Algoritma & Struktur Data
~ Ujian Tengah Semester ~
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NAMA : DHELA ASAFIANI AGATHA NIM : 20214920009 PRODI: STATISTIKA BISNIS (2021)
Kasus 1
Asumsikan Anda telah mengumpulkan beberapa kumpulan data dari perusahaan ABC Property seperti yang dapat kita lihat pada tabel berikut:
Id <- (1:10000)
Marketing_Name <- rep(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"), 200)
Work_Exp <- rep(c(1.3,2.4,2.5,3.6,3.7,4.7,5.7,6.7,7.7,7.3,
5.3,5.3,10,9.3,3.3,3.3,3.4,3.4,3.5,5.6,
3.5,4.6,4.6,5.7,6.2,4.4,6.4,6.4,3.5,7.5,
4.6,3.7,4.7,4.3,5.2,6.3,7.4,2.4,3.4,8.2,
6.4,7.2,1.5,7.5,10,4.5,6.5,7.2,7.1,7.6),200)
City <- sample(c("Jakarta","Bogor","Depok","Tengerang","Bekasi"),10000, replace = T)
Cluster <- sample(c("Victoria","Palmyra","Winona","Tiara", "Narada",
"Peronia","Lavesh","Alindra","Sweethome", "Asera",
"Teradamai","Albasia", "Adara","Neon","Arana",
"Asoka", "Primadona", "Mutiara","Permata","Alamanda" ), 10000, replace=T)
Price <- sample(c(7000:15000),10000, replace = T)
Date_Sales <- sample(seq(as.Date("2018/01/01"), by = "day", length.out = 1000),10000, replace = T)
Advertisement <- sample(c(1:20), 10000, replace = T)
Data <- data.frame(Id,
Marketing_Name,
Work_Exp,
City,
Cluster,
Price,
Date_Sales,
Advertisement)
library(DT)
datatable(Data)write.csv(Data,"C:\\Users\\Public\\YA.csv", row.names = FALSE)Soal 1
Kategorikan variabel Harga pada dataset di atas menjadi tiga kelompok sebagai berikut:
- \(\text{High} > 12000\)
- \(10000 \le \text{Medium} \le 12000\)
- \(\text{Low} < 10000\)
Tetapkan ke dalam variabel baru yang disebut Kelas dengan menggunakan fungsi kontrol If, else if, dan else.
R
x = Data$Price
a= ifelse((x>12000),print('High'),
ifelse((x>=10000 & x<= 12000), print("Medium"),print('Low')
))## [1] "High"
## [1] "Medium"
## [1] "Low"Data$Class = a
datatable(Data)Soal 2
Kategorikan variabel Harga pada dataset di atas menjadi enam kelompok sebagai berikut:
- Booking_fee nya 5 % jika \(\text{Price} < 8000\)
- Booking_fee nya 6 % jika \(8000 \le \text{Price} < 9000\)
- Booking_fee nya 7 % jika \(9000 \le \text{Price} < 10000\)
- Booking_fee nya 8 % jika \(10000 \le \text{Price} < 11000\)
- Booking_fee nya 9 % jika \(11000 \le \text{Price} < 13000\)
- Booking_fee nya 10 % jika \(13000 \le \text{Price} \le 15000\)
Tetapkan ke dalam variabel baru yang disebut Booking_fee dengan menggunakan fungsi kontrol If, else if, dan else.
R
x = Data$Price
Data$Booking_Fee <- ifelse(Data$Price < 8000, 0.05*Data$Price,
ifelse(Data$Price < 9000 & Data$Price > 8000 , 0.06*Data$Price,
ifelse(Data$Price < 10000 & Data$Price > 9000 , 0.07*Data$Price,
ifelse(Data$Price < 11000 & Data$Price > 10000 , 0.08*Data$Price,
ifelse(Data$Price < 12000 & Data$Price > 11000 , 0.09*Data$Price,
0.1*Data$Price
)))))
datatable(Data)Soal 3
Menurut kumpulan data akhir yang telah Anda buat pada soal no 2, saya berasumsi bahwa Anda telah bekerja sebagai pemasaran di perusahaan ABC Property, bagaimana Anda dapat mengumpulkan semua informasi tentang penjualan Anda dengan menggunakan pernyataan for.
