R

harga<- function(Data){
  if (Data$Price[i] > 12000){
    Data$Kelas[i] <- "High"}
  else if (Data$Price[i] >= 10000 & Data$Price[i] <= 12000){
    Data$Kelas[i] <- "Medium" }
  else{
    Data$Kelas[i] <- "Low"}
}
library(DT)
datatable(Data)

R

for (i in 1:nrow(Data)){
  if (Data$Price[i] < 8000){
    Data$Booking_fee[i] <- ((Data$Price[i]) * (5/100))
  }
  else if (Data$Price[i] >= 8000 & Data$Price[i] < 9000){
    Data$Booking_fee[i] <- ((Data$Price[i]) * (6/100))
  }
  else if (Data$Price[i] >= 9000 & Data$Price[i] < 10000){
    Data$Booking_fee[i] <- ((Data$Price[i]) * (7/100))
  }
  else if (Data$Price[i] >= 10000 & Data$Price[i] < 11000){
    Data$Booking_fee[i] <- ((Data$Price[i]) * (8/100))
  }
  else if (Data$Price[i] >= 11000 & Data$Price[i] < 13000){
    Data$Booking_fee[i] <- ((Data$Price[i])* (9/100))
  }
  else {
    Data$Booking_fee[i] <- ((Data$Price[i])* (10/100))
  }
}
transform(Data, Booking_fee = as.numeric(Booking_fee))

typeof(Data$Booking_fee)

## [1] "double"

library(DT)
datatable(Data)

