Lab4: R Basics
Vanessa Supit
September 24, 2020
1 Generate Data frame
Suppose you are a data scientist, and you get a project at a start-up company, for instance Kopi Kenangan. Let’s say, you are asking to generate the collection of any possible data set from their daily sales. If I asking you: what kind of data set that you can generate?. Here, I assume you want to provide them the following data set:
- Id : there are 5000 transactions.
- Date: daily 5000 transactions, start from 2018/01/01.
- Name: create 20 random cashier names (you can use names of your classmate including your self) to cover all 5000 transactions at
Kopi Kenangan.
- City: allocate this 5000 transactions to the biggest cities in Indonesia (with the same proportion). Here I assume,
- Jakarta
- Bogor
- Depok
- Tangerang
- Bekasi
- Outlet: allocate this 5000 transactions in five outlets. Here I assume,
- Outlet 1
- Outlet 2
- Outlet 3
- Outlet 4
- Outlet 5
- Menu: generate random sales of 5000 menu items at
Kopi Kenanganevery day. Here, I assume,- Cappucino
- Es Kopi Susu
- Hot Caramel Latte
- Hot Chocolate
- Hot Red Velvet Latte
- Ice Americano
- Ice Berry Coffe
- Ice Cafe Latte
- Ice Caramel Latte
- Ice Coffee Avocado
- Ice Coffee Lite
- Ice Matcha Espresso
- Ice Matcha Latte
- Ice Red Velvet Latte
- Price: generate random prices for each menu items above (min=18000, and max=45000)
- Discount: generate random discounts for each menu items above (min=0.05, and max=0.12)
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionId<-(1:5000)
Date<-seq(as.Date("2018/01/01"),by="day",length.out=5000)
Name<-sample(c('Vanessa','Nikita','Julian','Jocelyn','Jeffry','Sofia','Angel','Kefas','Ahmad','Deven','Maungzy','Celiboy','Zack','Qwynnzy','Reyzo','Andre','Kolop','Gloxenia','Bakti Siregar','Roger'),5000,replace=TRUE)
City1<-rep("Jakarta",times=1000)
City2<-rep("Bogor",times=1000)
City3<-rep("Depok",times=1000)
City4<-rep("Tangerang",times=1000)
City5<-rep("Bekasi",times=1000)
City<-sample(c(City1,City2,City3,City4,City5))
Outlet<-sample(c('Outlet 1','Outlet 2','Outlet 3','Outlet 4','Outlet 5'),5000,replace=TRUE)
Menu<-sample(c('Cappucino','Es Kopi Susu','Hot Caramel Latte','Hot Chocolate','Hot Red Velvet Latte','Ice Americano','Ice Berry Coffe','Ice Cafe Latte','Ice Caramel Latte','Ice Coffee Avocado','Ice Coffee Lite','Ice Matcha Espresso','Ice Matcha Espresso','Ice Matcha Latte','Ice Red Velvet Latte'),5000,replace=TRUE)
price<-runif(Menu,min=18000,max=45000)
Price<-round(price,digits=0)
discount<-runif(Menu,min=0.05,max=0.12)
Discount<-round(discount,digits=2)
KopiKenangan<-data.frame(Id,
Date,
Name,
City,
Outlet,
Menu,
Price,
Discount)
KopiKenangan%>%head(20)## Id Date Name City Outlet Menu Price Discount
