      7.3   Jakarta   Albasia 12909 2019-08-26
## 3410 3410           Lala      7.3   Jakarta     Asoka 13098 2019-08-07
## 3460 3460           Lala      7.3    Bekasi   Albasia 10817 2019-10-14
## 3510 3510           Lala      7.3   Jakarta    Winona  9652 2018-10-22
## 3560 3560           Lala      7.3     Bogor   Albasia 11805 2018-10-13
## 3610 3610           Lala      7.3    Bekasi   Alindra 13083 2018-01-24
## 3660 3660           Lala      7.3   Jakarta    Winona 14479 2018-03-28
## 3710 3710           Lala      7.3     Bogor    Narada 14360 2018-12-12
## 3760 3760           Lala      7.3     Depok     Asera  9578 2019-02-17
## 3810 3810           Lala      7.3   Jakarta Teradamai 13208 2019-04-19
## 3860 3860           Lala      7.3     Depok   Peronia 12965 2018-06-25
## 3910 3910           Lala      7.3     Bogor   Palmyra  9760 2020-02-19
## 3960 3960           Lala      7.3    Bekasi     Asera  9400 2019-02-18
## 4010 4010           Lala      7.3 Tengerang   Albasia 14597 2018-05-27
## 4060 4060           Lala      7.3   Jakarta   Peronia 13294 2018-09-10
## 4110 4110           Lala      7.3 Tengerang   Peronia  9945 2019-04-29
## 4160 4160           Lala      7.3     Depok   Mutiara 13610 2019-08-16
## 4210 4210           Lala      7.3     Bogor    Lavesh  9396 2020-03-22
## 4260 4260           Lala      7.3    Bekasi     Tiara 10209 2019-01-10
## 4310 4310           Lala      7.3   Jakarta     Asoka  7001 2018-04-24
## 4360 4360           Lala      7.3   Jakarta  Alamanda 12002 2018-03-28
## 4410 4410           Lala      7.3   Jakarta  Victoria  7062 2019-04-14
## 4460 4460           Lala      7.3     Depok   Mutiara  8334 2020-08-30
## 4510 4510           Lala      7.3   Jakarta      Neon 10925 2018-07-24
## 4560 4560           Lala      7.3   Jakarta     Asoka 11271 2020-07-02
## 4610 4610           Lala      7.3    Bekasi     Asoka 13868 2018-02-12
## 4660 4660           Lala      7.3 Tengerang   Peronia 11698 2020-06-06
## 4710 4710           Lala      7.3   Jakarta     Asoka 14706 2018-02-16
## 4760 4760           Lala      7.3     Bogor  Alamanda  8652 2019-03-27
## 4810 4810           Lala      7.3    Bekasi   Albasia 13009 2018-12-06
## 4860 4860           Lala      7.3    Bekasi    Winona  9187 2018-07-12
## 4910 4910           Lala      7.3     Depok     Adara 13340 2020-09-23
## 4960 4960           Lala      7.3     Bogor   Palmyra  8544 2019-04-13
## 5010 5010           Lala      7.3   Jakarta     Asera  9614 2019-03-09
## 5060 5060           Lala      7.3 Tengerang    Winona  7750 2018-12-23
## 5110 5110           Lala      7.3   Jakarta     Adara 12552 2018-05-06
## 5160 5160           Lala      7.3     Bogor   Palmyra  7562 2019-05-27
## 5210 5210           Lala      7.3   Jakarta   Peronia  8889 2018-05-04
## 5260 5260           Lala      7.3     Depok   Peronia  9975 2018-09-17
## 5310 5310           Lala      7.3   Jakarta Teradamai  7629 2019-07-18
## 5360 5360           Lala      7.3     Depok     Arana  8915 2018-09-22
## 5410 5410           Lala      7.3 Tengerang   Mutiara 11453 2018-09-23
## 5460 5460           Lala      7.3     Bogor     Adara  8934 2019-07-06
## 5510 5510           Lala      7.3 Tengerang Sweethome  8628 2019-11-13
## 5560 5560           Lala      7.3     Depok     Asoka 14781 2020-07-04
## 5610 5610           Lala      7.3   Jakarta    Narada 13554 2018-09-30
## 5660 5660           Lala      7.3 Tengerang     Asoka  9343 2018-01-16
## 5710 5710           Lala      7.3 Tengerang    Winona 10204 2019-03-30
## 5760 5760           Lala      7.3 Tengerang  Alamanda 10638 2018-06-11
## 5810 5810           Lala      7.3     Bogor   Permata 13292 2018-08-11
## 5860 5860           Lala      7.3     Bogor  Victoria 11457 2019-05-11
## 5910 5910           Lala      7.3    Bekasi      Neon  8308 2019-04-20
## 5960 5960           Lala      7.3   Jakarta     Arana 10854 2019-09-13
## 6010 6010           Lala      7.3 Tengerang   Alindra  9298 2018-07-09
## 6060 6060           Lala      7.3   Jakarta Primadona 12184 2018-04-19
## 6110 6110           Lala      7.3   Jakarta     Tiara 13046 2020-06-15
## 6160 6160           Lala      7.3 Tengerang    Winona  9890 2018-01-10
## 6210 6210           Lala      7.3 Tengerang  Victoria  8588 2019-03-06
## 6260 6260           Lala      7.3    Bekasi     Arana  8954 2018-04-24
## 6310 6310           Lala      7.3    Bekasi Sweethome  9325 2018-11-03
## 6360 6360           Lala      7.3     Bogor Teradamai  9207 2019-05-14
## 6410 6410           Lala      7.3 Tengerang  Victoria 12306 2020-07-10
## 6460 6460           Lala      7.3    Bekasi      Neon 11665 2019-08-09
## 6510 6510           Lala      7.3     Depok    Winona  8698 2020-02-13
## 6560 6560           Lala      7.3   