R

for (i in "Jeffry"){
  print(subset(Data, subset = (Marketing_Name ==i)))
}

##        Id Marketing_Name Work_Exp      City   Cluster Price Date_Sales
## 6       6         Jeffry      4.7     Depok   Mutiara 12058 2018-02-16
## 56     56         Jeffry      4.7   Jakarta    Lavesh 12825 2019-03-17
## 106   106         Jeffry      4.7     Depok   Peronia 11521 2020-05-10
## 156   156         Jeffry      4.7 Tengerang     Asoka 11611 2020-05-23
## 206   206         Jeffry      4.7     Bogor     Asoka 11316 2018-08-31
## 256   256         Jeffry      4.7     Depok Primadona 14055 2020-06-12
## 306   306         Jeffry      4.7     Bogor   Alindra 12752 2018-12-16
## 356   356         Jeffry      4.7     Depok   Palmyra  8309 2019-11-14
## 406   406         Jeffry      4.7    Bekasi     Adara 11887 2019-07-02
## 456   456         Jeffry      4.7     Depok  Alamanda 13036 2019-01-07
## 506   506         Jeffry      4.7   Jakarta Sweethome 12902 2018-08-27
## 556   556         Jeffry      4.7     Depok      Neon  7178 2018-09-15
## 606   606         Jeffry      4.7     Depok  Alamanda  8720 2019-12-31
## 656   656         Jeffry      4.7     Depok Sweethome 13947 2019-07-08
## 706   706         Jeffry      4.7    Bekasi     Arana  8950 2019-03-17
## 756   756         Jeffry      4.7     Depok   Peronia 11257 2018-07-23
## 806   806         Jeffry      4.7 Tengerang     Asoka 10139 2018-02-22
## 856   856         Jeffry      4.7    Bekasi   Albasia  7802 2018-02-22
## 906   906         Jeffry      4.7 Tengerang Teradamai  7797 2018-12-23
## 956   956         Jeffry      4.7     Depok   Albasia 10201 2020-01-21
## 1006 1006         Jeffry      4.7     Bogor     Tiara 14409 2019-10-06
## 1056 1056         Jeffry      4.7     Depok     Asoka 13239 2019-10-03
## 1106 1106         Jeffry      4.7    Bekasi Teradamai  9005 2018-04-11
## 1156 1156         Jeffry      4.7     Depok   Permata  7879 2018-07-13
## 1206 1206         Jeffry      4.7     Depok   Peronia  7098 2020-08-04
## 1256 1256         Jeffry      4.7   Jakarta   Permata 14442 2019-05-02
## 1306 1306         Jeffry      4.7    Bekasi   Palmyra 12413 2019-11-13
## 1356 1356         Jeffry      4.7   Jakarta     Asoka  9176 2019-06-16
## 1406 1406         Jeffry      4.7     Depok   Peronia  9597 2019-11-20
## 1456 1456         Jeffry      4.7   Jakarta  Alamanda 13183 2018-08-14
## 1506 1506         Jeffry      4.7    Bekasi     Tiara 11969 2019-07-11
## 1556 1556         Jeffry      4.7     Bogor  Victoria 14212 2019-08-28
## 1606 1606         Jeffry      4.7   Jakarta  Victoria 13581 2019-10-23
## 1656 1656         Jeffry      4.7     Depok   Alindra  8649 2018-08-02
## 1706 1706         Jeffry      4.7    Bekasi     Asera 11163 2018-05-16
## 1756 1756         Jeffry      4.7     Bogor   Mutiara 12881 2019-03-20
## 1806 1806         Jeffry      4.7   Jakarta     Asoka 12910 2018-10-24
## 1856 1856         Jeffry      4.7    Bekasi Sweethome  8429 2019-01-05
## 1906 1906         Jeffry      4.7     Depok  Alamanda 13265 2019-02-19
## 1956 1956         Jeffry      4.7    Bekasi Sweethome  9919 2018-05-04
## 2006 2006         Jeffry      4.7     Depok   Albasia  7230 2020-08-22
## 2056 2056         Jeffry      4.7     Depok     Adara 11151 2019-03-21
## 2106 2106         Jeffry      4.7 Tengerang   Mutiara 10501 2019-02-17
## 2156 2156         Jeffry      4.7     Depok  Alamanda 14730 2018-07-10
## 2206 2206         Jeffry      4.7     Bogor   Alindra  8771 2020-02-24
## 2256 2256         Jeffry      4.7   Jakarta  Victoria  9239 2018-07-22
## 2306 2306         Jeffry      4.7    Bekasi    Winona  9033 2019-04-03
