df_customer <- read.csv("df_customer.csv")
head(df_customer)## X ID_Pelanggan Jenis_Kelamin Tempat_Tinggal Penghasilan Total_Belanja
## 1 1 ID00031 Laki-laki Desa 2227350 2563031
## 2 2 ID00079 Perempuan Kota 9047608 8369550
## 3 3 ID00051 Perempuan Kota 9735540 8053033
## 4 4 ID00014 Laki-laki Kota 13510126 9799876
## 5 5 ID00067 Perempuan Desa 7773498 6982081
## 6 6 ID00042 Laki-laki Desa 6666740 4782002nrow(df_customer)## [1] 300length(unique(df_customer$ID_Pelanggan))## [1] 94sort(table(df_customer$ID_Pelanggan), decreasing = TRUE)[1:3]##
## ID00007 ID00025 ID00089
## 9 7 7aggregate(Penghasilan ~ Jenis_Kelamin, data = df_customer, mean)## Jenis_Kelamin Penghasilan
## 1 Laki-laki 8880902
## 2 Perempuan 8505199aggregate(Total_Belanja ~ Jenis_Kelamin, data = df_customer, mean)## Jenis_Kelamin Total_Belanja
## 1 Laki-laki 6034728
## 2 Perempuan 7114786aggregate(Penghasilan ~ Tempat_Tinggal, data = df_customer, mean)## Tempat_Tinggal Penghasilan
## 1 Desa 6249122
## 2 Kota 9878685aggregate(Total_Belanja ~ Tempat_Tinggal, data = df_customer, mean)## Tempat_Tinggal Total_Belanja
## 1 Desa 5022231
## 2 Kota 7520118df_customer[order(-df_customer$Total_Belanja), c("ID_Pelanggan", "Total_Belanja")] |> head(5)## ID_Pelanggan Total_Belanja
## 76 ID00034 11626302
## 175 ID00011 11527638
## 228 ID00057 11031197
## 287 ID00093 10984825
## 33 ID00007 10846012table(df_customer$Jenis_Kelamin)##
## Laki-laki Perempuan
## 121 179## df_customer$Kategori_Penghasilan <- cut(df_customer$Penghasilan,
breaks = c(-Inf, 5000000, 10000000, Inf),
labels = c("Rendah", "Menengah", "Tinggi"))
table(df_customer$Kategori_Penghasilan)##
## Rendah Menengah Tinggi
## 27 175 98df_customer$ID_Pelanggan## [1] "ID00031" "ID00079" "ID00051" "ID00014" "ID00067" "ID00042" "ID00050"
## [8] "ID00043" "ID00014" "ID00025" "ID00090" "ID00091" "ID00069" "ID00091"
## [15] "ID00057" "ID00092" "ID00009" "ID00093" "ID00099" "ID00072" "ID00026"
## [22] "ID00007" "ID00042" "ID00009" "ID00083" "ID00036" "ID00078" "ID00081"
## [29] "ID00043" "ID00076" "ID00015" "ID00032" "ID00007" "ID00009" "ID00041"
## [36] "ID00074" "ID00023" "ID00027" "ID00060" "ID00053" "ID00007" "ID00053"
## [43] "ID00027" "ID00096" "ID00038" "ID00089" "ID00034" "ID00093" "ID00069"
## [50] "ID00072" "ID00076" "ID00063" "ID00013" "ID00082" "ID00097" "ID00091"
## [57] "ID00025" "ID00038" "ID00021" "ID00079" "ID00041" "ID00047" "ID00090"
## [64] "ID00060" "ID00095" "ID00016" "ID00094" "ID00006" "ID00072" "ID00086"
## [71] "ID00086" "ID00039" "ID00031" "ID00081" "ID00050" "ID00034" "ID00004"
## [78] "ID00013" "ID00069" "ID00025" "ID00052" "ID00022" "ID00089" "ID00032"
## [85] "ID00025" "ID00087" "ID00035" "ID00040" "ID00030" "ID00012" "ID00031"