## 1 1 2018-01-01 Zack Bekasi Outlet 5 Es Kopi Susu 37910 0.11
## 2 2 2018-01-02 Roger Tangerang Outlet 1 Ice Cafe Latte 34473 0.07
## 3 3 2018-01-03 Kolop Jakarta Outlet 5 Ice Coffee Lite 29964 0.07
## 4 4 2018-01-04 Julian Bogor Outlet 2 Hot Red Velvet Latte 30909 0.12
## 5 5 2018-01-05 Jocelyn Depok Outlet 3 Ice Coffee Lite 23372 0.11
## 6 6 2018-01-06 Sofia Bogor Outlet 2 Ice Cafe Latte 38700 0.10
## 7 7 2018-01-07 Roger Jakarta Outlet 5 Ice Coffee Avocado 35835 0.11
## 8 8 2018-01-08 Jeffry Tangerang Outlet 3 Ice Coffee Avocado 24132 0.10
## 9 9 2018-01-09 Ahmad Tangerang Outlet 4 Ice Matcha Espresso 23071 0.11
## 10 10 2018-01-10 Julian Bogor Outlet 3 Ice Berry Coffe 33729 0.08
## 11 11 2018-01-11 Qwynnzy Jakarta Outlet 3 Ice Caramel Latte 24266 0.07
## 12 12 2018-01-12 Angel Bogor Outlet 4 Hot Caramel Latte 23594 0.05
## 13 13 2018-01-13 Roger Bogor Outlet 4 Hot Chocolate 21671 0.08
## 14 14 2018-01-14 Sofia Depok Outlet 3 Ice Coffee Avocado 39613 0.10
## 15 15 2018-01-15 Jeffry Bogor Outlet 5 Es Kopi Susu 20610 0.09
## 16 16 2018-01-16 Deven Bogor Outlet 3 Ice Americano 29616 0.08
## 17 17 2018-01-17 Deven Depok Outlet 1 Hot Caramel Latte 30065 0.06
## 18 18 2018-01-18 Ahmad Depok Outlet 5 Ice Americano 42604 0.09
## 19 19 2018-01-19 Andre Bekasi Outlet 1 Ice Matcha Latte 32530 0.07
## 20 20 2018-01-20 Angel Jakarta Outlet 2 Ice Red Velvet Latte 19951 0.102 Extraction
In this section, your expecter to be able apply a very basic data frame manipulation called Extraction. Please cover the following tasks:
- Extract all data set or transactions at
Kopi Kenangan, in the specific city for instanceJakarta.
## Id Date Name City Outlet Menu Price
## 1 3 2018-01-03 Kolop Jakarta Outlet 5 Ice Coffee Lite 29964
## 2 7 2018-01-07 Roger Jakarta Outlet 5 Ice Coffee Avocado 35835
## 3 11 2018-01-11 Qwynnzy Jakarta Outlet 3 Ice Caramel Latte 24266
## 4 20 2018-01-20 Angel Jakarta Outlet 2 Ice Red Velvet Latte 19951
## 5 28 2018-01-28 Maungzy Jakarta Outlet 5 Ice Coffee Avocado 41142
## 6 30 2018-01-30 Bakti Siregar Jakarta Outlet 2 Cappucino 38276
## 7 31 2018-01-31 Angel Jakarta Outlet 4 Hot Caramel Latte 42997
## 8 38 2018-02-07 Kefas Jakarta Outlet 1 Ice Matcha Espresso 32877
## 9 41 2018-02-10 Roger Jakarta Outlet 4 Es Kopi Susu 20348
## 10 42 2018-02-11 Gloxenia Jakarta Outlet 4 Hot Chocolate 44974
## 11 44 2018-02-13 Jeffry Jakarta Outlet 5 Ice Coffee Avocado 40639
## 12 65 2018-03-06 Jeffry Jakarta Outlet 5 Es Kopi Susu 43877
## 13 66 2018-03-07 Julian Jakarta Outlet 5 Ice Cafe Latte 28213
## 14 73 2018-03-14 Deven Jakarta Outlet 4 Ice Cafe Latte 38357
## 15 80 2018-03-21 Jocelyn Jakarta Outlet 3 Ice Matcha Espresso 27899
## 16 91 2018-04-01 Kefas Jakarta Outlet 3 Ice Berry Coffe 23920
## 17 93 2018-04-03 Ahmad Jakarta Outlet 5 Cappucino 26310
## 18 99 2018-04-09 Zack Jakarta Outlet 4 Ice Coffee Lite 25089
## 19 103 2018-04-13 Zack Jakarta Outlet 2 Ice Cafe Latte 28550
## 20 104 2018-04-14 Andre Jakarta Outlet 1 Ice Coffee Avocado 25947
## Discount
## 1 0.07
## 2 0.11
## 3 0.07
## 4 0.10
## 5 0.07
## 6 0.09
## 7 0.08
## 8 0.09
## 9 0.06
## 10 0.08
## 11 0.12
## 12 0.12
## 13 0.05
## 14 0.07
## 15 0.11
## 16 0.08
## 17 0.12
## 18 0.09
## 19 0.11
## 20 0.05- Extract all data set or transactions at
Kopi Kenangan, in the specific menu for instanceHot Chocolate.