## 2356 2356         Jeffry      4.7     Bogor  Alamanda  9271 2019-06-10
## 2406 2406         Jeffry      4.7     Bogor    Winona 11379 2020-02-19
## 2456 2456         Jeffry      4.7 Tengerang    Narada  8770 2020-05-04
## 2506 2506         Jeffry      4.7   Jakarta    Narada 11326 2019-03-02
## 2556 2556         Jeffry      4.7    Bekasi   Peronia  9995 2018-08-01
## 2606 2606         Jeffry      4.7 Tengerang  Victoria 14923 2019-04-13
## 2656 2656         Jeffry      4.7   Jakarta   Peronia 14185 2019-07-17
## 2706 2706         Jeffry      4.7 Tengerang     Tiara  8208 2019-02-13
## 2756 2756         Jeffry      4.7     Bogor    Narada 10215 2020-01-21
## 2806 2806         Jeffry      4.7    Bekasi   Permata  9253 2018-08-21
## 2856 2856         Jeffry      4.7   Jakarta     Adara  7067 2018-08-31
## 2906 2906         Jeffry      4.7     Bogor    Lavesh 11684 2020-09-22
## 2956 2956         Jeffry      4.7    Bekasi Teradamai  8251 2019-10-07
## 3006 3006         Jeffry      4.7   Jakarta   Permata  7472 2019-01-22
## 3056 3056         Jeffry      4.7 Tengerang      Neon  7546 2019-07-05
## 3106 3106         Jeffry      4.7     Depok Primadona 10246 2020-02-17
## 3156 3156         Jeffry      4.7   Jakarta   Mutiara  8305 2019-04-23
## 3206 3206         Jeffry      4.7     Bogor  Alamanda  8127 2020-01-21
## 3256 3256         Jeffry      4.7   Jakarta    Lavesh 14179 2018-03-09
## 3306 3306         Jeffry      4.7   Jakarta    Narada 14330 2019-06-19
## 3356 3356         Jeffry      4.7     Bogor     Asoka 11837 2018-07-05
## 3406 3406         Jeffry      4.7     Bogor     Arana  9671 2018-07-05
## 3456 3456         Jeffry      4.7   Jakarta  Victoria  9460 2019-11-29
## 3506 3506         Jeffry      4.7     Depok     Asoka  9599 2018-07-02
## 3556 3556         Jeffry      4.7    Bekasi  Alamanda  9172 2020-05-12
## 3606 3606         Jeffry      4.7     Bogor     Arana  9050 2018-10-24
## 3656 3656         Jeffry      4.7    Bekasi    Narada  8799 2019-02-25
## 3706 3706         Jeffry      4.7   Jakarta   Albasia 10217 2018-02-27
## 3756 3756         Jeffry      4.7   Jakarta Sweethome 12286 2018-06-17
## 3806 3806         Jeffry      4.7     Bogor     Arana 12687 2019-11-29
## 3856 3856         Jeffry      4.7     Depok   Permata 13920 2020-08-14
## 3906 3906         Jeffry      4.7    Bekasi   Peronia 12857 2020-01-29
## 3956 3956         Jeffry      4.7     Depok    Narada  7803 2018-06-16
## 4006 4006         Jeffry      4.7   Jakarta   Alindra 11133 2020-06-21
## 4056 4056         Jeffry      4.7 Tengerang Primadona 14783 2019-05-13
## 4106 4106         Jeffry      4.7   Jakarta     Asoka 13013 2018-09-15
## 4156 4156         Jeffry      4.7     Depok     Asera  9519 2018-06-05
## 4206 4206         Jeffry      4.7   Jakarta   Albasia 12785 2018-05-26
## 4256 4256         Jeffry      4.7 Tengerang Primadona  7085 2020-01-13
## 4306 4306         Jeffry      4.7   Jakarta     Tiara  7085 2019-03-06
## 4356 4356         Jeffry      4.7     Depok   Mutiara  8034 2019-06-01
## 4406 4406         Jeffry      4.7    Bekasi     Arana  7132 2019-11-01
## 4456 4456         Jeffry      4.7     Bogor   Palmyra  8563 2019-02-02
## 4506 4506         Jeffry      4.7    Bekasi   Mutiara 10495 2019-04-12
## 4556 4556         Jeffry      4.7 Tengerang Sweethome 10781 2018-10-15
## 4606 4606         Jeffry      4.7 Tengerang     Arana 14052 2018-12-30
## 4656 4656         Jeffry      4.7     Bogor   Peronia 14343 2018-01-13
## 4706 4706         Jeffry      4.7 Tengerang     Asoka  8501 2019-09-09
## 4756 4756         Jeffry      4.7   Jakarta   Peronia 10039 2020-02-14
## 