Lab4: R Basics
Sherly Taurin Siridion - 20194920011
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)
id <- 1:5000
#id
date <- sort(sample(seq(as.Date("2018/01/01"),as.Date("2020/09/26"), by="day"),5000,T))
#date
name <- sample(c("sherly","irene","kefas","julian","nikita","nessa","vanessa","putri","angel","ayu","siana","taurin","fallen","lala","ardifo","jeffry","difo","jocelyn","salomo","Bakti Siregar"),5000,replace=T)
#name
city <- sample(rep(c("jakarta","bogor","depok","tangerang","bekasi"),times=1000))
#city
outlet <- sample(c("outlet 1","outlet 2","outlet 3","outlet 4","outlet 5"),5000,replace=T)
#outlet
menu <- 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 Latte","Ice Red Velvet Latte")
menu_dipesan <- sample(menu,5000, replace=T)
#menu_dipesan
price <- sample(18000:45000,14,replace=T)
#price
df_menu_price <- data.frame(menu, price)
#view(df_menu_price)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Joining, by = "menu"#menu_dan_harga
discount <-round(runif(14,min=0.05,max=0.12),2)
#discount
menu_dan_discount <- data.frame(menu,discount)
#menu_dan_discount
df_menu_discount <- left_join(data.frame(menu = menu_dipesan),menu_dan_discount)## Joining, by = "menu"#df_menu_discount
menu_dan_harga$discount <- df_menu_discount$discount
#menu_dan_harga
DataKopiKenangan <- cbind(data.frame(id,
date,
name,
city,
outlet),
menu_dan_harga)
DataKopiKenangan%>%head(20)## id date name city outlet menu price discount
## 1 1 2018-01-01 jeffry bekasi outlet 1 Ice Caramel Latte 31405 0.11
## 2 2 2018-01-01 ardifo tangerang outlet 3 Ice Coffee Lite 42015 0.06
## 3 3 2018-01-01 siana tangerang outlet 5 Hot Chocolate 25018 0.10
## 4 4 2018-01-01 fallen tangerang outlet 1 Ice Americano 30772 0.11
## 5 5 2018-01-01 ayu bogor outlet 3 Ice Americano 30772 0.11
## 6 6 2018-01-01 nikita bogor outlet 1 Ice Berry Coffe 39581 0.05
## 7 7 2018-01-01 lala jakarta outlet 5 Hot Red Velvet Latte 36372 0.07
## 8 8 2018-01-02 angel bogor outlet 5 Hot Chocolate 25018 0.10
## 9 9 2018-01-02 salomo tangerang outlet 1 Hot Caramel Latte 25675 0.07
## 10 10 2018-01-02 jocelyn depok outlet 3 Hot Chocolate 25018 0.10
## 11 11 2018-01-02 jocelyn depok outlet 4 Ice Matcha Latte 34704 0.11
## 12 12 2018-01-02 sherly bogor outlet 4 Ice Coffee Lite 42015 0.06
## 13 13 2018-01-02 kefas jakarta outlet 3 Ice Red Velvet Latte 41193 0.10
## 14 14 2018-01-03 irene bogor outlet 4 Ice Red Velvet Latte 41193 0.10
## 15 15 2018-01-03 ardifo bogor outlet 4 Ice Matcha Latte 34704 0.11
## 16 16 2018-01-03 sherly bekasi outlet 2 Ice Coffee Avocado 37145 0.05
## 17 17 2018-01-04 salomo bogor outlet 4 Hot Red Velvet Latte 36372 0.07
## 18 18 2018-01-04 irene jakarta outlet 4 Hot Caramel Latte 25675 0.07
## 19 19 2018-01-04 fallen depok outlet 4 Es Kopi Susu 33813 0.11
## 20 20 2018-01-04 fallen depok outlet 3 Cappucino 37504 0.112 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 discount
## 1 7 2018-01-01 lala jakarta outlet 5 Hot Red Velvet Latte 36372 0.07
## 2 13 2018-01-02 kefas jakarta outlet 3 Ice Red Velvet Latte 41193 0.10
## 3 18 2018-01-04 irene jakarta outlet 4 Hot Caramel Latte 25675 0.07
## 4 23 2018-01-05 angel jakarta outlet 3 Ice Americano 30772 0.11
## 