Tugas Week 5

  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

df_customer <- read.csv("df_customer.csv")

hasil <- df_customer %>%
  filter(Total_Belanja > 5000000) %>%
  group_by(ID_Pelanggan) %>%
  summarise(JumlahTransaksi = n(), .groups = "drop") %>%
  filter(JumlahTransaksi == max(JumlahTransaksi))

print(hasil)

## # A tibble: 2 × 2
##   ID_Pelanggan JumlahTransaksi
##   <chr>                  <int>
## 1 ID00007                    7
## 2 ID00025                    7

hasil2 <- df_customer %>%
  filter(Total_Belanja > 5000000) %>%
  count(ID_Pelanggan, sort = TRUE) %>%
  slice_head(n=6)
hasil2

##   ID_Pelanggan n
## 1      ID00007 7
## 2      ID00025 7
## 3      ID00026 6
## 4      ID00089 6
## 5      ID00053 5
## 6      ID00079 5

jumlah_hasil <- df_customer %>%
  count(ID_Pelanggan, Jenis_Kelamin, Tempat_Tinggal, name = "Jumlah_Transaksi") %>%
  filter(Jenis_Kelamin == "Perempuan",
         Tempat_Tinggal == "Kota",
         Jumlah_Transaksi > 5) %>%
nrow()

print(paste("Jawabannya adalah:", jumlah_hasil, "orang."))

## [1] "Jawabannya adalah: 0 orang."

q33 <- df_customer %>%
  filter(Penghasilan > 5000000) %>%
  count(ID_Pelanggan, sort = TRUE) %>%
  slice_head(n = 1)
q33

##   ID_Pelanggan n
## 1      ID00007 9

q44 <- df_customer %>%
  filter(Tempat_Tinggal == "Desa", Total_Belanja > 5000000) %>%
  count(Jenis_Kelamin)
q44

##   Jenis_Kelamin  n
## 1     Laki-laki 10
## 2     Perempuan 37

q55 <- df_customer %>%
  filter(Tempat_Tinggal == "Desa", Total_Belanja > 5000000) %>%
  select(ID_Pelanggan, Penghasilan) %>%
  head(5)
q55

##   ID_Pelanggan Penghasilan
## 1      ID00067     7773498
## 2      ID00014     6776730
## 3      ID00027     8108645
## 4      ID00089     9032981
## 5      ID00034     5616450