## [92] "ID00030" "ID00064" "ID00099" "ID00014" "ID00093" "ID00096" "ID00071"
## [99] "ID00067" "ID00023" "ID00079" "ID00085" "ID00037" "ID00008" "ID00051"
## [106] "ID00074" "ID00050" "ID00098" "ID00074" "ID00086" "ID00076" "ID00084"
## [113] "ID00046" "ID00017" "ID00062" "ID00046" "ID00054" "ID00035" "ID00094"
## [120] "ID00079" "ID00024" "ID00087" "ID00007" "ID00093" "ID00079" "ID00023"
## [127] "ID00026" "ID00032" "ID00007" "ID00027" "ID00042" "ID00005" "ID00070"
## [134] "ID00016" "ID00024" "ID00032" "ID00021" "ID00055" "ID00075" "ID00036"
## [141] "ID00083" "ID00089" "ID00039" "ID00054" "ID00090" "ID00009" "ID00071"
## [148] "ID00098" "ID00048" "ID00077" "ID00083" "ID00056" "ID00039" "ID00068"
## [155] "ID00001" "ID00040" "ID00030" "ID00094" "ID00089" "ID00016" "ID00088"
## [162] "ID00054" "ID00075" "ID00048" "ID00020" "ID00067" "ID00093" "ID00036"
## [169] "ID00052" "ID00022" "ID00049" "ID00042" "ID00059" "ID00084" "ID00011"
## [176] "ID00055" "ID00008" "ID00046" "ID00085" "ID00066" "ID00077" "ID00046"
## [183] "ID00070" "ID00072" "ID00044" "ID00032" "ID00036" "ID00045" "ID00014"
## [190] "ID00016" "ID00087" "ID00033" "ID00040" "ID00040" "ID00010" "ID00089"
## [197] "ID00072" "ID00082" "ID00009" "ID00007" "ID00007" "ID00058" "ID00061"
## [204] "ID00074" "ID00024" "ID00063" "ID00054" "ID00023" "ID00026" "ID00033"
## [211] "ID00057" "ID00029" "ID00010" "ID00053" "ID00054" "ID00077" "ID00011"
## [218] "ID00025" "ID00052" "ID00026" "ID00007" "ID00025" "ID00087" "ID00090"
## [225] "ID00032" "ID00084" "ID00024" "ID00057" "ID00073" "ID00023" "ID00014"
## [232] "ID00006" "ID00091" "ID00001" "ID00090" "ID00058" "ID00081" "ID00029"
## [239] "ID00026" "ID00027" "ID00085" "ID00007" "ID00060" "ID00026" "ID00041"
## [246] "ID00084" "ID00006" "ID00094" "ID00031" "ID00093" "ID00017" "ID00064"
## [253] "ID00037" "ID00057" "ID00020" "ID00035" "ID00089" "ID00033" "ID00066"
## [260] "ID00004" "ID00074" "ID00097" "ID00005" "ID00025" "ID00008" "ID00055"
## [267] "ID00089" "ID00085" "ID00045" "ID00018" "ID00042" "ID00031" "ID00006"
## [274] "ID00071" "ID00061" "ID00048" "ID00017" "ID00045" "ID00092" "ID00063"
## [281] "ID00053" "ID00053" "ID00063" "ID00071" "ID00084" "ID00082" "ID00093"
## [288] "ID00017" "ID00097" "ID00002" "ID00049" "ID00002" "ID00013" "ID00024"
## [295] "ID00049" "ID00067" "ID00082" "ID00002" "ID00037" "ID00063"length(unique(df_customer$ID_Pelanggan))## [1] 94df_customer$Tempat_Tinggal## [1] "Desa" "Kota" "Kota" "Kota" "Desa" "Desa" "Desa" "Desa" "Desa" "Kota"
## [11] "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota"
## [21] "Kota" "Kota" "Kota" "Kota" "Desa" "Kota" "Kota" "Desa" "Kota" "Kota"
## [31] "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Desa" "Desa" "Kota" "Kota"