5 28 2018-01-06 vanessa jakarta outlet 4 Ice Coffee Avocado 37145 0.05
## 6 34 2018-01-07 nikita jakarta outlet 2 Ice Coffee Lite 42015 0.06
## 7 35 2018-01-07 sherly jakarta outlet 5 Ice Coffee Avocado 37145 0.05
## 8 40 2018-01-08 lala jakarta outlet 5 Ice Red Velvet Latte 41193 0.10
## 9 47 2018-01-09 angel jakarta outlet 2 Hot Red Velvet Latte 36372 0.07
## 10 49 2018-01-10 siana jakarta outlet 1 Hot Caramel Latte 25675 0.07
## 11 53 2018-01-12 taurin jakarta outlet 5 Ice Coffee Lite 42015 0.06
## 12 57 2018-01-13 jeffry jakarta outlet 1 Ice Caramel Latte 31405 0.11
## 13 59 2018-01-13 kefas jakarta outlet 1 Ice Red Velvet Latte 41193 0.10
## 14 62 2018-01-13 nessa jakarta outlet 4 Ice Coffee Avocado 37145 0.05
## 15 65 2018-01-14 taurin jakarta outlet 4 Es Kopi Susu 33813 0.11
## 16 67 2018-01-14 angel jakarta outlet 1 Hot Caramel Latte 25675 0.07
## 17 73 2018-01-15 ayu jakarta outlet 4 Ice Matcha Latte 34704 0.11
## 18 74 2018-01-16 kefas jakarta outlet 4 Ice Matcha Espresso 34215 0.07
## 19 90 2018-01-20 nikita jakarta outlet 5 Ice Matcha Latte 34704 0.11
## 20 116 2018-01-26 vanessa jakarta outlet 4 Cappucino 37504 0.11- 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 3 2018-01-01 siana tangerang outlet 5 Hot Chocolate 25018 0.1
## 2 8 2018-01-02 angel bogor outlet 5 Hot Chocolate 25018 0.1
## 3 10 2018-01-02 jocelyn depok outlet 3 Hot Chocolate 25018 0.1
## 4 60 2018-01-13 nessa depok outlet 3 Hot Chocolate 25018 0.1
## 5 89 2018-01-20 taurin bogor outlet 4 Hot Chocolate 25018 0.1
## 6 119 2018-01-27 salomo jakarta outlet 5 Hot Chocolate 25018 0.1
## 7 131 2018-01-29 putri bekasi outlet 5 Hot Chocolate 25018 0.1
## 8 199 2018-02-10 jeffry depok outlet 4 Hot Chocolate 25018 0.1
## 9 205 2018-02-11 kefas bogor outlet 2 Hot Chocolate 25018 0.1
## 10 214 2018-02-12 siana tangerang outlet 1 Hot Chocolate 25018 0.1
## 11 228 2018-02-14 ayu jakarta outlet 3 Hot Chocolate 25018 0.1
## 12 246 2018-02-18 salomo bekasi outlet 3 Hot Chocolate 25018 0.1
## 13 305 2018-02-28 jocelyn jakarta outlet 3 Hot Chocolate 25018 0.1
## 14 311 2018-03-01 julian depok outlet 4 Hot Chocolate 25018 0.1
## 15 312 2018-03-01 Bakti Siregar bekasi outlet 1 Hot Chocolate 25018 0.1
## 16 342 2018-03-06 lala tangerang outlet 3 Hot Chocolate 25018 0.1
## 17 375 2018-03-13 ayu jakarta outlet 2 Hot Chocolate 25018 0.1
## 18 384 2018-03-16 salomo bekasi outlet 4 Hot Chocolate 25018 0.1
## 19 391 2018-03-17 Bakti Siregar bekasi outlet 5 Hot Chocolate 25018 0.1
## 20 392 2018-03-17 kefas bekasi outlet 5 Hot Chocolate 25018 0.1- 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 32 2018-01-07 Bakti Siregar bekasi outlet 4 Es Kopi Susu 33813
## 2 79 2018-01-17 Bakti Siregar bekasi outlet 2 Cappucino 37504
## 3 99 2018-01-22 Bakti Siregar depok outlet 2 Ice Matcha Espresso 34215
## 4 168 2018-02-04 Bakti Siregar jakarta outlet 1 Ice Coffee Lite 42015
## 5 195 2018-02-09 Bakti Siregar bogor outlet 3 Ice Berry Coffe 39581
## 6 220 2018-02-13 Bakti Siregar tangerang outlet 5 Ice Matcha Latte 34704
## 7 236 2018-02-15 Bakti Siregar bogor outlet 3 Ice Americano 30772
## 8 244 2018-02-18 Bakti Siregar bogor outlet 2 Ice Caramel Latte 31405
## 9 261 2018-02-22 