4806 4806         Jeffry      4.7     Bogor   Albasia 10553 2019-03-01
## 4856 4856         Jeffry      4.7     Bogor Teradamai  8829 2019-10-07
## 4906 4906         Jeffry      4.7   Jakarta   Permata 12122 2018-01-30
## 4956 4956         Jeffry      4.7   Jakarta    Narada  7688 2018-04-14
## 5006 5006         Jeffry      4.7 Tengerang Primadona 14201 2018-11-08
## 5056 5056         Jeffry      4.7     Depok Teradamai  9597 2018-10-26
## 5106 5106         Jeffry      4.7    Bekasi   Albasia 13010 2018-10-12
## 5156 5156         Jeffry      4.7     Depok   Peronia  7027 2020-07-26
## 5206 5206         Jeffry      4.7     Depok      Neon 10495 2020-07-11
## 5256 5256         Jeffry      4.7 Tengerang   Palmyra 10875 2019-05-11
## 5306 5306         Jeffry      4.7 Tengerang    Narada 14505 2020-08-06
## 5356 5356         Jeffry      4.7     Bogor    Narada  7545 2020-02-05
## 5406 5406         Jeffry      4.7 Tengerang     Asoka 14877 2020-03-17
## 5456 5456         Jeffry      4.7    Bekasi   Alindra 14577 2020-09-20
## 5506 5506         Jeffry      4.7     Bogor    Winona  9571 2019-03-03
## 5556 5556         Jeffry      4.7     Bogor Teradamai 10075 2018-01-15
## 5606 5606         Jeffry      4.7 Tengerang     Arana 11146 2019-10-23
## 5656 5656         Jeffry      4.7    Bekasi    Narada  7613 2020-06-07
## 5706 5706         Jeffry      4.7    Bekasi    Winona 10875 2019-08-19
## 5756 5756         Jeffry      4.7     Bogor Primadona  7712 2018-11-25
## 5806 5806         Jeffry      4.7     Depok     Asera 11748 2018-06-14
## 5856 5856         Jeffry      4.7     Depok Teradamai 14991 2019-12-20
## 5906 5906         Jeffry      4.7 Tengerang     Tiara  9987 2018-08-07
## 5956 5956         Jeffry      4.7    Bekasi   Albasia 14014 2019-07-20
## 6006 6006         Jeffry      4.7     Depok   Palmyra 13222 2018-07-09
## 6056 6056         Jeffry      4.7 Tengerang  Victoria 11631 2018-11-05
## 6106 6106         Jeffry      4.7     Bogor     Adara  8551 2020-03-25
## 6156 6156         Jeffry      4.7    Bekasi     Adara 10452 2018-08-16
## 6206 6206         Jeffry      4.7     Bogor   Mutiara 14738 2018-05-29
## 6256 6256         Jeffry      4.7     Bogor     Arana 13418 2018-03-28
## 6306 6306         Jeffry      4.7   Jakarta   Alindra 13780 2018-11-16
## 6356 6356         Jeffry      4.7     Bogor Primadona  7770 2018-10-02
## 6406 6406         Jeffry      4.7     Depok Sweethome 10067 2018-06-02
## 6456 6456         Jeffry      4.7 Tengerang      Neon  9167 2018-04-15
## 6506 6506         Jeffry      4.7   Jakarta    Lavesh  8929 2019-08-21
## 6556 6556         Jeffry      4.7   Jakarta   Palmyra  8558 2019-04-11
## 6606 6606         Jeffry      4.7     Bogor Sweethome 14975 2018-01-28
## 6656 6656         Jeffry      4.7   Jakarta     Arana 12971 2020-07-27
## 6706 6706         Jeffry      4.7    Bekasi Primadona  8923 2018-02-14
## 6756 6756         Jeffry      4.7     Bogor   Palmyra  9915 2020-01-18
## 6806 6806         Jeffry      4.7     Depok Primadona 14271 2019-04-26
## 6856 6856         Jeffry      4.7    Bekasi     Asera 11941 2019-04-26
## 6906 6906         Jeffry      4.7    Bekasi   Permata 14735 2018-08-30
## 6956 6956         Jeffry      4.7 Tengerang     Tiara 13167 2018-08-28
## 7006 7006         Jeffry      4.7   Jakarta Teradamai 11476 2019-08-07
## 7056 7056         Jeffry      4.7     Bogor Teradamai 10886 2018-03-15
## 7106 7106         Jeffry      4.7     Bogor     Tiara 13445 2019-05-17
## 7156 7156         Jeffry      4.7     Bogor   Palmyra 13613 2018-10-12
## 7206 7206         Jeffry      4.7     Bogor     Arana 14427 2019-10-14
## 7256 