## Id Date Name City Outlet Menu Price Discount
## 1 13 2018-01-13 Roger Bogor Outlet 4 Hot Chocolate 21671 0.08
## 2 35 2018-02-04 Jocelyn Bekasi Outlet 4 Hot Chocolate 32786 0.08
## 3 42 2018-02-11 Gloxenia Jakarta Outlet 4 Hot Chocolate 44974 0.08
## 4 52 2018-02-21 Qwynnzy Bogor Outlet 2 Hot Chocolate 34712 0.07
## 5 56 2018-02-25 Sofia Bekasi Outlet 2 Hot Chocolate 42267 0.10
## 6 106 2018-04-16 Jocelyn Depok Outlet 2 Hot Chocolate 27001 0.08
## 7 122 2018-05-02 Andre Bekasi Outlet 2 Hot Chocolate 44699 0.07
## 8 138 2018-05-18 Bakti Siregar Jakarta Outlet 5 Hot Chocolate 30438 0.10
## 9 220 2018-08-08 Jocelyn Bekasi Outlet 1 Hot Chocolate 35892 0.10
## 10 243 2018-08-31 Deven Jakarta Outlet 3 Hot Chocolate 33744 0.08
## 11 287 2018-10-14 Kefas Tangerang Outlet 2 Hot Chocolate 34968 0.11
## 12 291 2018-10-18 Qwynnzy Bekasi Outlet 1 Hot Chocolate 41124 0.11
## 13 294 2018-10-21 Kefas Depok Outlet 2 Hot Chocolate 34707 0.07
## 14 310 2018-11-06 Jeffry Jakarta Outlet 2 Hot Chocolate 31562 0.05
## 15 314 2018-11-10 Reyzo Depok Outlet 1 Hot Chocolate 31870 0.06
## 16 327 2018-11-23 Reyzo Tangerang Outlet 5 Hot Chocolate 40393 0.08
## 17 345 2018-12-11 Bakti Siregar Tangerang Outlet 2 Hot Chocolate 22622 0.07
## 18 366 2019-01-01 Bakti Siregar Bekasi Outlet 2 Hot Chocolate 27686 0.10
## 19 374 2019-01-09 Jeffry Bogor Outlet 2 Hot Chocolate 29886 0.05
## 20 390 2019-01-25 Qwynnzy Bogor Outlet 2 Hot Chocolate 38743 0.11- Extract all data set or transactions at
Kopi Kenangan, in the specific cashier names for instanceBakti Siregar.