R
sales ="Matius"
z = for(i in sales){
print(subset(Data, subset=(Marketing_Name == i)))
}## Id Marketing_Name Work_Exp City Cluster Price Date_Sales
## 48 48 Matius 7.2 Bogor Asera 11250 2020-08-25
## 98 98 Matius 7.2 Tengerang Asera 11086 2020-09-26
## 148 148 Matius 7.2 Bogor Sweethome 12019 2018-09-03
## 198 198 Matius 7.2 Depok Teradamai 8504 2018-06-23
## 248 248 Matius 7.2 Jakarta Narada 11504 2018-06-16
## 298 298 Matius 7.2 Jakarta Alindra 7246 2020-01-25
## 348 348 Matius 7.2 Jakarta Alamanda 11663 2019-04-16
## 398 398 Matius 7.2 Bekasi Arana 14831 2019-12-22
## 448 448 Matius 7.2 Jakarta Arana 10286 2018-10-02
## 498 498 Matius 7.2 Bekasi Asera 14904 2020-07-20
## 548 548 Matius 7.2 Bogor Winona 9498 2018-10-19
## 598 598 Matius 7.2 Depok Asera 9409 2018-08-13
## 648 648 Matius 7.2 Jakarta Albasia 10481 2019-11-17
## 698 698 Matius 7.2 Depok Victoria 14724 2020-08-22
## 748 748 Matius 7.2 Tengerang Arana 14796 2019-05-22
## 798 798 Matius 7.2 Bekasi Narada 10394 2018-08-12
## 848 848 Matius 7.2 Depok Teradamai 8082 2019-10-03
## 898 898 Matius 7.2 Bogor Palmyra 10446 2020-06-09
## 948 948 Matius 7.2 Bogor Victoria 8971 2020-02-09
## 998 998 Matius 7.2 Bogor Narada 10369 2019-02-26
## 1048 1048 Matius 7.2 Bogor Neon 11196 2020-06-22
## 1098 1098 Matius 7.2 Tengerang Alindra 7912 2020-04-13
## 1148 1148 Matius 7.2 Depok Palmyra 11276 2020-08-01
## 1198 1198 Matius 7.2 Bogor Alamanda 14186 2019-11-11
## 1248 1248 Matius 7.2 Depok Albasia 13638 2020-09-23
## 1298 1298 Matius 7.2 Bogor Narada 9327 2018-04-22
## 1348 1348 Matius 7.2 Bekasi Winona 14675 2019-01-06
## 1398 1398 Matius 7.2 Tengerang Neon 11730 2020-07-31
## 1448 1448 Matius 7.2 Bekasi Teradamai 13516 2020-05-25
## 1498 1498 Matius 7.2 Bogor Arana 13658 2020-04-02
## 1548 1548 Matius 7.2 Bekasi Albasia 12607 2019-08-04
## 1598 1598 Matius 7.2 Jakarta Permata 9611 2019-12-12
## 1648 1648 Matius 7.2 Bogor Mutiara 14608 2018-11-28
## 1698 1698 Matius 7.2 Jakarta Albasia 9499 2020-01-28
## 1748 1748 Matius 7.2 Tengerang Narada 7608 2018-01-17
## 1798 1798 Matius 7.2 Tengerang Alindra 14785 2019-02-02
## 1848 1848 Matius 7.2 Jakarta Palmyra 10989 2019-07-19
## 1898 1898 Matius 7.2 Bogor Permata 9378 2019-03-11
## 1948 1948 Matius 7.2 Bekasi Mutiara 10639 2018-09-01
## 1998 1998 Matius 7.2 Bekasi Albasia 9841 2018-12-25
## 2048 2048 Matius 7.2 Bogor Lavesh 13776 2019-12-07
## 2098 2098 Matius 7.2 Tengerang Albasia 9710 2018-07-23
## 2148 2148 Matius 7.2 Depok Palmyra 10073 2018-07-03
## 2198 2198 Matius 7.2 Bekasi Alamanda 7121 2020-06-27
## 2248 2248 Matius 7.2 Depok Alamanda 10462 2019-09-03
## 2298 2298 Matius 7.2 Bogor Arana 13399 2020-01-23
## 2348 2348 Matius 7.2 Jakarta Primadona 8776 2020-06-14
## 2398 2398 Matius 7.2 Jakarta Neon 13043 2018-08-19
## 2448 2448 Matius 7.2 Tengerang Palmyra 14225 2019-02-23
## 2498 2498 Matius 7.2 Depok Permata 7763 2020-06-06
## 2548 2548 Matius 7.2 Depok Tiara 9865 2019-07-28
## 2598 2598 Matius 7.2 Tengerang Sweethome 13865 2018-08-26
## 2648 2648 Matius 7.2 Bogor Alamanda 14670 2018-10-19
## 2698 2698 Matius 7.2 Tengerang Alindra 7704 2020-02-01
## 2748 2748 Matius 7.2 Jakarta Mutiara 7279 2019-08-27
## 2798 2798 Matius 7.2 Bogor Primadona 7639 2019-10-01
## 2848 2848 Matius 7.2 Depok Palmyra 10785 2018-01-13
## 2898 2898 Matius 7.2 Bogor Alamanda 8684 2020-06-09
## 2948 2948 Matius 7.2 Jakarta Albasia 8722 2018-08-23
## 2998 2998 Matius 7.2 Bogor Peronia 10856 2019-04-05
## 3048 3048 Matius 7.2 Depok Narada 10587 2020-02-08
## 3098 3098 Matius 7.2 Tengerang Asoka 11562 2018-06-22
## 3148 3148 Matius 7.2 Tengerang Mutiara 13976 2020-02-16
## 3198 3198 Matius 7.2 Jakarta Albasia 14086 2019-10-14
## 3248 3248 Matius 7.2 Depok Teradamai 8793 2018-03-19
## 3298 3298 Matius 7.2 Bekasi Peronia 13549 2018-08-12
## 3348 3348 Matius 7.2 Depok Narada 12039 2018-07-16
## 3398 3398 Matius 7.2 Bekasi Asoka 11744 2019-12-17
## 3448 3448 Matius 7.2 Depok Adara 10415 2020-02-02
## 3498 3498 Matius 7.2 Bogor Asoka 10852 2019-02-02
## 3548 3548 Matius 7.2 Depok Lavesh 11877 