7256         Jeffry      4.7     Depok      Neon  8765 2018-12-01
## 7306 7306         Jeffry      4.7    Bekasi   Permata 12513 2019-07-06
## 7356 7356         Jeffry      4.7   Jakarta   Mutiara 12940 2019-08-03
## 7406 7406         Jeffry      4.7   Jakarta Sweethome  7696 2018-07-03
## 7456 7456         Jeffry      4.7   Jakarta   Peronia 10221 2019-10-27
## 7506 7506         Jeffry      4.7 Tengerang     Asera  7304 2019-10-29
## 7556 7556         Jeffry      4.7     Depok     Tiara 11812 2018-04-13
## 7606 7606         Jeffry      4.7     Bogor     Asoka  9068 2018-04-02
## 7656 7656         Jeffry      4.7    Bekasi   Albasia 14290 2018-11-09
## 7706 7706         Jeffry      4.7   Jakarta   Mutiara  8335 2018-06-21
## 7756 7756         Jeffry      4.7    Bekasi   Palmyra  8931 2020-03-25
## 7806 7806         Jeffry      4.7 Tengerang   Permata  8052 2019-06-04
## 7856 7856         Jeffry      4.7     Bogor  Victoria 11838 2019-11-03
## 7906 7906         Jeffry      4.7 Tengerang Primadona  9071 2019-01-23
## 7956 7956         Jeffry      4.7     Depok   Albasia 12595 2018-02-26
## 8006 8006         Jeffry      4.7    Bekasi  Victoria 10462 2020-06-15
## 8056 8056         Jeffry      4.7     Depok   Permata  7062 2019-06-08
## 8106 8106         Jeffry      4.7 Tengerang   Albasia 12948 2018-07-30
## 8156 8156         Jeffry      4.7     Bogor     Asera  8030 2018-01-10
## 8206 8206         Jeffry      4.7 Tengerang Primadona  7980 2019-02-05
## 8256 8256         Jeffry      4.7 Tengerang     Arana 10370 2018-04-19
## 8306 8306         Jeffry      4.7 Tengerang    Lavesh 11179 2019-04-21
## 8356 8356         Jeffry      4.7 Tengerang  Alamanda 11992 2019-07-08
## 8406 8406         Jeffry      4.7     Bogor     Arana 12402 2020-07-06
## 8456 8456         Jeffry      4.7     Bogor   Palmyra 10802 2020-09-19
## 8506 8506         Jeffry      4.7    Bekasi  Victoria 10791 2020-03-12
## 8556 8556         Jeffry      4.7    Bekasi      Neon 10978 2018-11-19
## 8606 8606         Jeffry      4.7 Tengerang  Victoria 13491 2018-01-06
## 8656 8656         Jeffry      4.7     Depok     Asera 13605 2018-12-07
## 8706 8706         Jeffry      4.7   Jakarta  Alamanda 12559 2020-01-30
## 8756 8756         Jeffry      4.7    Bekasi   Peronia 14758 2019-08-13
## 8806 8806         Jeffry      4.7     Depok     Adara  7753 2019-07-07
## 8856 8856         Jeffry      4.7    Bekasi   Peronia  9915 2019-12-15
## 8906 8906         Jeffry      4.7     Depok   Alindra 10283 2020-02-22
## 8956 8956         Jeffry      4.7    Bekasi   Alindra  8091 2020-06-16
## 9006 9006         Jeffry      4.7    Bekasi   Palmyra 13113 2018-04-25
## 9056 9056         Jeffry      4.7     Bogor   Alindra 14719 2019-08-14
## 9106 9106         Jeffry      4.7 Tengerang  Alamanda 12260 2019-08-20
## 9156 9156         Jeffry      4.7     Bogor Teradamai  8363 2020-09-12
## 9206 9206         Jeffry      4.7   Jakarta Teradamai  8161 2019-06-17
## 9256 9256         Jeffry      4.7    Bekasi     Adara 14928 2020-09-07
## 9306 9306         Jeffry      4.7 Tengerang   Mutiara  9553 2018-12-24
## 9356 9356         Jeffry      4.7     Bogor     Asoka  9982 2020-03-06
## 9406 9406         Jeffry      4.7 Tengerang   Albasia 12345 2020-08-02
## 9456 9456         Jeffry      4.7     Depok   Albasia  8111 2019-10-13
## 9506 9506         Jeffry      4.7 Tengerang    Winona 13276 2018-08-29
## 9556 9556         Jeffry      4.7     Depok    Lavesh  7528 2019-12-07
## 9606 9606         Jeffry      4.7    Bekasi Primadona  9562 2020-03-17
## 9656 9656         Jeffry      4.7 Tengerang Primadona  7266 2019-01-31
## 9706 9706         