UTS

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

penjualan <- read.csv("data_penjualan.csv")

#1 
# Produk dengan total penjualan terbesar
penjualan %>%
  group_by(Produk) %>%
  summarise(Total = sum(Total_Penjualan, na.rm = TRUE)) %>%
  arrange(desc(Total))

## # A tibble: 12 × 2
##    Produk                 Total
##    <chr>                  <int>
##  1 Smartphone X5      314160000
##  2 Laptop Ultra 14    281675000
##  3 Earbuds Pro        158340000
##  4 Smartwatch Fit      70560000
##  5 Rak Buku Minimalis  59815000
##  6 Printer Tinta       55417500
##  7 Tas Ransel Laptop   47162500
##  8 Meja Kerja Lipat    34637500
##  9 Power Bank 20000    19362000
## 10 Kursi Ergonomis     17100000
## 11 Kertas A4 (Rim)     14789500
## 12 Paket Alat Tulis    11844000

# Produk dengan jumlah unit terjual terbanyak
penjualan %>%
  group_by(Produk) %>%
  summarise(Total_Unit = sum(Jumlah, na.rm = TRUE)) %>%
  arrange(desc(Total_Unit))

## # A tibble: 12 × 2
##    Produk             Total_Unit
##    <chr>                   <int>
##  1 Kertas A4 (Rim)           289
##  2 Earbuds Pro               270
##  3 Tas Ransel Laptop         146
##  4 Paket Alat Tulis          115
##  5 Rak Buku Minimalis         94
##  6 Smartphone X5              79
##  7 Power Bank 20000           78
##  8 Meja Kerja Lipat           44
##  9 Printer Tinta              44
## 10 Smartwatch Fit             42
## 11 Laptop Ultra 14            34
## 12 Kursi Ergonomis            13

#2
hasil2 <- penjualan %>%
  filter(Kategori == "Elektronik",
         Diskon_Persen >= 10) %>%
  group_by(Cabang) %>%
  summarise(Total = sum(Total_Penjualan, na.rm = TRUE)) %>%
  arrange(desc(Total))

hasil2

## # A tibble: 4 × 2
##   Cabang       Total
##   <chr>        <int>
## 1 Surabaya 155022500
## 2 Jakarta   92865000
## 3 Semarang  52305000
## 4 Bandung   32017500

#3A
penjualan %>%
  filter(Cabang == "Jakarta",
         Metode_Pembayaran == "E-Wallet") %>%
  summarise(
    Jumlah_Transaksi = n(),
    Rata_Rata = mean(Total_Penjualan, na.rm = TRUE)
  )

##   Jumlah_Transaksi Rata_Rata
## 1               38   3793776

#3B
penjualan %>%
  filter(Cabang == "Jakarta") %>%
  group_by(Metode_Pembayaran) %>%
  summarise(
    Rata_Rata = mean(Total_Penjualan, na.rm = TRUE)
  ) %>%
  arrange(desc(Rata_Rata))

## # A tibble: 4 × 2
##   Metode_Pembayaran Rata_Rata
##   <chr>                 <dbl>
## 1 Kartu Kredit       4906550 
## 2 E-Wallet           3793776.
## 3 Transfer           3194140 
## 4 Tunai              2838809.

#4
hasil4 <- penjualan %>%
  filter(Jumlah >= 5, Diskon_Persen > 0) %>%
  group_by(Admin) %>%
  summarise(
    Total_Penjualan = sum(Total_Penjualan, na.rm = TRUE),
    Jumlah_Transaksi = n()
  ) %>%
  arrange(desc(Total_Penjualan))

hasil4

## # A tibble: 9 × 3
##   Admin   Total_Penjualan Jumlah_Transaksi
##   <chr>             <int>            <int>
## 1 Rina           24523750                9
## 2 Lestari        24267500                7
## 3 Agus           23678250                9
## 4 Doni           22571250                9
## 5 Maya           20709000                9
## 6 Wawan          19378750                7
## 7 Sari           13395500               10
## 8 Putri           7640500                5
## 9 Budi            6723750                6

# 5A
penjualan %>%
  group_by(Cabang) %>%
  summarise(
    Total = sum(Total_Penjualan, na.rm = TRUE)
  ) %>%
  arrange(desc(Total))

## # A tibble: 4 × 2
##   Cabang       Total
##   <chr>        <int>
## 1 Surabaya 342730000
## 2 Jakarta  321342250
## 3 Semarang 251269250
## 4 Bandung  169521500

# 5B
penjualan %>%
  group_by(Produk) %>%
  summarise(
    Total_Unit = sum(Jumlah, na.rm = TRUE)
  ) %>%
  arrange(desc(Total_Unit))

## # A tibble: 12 × 2
##    Produk             Total_Unit
##    <chr>                   <int>
##  1 Kertas A4 (Rim)           289
##  2 Earbuds Pro               270
##  3 Tas Ransel Laptop         146
##  4 Paket Alat Tulis          115
##  5 Rak Buku Minimalis         94
##  6 Smartphone X5              79
##  7 Power Bank 20000           78
##  8 Meja Kerja Lipat           44
##  9 Printer Tinta              44
## 10 Smartwatch Fit             42
## 11 Laptop Ultra 14            34
## 12 Kursi Ergonomis            13

# 5C
penjualan %>%
  group_by(Metode_Pembayaran) %>%
  summarise(
    Rata_Rata = mean(Total_Penjualan, na.rm = TRUE),
    Jumlah_Transaksi = n()
  ) %>%
  arrange(desc(Rata_Rata))

## # A tibble: 4 × 3
##   Metode_Pembayaran Rata_Rata Jumlah_Transaksi
##   <chr>                 <dbl>            <int>
## 1 E-Wallet           4031159.              113
## 2 Kartu Kredit       3971900                45
## 3 Transfer           3319620.               98
## 4 Tunai              2847358.               44

# 5D
penjualan %>%
  group_by(Diskon_Persen) %>%
  summarise(
    Rata_Rata = mean(Total_Penjualan, na.rm = TRUE),
    Jumlah_Transaksi = n()
  ) %>%
  arrange(Diskon_Persen)

## # A tibble: 6 × 3
##   Diskon_Persen Rata_Rata Jumlah_Transaksi
##           <int>     <dbl>            <int>
## 1             0  4411352.              122
## 2             5  2764628.               37
## 3            10  3049625                36
## 4            15  2697475                40
## 5            20  3080800                25
## 6            25  3742031.               40

#5E
penjualan %>%
  summarise(
    Total_Data = n(),
    Rating_Kosong = sum(is.na(Rating)),
    Persen_Kosong = mean(is.na(Rating)) * 100
  )

##   Total_Data Rating_Kosong Persen_Kosong
## 1        300             8      2.666667