Bakti Siregar depok outlet 4 Hot Caramel Latte 25675
## 10 265 2018-02-22 Bakti Siregar bekasi outlet 3 Ice Cafe Latte 35884
## 11 269 2018-02-23 Bakti Siregar bekasi outlet 1 Ice Matcha Espresso 34215
## 12 277 2018-02-24 Bakti Siregar bogor outlet 1 Ice Coffee Avocado 37145
## 13 285 2018-02-25 Bakti Siregar jakarta outlet 4 Cappucino 37504
## 14 289 2018-02-26 Bakti Siregar depok outlet 2 Es Kopi Susu 33813
## 15 302 2018-02-28 Bakti Siregar tangerang outlet 1 Cappucino 37504
## 16 312 2018-03-01 Bakti Siregar bekasi outlet 1 Hot Chocolate 25018
## 17 355 2018-03-09 Bakti Siregar jakarta outlet 5 Ice Cafe Latte 35884
## 18 371 2018-03-13 Bakti Siregar depok outlet 2 Ice Caramel Latte 31405
## 19 373 2018-03-13 Bakti Siregar tangerang outlet 3 Ice Matcha Espresso 34215
## 20 391 2018-03-17 Bakti Siregar bekasi outlet 5 Hot Chocolate 25018
## discount
## 1 0.11
## 2 0.11
## 3 0.07
## 4 0.06
## 5 0.05
## 6 0.11
## 7 0.11
## 8 0.11
## 9 0.07
## 10 0.07
## 11 0.07
## 12 0.05
## 13 0.11
## 14 0.11
## 15 0.11
## 16 0.10
## 17 0.07
## 18 0.11
## 19 0.07
## 20 0.10- Extract all data set or transactions at
Kopi Kenangan, in the specific price for instance>=40000.
## id date name city outlet menu price
## 1 2 2018-01-01 ardifo tangerang outlet 3 Ice Coffee Lite 42015
## 2 12 2018-01-02 sherly bogor outlet 4 Ice Coffee Lite 42015
## 3 13 2018-01-02 kefas jakarta outlet 3 Ice Red Velvet Latte 41193
## 4 14 2018-01-03 irene bogor outlet 4 Ice Red Velvet Latte 41193
## 5 21 2018-01-05 lala bogor outlet 1 Ice Red Velvet Latte 41193
## 6 24 2018-01-06 fallen tangerang outlet 2 Ice Red Velvet Latte 41193
## 7 34 2018-01-07 nikita jakarta outlet 2 Ice Coffee Lite 42015
## 8 37 2018-01-08 angel tangerang outlet 4 Ice Red Velvet Latte 41193
## 9 40 2018-01-08 lala jakarta outlet 5 Ice Red Velvet Latte 41193
## 10 53 2018-01-12 taurin jakarta outlet 5 Ice Coffee Lite 42015
## 11 54 2018-01-12 kefas bekasi outlet 1 Ice Coffee Lite 42015
## 12 55 2018-01-12 ayu tangerang outlet 3 Ice Coffee Lite 42015
## 13 59 2018-01-13 kefas jakarta outlet 1 Ice Red Velvet Latte 41193
## 14 64 2018-01-14 jeffry depok outlet 4 Ice Red Velvet Latte 41193
## 15 66 2018-01-14 irene tangerang outlet 3 Ice Red Velvet Latte 41193
## 16 70 2018-01-15 irene depok outlet 1 Ice Red Velvet Latte 41193
## 17 91 2018-01-20 vanessa tangerang outlet 3 Ice Red Velvet Latte 41193
## 18 95 2018-01-21 salomo bogor outlet 1 Ice Coffee Lite 42015
## 19 100 2018-01-23 nikita tangerang outlet 2 Ice Red Velvet Latte 41193
## 20 103 2018-01-23 jeffry bekasi outlet 1 Ice Coffee Lite 42015
## discount
## 1 0.06
## 2 0.06
## 3 0.10
## 4 0.10
## 5 0.10
## 6 0.10
## 7 0.06
## 8 0.10
## 9 0.10
## 10 0.06
## 11 0.06
## 12 0.06
## 13 0.10
## 14 0.10
## 15 0.10
## 16 0.10
## 17 0.10
## 18 0.06
## 19 0.10
## 20 0.06- Add a new variable, call
Total_Priceto your data frame (data frame that you have done above)
harga_setelah_discount <- price - round((discount*price),0)
df_menu_discount <- data.frame(menu,harga_setelah_discount)
df_menu_discount1 <- left_join(data.frame(menu = menu_dipesan),df_menu_discount)## Joining, by = "menu"DataKopiKenangan$Total_Price <- df_menu_discount1$harga_setelah_discount