Jakarta Teradamai 11723 2020-08-05
## 6610 6610           Lala      7.3   Jakarta    Winona 10326 2019-12-02
## 6660 6660           Lala      7.3    Bekasi      Neon  7899 2019-08-06
## 6710 6710           Lala      7.3 Tengerang    Lavesh 13904 2018-05-12
## 6760 6760           Lala      7.3   Jakarta   Albasia 11433 2019-07-10
## 6810 6810           Lala      7.3 Tengerang   Mutiara 10595 2020-02-02
## 6860 6860           Lala      7.3     Depok   Permata  9742 2018-07-01
## 6910 6910           Lala      7.3 Tengerang   Peronia  9592 2019-02-27
## 6960 6960           Lala      7.3     Bogor    Narada 14658 2019-07-05
## 7010 7010           Lala      7.3    Bekasi     Asera  9666 2019-09-30
## 7060 7060           Lala      7.3    Bekasi     Tiara 13720 2019-11-28
## 7110 7110           Lala      7.3    Bekasi     Asera  9454 2020-07-28
## 7160 7160           Lala      7.3     Bogor     Asoka 10464 2019-01-01
## 7210 7210           Lala      7.3 Tengerang   Albasia 10444 2020-07-12
## 7260 7260           Lala      7.3    Bekasi   Peronia  7210 2018-07-04
## 7310 7310           Lala      7.3 Tengerang     Asera 10015 2020-06-11
## 7360 7360           Lala      7.3    Bekasi    Winona  7716 2019-07-06
## 7410 7410           Lala      7.3     Depok    Lavesh 14939 2019-08-06
## 7460 7460           Lala      7.3 Tengerang  Victoria  9768 2020-08-06
## 7510 7510           Lala      7.3   Jakarta     Asera  7848 2018-07-29
## 7560 7560           Lala      7.3     Depok     Adara 13246 2019-03-28
## 7610 7610           Lala      7.3 Tengerang   Mutiara  8824 2019-10-17
## 7660 7660           Lala      7.3     Bogor  Victoria 14036 2018-08-29
## 7710 7710           Lala      7.3 Tengerang   Alindra  7617 2018-04-09
## 7760 7760           Lala      7.3     Bogor Primadona 11282 2018-08-25
## 7810 7810           Lala      7.3     Depok   Albasia 14576 2018-10-27
## 7860 7860           Lala      7.3   Jakarta    Lavesh 10454 2018-10-20
## 7910 7910           Lala      7.3   Jakarta     Asoka 13768 2020-08-23
## 7960 7960           Lala      7.3 Tengerang     Adara 11561 2019-05-24
## 8010 8010           Lala      7.3     Depok     Asera 12524 2018-01-30
## 8060 8060           Lala      7.3 Tengerang Sweethome  9135 2019-10-16
## 8110 8110           Lala      7.3    Bekasi    Lavesh 14015 2018-01-19
## 8160 8160           Lala      7.3     Bogor      Neon  8337 2020-01-09
## 8210 8210           Lala      7.3   Jakarta  Victoria  7765 2020-06-27
## 8260 8260           Lala      7.3 Tengerang     Tiara 11014 2018-09-24
## 8310 8310           Lala      7.3 Tengerang     Tiara 10830 2019-12-29
## 8360 8360           Lala      7.3    Bekasi   Peronia 12225 2020-04-30
## 8410 8410           Lala      7.3     Depok Primadona  9787 2018-09-28
## 8460 8460           Lala      7.3 Tengerang Sweethome 10126 2020-07-22
## 8510 8510           Lala      7.3     Bogor    Narada 12595 2020-08-10
## 8560 8560           Lala      7.3     Depok Sweethome  9071 2018-05-29
## 8610 8610           Lala      7.3 Tengerang    Lavesh 13265 2020-01-10
## 8660 8660           Lala      7.3   Jakarta Primadona 12221 2018-06-20
## 8710 8710           Lala      7.3    Bekasi      Neon 12289 2018-05-17
## 8760 8760           Lala      7.3   Jakarta   Albasia 13899 2018-10-10
## 8810 8810           Lala      7.3     Depok   Peronia  8377 2020-03-01
## 8860 8860           Lala      7.3   Jakarta  Victoria 14904 2020-08-09
## 8910 8910           Lala      7.3    Bekasi      Neon  7039 2018-04-13
## 8960 8960           Lala      7.3     Bogor   Alindra  7218 2019-12-28
## 9010 9010           Lala      7.3     Bogor  Alamanda  9246 2018-03-09
## 9060 9060           Lala      7.3    Bekasi    Winona 10193 2020-03-27
## 9110 9110           Lala      7.3     Depok   Alindra  7029 2020-04-07
## 9160 9160           Lala      7.3     Depok      Neon 12884 2020-07-20
## 9210 9210           Lala      7.3 Tengerang Sweethome  8307 2020-02-20
## 9260 9260           Lala      7.3     Bogor Teradamai 14497 2018-03-16
## 9310 9310           Lala      7.3    Bekasi Teradamai 12379 2019-01-14
## 9360 9360           Lala      7.3     Depok    Lavesh 13835 2018-11-17
## 9410 9410           Lala      7.3   Jakarta   Peronia  9843 2020-03-27
## 9460 9460           Lala      7.3     Bogor   Alindra 14736 2018-07-02
## 9510 9510           Lala      7.3     Bogor   Albasia 12801 2019-02-09
## 9560 9560           Lala      7.3     Depok   Peronia  8652 2019-07-31
## 9610 9610           Lala      7.3   Jakarta  Victoria  8600 2018-02-03
## 9660 9660           Lala      7.3 Tengerang   Alindra 11721 2018-03-07
## 9710 9710           Lala      7.3    Bekasi   Permata 13917 2020-06-15
## 9760 9760           Lala      7.3    Bekasi    