Jeffry      4.7   Jakarta   Peronia 14552 2019-10-17
## 9756 9756         Jeffry      4.7     Bogor Primadona 13473 2020-03-08
## 9806 9806         Jeffry      4.7     Depok      Neon 10387 2019-06-29
## 9856 9856         Jeffry      4.7   Jakarta   Albasia 12484 2018-12-11
## 9906 9906         Jeffry      4.7 Tengerang      Neon  9859 2019-10-13
## 9956 9956         Jeffry      4.7 Tengerang Primadona  9792 2020-02-04
##      Advertisement Booking_fee
## 6                9     1085.22
## 56              17     1154.25
## 106             20     1036.89
## 156             15     1044.99
## 206             16     1018.44
## 256              3     1405.50
## 306             12     1147.68
## 356             19      498.54
## 406              5     1069.83
## 456              8     1303.60
## 506              8     1161.18
## 556              4      358.90
## 606             17      523.20
## 656              4     1394.70
## 706             16      537.00
## 756             16     1013.13
## 806             14      811.12
## 856             20      390.10
## 906              6      389.85
## 956             19      816.08
## 1006             8     1440.90
## 1056            10     1323.90
## 1106            16      630.35
## 1156            17      393.95
## 1206             1      354.90
## 1256            13     1444.20
## 1306            11     1117.17
## 1356            19      642.32
## 1406            14      671.79
## 1456             8     1318.30
## 1506             9     1077.21
## 1556            11     1421.20
## 1606             3     1358.10
## 1656            20      518.94
## 1706            10     1004.67
## 1756             4     1159.29
## 1806             8     1161.90
## 1856             9      505.74
## 1906             1     1326.50
## 1956             9      694.33
## 2006            18      361.50
## 2056            14     1003.59
## 2106            13      840.08
## 2156             9     1473.00
## 2206            10      526.26
## 2256            16      646.73
## 2306             6      632.31
## 2356             1      648.97
## 2406            14     1024.11
## 2456             8      526.20
## 2506             5     1019.34
## 2556            11      699.65
## 2606             8     1492.30
## 2656            10     1418.50
## 2706             4      492.48
## 2756             4      817.20
## 2806            11      647.71
## 2856             3      353.35
## 2906            13     1051.56
## 2956            13      495.06
## 3006             7      373.60
## 3056             7      377.30
## 3106            17      819.68
## 3156             9      498.30
## 3206            17      487.62
## 3256             1     1417.90
## 3306            17     1433.00
## 3356            17     1065.33
## 3406            17      676.97
## 3456             2      662.20
## 3506             6      671.93
## 3556             5      642.04
## 3606             6      633.50
## 3656             7      527.94
## 3706            17      817.36
## 3756             4     1105.74
## 3806             4     1141.83
## 3856             7     1392.00
## 3906            20     1157.13
## 3956            17      390.15
## 4006             6     1001.97
## 4056             3     1478.30
## 4106            11     1301.30
## 4156            12      666.33
## 4206             2     1150.65
## 4256            11      354.25
## 4306            13      354.25
## 4356             7      482.04
## 4406             9      356.60
## 4456            10      513.78
## 4506            19      839.60
## 4556             3      862.48
## 4606             4     1405.20
## 