## Id Date Name City Outlet Menu Price
## 1 30 2018-01-30 Bakti Siregar Jakarta Outlet 2 Cappucino 38276
## 2 54 2018-02-23 Bakti Siregar Bekasi Outlet 5 Ice Coffee Avocado 30829
## 3 55 2018-02-24 Bakti Siregar Bekasi Outlet 1 Hot Red Velvet Latte 18354
## 4 70 2018-03-11 Bakti Siregar Tangerang Outlet 4 Ice Red Velvet Latte 21633
## 5 75 2018-03-16 Bakti Siregar Bogor Outlet 4 Ice Red Velvet Latte 31008
## 6 84 2018-03-25 Bakti Siregar Depok Outlet 5 Ice Matcha Espresso 44887
## 7 138 2018-05-18 Bakti Siregar Jakarta Outlet 5 Hot Chocolate 30438
## 8 183 2018-07-02 Bakti Siregar Jakarta Outlet 4 Ice Coffee Avocado 30524
## 9 185 2018-07-04 Bakti Siregar Jakarta Outlet 4 Ice Matcha Espresso 43429
## 10 204 2018-07-23 Bakti Siregar Bekasi Outlet 4 Ice Coffee Lite 38385
## 11 209 2018-07-28 Bakti Siregar Bekasi Outlet 1 Ice Matcha Espresso 29210
## 12 216 2018-08-04 Bakti Siregar Tangerang Outlet 1 Ice Americano 27931
## 13 221 2018-08-09 Bakti Siregar Bekasi Outlet 1 Ice Caramel Latte 19993
## 14 229 2018-08-17 Bakti Siregar Tangerang Outlet 1 Ice Coffee Lite 36171
## 15 250 2018-09-07 Bakti Siregar Jakarta Outlet 1 Ice Caramel Latte 41580
## 16 273 2018-09-30 Bakti Siregar Tangerang Outlet 5 Ice Berry Coffe 25781
## 17 302 2018-10-29 Bakti Siregar Jakarta Outlet 1 Ice Coffee Avocado 39409
## 18 307 2018-11-03 Bakti Siregar Tangerang Outlet 4 Es Kopi Susu 43265
## 19 319 2018-11-15 Bakti Siregar Bogor Outlet 5 Cappucino 43775
## 20 333 2018-11-29 Bakti Siregar Jakarta Outlet 2 Hot Caramel Latte 29571
## Discount
## 1 0.09
## 2 0.09
## 3 0.09
## 4 0.10
## 5 0.10
## 6 0.09
## 7 0.10
## 8 0.11
## 9 0.06
## 10 0.09
## 11 0.09
## 12 0.09
## 13 0.05
## 14 0.10
## 15 0.08
## 16 0.09
## 17 0.08
## 18 0.09
## 19 0.06
## 20 0.09- Extract all data set or transactions at
Kopi Kenangan, in the specific price for instance>=40000.
## Id Date Name City Outlet Menu Price
## 1 18 2018-01-18 Ahmad Depok Outlet 5 Ice Americano 42604
## 2 21 2018-01-21 Zack Depok Outlet 2 Ice Cafe Latte 44017
## 3 23 2018-01-23 Jeffry Depok Outlet 3 Ice Caramel Latte 43145
## 4 25 2018-01-25 Jocelyn Tangerang Outlet 5 Ice Cafe Latte 43603
## 5 28 2018-01-28 Maungzy Jakarta Outlet 5 Ice Coffee Avocado 41142
## 6 31 2018-01-31 Angel Jakarta Outlet 4 Hot Caramel Latte 42997
## 7 32 2018-02-01 Celiboy Bekasi Outlet 1 Es Kopi Susu 41206
## 8 36 2018-02-05 Roger Bogor Outlet 4 Cappucino 44394
## 9 42 2018-02-11 Gloxenia Jakarta Outlet 4 Hot Chocolate 44974
## 10 44 2018-02-13 Jeffry Jakarta Outlet 5 Ice Coffee Avocado 40639
## 11 47 2018-02-16 Kefas Bogor Outlet 2 Hot Red Velvet Latte 41805
## 12 50 2018-02-19 Zack Bogor Outlet 5 Ice Caramel Latte 41540
## 13 56 2018-02-25 Sofia Bekasi Outlet 2 Hot Chocolate 42267
## 14 63 2018-03-04 Reyzo Tangerang Outlet 