Winona 12079 2020-08-12
## 9810 9810           Lala      7.3    Bekasi    Lavesh  9584 2018-02-09
## 9860 9860           Lala      7.3     Bogor      Neon 13996 2020-03-13
## 9910 9910           Lala      7.3 Tengerang   Palmyra  8092 2020-02-13
## 9960 9960           Lala      7.3    Bekasi   Albasia 12583 2018-09-11
##      Advertisement
## 10               9
## 60              17
## 110             11
## 160             12
## 210              3
## 260              6
## 310              6
## 360              8
## 410             13
## 460              4
## 510              2
## 560             19
## 610              9
## 660              1
## 710             12
## 760              1
## 810             16
## 860              7
## 910             16
## 960              9
## 1010             4
## 1060            14
## 1110             8
## 1160             9
## 1210            20
## 1260            12
## 1310            20
## 1360             3
## 1410            16
## 1460            19
## 1510             6
## 1560            17
## 1610             5
## 1660             6
## 1710            20
## 1760            19
## 1810             8
## 1860            20
## 1910            18
## 1960            12
## 2010            18
## 2060            19
## 2110            15
## 2160            16
## 2210            14
## 2260             5
## 2310             2
## 2360            18
## 2410            11
## 2460            10
## 2510            17
## 2560             5
## 2610            11
## 2660            10
## 2710            13
## 2760             6
## 2810            19
## 2860            18
## 2910             7
## 2960            20
## 3010            13
## 3060            18
## 3110            13
## 3160             9
## 3210            12
## 3260             8
## 3310             9
## 3360            20
## 3410             1
## 3460             2
## 3510             8
## 3560            12
## 3610             7
## 3660             9
## 3710             1
## 3760            13
## 3810            11
## 3860             8
## 3910             1
## 3960             7
## 4010            20
## 4060             2
## 4110             8
## 4160            16
## 4210            15
## 4260             6
## 4310            17
## 4360            19
## 4410             3
## 4460            20
## 4510            18
## 4560             9
## 4610             8
## 4660             3
## 4710            16
## 4760             4
## 4810            10
## 4860            14
## 4910            20
## 4960             1
## 5010             2
## 5060            17
## 5110            18
## 5160             2
## 5210            11
## 5260            13
## 5310             3
## 5360             7
## 5410             7
## 5460            17
## 5510            19
## 5560             9
## 5610            14
## 5660             1
## 5710             6
## 5760            13
## 5810            20
## 5860            12
## 5910            17
## 5960            14
## 6010             8
## 6060             7
## 6110            16
## 6160            11
## 6210             8
## 6260            13
## 6310             7
## 6360            19
## 6410            11
## 6460            16
## 6510            17
## 6560            14
## 6610             4
## 6660            14
## 6710            19
## 6760             6
## 6810            19
## 6860             4
## 6910             6
## 6960            13
## 7010            18
## 7060            12
## 7110             3
## 7160             8
## 7210            13
## 7260            13
## 7310            10
## 7360            12
## 7410            20
## 7460             7
## 7510             5
## 7560             4
## 7610             7
## 7660             4
## 7710            10
## 7760            18
## 7810            18
## 7860            16
## 7910            16
## 7960             2
## 8010             1
## 8060            18
## 8110            16
## 8160             5
## 8210             7
## 8260             9
## 8310            15
## 8360             7
## 8410            10
## 8460            15
## 8510             3
## 8560            11
## 8610            20
## 8660            12
## 8710             5
## 8760            19
## 8810            14
## 8860             1
## 8910             7
## 8960            19
## 9010            16
## 9060             5
## 9110             1
## 9160            16
## 9210             7
## 9260             9
## 9310             3
## 9360            20
## 9410            12
## 9460             2
## 9510            17
## 9560            11
## 9610            20
## 9660            18
## 9710            19
## 9760            16
## 9810            15
## 9860            17
## 9910            13
## 9960             4