DataKopiKenangan %>% head(30)## id date name city outlet menu price discount
## 1 1 2018-01-01 jeffry bekasi outlet 1 Ice Caramel Latte 31405 0.11
## 2 2 2018-01-01 ardifo tangerang outlet 3 Ice Coffee Lite 42015 0.06
## 3 3 2018-01-01 siana tangerang outlet 5 Hot Chocolate 25018 0.10
## 4 4 2018-01-01 fallen tangerang outlet 1 Ice Americano 30772 0.11
## 5 5 2018-01-01 ayu bogor outlet 3 Ice Americano 30772 0.11
## 6 6 2018-01-01 nikita bogor outlet 1 Ice Berry Coffe 39581 0.05
## 7 7 2018-01-01 lala jakarta outlet 5 Hot Red Velvet Latte 36372 0.07
## 8 8 2018-01-02 angel bogor outlet 5 Hot Chocolate 25018 0.10
## 9 9 2018-01-02 salomo tangerang outlet 1 Hot Caramel Latte 25675 0.07
## 10 10 2018-01-02 jocelyn depok outlet 3 Hot Chocolate 25018 0.10
## 11 11 2018-01-02 jocelyn depok outlet 4 Ice Matcha Latte 34704 0.11
## 12 12 2018-01-02 sherly bogor outlet 4 Ice Coffee Lite 42015 0.06
## 13 13 2018-01-02 kefas jakarta outlet 3 Ice Red Velvet Latte 41193 0.10
## 14 14 2018-01-03 irene bogor outlet 4 Ice Red Velvet Latte 41193 0.10
## 15 15 2018-01-03 ardifo bogor outlet 4 Ice Matcha Latte 34704 0.11
## 16 16 2018-01-03 sherly bekasi outlet 2 Ice Coffee Avocado 37145 0.05
## 17 17 2018-01-04 salomo bogor outlet 4 Hot Red Velvet Latte 36372 0.07
## 18 18 2018-01-04 irene jakarta outlet 4 Hot Caramel Latte 25675 0.07
## 19 19 2018-01-04 fallen depok outlet 4 Es Kopi Susu 33813 0.11
## 20 20 2018-01-04 fallen depok outlet 3 Cappucino 37504 0.11
## 21 21 2018-01-05 lala bogor outlet 1 Ice Red Velvet Latte 41193 0.10
## 22 22 2018-01-05 fallen tangerang outlet 5 Es Kopi Susu 33813 0.11
## 23 23 2018-01-05 angel jakarta outlet 3 Ice Americano 30772 0.11
## 24 24 2018-01-06 fallen tangerang outlet 2 Ice Red Velvet Latte 41193 0.10
## 25 25 2018-01-06 vanessa bekasi outlet 1 Ice Matcha Latte 34704 0.11
## 26 26 2018-01-06 difo bogor outlet 3 Ice Berry Coffe 39581 0.05
## 27 27 2018-01-06 taurin bogor outlet 4 Ice Caramel Latte 31405 0.11
## 28 28 2018-01-06 vanessa jakarta outlet 4 Ice Coffee Avocado 37145 0.05
## 29 29 2018-01-07 jocelyn tangerang outlet 2 Ice Caramel Latte 31405 0.11
## 30 30 2018-01-07 irene depok outlet 5 Ice Berry Coffe 39581 0.05
## Total_Price
## 1 27950
## 2 39494
## 3 22516
## 4 27387
## 5 27387
## 6 37602
## 7 33826
## 8 22516
## 9 23878
## 10 22516
## 11 30887
## 12 39494
## 13 37074
## 14 37074
## 15 30887
## 16 35288
## 17 33826
## 18 23878
## 19 30094
## 20 33379
## 21 37074
## 22 30094
## 23 27387
## 24 37074
## 25 30887
## 26 37602
## 27 27950
## 28 35288
## 29 27950
## 30 37602- 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”.
DataKopiKenangan$Category_Price <- ifelse(DataKopiKenangan$price < 25000, "cheap",
ifelse(DataKopiKenangan$price > 35000, "expensive" , "so-so"))
DataKopiKenangan %>% head(20)## id date name city outlet menu price discount