4656            11     1434.30
## 4706             8      510.06
## 4756             5      803.12
## 4806            17      844.24
## 4856             8      529.74
## 4906            16     1090.98
## 4956            11      384.40
## 5006             3     1420.10
## 5056            19      671.79
## 5106            13     1301.00
## 5156            19      351.35
## 5206            16      839.60
## 5256             6      870.00
## 5306             8     1450.50
## 5356             9      377.25
## 5406            12     1487.70
## 5456            11     1457.70
## 5506            20      669.97
## 5556             3      806.00
## 5606            18     1003.14
## 5656            17      380.65
## 5706             9      870.00
## 5756             2      385.60
## 5806            19     1057.32
## 5856            13     1499.10
## 5906            15      699.09
## 5956             5     1401.40
## 6006             9     1322.20
## 6056             8     1046.79
## 6106             2      513.06
## 6156             6      836.16
## 6206             3     1473.80
## 6256            18     1341.80
## 6306             6     1378.00
## 6356             8      388.50
## 6406             7      805.36
## 6456            19      641.69
## 6506            18      535.74
## 6556            10      513.48
## 6606             5     1497.50
## 6656            11     1167.39
## 6706             3      535.38
## 6756            13      694.05
## 6806             3     1427.10
## 6856             9     1074.69
## 6906            10     1473.50
## 6956             7     1316.70
## 7006            18     1032.84
## 7056            19      870.88
## 7106             7     1344.50
## 7156             3     1361.30
## 7206             7     1442.70
## 7256            20      525.90
## 7306             9     1126.17
## 7356            19     1164.60
## 7406            10      384.80
## 7456            10      817.68
## 7506             3      365.20
## 7556             7     1063.08
## 7606             8      634.76
## 7656             6     1429.00
## 7706             8      500.10
## 7756            15      535.86
## 7806            15      483.12
## 7856             3     1065.42
## 7906             9      634.97
## 7956            12     1133.55
## 8006             3      836.96
## 8056             4      353.10
## 8106             5     1165.32
## 8156            10      481.80
## 8206            17      399.00
## 8256            14      829.60
## 8306            14     1006.11
## 8356            11     1079.28
## 8406            20     1116.18
## 8456            16      864.16
## 8506            17      863.28
## 8556            14      878.24
## 8606            16     1349.10
## 8656            13     1360.50
## 8706            19     1130.31
## 8756             2     1475.80
## 8806            20      387.65
## 8856            17      694.05
## 8906             7      822.64
## 8956            20      485.46
## 9006            13     1311.30
## 9056             7     1471.90
## 9106            11     1103.40
## 9156             6      501.78
## 9206             1      489.66
## 9256            16     1492.80
## 9306             7      668.71
## 9356             2      698.74
## 9406             4     1111.05
## 9456            14      486.66
## 9506             4     1327.60
## 9556            17      376.40
## 9606            13      669.34
## 9656             7      363.30
## 9706            18     1455.20
## 9756             2     1347.30
## 9806             6      830.96
## 9856            18     1123.56
## 9906            19      690.13
## 9956             5      685.44