2019-08-25
## 3598 3598 Matius 7.2 Depok Peronia 13628 2019-02-10
## 3648 3648 Matius 7.2 Bogor Winona 7111 2018-03-16
## 3698 3698 Matius 7.2 Tengerang Neon 10273 2019-12-17
## 3748 3748 Matius 7.2 Tengerang Victoria 12587 2019-12-13
## 3798 3798 Matius 7.2 Tengerang Asera 9773 2018-07-20
## 3848 3848 Matius 7.2 Bekasi Victoria 14650 2018-06-21
## 3898 3898 Matius 7.2 Depok Arana 8395 2018-08-07
## 3948 3948 Matius 7.2 Bekasi Asera 12707 2018-04-02
## 3998 3998 Matius 7.2 Jakarta Palmyra 8576 2018-04-13
## 4048 4048 Matius 7.2 Bekasi Teradamai 7525 2018-08-01
## 4098 4098 Matius 7.2 Jakarta Narada 10491 2019-07-10
## 4148 4148 Matius 7.2 Depok Peronia 13374 2020-05-28
## 4198 4198 Matius 7.2 Depok Asoka 12105 2019-07-23
## 4248 4248 Matius 7.2 Jakarta Mutiara 10911 2020-09-03
## 4298 4298 Matius 7.2 Tengerang Neon 13434 2018-09-24
## 4348 4348 Matius 7.2 Depok Adara 14944 2018-06-06
## 4398 4398 Matius 7.2 Tengerang Victoria 8720 2020-04-04
## 4448 4448 Matius 7.2 Bogor Arana 9942 2019-03-20
## 4498 4498 Matius 7.2 Bekasi Permata 8179 2018-03-27
## 4548 4548 Matius 7.2 Bekasi Winona 13269 2018-10-01
## 4598 4598 Matius 7.2 Tengerang Palmyra 12748 2019-01-15
## 4648 4648 Matius 7.2 Tengerang Sweethome 8109 2018-02-26
## 4698 4698 Matius 7.2 Bekasi Winona 8287 2018-05-23
## 4748 4748 Matius 7.2 Bekasi Permata 13129 2018-12-04
## 4798 4798 Matius 7.2 Bekasi Winona 8424 2019-09-07
## 4848 4848 Matius 7.2 Tengerang Alindra 9769 2018-12-28
## 4898 4898 Matius 7.2 Bogor Palmyra 13094 2018-10-02
## 4948 4948 Matius 7.2 Depok Tiara 10382 2019-11-14
## 4998 4998 Matius 7.2 Depok Primadona 11071 2020-08-27
## 5048 5048 Matius 7.2 Jakarta Arana 11122 2019-12-10
## 5098 5098 Matius 7.2 Jakarta Narada 9457 2018-07-23
## 5148 5148 Matius 7.2 Bekasi Mutiara 11048 2018-07-27
## 5198 5198 Matius 7.2 Bogor Alindra 11987 2019-09-14
## 5248 5248 Matius 7.2 Tengerang Neon 13316 2019-12-24
## 5298 5298 Matius 7.2 Bogor Asera 7179 2019-01-17
## 5348 5348 Matius 7.2 Bekasi Asera 12793 2019-09-27
## 5398 5398 Matius 7.2 Jakarta Adara 12578 2020-04-10
## 5448 5448 Matius 7.2 Jakarta Alamanda 13677 2020-02-08
## 5498 5498 Matius 7.2 Jakarta Sweethome 13847 2018-07-02
## 5548 5548 Matius 7.2 Depok Primadona 9640 2019-03-09
## 5598 5598 Matius 7.2 Bogor Tiara 9646 2019-08-25
## 5648 5648 Matius 7.2 Bogor Palmyra 9894 2019-02-12
## 5698 5698 Matius 7.2 Bogor Primadona 9655 2019-08-12
## 5748 5748 Matius 7.2 Bekasi Peronia 8176 2019-05-11
## 5798 5798 Matius 7.2 Bogor Palmyra 13210 2019-11-09
## 5848 5848 Matius 7.2 Tengerang Alindra 12893 2019-10-18
## 5898 5898 Matius 7.2 Tengerang Adara 12115 2019-04-12
## 5948 5948 Matius 7.2 Tengerang Palmyra 11405 2018-05-24
## 5998 5998 Matius 7.2 Bekasi Teradamai 10385 2020-06-14
## 6048 6048 Matius 7.2 Bogor Primadona 13529 2019-09-30
## 6098 6098 Matius 7.2 Bogor Neon 11179 2019-06-08
## 6148 6148 Matius 7.2 Bekasi Teradamai 13100 2019-11-27
## 6198 6198 Matius 7.2 Jakarta Neon 7533 2018-07-02
## 6248 6248 Matius 7.2 Jakarta Permata 11278 2018-12-21
## 6298 6298 Matius 7.2 Bekasi Tiara 12705 2019-10-02
## 6348 6348 Matius 7.2 Bekasi Arana 7213 2020-03-28
## 6398 6398 Matius 7.2 Depok Adara 10858 2020-04-09
## 6448 6448 Matius 7.2 Tengerang Palmyra 9630 2019-10-21
## 6498 6498 Matius 7.2 Bogor Asoka 7233 2019-11-09
## 6548 6548 Matius 7.2 Bogor Palmyra 12569 2019-09-22
## 6598 6598 Matius 7.2 Jakarta Palmyra 9990 2018-09-30
## 6648 6648 Matius 7.2 Depok Alamanda 14544 2018-10-14
## 6698 6698 Matius 7.2 Bekasi Arana 8457 2019-03-22
## 6748 6748 Matius 7.2 Bekasi Arana 8184 2019-03-23
## 6798 6798 Matius 7.2 Tengerang Peronia 8918 2019-08-12
## 6848 6848 Matius 7.2 Depok Albasia 11763 2019-04-24
## 6898 6898 Matius 7.2 Tengerang Palmyra 12156 2020-01-05
## 6948 6948 Matius 7.2 Jakarta Lavesh 11367 2019-03-06
## 6998 6998 Matius 7.2 Tengerang Tiara 8102 2018-04-18
## 7048 7048 Matius 7.2 Tengerang Arana 13367 2018-03-07
## 7098 7098 Matius 7.2 Jakarta Asera 14550 2020-03-27
## 7148 7148 Matius 7.2 Bogor Alamanda 7161 2019-10-05
## 7198 7198 Matius 7.2 Bekasi