## [41] "Kota" "Kota" "Desa" "Kota" "Kota" "Desa" "Desa" "Desa" "Kota" "Desa"
## [51] "Desa" "Kota" "Desa" "Kota" "Kota" "Desa" "Kota" "Desa" "Kota" "Kota"
## [61] "Desa" "Desa" "Kota" "Kota" "Desa" "Kota" "Kota" "Kota" "Kota" "Kota"
## [71] "Desa" "Kota" "Desa" "Desa" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota"
## [81] "Kota" "Desa" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota"
## [91] "Kota" "Kota" "Desa" "Desa" "Desa" "Desa" "Desa" "Desa" "Kota" "Kota"
## [101] "Kota" "Kota" "Kota" "Desa" "Kota" "Desa" "Kota" "Desa" "Desa" "Kota"
## [111] "Kota" "Kota" "Kota" "Desa" "Desa" "Kota" "Kota" "Kota" "Desa" "Kota"
## [121] "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Desa"
## [131] "Kota" "Kota" "Desa" "Desa" "Kota" "Desa" "Kota" "Desa" "Desa" "Kota"
## [141] "Kota" "Kota" "Kota" "Kota" "Desa" "Kota" "Kota" "Desa" "Desa" "Kota"
## [151] "Kota" "Kota" "Kota" "Desa" "Kota" "Desa" "Kota" "Desa" "Desa" "Kota"
## [161] "Desa" "Desa" "Kota" "Kota" "Desa" "Desa" "Kota" "Kota" "Kota" "Kota"
## [171] "Desa" "Kota" "Kota" "Kota" "Kota" "Desa" "Desa" "Desa" "Kota" "Kota"
## [181] "Desa" "Kota" "Kota" "Desa" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota"
## [191] "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Desa" "Kota" "Desa" "Desa"
## [201] "Kota" "Desa" "Desa" "Kota" "Kota" "Desa" "Desa" "Kota" "Desa" "Desa"
## [211] "Kota" "Kota" "Kota" "Kota" "Kota" "Desa" "Desa" "Kota" "Desa" "Desa"
## [221] "Kota" "Kota" "Kota" "Desa" "Desa" "Kota" "Desa" "Kota" "Desa" "Kota"
## [231] "Desa" "Desa" "Desa" "Kota" "Kota" "Kota" "Desa" "Kota" "Kota" "Desa"
## [241] "Kota" "Kota" "Kota" "Kota" "Desa" "Kota" "Kota" "Desa" "Desa" "Desa"
## [251] "Kota" "Kota" "Kota" "Desa" "Desa" "Kota" "Desa" "Kota" "Desa" "Kota"
## [261] "Kota" "Desa" "Desa" "Kota" "Desa" "Kota" "Kota" "Kota" "Kota" "Kota"
## [271] "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Desa" "Kota" "Kota" "Desa"
## [281] "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Kota" "Desa" "Kota"
## [291] "Kota" "Kota" "Desa" "Kota" "Desa" "Kota" "Desa" "Kota" "Kota" "Kota"sort(table(df_customer$ID_Pelanggan),decreasing = TRUE)[1:5]##
## ID00007 ID00025 ID00089 ID00093 ID00026
## 9 7 7 7 6sort(table(df_customer$ID_Pelanggan),decreasing = TRUE)[1:10]##
## ID00007 ID00025 ID00089 ID00093 ID00026 ID00032 ID00009 ID00014 ID00023 ID00024
## 9 7 7 7 6 6 5 5 5 5// 1.Siapa pelanggan yang paling sering membeli dengan total belanja lebih dari 5000000
df_customer$Kategori_Penghasilan <- cut(df_customer$Penghasilan,
breaks = c(5000000, Inf),
labels = c("Tinggi"))
table(df_customer$Kategori_Penghasilan)##