## 1 1 2018-01-01 jeffry bekasi outlet 1 Ice Caramel Latte 31405 0.11
## 2 2 2018-01-01 ardifo tangerang outlet 3 Ice Coffee Lite 42015 0.06
## 3 3 2018-01-01 siana tangerang outlet 5 Hot Chocolate 25018 0.10
## 4 4 2018-01-01 fallen tangerang outlet 1 Ice Americano 30772 0.11
## 5 5 2018-01-01 ayu bogor outlet 3 Ice Americano 30772 0.11
## 6 6 2018-01-01 nikita bogor outlet 1 Ice Berry Coffe 39581 0.05
## 7 7 2018-01-01 lala jakarta outlet 5 Hot Red Velvet Latte 36372 0.07
## 8 8 2018-01-02 angel bogor outlet 5 Hot Chocolate 25018 0.10
## 9 9 2018-01-02 salomo tangerang outlet 1 Hot Caramel Latte 25675 0.07
## 10 10 2018-01-02 jocelyn depok outlet 3 Hot Chocolate 25018 0.10
## 11 11 2018-01-02 jocelyn depok outlet 4 Ice Matcha Latte 34704 0.11
## 12 12 2018-01-02 sherly bogor outlet 4 Ice Coffee Lite 42015 0.06
## 13 13 2018-01-02 kefas jakarta outlet 3 Ice Red Velvet Latte 41193 0.10
## 14 14 2018-01-03 irene bogor outlet 4 Ice Red Velvet Latte 41193 0.10
## 15 15 2018-01-03 ardifo bogor outlet 4 Ice Matcha Latte 34704 0.11
## 16 16 2018-01-03 sherly bekasi outlet 2 Ice Coffee Avocado 37145 0.05
## 17 17 2018-01-04 salomo bogor outlet 4 Hot Red Velvet Latte 36372 0.07
## 18 18 2018-01-04 irene jakarta outlet 4 Hot Caramel Latte 25675 0.07
## 19 19 2018-01-04 fallen depok outlet 4 Es Kopi Susu 33813 0.11
## 20 20 2018-01-04 fallen depok outlet 3 Cappucino 37504 0.11
## Total_Price Category_Price
## 1 27950 so-so
## 2 39494 expensive
## 3 22516 so-so
## 4 27387 so-so
## 5 27387 so-so
## 6 37602 expensive
## 7 33826 expensive
## 8 22516 so-so
## 9 23878 so-so
## 10 22516 so-so
## 11 30887 so-so
## 12 39494 expensive
## 13 37074 expensive
## 14 37074 expensive
## 15 30887 so-so
## 16 35288 expensive
## 17 33826 expensive
## 18 23878 so-so
## 19 30094 so-so
## 20 33379 expensive3 Renames Data Frame
Please rename all variables of your data frame (data frame that you have done above) in your language.
## -- Attaching packages -------------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v stringr 1.4.0
## v tidyr 1.1.2 v forcats 0.5.0
## v readr 1.3.1## -- Conflicts ----------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()DataKopiKenangan %>%
rename("Nomor_Transaksi"="id",
"Tanggal"="date",
"Nama"="name",
"Kota"="city",
"Outlet"="outlet",
"Menu"="menu",
"Harga"="price",
"Diskon"="discount",
"Harga_Total"="Total_Price",
"Kategori_Harga"="Category_Price") %>%
head(20) %>%
print()## Nomor_Transaksi Tanggal Nama Kota Outlet Menu
## 1 1 2018-01-01 jeffry bekasi outlet 1 Ice Caramel Latte
## 2 2 2018-01-01 ardifo tangerang outlet 3 Ice Coffee Lite
## 3 3 2018-01-01 siana tangerang outlet 5 Hot Chocolate
## 4 4 2018-01-01 fallen tangerang outlet 1 Ice Americano
## 5 5 2018-01-01 ayu bogor outlet 3 Ice Americano
## 6 6 2018-01-01 nikita bogor outlet 1 Ice Berry Coffe
## 7 7 2018-01-01 lala jakarta outlet 5 Hot Red Velvet Latte
## 8 8 2018-01-02 angel bogor outlet 5 Hot Chocolate
## 9 9 2018-01-02 salomo tangerang outlet 1 Hot Caramel Latte
## 10 10 2018-01-02 jocelyn depok outlet 3 Hot Chocolate
## 11 11 2018-01-02 jocelyn depok outlet 4 