1.Business Understanding

a.Goals: 1, Menentukan Produk Unggulan untuk penjualan. 2, Saran Penjualan Berdasarkan Tren yang telah terjadi.

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
library(readxl)
library(ggplot2)
library(skimr)
## Warning: package 'skimr' was built under R version 4.5.2
library(corrplot)  #digunakan untuk visualisasi korelasi
## Warning: package 'corrplot' was built under R version 4.5.2
## corrplot 0.95 loaded

2.Data Cleaning

onret <- read_excel("Online Retail.xlsx")
onret
## # A tibble: 541,909 × 8
##    InvoiceNo StockCode Description        Quantity InvoiceDate         UnitPrice
##    <chr>     <chr>     <chr>                 <dbl> <dttm>                  <dbl>
##  1 536365    85123A    WHITE HANGING HEA…        6 2010-12-01 08:26:00      2.55
##  2 536365    71053     WHITE METAL LANTE…        6 2010-12-01 08:26:00      3.39
##  3 536365    84406B    CREAM CUPID HEART…        8 2010-12-01 08:26:00      2.75
##  4 536365    84029G    KNITTED UNION FLA…        6 2010-12-01 08:26:00      3.39
##  5 536365    84029E    RED WOOLLY HOTTIE…        6 2010-12-01 08:26:00      3.39
##  6 536365    22752     SET 7 BABUSHKA NE…        2 2010-12-01 08:26:00      7.65
##  7 536365    21730     GLASS STAR FROSTE…        6 2010-12-01 08:26:00      4.25
##  8 536366    22633     HAND WARMER UNION…        6 2010-12-01 08:28:00      1.85
##  9 536366    22632     HAND WARMER RED P…        6 2010-12-01 08:28:00      1.85
## 10 536367    84879     ASSORTED COLOUR B…       32 2010-12-01 08:34:00      1.69
## # ℹ 541,899 more rows
## # ℹ 2 more variables: CustomerID <dbl>, Country <chr>
summary(onret)
##   InvoiceNo          StockCode         Description           Quantity         
##  Length:541909      Length:541909      Length:541909      Min.   :-80995.000  
##  Class :character   Class :character   Class :character   1st Qu.:     1.000  
##  Mode  :character   Mode  :character   Mode  :character   Median :     3.000  
##                                                           Mean   :     9.552  
##                                                           3rd Qu.:    10.000  
##                                                           Max.   : 80995.000  
##                                                                               
##   InvoiceDate                    UnitPrice            CustomerID    
##  Min.   :2010-12-01 08:26:00   Min.   :-11062.060   Min.   :12346   
##  1st Qu.:2011-03-28 11:34:00   1st Qu.:     1.250   1st Qu.:13953   
##  Median :2011-07-19 17:17:00   Median :     2.080   Median :15152   
##  Mean   :2011-07-04 13:34:57   Mean   :     4.611   Mean   :15288   
##  3rd Qu.:2011-10-19 11:27:00   3rd Qu.:     4.130   3rd Qu.:16791   
##  Max.   :2011-12-09 12:50:00   Max.   : 38970.000   Max.   :18287   
##                                                     NA's   :135080  
##    Country         
##  Length:541909     
##  Class :character  
##  Mode  :character  
##                    
##                    
##                    
## 
str(onret)
