R Markdown

# Contoh (Ganti data)
data <- data.frame(
  Pelanggan = c("apel", "pisang", "jeruk", "nanas", "mangga", "kiwi", "melon"),
  Total_Belanja = c(9000000, 8000000, 7000000, 5000000, 6000000, 2000000, 30000000)
)
# Filter hanya transaksi dengan belanja > 5.000.000
data_filter <- subset(data, Total_Belanja > 5000000)
# Hitung frekuensi belanja per pelanggan
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
hasil <- data_filter %>%
  group_by(Pelanggan) %>%
  summarise(Jumlah_Transaksi = n()) %>%
  arrange(desc(Jumlah_Transaksi))
# Tampilkan pelanggan yang paling sering belanja > 5 juta
hasil
## # A tibble: 5 × 2
##   Pelanggan Jumlah_Transaksi
##   <chr>                <int>
## 1 apel                     1
## 2 jeruk                    1
## 3 mangga                   1
## 4 melon                    1
## 5 pisang                   1
data <- data.frame(
  Pelanggan = c("apel","pisang","jeruk","nanas","mangga","kiwi","melon","apel","jeruk","pisang"),
  Penghasilan = c(9000000,8000000,7000000,5000000,6000000,
                  2000000,3000000,9000000,7000000,8000000)
)
library(dplyr)

hasil <- data %>%
  filter(Penghasilan > 5000000) %>%
  count(Pelanggan, sort = TRUE)

head(hasil, 1)
##   Pelanggan n
## 1      apel 2
data <- data.frame(
  Pelanggan = c("apel","pisang","jeruk","nanas","mangga","kiwi","melon","apel","jeruk","pisang"),
  Penghasilan = c(9000000,8000000,7000000,5000000,6000000,
                  2000000,3000000,9000000,7000000,8000000)
)
# Filter hanya penghasilan > 5 juta
data_filter <- data[data$Penghasilan > 5000000, ]
# Hitung siapa yang paling sering
tab <- table(data_filter$Pelanggan)
top_pelanggan <- names(which.max(tab))
cat("Pelanggan yang paling sering membeli dengan penghasilan > 5 juta adalah:",
    top_pelanggan, "dengan", max(tab), "kali transaksi\n")
## Pelanggan yang paling sering membeli dengan penghasilan > 5 juta adalah: apel dengan 2 kali transaksi
# Contoh data
data <- data.frame(
  Pelanggan = c("apel","pisang","jeruk","nanas","mangga","kiwi"),
  Jenis_Kelamin = c("P","L","P","L","P","L"),
  Tempat_Tinggal = c("Kota","Desa","Desa","kota","Kota","Desa"),
  Total_Belanja = c(9000000,8000000,7000000,5000000,6000000,2000000)
)
# Filter: tinggal di desa & belanja > 5 juta
data_filter <- subset(data, Tempat_Tinggal == "Desa" & Total_Belanja > 5000000)
# Ambil jenis kelamin pelanggan
print(data_filter[, c("Pelanggan","Jenis_Kelamin","Total_Belanja")])
##   Pelanggan Jenis_Kelamin Total_Belanja
## 2    pisang             L         8e+06
## 3     jeruk             P         7e+06
# Contoh data
data <- data.frame(
  Pelanggan = c("apel","pisang","jeruk","nanas","mangga","kiwi"),
  Jenis_Kelamin = c("P","L","P","L","P","L"),
  Tempat_Tinggal = c("Kota","Desa","Desa","kota","Kota","Desa"),
  Total_Belanja = c(9000000,8000000,7000000,5000000,6000000,2000000),
  Penghasilan = c(5000000,6000000,8000000,5000000,10000000,3000000)
)
# Filter: tinggal di desa & belanja > 5 juta
data_filter <- subset(data, Tempat_Tinggal == "Desa" & Total_Belanja > 5000000)
# Tampilkan nama, penghasilan, dan total belanja
print(data_filter[, c("Pelanggan","Penghasilan","Total_Belanja")])
##   Pelanggan Penghasilan Total_Belanja
## 2    pisang       6e+06         8e+06
## 3     jeruk       8e+06         7e+06

```