Primadona 10445 2020-03-26
## 7248 7248 Matius 7.2 Tengerang Tiara 11458 2019-07-29
## 7298 7298 Matius 7.2 Depok Winona 11608 2019-06-01
## 7348 7348 Matius 7.2 Tengerang Asera 14265 2018-09-05
## 7398 7398 Matius 7.2 Bekasi Lavesh 13783 2018-10-01
## 7448 7448 Matius 7.2 Bogor Victoria 11460 2019-07-18
## 7498 7498 Matius 7.2 Depok Peronia 13867 2018-02-10
## 7548 7548 Matius 7.2 Jakarta Arana 9477 2019-03-26
## 7598 7598 Matius 7.2 Tengerang Victoria 7167 2019-06-08
## 7648 7648 Matius 7.2 Jakarta Alamanda 7272 2018-01-05
## 7698 7698 Matius 7.2 Depok Asera 10453 2019-09-21
## 7748 7748 Matius 7.2 Bekasi Palmyra 14071 2019-10-10
## 7798 7798 Matius 7.2 Bekasi Teradamai 12248 2018-01-15
## 7848 7848 Matius 7.2 Tengerang Narada 11293 2020-06-12
## 7898 7898 Matius 7.2 Jakarta Alamanda 12920 2019-08-13
## 7948 7948 Matius 7.2 Tengerang Teradamai 14530 2018-07-18
## 7998 7998 Matius 7.2 Depok Permata 8432 2018-12-16
## 8048 8048 Matius 7.2 Tengerang Asoka 11560 2019-02-14
## 8098 8098 Matius 7.2 Bekasi Sweethome 9288 2019-05-31
## 8148 8148 Matius 7.2 Bekasi Victoria 10903 2019-10-20
## 8198 8198 Matius 7.2 Bogor Tiara 7825 2018-04-01
## 8248 8248 Matius 7.2 Tengerang Neon 9505 2020-08-19
## 8298 8298 Matius 7.2 Bogor Adara 8990 2020-08-05
## 8348 8348 Matius 7.2 Bekasi Asoka 12973 2020-07-02
## 8398 8398 Matius 7.2 Bogor Palmyra 8020 2018-07-29
## 8448 8448 Matius 7.2 Depok Primadona 9440 2018-02-20
## 8498 8498 Matius 7.2 Bogor Asoka 8366 2019-04-17
## 8548 8548 Matius 7.2 Jakarta Arana 10901 2018-12-15
## 8598 8598 Matius 7.2 Bogor Alindra 8679 2018-09-28
## 8648 8648 Matius 7.2 Bogor Neon 11842 2020-06-21
## 8698 8698 Matius 7.2 Depok Neon 8025 2019-01-20
## 8748 8748 Matius 7.2 Tengerang Victoria 13152 2019-09-20
## 8798 8798 Matius 7.2 Depok Victoria 12798 2019-07-12
## 8848 8848 Matius 7.2 Depok Primadona 8859 2018-11-05
## 8898 8898 Matius 7.2 Bogor Arana 8741 2020-03-04
## 8948 8948 Matius 7.2 Tengerang Arana 11178 2020-01-30
## 8998 8998 Matius 7.2 Bogor Teradamai 9641 2020-09-09
## 9048 9048 Matius 7.2 Tengerang Alamanda 7008 2018-02-23
## 9098 9098 Matius 7.2 Tengerang Adara 12730 2019-05-24
## 9148 9148 Matius 7.2 Depok Primadona 12773 2020-07-19
## 9198 9198 Matius 7.2 Bekasi Victoria 10341 2020-07-22
## 9248 9248 Matius 7.2 Bekasi Winona 8814 2020-04-21
## 9298 9298 Matius 7.2 Tengerang Neon 8592 2020-09-16
## 9348 9348 Matius 7.2 Tengerang Victoria 8661 2018-07-09
## 9398 9398 Matius 7.2 Tengerang Permata 13966 2018-07-27
## 9448 9448 Matius 7.2 Tengerang Arana 13827 2018-07-02
## 9498 9498 Matius 7.2 Tengerang Albasia 10689 2019-09-24
## 9548 9548 Matius 7.2 Bekasi Mutiara 10283 2018-07-28
## 9598 9598 Matius 7.2 Depok Alamanda 9292 2019-12-19
## 9648 9648 Matius 7.2 Depok Albasia 11120 2020-05-02
## 9698 9698 Matius 7.2 Bogor Mutiara 12185 2018-03-09
## 9748 9748 Matius 7.2 Jakarta Winona 10864 2019-02-18
## 9798 9798 Matius 7.2 Tengerang Adara 9262 2018-04-13
## 9848 9848 Matius 7.2 Bekasi Tiara 8533 2018-11-25
## 9898 9898 Matius 7.2 Depok Sweethome 10076 2018-07-02
## 9948 9948 Matius 7.2 Bekasi Narada 8419 2020-09-12
## 9998 9998 Matius 7.2 Jakarta Peronia 10628 2019-10-06
## Advertisement Class Booking_Fee
## 48 9 Medium 1012.50
## 98 18 Medium 997.74
## 148 8 High 1201.90
## 198 2 Low 510.24
## 248 14 Medium 1035.36
## 298 8 Low 362.30
## 348 18 Medium 1049.67
## 398 15 High 1483.10
## 448 14 Medium 822.88
## 498 18 High 1490.40
## 548 4 Low 664.86
## 598 16 Low 658.63
## 648 16 Medium 838.48
## 698 6 High 1472.40
## 748 18 High 1479.60
## 798 13 Medium 831.52
## 848 3 Low 484.92
## 898 19 Medium 835.68
## 948 11 Low 538.26
## 998 16 Medium 829.52
## 1048 3 Medium 1007.64
## 1098 15 Low 395.60
## 1148 1 Medium 1014.84
## 1198 20 High 1418.60
## 1248 20 High 1363.80
## 1298 14 Low 652.89
## 1348 20 High 1467.50
## 1398 2 Medium 1055.70
## 1448 2 High 1351.60
## 1498 8 High 1365.80
## 1548 15 High 1260.70
## 1598 15 Low 672.77
## 1648 20 High 1460.80
## 1698 3 Low 664.93
## 1748 3 Low 380.40
## 1798 6 High 1478.50
## 1848 18 Medium 879.12
## 