## tibble [541,909 × 8] (S3: tbl_df/tbl/data.frame)
##  $ InvoiceNo  : chr [1:541909] "536365" "536365" "536365" "536365" ...
##  $ StockCode  : chr [1:541909] "85123A" "71053" "84406B" "84029G" ...
##  $ Description: chr [1:541909] "WHITE HANGING HEART T-LIGHT HOLDER" "WHITE METAL LANTERN" "CREAM CUPID HEARTS COAT HANGER" "KNITTED UNION FLAG HOT WATER BOTTLE" ...
##  $ Quantity   : num [1:541909] 6 6 8 6 6 2 6 6 6 32 ...
##  $ InvoiceDate: POSIXct[1:541909], format: "2010-12-01 08:26:00" "2010-12-01 08:26:00" ...
##  $ UnitPrice  : num [1:541909] 2.55 3.39 2.75 3.39 3.39 7.65 4.25 1.85 1.85 1.69 ...
##  $ CustomerID : num [1:541909] 17850 17850 17850 17850 17850 ...
##  $ Country    : chr [1:541909] "United Kingdom" "United Kingdom" "United Kingdom" "United Kingdom" ...
colSums(is.na(onret))  #cek missing value
##   InvoiceNo   StockCode Description    Quantity InvoiceDate   UnitPrice 
##           0           0        1454           0           0           0 
##  CustomerID     Country 
##      135080           0
onret_1 <- onret %>%  #menghapus baris yang mengandung missing value
  filter(if_all(c(Description, CustomerID),~!is.na(.)))
colSums(is.na(onret_1))  #cek apakah masih ada
##   InvoiceNo   StockCode Description    Quantity InvoiceDate   UnitPrice 
##           0           0           0           0           0           0 
##  CustomerID     Country 
##           0           0
onret_2 <- onret_1 %>%  #cek duplikasi
  filter(duplicated(.))
onret_3 <- onret_2 %>%
  group_by(across(everything())) %>%
  filter(n()>1)
onret_3
## # A tibble: 678 × 8
## # Groups:   InvoiceNo, StockCode, Description, Quantity, InvoiceDate,
## #   UnitPrice, CustomerID, Country [290]
##    InvoiceNo StockCode Description        Quantity InvoiceDate         UnitPrice
##    <chr>     <chr>     <chr>                 <dbl> <dttm>                  <dbl>
##  1 536412    21448     12 DAISY PEGS IN …        2 2010-12-01 11:49:00      1.65
##  2 536412    21448     12 DAISY PEGS IN …        2 2010-12-01 11:49:00      1.65
##  3 536464    22866     HAND WARMER SCOTT…        1 2010-12-01 12:23:00      2.1 
##  4 536464    22866     HAND WARMER SCOTT…        1 2010-12-01 12:23:00      2.1 
##  5 536749    21415     CLAM SHELL SMALL          2 2010-12-02 13:49:00      2.1 
##  6 536749    22174     PHOTO CUBE                2 2010-12-02 13:49:00      1.65
##  7 536749    21415     CLAM SHELL SMALL          2 2010-12-02 13:49:00      2.1 
##  8 536749    22174     PHOTO CUBE                2 2010-12-02 13:49:00      1.65
##  9 536796    21967     PACK OF 12 SKULL …        1 2010-12-02 15:46:00      0.29
## 10 536796    21967     PACK OF 12 SKULL …        1 2010-12-02 15:46:00      0.29
## # ℹ 668 more rows
## # ℹ 2 more variables: CustomerID <dbl>, Country <chr>
onret_4 <- onret_3 %>%  #menghapus duplikasi
  distinct()
duplicated(onret_4)
##   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE
dacl <- onret_4 %>%  #mengubah kolom menjadi biner
  mutate(UnitPrice = ifelse(UnitPrice > 3, 0, 1))