R

sales = "Jeffry"
x <- subset(Data, subset = (Marketing_Name == sales)) 

for (i in 1:nrow(x)){
  if (x$Work_Exp [i] > 3){
    x$Bonus [i] <- ((x$Booking_fee[i])*(3/100))
    }
  else {
    x$Bonus [i] <- ((x$Booking_fee[i])*(2/100))
  }
}
x

Total = sum(x$Bonus)
Total

## [1] 5376.997

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 == "Falen"))
Ardifo = subset(Data, subset =(Marketing_Name == "Ardifo"))
Kevin = subset(Data, subset =(Marketing_Name == "Kevin"))
Juen = subset(Data, subset =(Marketing_Name == "Sherly"))
Jerrel = subset(Data, subset =(Marketing_Name == "Vanessa"))
Imelda = subset(Data, subset =(Marketing_Name == "Irene"))
Widi = subset(Data, subset =(Marketing_Name == "Julian"))
Theodora = subset(Data, subset =(Marketing_Name == "Jeffry"))
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 == "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"))

Sales_name = 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")

total_sales = 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$Price), sum(James$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))

Marketing=data.frame(Sales_name,total_sales)
Marketing

#marketing terbaik
Marketing[which.max(Marketing$total_sales),]

#kota dan cluster
Untung=Data[c("City","Cluster","Price")]
Untung[which.max(Untung$Price),]

review <- function(x){
  total_iklan = sum(x$Advertisement)*4
  rerata_iklan = total_iklan/sum(x$Advertisement)
  pendapatan = sum(x$Booking_fee) + sum(x$Bonus)
  return(cat(c("Total biaya iklan :", total_iklan, "\n",
               "Rata-rata biaya iklan:", rerata_iklan, "\n",
               "Total pendapatan bulanan:", pendapatan, "\n")))
}
review(x)

## Total biaya iklan : 8396 
##  Rata-rata biaya iklan: 4 
##  Total pendapatan bulanan: 184610.2166

R

No <- 1:50000

Marital_Status <- sample(c("Ya","Tidak"),50000,replace=TRUE)

Addres <- sample(c("Jakarta","Bogor","Depok","Tangerang","Bekasi"),50000, replace = TRUE)

Work_location <- sample(c("Jakarta","Bogor","Depok","Tangerang","Bekasi"),50000, replace=TRUE)

Age <- sample(c(19:60),50000,replace=TRUE)

Academic <- rep(c("J.School", "H.School", "Sarjana", "Magister", "Phd"),10000)

Job <- sample(c("Pengusaha","Pegawai","Supir","Perawat","Guru","Pelayan","Designer","Ibu_rumah_tangga","Asisten_rumah_tangga","fotografer","Artis","Penyanyi","Model","Koki","Arsitek","Ojek","Montir","Hair_stylish","Kolektor","Akuntan","Programmer","Atlet","MC","Wedding_Organizer","Vlogger","Selebgram","Sales","Pramugari","Pilot","Pelukis","Wartawan","Petani","Pesulap","Polisi","Pemadam_kebakaran","Penulis","Seniman","ilmuwan","Buruh","Tentara","Psikolog","Nelayan","Tukang_bangunan","Penjahit","Peternak","Teknisi","Komedian","Gammers","Youtuber","Sutradara"),50000,replace=TRUE)

Grade <- sample(c("A","B","C","D","E"),50000,replace=TRUE)

Income <- sample(c('<1000.000','1.000.000-2.000.000','2.000.000-3.000.000','3.000.000-4.000.000','4.000.000-5.000.000','>5.000.000'),50000,replace=TRUE)

Spending <- sample(c('<1000.000','1.000.000-2.000.000','2.000.000-3.000.000','3.000.000-4.000.000','4.000.000-5.000.000','>5.000.000'),50000,replace=TRUE)

Number_of_children <- ifelse(Marital_Status == "Ya", sample(c(1:10),25000,replace=TRUE),0)

Private_vehicle <- sample(c("Mobil","Motor","Umum"),50000,replace=TRUE)

Home <- sample(c("sewa","milik","kredit"),50000,replace=TRUE)

Asuransi <- data.frame(No,
                       Marital_Status,
                       Addres,
                       Work_location,
                       Age,
                       Academic,
                       Job,
                       Grade,
                       Income,
                       Spending,
                       Number_of_children,
                       Private_vehicle,
                       Home)

library(DT)
datatable(Asuransi)

## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html

R

Asuransi$Age -> Umur

Statistik <- function(Umur)
{rerata = sum(Umur)/length(Umur)
 Maks = max(Umur)
 Min = min(Umur)
 Median = median(Umur)
 return(cat(c("Rata-rata umur :", rerata, "\n",
              "Maksimal Umur :", Maks, "\n",
              "Minimal umur :", Min, "\n",
              "Median dari umur :", Median, "\n")))}

Statistik(Umur)

## Rata-rata umur : 39.43988 
##  Maksimal Umur : 60 
##  Minimal umur : 19 
##  Median dari umur : 39

R

for (i in 1:nrow(Asuransi)){
  if (Asuransi$Home[i] == "milik")
    if(Asuransi$Income[i] == ">5.000.000"){
    Asuransi$Status[i] <- "Pertahankan"}
  else{
    Asuransi$Status[i] <- "Tidak"}
}

library(DT)
datatable(Asuransi)

## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html