1898 1 Low 656.46
## 1948 16 Medium 851.12
## 1998 19 Low 688.87
## 2048 8 High 1377.60
## 2098 7 Low 679.70
## 2148 10 Medium 805.84
## 2198 9 Low 356.05
## 2248 15 Medium 836.96
## 2298 11 High 1339.90
## 2348 16 Low 526.56
## 2398 10 High 1304.30
## 2448 5 High 1422.50
## 2498 6 Low 388.15
## 2548 14 Low 690.55
## 2598 5 High 1386.50
## 2648 11 High 1467.00
## 2698 14 Low 385.20
## 2748 14 Low 363.95
## 2798 4 Low 381.95
## 2848 13 Medium 862.80
## 2898 14 Low 521.04
## 2948 20 Low 523.32
## 2998 20 Medium 868.48
## 3048 9 Medium 846.96
## 3098 6 Medium 1040.58
## 3148 9 High 1397.60
## 3198 6 High 1408.60
## 3248 14 Low 527.58
## 3298 8 High 1354.90
## 3348 3 High 1203.90
## 3398 15 Medium 1056.96
## 3448 15 Medium 833.20
## 3498 18 Medium 868.16
## 3548 6 Medium 1068.93
## 3598 11 High 1362.80
## 3648 18 Low 355.55
## 3698 15 Medium 821.84
## 3748 1 High 1258.70
## 3798 7 Low 684.11
## 3848 16 High 1465.00
## 3898 8 Low 503.70
## 3948 7 High 1270.70
## 3998 14 Low 514.56
## 4048 9 Low 376.25
## 4098 20 Medium 839.28
## 4148 9 High 1337.40
## 4198 10 High 1210.50
## 4248 9 Medium 872.88
## 4298 15 High 1343.40
## 4348 3 High 1494.40
## 4398 4 Low 523.20
## 4448 13 Low 695.94
## 4498 15 Low 490.74
## 4548 13 High 1326.90
## 4598 2 High 1274.80
## 4648 19 Low 486.54
## 4698 9 Low 497.22
## 4748 8 High 1312.90
## 4798 19 Low 505.44
## 4848 5 Low 683.83
## 4898 14 High 1309.40
## 4948 2 Medium 830.56
## 4998 6 Medium 996.39
## 5048 9 Medium 1000.98
## 5098 8 Low 661.99
## 5148 20 Medium 994.32
## 5198 7 Medium 1078.83
## 5248 17 High 1331.60
## 5298 15 Low 358.95
## 5348 16 High 1279.30
## 5398 4 High 1257.80
## 5448 12 High 1367.70
## 5498 5 High 1384.70
## 5548 17 Low 674.80
## 5598 9 Low 675.22
## 5648 1 Low 692.58
## 5698 20 Low 675.85
## 5748 14 Low 490.56
## 5798 11 High 1321.00
## 5848 10 High 1289.30
## 5898 17 High 1211.50
## 5948 10 Medium 1026.45
## 5998 18 Medium 830.80
## 6048 4 High 1352.90
## 6098 4 Medium 1006.11
## 6148 7 High 1310.00
## 6198 5 Low 376.65
## 6248 10 Medium 1015.02
## 6298 15 High 1270.50
## 6348 17 Low 360.65
## 6398 19 Medium 868.64
## 6448 7 Low 674.10
## 6498 2 Low 361.65
## 6548 12 High 1256.90
## 6598 16 Low 699.30
## 6648 6 High 1454.40
## 6698 20 Low 507.42
## 6748 18 Low 491.04
## 6798 10 Low 535.08
## 6848 12 Medium 1058.67
## 6898 19 High 1215.60
## 6948 13 Medium 1023.03
## 6998 16 Low 486.12
## 7048 10 High 1336.70
## 7098 12 High 1455.00
## 7148 18 Low 358.05
## 7198 15 Medium 835.60
## 7248 20 Medium 1031.22
## 7298 11 Medium 1044.72
## 7348 9 High 1426.50
## 7398 13 High 1378.30
## 7448 19 Medium 1031.40
## 7498 13 High 1386.70
## 7548 8 Low 663.39
## 7598 12 Low 358.35
## 7648 11 Low 363.60
## 7698 4 Medium 836.24
## 7748 14 High 1407.10
## 7798 5 High 1224.80
## 7848 9 Medium 1016.37
## 7898 8 High 1292.00
## 7948 20 High 1453.00
## 7998 7 Low 505.92
## 8048 20 Medium 1040.40
## 8098 1 Low 650.16
## 8148 5 Medium 872.24
## 8198 7 Low 391.25
## 8248 20 Low 665.35
## 8298 17 Low 539.40
## 8348 17 High 1297.30
## 8398 9 Low 481.20
## 8448 13 Low 660.80
## 8498 7 Low 501.96
## 8548 16 Medium 872.08
## 8598 11 Low 520.74
## 8648 9 Medium 1065.78
## 8698 11 Low 481.50
## 8748 18 High 1315.20
## 8798 9 High 1279.80
## 8848 16 Low 531.54
## 8898 12 Low 524.46
## 8948 4 Medium 1006.02
## 8998 20 Low 674.87
## 9048 17 Low 350.40
## 9098 1 High 1273.00
## 9148 3 High 1277.30
## 9198 14 Medium 827.28
## 9248 2 Low 528.84
## 9298 6 Low 515.52
## 9348 10 Low 519.66
## 9398 4 High 1396.60
## 9448 14 High 1382.70
## 9498 7 Medium 855.12
## 9548 11 Medium 822.64
## 9598 1 Low 650.44
## 9648 16 Medium 1000.80
## 9698 7 High 1218.50
## 9748 20 Medium 869.12
## 9798 13 Low 648.34
## 9848 14 Low 511.98
## 9898 10 Medium 806.08
## 9948 7 Low 505.14
## 9998 18 Medium 850.24Soal 4
Jika Anda akan mendapatkan bonus 2% dari Booking fee per unit sebagai pemasaran dan juga mendapatkan bonus tambahan 1% jika Anda telah bekerja di perusahaan ini selama lebih dari 3 tahun. Silakan hitung total bonus dengan menggunakan pernyataan if, for, dan break.