3 Ice Caramel Latte 42985
## 15 65 2018-03-06 Jeffry Jakarta Outlet 5 Es Kopi Susu 43877
## 16 67 2018-03-08 Deven Tangerang Outlet 3 Cappucino 42365
## 17 71 2018-03-12 Reyzo Tangerang Outlet 4 Ice Coffee Lite 40101
## 18 84 2018-03-25 Bakti Siregar Depok Outlet 5 Ice Matcha Espresso 44887
## 19 92 2018-04-02 Angel Bekasi Outlet 1 Ice Red Velvet Latte 42493
## 20 95 2018-04-05 Celiboy Bogor Outlet 2 Ice Red Velvet Latte 41099
## Discount
## 1 0.09
## 2 0.11
## 3 0.07
## 4 0.05
## 5 0.07
## 6 0.08
## 7 0.12
## 8 0.08
## 9 0.08
## 10 0.12
## 11 0.06
## 12 0.07
## 13 0.10
## 14 0.07
## 15 0.12
## 16 0.09
## 17 0.10
## 18 0.09
## 19 0.11
## 20 0.05- Add a new variable, call
Total_Priceto your data frame (data frame that you have done above)
library(dplyr)
KopiKenangan$Total_Price<-KopiKenangan$Price-(KopiKenangan$Price*KopiKenangan$Discount)
KopiKenangan%>%head(20) ## Id Date Name City Outlet Menu Price Discount
## 1 1 2018-01-01 Zack Bekasi Outlet 5 Es Kopi Susu 37910 0.11
## 2 2 2018-01-02 Roger Tangerang Outlet 1 Ice Cafe Latte 34473 0.07
## 3 3 2018-01-03 Kolop Jakarta Outlet 5 Ice Coffee Lite 29964 0.07
## 4 4 2018-01-04 Julian Bogor Outlet 2 Hot Red Velvet Latte 30909 0.12
## 5 5 2018-01-05 Jocelyn Depok Outlet 3 Ice Coffee Lite 23372 0.11
## 6 6 2018-01-06 Sofia Bogor Outlet 2 Ice Cafe Latte 38700 0.10
## 7 7 2018-01-07 Roger Jakarta Outlet 5 Ice Coffee Avocado 35835 0.11
## 8 8 2018-01-08 Jeffry Tangerang Outlet 3 Ice Coffee Avocado 24132 0.10
## 9 9 2018-01-09 Ahmad Tangerang Outlet 4 Ice Matcha Espresso 23071 0.11
## 10 10 2018-01-10 Julian Bogor Outlet 3 Ice Berry Coffe 33729 0.08
## 11 11 2018-01-11 Qwynnzy Jakarta Outlet 3 Ice Caramel Latte 24266 0.07
## 12 12 2018-01-12 Angel Bogor Outlet 4 Hot Caramel Latte 23594 0.05
## 13 13 2018-01-13 Roger Bogor Outlet 4 Hot Chocolate 21671 0.08
## 14 14 2018-01-14 Sofia Depok Outlet 3 Ice Coffee Avocado 39613 0.10
## 15 15 2018-01-15 Jeffry Bogor Outlet 5 Es Kopi Susu 20610 0.09
## 16 16 2018-01-16 Deven Bogor Outlet 3 Ice Americano 29616 0.08
## 17 17 2018-01-17 Deven Depok Outlet 1 Hot Caramel Latte 30065 0.06
## 18 18 2018-01-18 Ahmad Depok Outlet 5 Ice Americano 42604 0.09
## 19 19 2018-01-19 Andre Bekasi Outlet 1 Ice Matcha Latte 32530 0.07
## 20 20 2018-01-20 Angel Jakarta Outlet 2 Ice Red Velvet Latte 19951 0.10
## Total_Price
## 1 33739.90
## 2 32059.89
## 3 27866.52
## 4 27199.92
## 5 20801.08
## 6 34830.00
## 7 31893.15
## 8 21718.80
## 9 20533.19
## 10 31030.68
## 11 22567.38
## 12 22414.30
## 13 19937.32
## 14 35651.70
## 15 18755.10
## 16 27246.72
## 17 28261.10
## 18 38769.64
## 19 30252.90
## 20 17955.90- Add a new variable, call
Category_Priceto your data frame (data frame that you have done above), Here, I assume: “expensive”, “so-so”, and “cheap”.