R

x <- subset(Data, subset=(Marketing_Name == "Lala"))

y= ifelse((x$Work_Exp <3),
          (x$Price * x$Booking_Fee) *(0.02),
          (x$Price * x$Booking_Fee) *(0.01))

x$Bonus =y
x

Bonus=sum(x$Bonus)
Bonus

## [1] NA

R

 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 == "Jerrel"))
Imelda =subset (Data, subset=(Marketing_Name == "Imelda")) 
Widi =subset(Data, subset=(Marketing_Name == "Widi")) 
Theodor = subset (Data, subset=(Marketing_Name == "Theodor")) 
Elvani =subset (Data, subset=(Marketing_Name == "Elvani")) 
Jonathan = subset (Data, subset=(Marketing_Name == "Jonathan")) 
Sofia =subset (Data, subset=(Marketing_Name == "Sofia")) 
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 == "Selfi")) 
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")) 
  
Nama_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")
propertysold = 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$Price), sum(Theodor$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$Price), sum (James$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))

datamarketing= data.frame(Nama_Sales, propertysold)

datamarketing

# Best Marketing
datamarketing[which.max(datamarketing$propertysold),]

#Kota dan Cluster Paling Menguntungkan
profitable= Data[,c("City","Cluster","Price")]

profitable[which.max(profitable$Price),]