R
Yap = subset(Data, subset=(Marketing_Name == "Matius"))
p = ifelse((Yap$Work_Exp < 3),
(Yap$Price * Yap$Booking_Fee)*(0.02),
(Yap$Price * Yap$Booking_Fee)*(0.03))
Yap$Bonus = p
YapBonus = sum(Yap$Bonus)
Bonus## [1] 64473077Soal 5
Pada bagian ini, Anda diharapkan dapat membuat fungsi yang dapat menjawab setiap penyataan dibawah ini dengan melibatkan setiap fungsi kontrol yang dipelajari pada pertemuan 7.
- Siapa nama marketing pemasaran terbaik?
- Kota dan Cluster mana yang paling menguntungkan?
- Hitung total biaya iklan Anda, jika Anda harus membayarnya $4 setiap kali iklan.
- Hitung rata-rata biaya iklan untuk setiap marketing di Perusahaan tersebut.
- Hitung Total Pendapatan (dalam Bulanan)
R
Siapa nama marketing pemasaran terbaik?
MarketName = 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")
Jonathan = subset(Data, subset=(Marketing_Name == "Jonathan"))
Theodora = subset(Data, subset=(Marketing_Name == "Theodora"))
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"))
Jonatha = subset(Data, subset=(Marketing_Name == "Jonatha"))
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"))
sum =
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$Price))
m_ = data.frame(MarketName,
sum)
m_max(sum)## [1] 2252960Cari = which.max(m_$sum)
TERBAIK.M = m_[Cari,]
TERBAIK.MKota dan Cluster mana yang paling menguntungkan?
#KOTA
city = c("Jakarta","Bogor", "Tangerang","Depok","Bekasi")
Jakarta = subset(Data, subset=(City == "Jakarta"))
Bogor = subset(Data, subset=(City == "Bogor"))
Tangerang = subset(Data, subset=(City == "Tengerang"))
Depok = subset(Data, subset=(City == "Depok"))
Bekasi = subset(Data, subset=(City == "Bekasi"))
mean = 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))
df_city = data.frame(city,
mean)
df_citymax(mean)## [1] 11016.98Cari = which.max(df_city$mean)
TERBAIK.C = df_city[Cari,]
TERBAIK.C#CLUSTER
cluster = c("Victoria","Palmyra","Winona","Tiara", "Narada",
"Peronia","Lavesh","Alindra","Sweethome", "Asera",
"Teradamai","Albasia", "Adara","Neon","Arana",
"Asoka", "Primadona", "Mutiara","Permata","Alamanda")
Teradamai = subset(Data, subset=( Cluster == "Teradamai"))
Victoria = subset(Data, subset=( Cluster == "Victoria"))
Palmyra = subset(Data, subset=( Cluster == "Palmyra"))
Winona = subset(Data, subset=( Cluster == "Winona"))
Tiara = subset(Data, subset=( Cluster == "Tiara"))
Narada = subset(Data, subset=( Cluster == "Narada"))
Peronia = subset(Data, subset=( Cluster == "Peronia"))
Lavesh = subset(Data, subset=( Cluster == "Lavesh"))
Alindra = subset(Data, subset=( Cluster == "Alindra"))
Sweethome = subset(Data, subset=( Cluster == "Sweethome"))
Asera = subset(Data, subset=( Cluster == "Asera"))
Teradamai = subset(Data, subset=( Cluster == "Teradamai"))
Albasia = subset(Data, subset=( Cluster == "Albasia "))
Adara = subset(Data, subset=( Cluster == "Adara"))
Neon = subset(Data, subset=( Cluster == "Neon"))
Arana = subset(Data, subset=( Cluster == "Arana"))
Asoka = subset(Data, subset=( Cluster == "Asoka "))
Primadona = subset(Data, subset=( Cluster == "Primadona"))
Mutiara = subset(Data, subset=( Cluster == "Mutiara"))
Permata = subset(Data, subset=( Cluster == "Permata"))
Alamanda = subset(Data, subset=( Cluster == "Alamanda"))
mean = c(sum(Victoria$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$Id), sum(Alindra$Price)/length( Alindra$Id),
sum(Sweethome$Price)/length( Sweethome$Id), sum(Asera$Price)/length( Asera$Id),
sum(Teradamai$Price)/length( Teradamai$Id), sum(Albasia$Price)/length( Albasia$Id),
sum(Adara$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$Id))
df_cluster = data.frame(cluster,mean)
df_clustermax(mean)## [1] NaNCari = which.max(df_cluster$mean)
TERBAIK.CL = df_cluster[Cari,]
TERBAIK.CLHitung total biaya iklan Anda, jika Anda harus membayarnya $4 setiap kali iklan
M.Name = "Matius"
Mar.Name = subset(Data, subset=(Marketing_Name == M.Name))
Iklan = ( Mar.Name$Advertisement * 4)
t.iklan = print(sum(Iklan))## [1] 9000iklann =c(M.Name, unlist(t.iklan))