dacl
## # A tibble: 290 × 8
## # Groups:   InvoiceNo, StockCode, Description, Quantity, InvoiceDate,
## #   UnitPrice, CustomerID, Country [290]
##    InvoiceNo StockCode Description        Quantity InvoiceDate         UnitPrice
##    <chr>     <chr>     <chr>                 <dbl> <dttm>                  <dbl>
##  1 536412    21448     12 DAISY PEGS IN …        2 2010-12-01 11:49:00         1
##  2 536464    22866     HAND WARMER SCOTT…        1 2010-12-01 12:23:00         1
##  3 536749    21415     CLAM SHELL SMALL          2 2010-12-02 13:49:00         1
##  4 536749    22174     PHOTO CUBE                2 2010-12-02 13:49:00         1
##  5 536796    21967     PACK OF 12 SKULL …        1 2010-12-02 15:46:00         1
##  6 536874    22866     HAND WARMER SCOTT…        1 2010-12-03 11:35:00         1
##  7 537042    21579     LOLITA  DESIGN  C…        1 2010-12-05 10:45:00         1
##  8 537051    22730     ALARM CLOCK BAKEL…        1 2010-12-05 11:12:00         0
##  9 537144    22086     PAPER CHAIN KIT 5…        1 2010-12-05 13:00:00         1
## 10 537144    21882     SKULLS TAPE               1 2010-12-05 13:00:00         1
## # ℹ 280 more rows
## # ℹ 2 more variables: CustomerID <dbl>, Country <chr>
head(dacl)
## # A tibble: 6 × 8
## # Groups:   InvoiceNo, StockCode, Description, Quantity, InvoiceDate,
## #   UnitPrice, CustomerID, Country [6]
##   InvoiceNo StockCode Description         Quantity InvoiceDate         UnitPrice
##   <chr>     <chr>     <chr>                  <dbl> <dttm>                  <dbl>
## 1 536412    21448     12 DAISY PEGS IN W…        2 2010-12-01 11:49:00         1
## 2 536464    22866     HAND WARMER SCOTTY…        1 2010-12-01 12:23:00         1
## 3 536749    21415     CLAM SHELL SMALL           2 2010-12-02 13:49:00         1
## 4 536749    22174     PHOTO CUBE                 2 2010-12-02 13:49:00         1
## 5 536796    21967     PACK OF 12 SKULL T…        1 2010-12-02 15:46:00         1
## 6 536874    22866     HAND WARMER SCOTTY…        1 2010-12-03 11:35:00         1
## # ℹ 2 more variables: CustomerID <dbl>, Country <chr>
str(dacl)
## gropd_df [290 × 8] (S3: grouped_df/tbl_df/tbl/data.frame)
##  $ InvoiceNo  : chr [1:290] "536412" "536464" "536749" "536749" ...
##  $ StockCode  : chr [1:290] "21448" "22866" "21415" "22174" ...
##  $ Description: chr [1:290] "12 DAISY PEGS IN WOOD BOX" "HAND WARMER SCOTTY DOG DESIGN" "CLAM SHELL SMALL" "PHOTO CUBE" ...
##  $ Quantity   : num [1:290] 2 1 2 2 1 1 1 1 1 1 ...
##  $ InvoiceDate: POSIXct[1:290], format: "2010-12-01 11:49:00" "2010-12-01 12:23:00" ...
##  $ UnitPrice  : num [1:290] 1 1 1 1 1 1 1 0 1 1 ...
##  $ CustomerID : num [1:290] 17920 17968 17976 17976 15574 ...
##  $ Country    : chr [1:290] "United Kingdom" "United Kingdom" "United Kingdom" "United Kingdom" ...
##  - attr(*, "groups")= tibble [290 × 9] (S3: tbl_df/tbl/data.frame)
##   ..$ InvoiceNo  : chr [1:290] "536412" "536464" "536749" "536749" ...
##   ..$ StockCode  : chr [1:290] "21448" "22866" "21415" "22174" ...
##   ..$ Description: chr [1:290] "12 DAISY PEGS IN WOOD BOX" "HAND WARMER SCOTTY DOG DESIGN" "CLAM SHELL SMALL" "PHOTO CUBE" ...
##   ..$ Quantity   : num [1:290] 2 1 2 2 1 1 1 1 1 1 ...
##   ..$ InvoiceDate: POSIXct[1:290], format: "2010-12-01 11:49:00" "2010-12-01 12:23:00" ...
##   ..$ UnitPrice  : num [1:290] 1 1 1 1 1 1 1 0 1 1 ...
##   ..$ CustomerID : num [1:290] 17920 17968 17976 17976 15574 ...
##   ..$ Country    : chr [1:290] "United Kingdom" "United Kingdom" "United Kingdom" "United Kingdom" ...
##   ..$ .rows      : list<int> [1:290] 
##   .. ..$ : int 1
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##   .. .. [list output truncated]
##   .. ..@ ptype: int(0) 
##   ..- attr(*, ".drop")= logi TRUE