library(dplyr)
KopiKenangan$Category_Price<-ifelse(KopiKenangan$Price>36000,"Expensive",ifelse(KopiKenangan$Price>27000,"so-so","cheap"))
KopiKenangan%>%head(20)## Id Date Name City Outlet Menu Price Discount
## 1 1 2018-01-01 Zack Bekasi Outlet 5 Es Kopi Susu 37910 0.11
## 2 2 2018-01-02 Roger Tangerang Outlet 1 Ice Cafe Latte 34473 0.07
## 3 3 2018-01-03 Kolop Jakarta Outlet 5 Ice Coffee Lite 29964 0.07
## 4 4 2018-01-04 Julian Bogor Outlet 2 Hot Red Velvet Latte 30909 0.12
## 5 5 2018-01-05 Jocelyn Depok Outlet 3 Ice Coffee Lite 23372 0.11
## 6 6 2018-01-06 Sofia Bogor Outlet 2 Ice Cafe Latte 38700 0.10
## 7 7 2018-01-07 Roger Jakarta Outlet 5 Ice Coffee Avocado 35835 0.11
## 8 8 2018-01-08 Jeffry Tangerang Outlet 3 Ice Coffee Avocado 24132 0.10
## 9 9 2018-01-09 Ahmad Tangerang Outlet 4 Ice Matcha Espresso 23071 0.11
## 10 10 2018-01-10 Julian Bogor Outlet 3 Ice Berry Coffe 33729 0.08
## 11 11 2018-01-11 Qwynnzy Jakarta Outlet 3 Ice Caramel Latte 24266 0.07
## 12 12 2018-01-12 Angel Bogor Outlet 4 Hot Caramel Latte 23594 0.05
## 13 13 2018-01-13 Roger Bogor Outlet 4 Hot Chocolate 21671 0.08
## 14 14 2018-01-14 Sofia Depok Outlet 3 Ice Coffee Avocado 39613 0.10
## 15 15 2018-01-15 Jeffry Bogor Outlet 5 Es Kopi Susu 20610 0.09
## 16 16 2018-01-16 Deven Bogor Outlet 3 Ice Americano 29616 0.08
## 17 17 2018-01-17 Deven Depok Outlet 1 Hot Caramel Latte 30065 0.06
## 18 18 2018-01-18 Ahmad Depok Outlet 5 Ice Americano 42604 0.09
## 19 19 2018-01-19 Andre Bekasi Outlet 1 Ice Matcha Latte 32530 0.07
## 20 20 2018-01-20 Angel Jakarta Outlet 2 Ice Red Velvet Latte 19951 0.10
## Total_Price Category_Price
## 1 33739.90 Expensive
## 2 32059.89 so-so
## 3 27866.52 so-so
## 4 27199.92 so-so
## 5 20801.08 cheap
## 6 34830.00 Expensive
## 7 31893.15 so-so
## 8 21718.80 cheap
## 9 20533.19 cheap
## 10 31030.68 so-so
## 11 22567.38 cheap
## 12 22414.30 cheap
## 13 19937.32 cheap
## 14 35651.70 Expensive
## 15 18755.10 cheap
## 16 27246.72 so-so
## 17 28261.10 so-so
## 18 38769.64 Expensive
## 19 30252.90 so-so
## 20 17955.90 cheap3 Renames Data Frame
Please rename all variables of your data frame (data frame that you have done above) in your language.