Ice Matcha Latte
## 12 12 2018-01-02 sherly bogor outlet 4 Ice Coffee Lite
## 13 13 2018-01-02 kefas jakarta outlet 3 Ice Red Velvet Latte
## 14 14 2018-01-03 irene bogor outlet 4 Ice Red Velvet Latte
## 15 15 2018-01-03 ardifo bogor outlet 4 Ice Matcha Latte
## 16 16 2018-01-03 sherly bekasi outlet 2 Ice Coffee Avocado
## 17 17 2018-01-04 salomo bogor outlet 4 Hot Red Velvet Latte
## 18 18 2018-01-04 irene jakarta outlet 4 Hot Caramel Latte
## 19 19 2018-01-04 fallen depok outlet 4 Es Kopi Susu
## 20 20 2018-01-04 fallen depok outlet 3 Cappucino
## Harga Diskon Harga_Total Kategori_Harga
## 1 31405 0.11 27950 so-so
## 2 42015 0.06 39494 expensive
## 3 25018 0.10 22516 so-so
## 4 30772 0.11 27387 so-so
## 5 30772 0.11 27387 so-so
## 6 39581 0.05 37602 expensive
## 7 36372 0.07 33826 expensive
## 8 25018 0.10 22516 so-so
## 9 25675 0.07 23878 so-so
## 10 25018 0.10 22516 so-so
## 11 34704 0.11 30887 so-so
## 12 42015 0.06 39494 expensive
## 13 41193 0.10 37074 expensive
## 14 41193 0.10 37074 expensive
## 15 34704 0.11 30887 so-so
## 16 37145 0.05 35288 expensive
## 17 36372 0.07 33826 expensive
## 18 25675 0.07 23878 so-so
## 19 33813 0.11 30094 so-so
## 20 37504 0.11 33379 expensive4 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!
## Var1 Freq
## 1 Ice Caramel Latte 349
## 2 Cappucino 366
## 3 Es Kopi Susu 332
## 4 Hot Caramel Latte 333
## 5 Hot Chocolate 365
## 6 Hot Red Velvet Latte 366
## 7 Ice Americano 355
## 8 Ice Berry Coffe 395
## 9 Ice Cafe Latte 351
## 10 Ice Coffee Avocado 348
## 11 Ice Coffee Lite 342
## 12 Ice Matcha Espresso 348
## 13 Ice Matcha Latte 354
## 14 Ice Red Velvet Latte 396bestsellingmenu <- total_penjualan_permenu[order(total_penjualan_permenu$Freq, decreasing = T),] %>%
head(1) %>%
print()## Var1 Freq
## 14 Ice Red Velvet Latte 396atau
best_selling_menu <- total_penjualan_permenu %>%
filter(Freq == max(total_penjualan_permenu$Freq)) %>%
print()## Var1 Freq
## 1 Ice Red Velvet Latte 396 * Find out which city got the most sales!
income_perkota <-data.frame(aggregate(DataKopiKenangan$price, by=list(city = DataKopiKenangan$city), FUN=sum))
income_perkota## city x
## 1 bekasi 34620816
## 2 bogor 34968282
## 3 depok 34773502
## 4 jakarta 34730630
## 5 tangerang 34766885## city x
## 1 bogor 34968282atau
## city x
## 2 bogor 34968282 * Find out which city has the most discounted sales!
income_perkota1 <-data.frame(aggregate(DataKopiKenangan$Total_Price, by=list(city = DataKopiKenangan$city), FUN=sum))
income_perkota1## city x
## 1 bekasi 31725372
## 2 bogor 32062208
## 3 depok 31806861
## 4 jakarta 31813248
## 5 tangerang 31869394## city x
## 1 bogor 32062208atau
## city x
## 2 bogor 32062208 * what year were the most sales?
sepdate<- DataKopiKenangan%>%
separate(date, c("year", "month","day"), sep="-")
#sepdate
tabeldate <- data.frame(table(sepdate$year))
tabeldate ## Var1 Freq
## 1 2018 1847
## 2 2019 1814
## 3 2020 1339## Var1 Freq
## 1 2018 1847atau
## Var1 Freq
## 1 2018 1847