susah_pak = function(x){
print(iklann)
print(c(cat("Marketing terbaik adalah", unlist(TERBAIK.M),"\n",
"cluster menguntungkan", unlist(TERBAIK.CL),"\n",
"Kota menguntungkan ",unlist(TERBAIK.C),"\n")))
}
susah_pak(x)## [1] "Matius" "9000"
## Marketing terbaik adalah Theodora 2252960
## cluster menguntungkan Adara 11278.2816091954
## Kota menguntungkan Bekasi 11016.9816377171
## NULLRata-rata biaya iklan perbulan setiap marketing
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")
Iklan_M = for (x in Name){
t = subset(Data, subset=(Marketing_Name == x))
i = sum(t$Advertisement * 4)
print(cat(sum(i)/length(t$Id),(cat(x," rata-rata iklannya " ))))}## Angel rata-rata iklannya 40.24NULL
## Sherly rata-rata iklannya 41.34NULL
## Vanessa rata-rata iklannya 43.16NULL
## Irene rata-rata iklannya 40.72NULL
## Julian rata-rata iklannya 40.1NULL
## Jeffry rata-rata iklannya 44.2NULL
## Nikita rata-rata iklannya 42.02NULL
## Kefas rata-rata iklannya 37.4NULL
## Siana rata-rata iklannya 42.16NULL
## Lala rata-rata iklannya 41.86NULL
## Fallen rata-rata iklannya 43.76NULL
## Ardifo rata-rata iklannya 40.34NULL
## Kevin rata-rata iklannya 41.54NULL
## Juen rata-rata iklannya 38.7NULL
## Jerrel rata-rata iklannya 40.52NULL
## Imelda rata-rata iklannya 41.58NULL
## Widi rata-rata iklannya 42.12NULL
## Theodora rata-rata iklannya 45.2NULL
## Elvani rata-rata iklannya 39.78NULL
## Jonathan rata-rata iklannya 42.96NULL
## Sofia rata-rata iklannya 44.5NULL
## Abraham rata-rata iklannya 40.7NULL
## Siti rata-rata iklannya 39.8NULL
## Niko rata-rata iklannya 43.26NULL
## Sefli rata-rata iklannya 42.68NULL
## Bene rata-rata iklannya 42.1NULL
## Diana rata-rata iklannya 42.1NULL
## Pupe rata-rata iklannya 40.94NULL
## Andi rata-rata iklannya 44.2NULL
## Tatha rata-rata iklannya 41.58NULL
## Endri rata-rata iklannya 43.6NULL
## Monika rata-rata iklannya 42.42NULL
## Hans rata-rata iklannya 42.22NULL
## Debora rata-rata iklannya 46.78NULL
## Hanifa rata-rata iklannya 39.76NULL
## James rata-rata iklannya 39.86NULL
## Jihan rata-rata iklannya 44.8NULL
## Friska rata-rata iklannya 39.28NULL
## Ardiwan rata-rata iklannya 40.28NULL
## Bakti rata-rata iklannya 45.16NULL
## Anthon rata-rata iklannya 41.94NULL
## Amry rata-rata iklannya 41.74NULL
## Wiwik rata-rata iklannya 43.9NULL
## Bastian rata-rata iklannya 42.74NULL
## Budi rata-rata iklannya 40.24NULL
## Leo rata-rata iklannya 43.56NULL
## Simon rata-rata iklannya 42.58NULL
## Matius rata-rata iklannya 45NULL
## Arry rata-rata iklannya 45.02NULL
## Eliando rata-rata iklannya 39.44NULLHitung Total Pendapatan (dalam Bulanan)
Totalp = function(x)
{
Sum_Dulu = sum(Data$Price)-(sum(Data$Advertisement)*4)
return(cat("Total Pendapatan perbulan adalah", Sum_Dulu))
}
Totalp(x)## Total Pendapatan perbulan adalah 109168563Kasus 2
Misalkan Anda memiliki proyek riset pasar untuk mempertahankan beberapa pelanggan potensial di perusahaan Anda. Mari kita asumsikan Anda bekerja di perusahaan asuransi ABC. Untuk melakukannya, Anda ingin mengumpulkan kumpulan data berikut:
- Marital_Status : menetapkan status perkawinan acak (“Ya”, “Tidak”)
- Address : berikan alamat acak (JABODETABEK)
- Work_Location : menetapkan lokasi kerja secara acak (JABODETABEK)
- Age : menetapkan urutan angka acak (dari 19 hingga 60)
- Academi : menetapkan tingkat akademik acak (“J.School”, “H.School”, “Sarjana”, “Magister”, “Phd”)
- Job : 10 pekerjaan acak untuk setiap tingkat akademik
- Grade : 5 nilai acak untuk setiap Pekerjaan
- Income : tetapkan pendapatan yang mungkin untuk setiap Pekerjaan
- Spending : tetapkan kemungkinan pengeluaran untuk setiap Pekerjaan
- Number_of_children: menetapkan nomor acak di antara 0 dan 10 (sesuai dengan status perkawinan)
- Private_vehicle : menetapkan kemungkinan kendaraan pribadi untuk setiap orang (“Mobil”, “sepeda motor”, “Umum”)
- Home : “Sewa”, “Milik”, “Kredit”
Soal 1
Tolong berikan saya kumpulan data tentang informasi 50000 pelanggan yang mengacu pada setiap variabel di atas!