## Tinggi
## 273sort(table(df_customer$ID_Pelanggan))##
## ID00012 ID00015 ID00018 ID00044 ID00047 ID00056 ID00059 ID00062 ID00068 ID00073
## 1 1 1 1 1 1 1 1 1 1
## ID00078 ID00088 ID00095 ID00001 ID00004 ID00005 ID00010 ID00011 ID00020 ID00021
## 1 1 1 2 2 2 2 2 2 2
## ID00022 ID00029 ID00034 ID00038 ID00043 ID00051 ID00058 ID00061 ID00064 ID00066
## 2 2 2 2 2 2 2 2 2 2
## ID00070 ID00075 ID00092 ID00096 ID00098 ID00099 ID00002 ID00008 ID00013 ID00030
## 2 2 2 2 2 2 3 3 3 3
## ID00033 ID00035 ID00037 ID00039 ID00041 ID00045 ID00048 ID00049 ID00050 ID00052
## 3 3 3 3 3 3 3 3 3 3
## ID00055 ID00060 ID00069 ID00076 ID00077 ID00081 ID00083 ID00086 ID00097 ID00006
## 3 3 3 3 3 3 3 3 3 4
## ID00016 ID00017 ID00027 ID00036 ID00040 ID00046 ID00057 ID00067 ID00071 ID00082
## 4 4 4 4 4 4 4 4 4 4
## ID00085 ID00087 ID00091 ID00094 ID00009 ID00014 ID00023 ID00024 ID00031 ID00042
## 4 4 4 4 5 5 5 5 5 5
## ID00053 ID00054 ID00063 ID00072 ID00074 ID00079 ID00084 ID00090 ID00026 ID00032
## 5 5 5 5 5 5 5 5 6 6
## ID00025 ID00089 ID00093 ID00007
## 7 7 7 9sort(table(df_customer$Total_Belanja>5000000))##
## FALSE TRUE
## 62 238q1 <- df_customer[df_customer$Total_Belanja > 5000000,]
q1 <- sort(table(q1$ID_Pelanggan), decreasing = TRUE)
head(q1)##
## ID00007 ID00025 ID00026 ID00089 ID00053 ID00079
## 7 7 6 6 5 5//2. Ada berapa banyak perempuan di kota yang berbelanja lebih dari 5x
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionq22 <- df_customer %>%
filter(Jenis_Kelamin == "Perempuan", Tempat_Tinggal == "Kota") %>%
count(ID_Pelanggan) %>%
filter(n > 5) %>%
summarise(jumlah = n())
q22## jumlah
## 1 0//3. Siapa pelanggan yang paling sering membeli dengan penghasilan lebih dari 5000000
q3 <- df_customer[df_customer$Penghasilan > 5000000, ]
q3 <- sort(table(q3$ID_Pelanggan), decreasing = TRUE)
head(q3, 1)##
## ID00007
## 9library(dplyr)
q33 <- df_customer %>%
filter(Penghasilan > 5000000) %>%
count(ID_Pelanggan, sort = TRUE) %>%
slice_head(n = 1)
q33## ID_Pelanggan n
## 1 ID00007 9//4. Berjenis kelamin apa pelanggan yang tinggal di desa namun memiliki total belanja yang lebih dari 5000000
q4 <- subset(df_customer, Tempat_Tinggal == "Desa" & Total_Belanja > 5000000)
table(q4$Jenis_Kelamin)##
## Laki-laki Perempuan
## 10 37library(dplyr)
q44 <- df_customer %>%
filter(Tempat_Tinggal == "Desa", Total_Belanja > 5000000) %>%
count(Jenis_Kelamin)
q44## Jenis_Kelamin n
## 1 Laki-laki 10
## 2 Perempuan 37//5. Berpenghasilan berapa pelanggan yang tinggal di desa namun memiliki total belanja lebih dari 5000000
q5 <- subset(df_customer, Tempat_Tinggal == "Desa" & Total_Belanja > 5000000)
head(q5[, c("ID_Pelanggan", "Penghasilan")], 5)## ID_Pelanggan Penghasilan
## 5 ID00067 7773498
## 9 ID00014 6776730
## 43 ID00027 8108645
## 46 ID00089 9032981
## 47 ID00034 5616450library(dplyr)
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