#Total Biaya Sdvertisment irene
costforad = subset(Data, subset=(Marketing_Name == "Irene"))

adscost = ( costforad$Advertisement * 4)

Totalcost=print(sum(adscost))

## [1] 8044

# Rata-Rata cost iklan
Namasales = 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")
ratacost = for (x in Namasales) {
  f= subset ( Data, subset=(Marketing_Name == x ))
  c=sum(f$Advertisement * 4)
  print (cat(sum(c)/length(f$Id), (cat(x, 'Rata-rata pengeluaran untuk iklan'))))}

## Angel Rata-rata pengeluaran untuk iklan41.7NULL
## Sherly Rata-rata pengeluaran untuk iklan43.04NULL
## Vanessa Rata-rata pengeluaran untuk iklan40.1NULL
## Irene Rata-rata pengeluaran untuk iklan40.22NULL
## Julian Rata-rata pengeluaran untuk iklan41.92NULL
## Jeffry Rata-rata pengeluaran untuk iklan41.6NULL
## Nikita Rata-rata pengeluaran untuk iklan40.84NULL
## Kefas Rata-rata pengeluaran untuk iklan41.32NULL
## Siana Rata-rata pengeluaran untuk iklan40.46NULL
## Lala Rata-rata pengeluaran untuk iklan44.54NULL
## Fallen Rata-rata pengeluaran untuk iklan39.52NULL
## Ardifo Rata-rata pengeluaran untuk iklan41.06NULL
## Kevin Rata-rata pengeluaran untuk iklan45.14NULL
## Juen Rata-rata pengeluaran untuk iklan44.14NULL
## Jerrel Rata-rata pengeluaran untuk iklan42.08NULL
## Imelda Rata-rata pengeluaran untuk iklan43.16NULL
## Widi Rata-rata pengeluaran untuk iklan41.48NULL
## Theodora Rata-rata pengeluaran untuk iklan42.1NULL
## Elvani Rata-rata pengeluaran untuk iklan42.74NULL
## Jonathan Rata-rata pengeluaran untuk iklan39.42NULL
## Sofia Rata-rata pengeluaran untuk iklan40.84NULL
## Abraham Rata-rata pengeluaran untuk iklan42.34NULL
## Siti Rata-rata pengeluaran untuk iklan43.08NULL
## Niko Rata-rata pengeluaran untuk iklan39.18NULL
## Sefli Rata-rata pengeluaran untuk iklan40.8NULL
## Bene Rata-rata pengeluaran untuk iklan42.26NULL
## Diana Rata-rata pengeluaran untuk iklan41.46NULL
## Pupe Rata-rata pengeluaran untuk iklan41.64NULL
## Andi Rata-rata pengeluaran untuk iklan39.94NULL
## Tatha Rata-rata pengeluaran untuk iklan37.8NULL
## Endri Rata-rata pengeluaran untuk iklan39.6NULL
## Monika Rata-rata pengeluaran untuk iklan44.56NULL
## Hans Rata-rata pengeluaran untuk iklan45.9NULL
## Debora Rata-rata pengeluaran untuk iklan44.92NULL
## Hanifa Rata-rata pengeluaran untuk iklan41.78NULL
## James Rata-rata pengeluaran untuk iklan39.42NULL
## Jihan Rata-rata pengeluaran untuk iklan41.4NULL
## Friska Rata-rata pengeluaran untuk iklan39.18NULL
## Ardiwan Rata-rata pengeluaran untuk iklan42.34NULL
## Bakti Rata-rata pengeluaran untuk iklan41.3NULL
## Anthon Rata-rata pengeluaran untuk iklan43.54NULL
## Amry Rata-rata pengeluaran untuk iklan40.92NULL
## Wiwik Rata-rata pengeluaran untuk iklan43.06NULL
## Bastian Rata-rata pengeluaran untuk iklan41.64NULL
## Budi Rata-rata pengeluaran untuk iklan41.3NULL
## Leo Rata-rata pengeluaran untuk iklan41.82NULL
## Simon Rata-rata pengeluaran untuk iklan41.98NULL
## Matius Rata-rata pengeluaran untuk iklan43.1NULL
## Arry Rata-rata pengeluaran untuk iklan39.56NULL
## Eliando Rata-rata pengeluaran untuk iklan42.74NULL