library(dplyr)
rename_1<-KopiKenangan
names(rename_1)<-c("Nomor","Tanggal","Nama","Kota","Toko","Daftar Minuman","Harga","Potongan","Total_harga","Kategori_harga")
rename_1%>%head(20)## Nomor Tanggal Nama Kota Toko Daftar Minuman Harga
## 1 1 2018-01-01 Zack Bekasi Outlet 5 Es Kopi Susu 37910
## 2 2 2018-01-02 Roger Tangerang Outlet 1 Ice Cafe Latte 34473
## 3 3 2018-01-03 Kolop Jakarta Outlet 5 Ice Coffee Lite 29964
## 4 4 2018-01-04 Julian Bogor Outlet 2 Hot Red Velvet Latte 30909
## 5 5 2018-01-05 Jocelyn Depok Outlet 3 Ice Coffee Lite 23372
## 6 6 2018-01-06 Sofia Bogor Outlet 2 Ice Cafe Latte 38700
## 7 7 2018-01-07 Roger Jakarta Outlet 5 Ice Coffee Avocado 35835
## 8 8 2018-01-08 Jeffry Tangerang Outlet 3 Ice Coffee Avocado 24132
## 9 9 2018-01-09 Ahmad Tangerang Outlet 4 Ice Matcha Espresso 23071
## 10 10 2018-01-10 Julian Bogor Outlet 3 Ice Berry Coffe 33729
## 11 11 2018-01-11 Qwynnzy Jakarta Outlet 3 Ice Caramel Latte 24266
## 12 12 2018-01-12 Angel Bogor Outlet 4 Hot Caramel Latte 23594
## 13 13 2018-01-13 Roger Bogor Outlet 4 Hot Chocolate 21671
## 14 14 2018-01-14 Sofia Depok Outlet 3 Ice Coffee Avocado 39613
## 15 15 2018-01-15 Jeffry Bogor Outlet 5 Es Kopi Susu 20610
## 16 16 2018-01-16 Deven Bogor Outlet 3 Ice Americano 29616
## 17 17 2018-01-17 Deven Depok Outlet 1 Hot Caramel Latte 30065
## 18 18 2018-01-18 Ahmad Depok Outlet 5 Ice Americano 42604
## 19 19 2018-01-19 Andre Bekasi Outlet 1 Ice Matcha Latte 32530
## 20 20 2018-01-20 Angel Jakarta Outlet 2 Ice Red Velvet Latte 19951
## Potongan Total_harga Kategori_harga
## 1 0.11 33739.90 Expensive
## 2 0.07 32059.89 so-so
## 3 0.07 27866.52 so-so
## 4 0.12 27199.92 so-so
## 5 0.11 20801.08 cheap
## 6 0.10 34830.00 Expensive
## 7 0.11 31893.15 so-so
## 8 0.10 21718.80 cheap
## 9 0.11 20533.19 cheap
## 10 0.08 31030.68 so-so
## 11 0.07 22567.38 cheap
## 12 0.05 22414.30 cheap
## 13 0.08 19937.32 cheap
## 14 0.10 35651.70 Expensive
## 15 0.09 18755.10 cheap
## 16 0.08 27246.72 so-so
## 17 0.06 28261.10 so-so
## 18 0.09 38769.64 Expensive
## 19 0.07 30252.90 so-so
## 20 0.10 17955.90 cheap4 Case Study
According to your data frame, pleas provide me the following tasks:
- Find out the frequency of sales of which menu items are best-selling in Kopi Kenangan!