R
Id <- (1:50000)
Marital_Status <- sample(c("Ya", "Tidak"), 50000, replace = T)
Adress <- sample(c("Jakarta","Bogor","Depok","Tangerang","Bekasi"),50000,replace = T)
Work_Location <- sample(c("Jakarta","Bogor","Depok","Tengerang","Bekasi"),50000, replace = T)
Age <- sample(c(19:60), 50000, replace=T)
Academi <- sample(c("J.School", "H.School", "Sarjana", "Magister", "Phd"), 50000, replace = T)
Grade <- sample(c("A","B","C","D","E"), 50000, replace = T)
x <- sample(c(1:10), 50000, replace=T)
Pelanggan <- data.frame(Id,
Marital_Status,
Adress,
Work_Location,
Age,
Academi,
Grade)
kerja = c("Doctor", "Teacher", "Karyawan", "Musician", "Actuarial", "Data Analyst", "Pilot", "Chef", "Translator")
Pelanggan$Job <- ifelse(Pelanggan$Academi == "J.School", "Student", ifelse(Pelanggan$Academi == "H.School", "Student", sample(kerja, 40000, replace =T)))
Pelanggan$Income <- ifelse(Pelanggan$Job == "Doctor", 25000,
ifelse(Pelanggan$Job == "Teacher", 14000,
ifelse(Pelanggan$Job == "Karyawan", 15000,
ifelse(Pelanggan$Job == "Musician", 19000,
ifelse(Pelanggan$Job == "Actuarial",35000,
ifelse(Pelanggan$Job == "Data Analyst", 36000,
ifelse(Pelanggan$Job == "Pilot", 32000,
ifelse(Pelanggan$Job == "Chef", 24000,
ifelse(Pelanggan$Job == "Translator", 27000, 10000)))))))))
Pelanggan$Spending <- ifelse(Pelanggan$Job == "Doctor", 15000,
ifelse(Pelanggan$Job == "Teacher", 4000,
ifelse(Pelanggan$Job == "Karyawan", 5000,
ifelse(Pelanggan$Job == "Musician", 9000,
ifelse(Pelanggan$Job == "Actuarial",25000,
ifelse(Pelanggan$Job == "Data Analyst", 26000,
ifelse(Pelanggan$Job == "Pilot", 22000,
ifelse(Pelanggan$Job == "Chef", 14000,
ifelse(Pelanggan$Job == "Translator", 17000, 5000)))))))))
Pelanggan$Number_of_CHildren <- ifelse(Pelanggan$Marital_Status == "Iya", sample(c(1:10)),
ifelse(Pelanggan$Marital_Status == "Tidak", 0, sample(c(1:10) )))
Pelanggan$Private_vehicle <- sample(c("Mobil", "Sepeda Motor", "Umum"), 50000, replace = T)
Pelanggan$Home <- sample(c("Sewa","Milik","Kredit"), 50000, replace = T)
Pelanggan Soal 2
Ringkasan Statistik penting seperti apa yang bisa Anda dapatkan dari kumpulan data Anda?
R
Pelanggan1 = function (x)
{
min = min(x)
max = max(x)
median = x[median.default(x)]
modus =x[which.max(x)]
mean = round(sum(x)/length(x), digits=2)
var = sum((x-mean)^2) / (length(x)-1)
sd = sqrt(sum((x-mean)^2) / (length(x)-1))
return(cat(c("minimum =", min,"\n",
"maksimum =", max,"\n",
"median =",median,"\n",
"modus =", modus,"\n",
"rata-rata =", mean,"\n",
"varians = ", var,"\n",
"standar deviasi =", sd,"\n"
)))}Pelanggan1(Pelanggan$Id)## minimum = 1
## maksimum = 50000
## median = 25000
## modus = 50000
## rata-rata = 25000.5
## varians = 208337500
## standar deviasi = 14433.9010665863Pelanggan1(Pelanggan$Age)## minimum = 19
## maksimum = 60
## median = 43
## modus = 60
## rata-rata = 39.42
## varians = 146.969659393188
## standar deviasi = 12.1231043628762Pelanggan1(Pelanggan$Income)## minimum = 10000
## maksimum = 36000
## median = 19000
## modus = 36000
## rata-rata = 19089.48
## varians = 90439522.1200424
## standar deviasi = 9509.9696171987Pelanggan1(Pelanggan$Spending)## minimum = 4000
## maksimum = 26000
## median = 14000
## modus = 26000
## rata-rata = 11086.98
## varians = 60123936.9583392
## standar deviasi = 7753.96266165495Pelanggan1(Pelanggan$Number_of_Children)## minimum = Inf
## maksimum = -Inf
## median =
## modus =
## rata-rata = NaN
## varians = 0
## standar deviasi = 0# tipe data character
typeof(Pelanggan$Marital_Status)## [1] "character"typeof(Pelanggan$Address)## [1] "NULL"typeof(Pelanggan$Work_Location)## [1] "character"typeof(Pelanggan$Grade)## [1] "character"typeof(Pelanggan$Private_vehicle)## [1] "character"typeof(Pelanggan$Home)## [1] "character"typeof(Pelanggan$Academi)## [1] "character"typeof(Pelanggan$Job)## [1] "character"summary(Pelanggan)## Id Marital_Status Adress Work_Location
## Min. : 1 Length:50000 Length:50000 Length:50000
## 1st Qu.:12501 Class :character Class :character Class :character
## Median :25001 Mode :character Mode :character Mode :character
## Mean :25001
## 3rd Qu.:37500
## Max. :50000
## Age Academi Grade Job
## Min. :19.00 Length:50000 Length:50000 Length:50000
## 1st Qu.:29.00 Class :character Class :character Class :character
## Median :39.00 Mode :character Mode :character Mode :character
## Mean :39.42
## 3rd Qu.:50.00
## Max. :60.00
## Income Spending Number_of_CHildren Private_vehicle
## Min. :10000 Min. : 4000 Min. : 0.000 Length:50000
## 1st Qu.:10000 1st Qu.: 5000 1st Qu.: 0.000 Class :character
## Median :15000 Median : 5000 Median : 1.000 Mode :character
## Mean :19089 Mean :11087 Mean : 2.754
## 3rd Qu.:27000 3rd Qu.:17000 3rd Qu.: 6.000
## Max. :36000 Max. :26000 Max. :10.000
## Home
## Length:50000
## Class :character
## Mode :character
##
##
## Soal 3
Menurut perhitungan dan analisis Anda, pelanggan mana yang potensial untuk Anda pertahankan?
Pelanggan$Potential <- ifelse((Pelanggan$Income - Pelanggan$Spending) > 0.5*Pelanggan$Income, "yes", "no")
Potential <- Pelanggan[Pelanggan$Potential == "yes",]
Potential