3.EXPLORATORY DATA ANALYSIS (EDA)

Yang diperlukan

dacl_1 <- dacl %>%  #buat kolom TotalSales
  mutate(TotalSales = Quantity*UnitPrice)   #mutate untuk menambahkan kolom
skim(dacl_1[, c("Quantity", "UnitPrice", "TotalSales")])
Data summary
Name dacl_1[, c(“Quantity”, “U…
Number of rows 290
Number of columns 3
_______________________
Column type frequency:
numeric 1
________________________
Group variables Quantity, UnitPrice

Variable type: numeric

skim_variable Quantity UnitPrice n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
TotalSales -12 1 0 1 -12 NA -12 -12 -12 -12 -12 ▁▁▇▁▁
TotalSales -1 0 0 1 0 0 0 0 0 0 0 ▁▁▇▁▁
TotalSales 1 0 0 1 0 0 0 0 0 0 0 ▁▁▇▁▁
TotalSales 1 1 0 1 1 0 1 1 1 1 1 ▁▁▇▁▁
TotalSales 2 0 0 1 0 0 0 0 0 0 0 ▁▁▇▁▁
TotalSales 2 1 0 1 2 0 2 2 2 2 2 ▁▁▇▁▁
TotalSales 3 0 0 1 0 NA 0 0 0 0 0 ▁▁▇▁▁
TotalSales 3 1 0 1 3 NA 3 3 3 3 3 ▁▁▇▁▁
TotalSales 4 1 0 1 4 0 4 4 4 4 4 ▁▁▇▁▁
TotalSales 5 1 0 1 5 NA 5 5 5 5 5 ▁▁▇▁▁
TotalSales 6 1 0 1 6 0 6 6 6 6 6 ▁▁▇▁▁
TotalSales 8 1 0 1 8 0 8 8 8 8 8 ▁▁▇▁▁
TotalSales 10 1 0 1 10 NA 10 10 10 10 10 ▁▁▇▁▁
TotalSales 12 1 0 1 12 0 12 12 12 12 12 ▁▁▇▁▁
TotalSales 24 1 0 1 24 0 24 24 24 24 24 ▁▁▇▁▁
TotalSales 25 1 0 1 25 NA 25 25 25 25 25 ▁▁▇▁▁
TotalSales 48 1 0 1 48 NA 48 48 48 48 48 ▁▁▇▁▁

Visualisasi Distribusi

ggplot(dacl_1, aes(x = Quantity)) +
  geom_histogram(binwidth = 3, fill = "dodgerblue") +
  theme_minimal() +
  labs(title = "Distribusi Quantity")

ggplot(dacl_1, aes(x = UnitPrice)) +
  geom_histogram(binwidth = 0.5, fill = "turquoise") +
  theme_light() +
  labs(title = "Distribusi UnitPrice")

ggplot(dacl_1, aes(x = TotalSales)) +
  geom_histogram(binwidth = 5, fill = "royalblue") +
  theme_classic() +
  labs(title = "Distribusi TotalSales")

4. Model Penyelesaian

produk_unggulan <- dacl_1 %>%
  group_by(StockCode) %>%   #pengelompokkan data berdasarkan stockcode
  summarise(TotalSales = sum(TotalSales)) %>%   #menghitung total penjualan perproduk
  arrange(desc(TotalSales)) %>%  #Pengurutan
  slice_head(n = 5)   #mengambil 5 produk teratas
ggplot(produk_unggulan, aes(x = reorder(StockCode, TotalSales), y = TotalSales)) +
#membuat grafik dari data produk_terlaris
  geom_bar(stat = "identity", fill = "olivedrab") +   #Untuk membuat grafik 
  coord_flip() +   #Untuk memudahkan membaca nama produk
  labs(title = "5 Produk Unggulan Berdasarkan StockCode", x = "StockCode", y = "Total Penjualan") +   #Pelabelan dan penjudulan
  theme_minimal() +
  theme(axis.text.y = element_text(size = 10))

produk_unggulan
## # A tibble: 5 × 2
##   StockCode TotalSales
##   <chr>          <dbl>
## 1 M                 48
## 2 21497             25
## 3 17136A            24
## 4 20668             24
## 5 22294             24
tren_penjualan <- dacl_1 %>%
  group_by(Tanggal = as.Date(InvoiceDate)) %>%  #pengelompokkan data 
  summarise(TotalSales = sum(TotalSales))  #penjumlahkan total penjualan perhari
ggplot(tren_penjualan, aes(x = Tanggal, y = TotalSales)) +
  geom_line(color = "gray13") +  #menampilkan grafik
  theme_minimal() +
  labs(title = "Tren Penjualan Harian", x = "Tanggal", y = "Total Penjualan")   #penjudulan dan pelabelan sumbu bertujan untuk mempermudah membaca 

tren_penjualan
## # A tibble: 121 × 2
##    Tanggal    TotalSales
##    <date>          <dbl>
##  1 2010-12-01          3
##  2 2010-12-02          5
##  3 2010-12-03          1
##  4 2010-12-05          6
##  5 2010-12-06         15
##  6 2010-12-07          1
##  7 2010-12-08          3
##  8 2010-12-10          0
##  9 2010-12-12          1
## 10 2010-12-13          0
## # ℹ 111 more rows