library(dplyr)
Menu.freq<-data.frame(table(KopiKenangan$Menu))
max_menu<-max(Menu.freq$Freq)
Most_sales_menu<-Menu.freq%>%filter(Freq==max_menu)%>%print()## Var1 Freq
## 1 Ice Matcha Espresso 636- Find out which city got the most sales!
library(dplyr)
Cityprice<-data.frame(KopiKenangan$City,KopiKenangan$Price)
Jakarta.Sales<-Cityprice%>%filter(City=="Jakarta")
Jakarta.Sales.sum<-sum(Jakarta.Sales[,2])
Bogor.Sales<-Cityprice%>%filter(City=="Bogor")
Bogor.Sales.sum<-sum(Bogor.Sales[,2])
Depok.Sales<-Cityprice%>%filter(City=="Depok")
Depok.Sales.sum<-sum(Depok.Sales[,2])
Tangerang.Sales<-Cityprice%>%filter(City=="Tangerang")
Tangerang.Sales.sum<-sum(Tangerang.Sales[,2])
Bekasi.Sales<-Cityprice%>%filter(City=="Bekasi")
Bekasi.Sales.sum<-sum(Bekasi.Sales[,2])
sales_table<-data.frame(Jakarta.Sales.sum,Bogor.Sales.sum,Depok.Sales.sum,Tangerang.Sales.sum,Bekasi.Sales.sum)
max_sales<-max(sales_table)
Most_sales_city<-sales_table[order(sales_table,decreasing=T)]
Most_sales_city## Jakarta.Sales.sum Bekasi.Sales.sum Depok.Sales.sum Bogor.Sales.sum
## 1 32035673 31860070 31738978 31595861
## Tangerang.Sales.sum
## 1 31458279City_sales<-aggregate(KopiKenangan$Price,list(KopiKenangan$City),FUN=sum)
Max_sales<-max(City_sales$x)
Most_sales_city<-City_sales%>%filter(x==Max_sales)%>%head(1)%>%print()## Group.1 x
## 1 Jakarta 32035673- Find out which city has the most discounted sales!
library(dplyr)
Citydiscount<-data.frame(KopiKenangan$City,KopiKenangan$Discount)
Jakarta.discount<-Citydiscount%>%filter(City=="Jakarta")
Jakarta.discount.sum<-sum(Jakarta.discount[,2])
Bogor.discount<-Citydiscount%>%filter(City=="Bogor")
Bogor.discount.sum<-sum(Bogor.discount[,2])
Depok.discount<-Citydiscount%>%filter(City=="Depok")
Depok.discount.sum<-sum(Depok.discount[,2])
Tangerang.discount<-Citydiscount%>%filter(City=="Tangerang")
Tangerang.discount.sum<-sum(Tangerang.discount[,2])
Bekasi.discount<-Citydiscount%>%filter(City=="Bekasi")
Bekasi.discount.sum<-sum(Bekasi.discount[,2])
discount_table<-data.frame(Jakarta.discount.sum,Bogor.discount.sum,Depok.discount.sum,Tangerang.discount.sum,Bekasi.discount.sum)
max_discount<-max(discount_table)
Most_discount_city<-discount_table[order(discount_table,decreasing=T)]
Most_discount_city## Bekasi.discount.sum Bogor.discount.sum Jakarta.discount.sum
## 1 85.72 85.69 85.2
## Depok.discount.sum Tangerang.discount.sum
## 1 84.59 83.35City_discount<-aggregate(KopiKenangan$Discount,list(KopiKenangan$City),FUN=sum)
Max_discount<-max(City_discount$x)
Most_sales_city<-City_discount%>%filter(x==Max_discount)%>%print()## Group.1 x
## 1 Bekasi 85.72- what year were the most sales?
library(dplyr)
year<-format(Date,"%Y")
sales_year<-aggregate(KopiKenangan$Price,list(year),sum)
most_sales_year<-sales_year%>%filter(x==max(sales_year$x))%>%print()## Group.1 x
## 1 2020 11721601we can use tapply() fucntion
library(dplyr)
year <-format(Date,"%Y")
sales_year<- tapply(KopiKenangan$Price,year,sum)
most_sales_year<-sales_year[sales_year==max(sales_year)]%>%print()## 2020
## 11721601