# Pendapatan

revenue= (sum(Data$Price)-(sum(Data$Advertisment) * 4))/((max(Data$Work_Exp))*12)

revenue

## [1] 914675.9

R

marital_status <- sample(c("Yes", "No"), 50000, replace = T) 
Address <-sample(c("Jakarta", "Bogor", "Depok", "Tangerang", "Bekasi"),50000, replace=T)
Work_Location <-sample(c("Jakarta", "Bogor", "Depok", "Tangerang", "Bekasi"),50000, replace=T)
Age <-floor (runif(50000,19,60))
Academic <-sample(c("J.School", "H.School", "Undergraduate", "Master",
"Phd"), 50000, replace = T) 
Job <-ifelse (Academic=="J.School", sample(c("Security","Storekeeper" ,
"Supir", "Office Boy", "Cleaning Service", " Helper", "Lifeguard", "Cashier", "Buka Toko", "Buruh Angkut"),
length(Academic=="J.School"), replace=T), ifelse (Academic=="H. School", sample(c("Influencer", "Cashier",
"Waiter", "Tutor Les", "Customer Service", "Operator", "Security", "Admin", "Gojek Driver", "Grab Driver", ),
length(Academic=="H.School"), replace=T), ifelse(Academic=="Undergraduate", sample(c("Nurse", "Business StartUp Development",
"Personal Assistant", "PNS", "Accountant", "Sales", "Private Driver", "Admin of Online Shop"), length(Academic=="Undergraduate"),
replace=T), ifelse(Academic=="Master", sample(c("Software Developer", "Actuarist",
"Creative Team","Designer","Business analyst", "Graphic Designer", "Engineer", "Chemist", "Data Analyst", "Accountant", "Statistician"),
length(Academic=="Master"), replace=T), sample(c("HRD Manager", "CEO",
" Consultant", "Head of Research", "Entrepreneurship", "Partner in Finance", "Mathematicians", "System Developer", "Shrink", "General  Manager", "Board Director"),
length (Academic=="Phd"), replace=T))))) 
Grade <-sample(c("Expert", "Master", "Intermediate", "Skilled",
"Newbie"), 50000, replace=T) 
Income <-sample(c(5000:15000),50000, replace = T)
Spending <-sample(c(3000:10000), 50000, replace = T)
Number_of_children <- ifelse(marital_status =="Yes", sample((c(0:5)), length(marital_status =="Yes"), replace=T), "0")
Private_vehicle <- sample(c("Car", "Motorcycle", "Public"),
50000, replace=T)
Home <- sample(c("Rent", "Own", "Credit"), 50000, replace=T) 

Data2 <-data.frame (marital_status, Address, Work_Location, Age, Academic, Job, Grade, Income, Spending, Number_of_children, Private_vehicle, Home)

library(data.table)
data.table(Data2)

R

summary(Data2)

##  marital_status       Address          Work_Location           Age       
##  Length:50000       Length:50000       Length:50000       Min.   :19.00  
##  Class :character   Class :character   Class :character   1st Qu.:29.00  
##  Mode  :character   Mode  :character   Mode  :character   Median :39.00  
##                                                           Mean   :38.96  
##                                                           3rd Qu.:49.00  
##                                                           Max.   :59.00  
##    Academic             Job               Grade               Income     
##  Length:50000       Length:50000       Length:50000       Min.   : 5000  
##  Class :character   Class :character   Class :character   1st Qu.: 7507  
##  Mode  :character   Mode  :character   Mode  :character   Median :10027  
##                                                           Mean   :10012  
##                                                           3rd Qu.:12528  
##                                                           Max.   :15000  
##     Spending     Number_of_children Private_vehicle        Home          
##  Min.   : 3000   Length:50000       Length:50000       Length:50000      
##  1st Qu.: 4765   Class :character   Class :character   Class :character  
##  Median : 6501   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 6507                                                           
##  3rd Qu.: 8256                                                           
##  Max.   :10000

`

R

Data2$NetWorth = Data2$Income-Data2$Spending

Data2

#Potential untuk dipertahankan
subset(Data2, NetWorth>= 7000)