Retail Sales
Ivan Raditya Musyaffa (023)
Rafael Farjid Nurwimaya (022)
Mohammad Dwiki Kurniawan (021)
2026-05-12
Import Library
library(readxl)## Warning: package 'readxl' was built under R version 4.5.3library(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, unionlibrary(ggplot2)
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionImport Dataset
sales <- read_excel("01_RETAIL_SALES.xlsx")
head(sales)## # A tibble: 6 × 7
## transaction_id date product quantity price channel store_id
## <dbl> <dttm> <chr> <dbl> <dbl> <chr> <chr>
## 1 1 2023-10-01 00:00:00 Sports 7 5 e5 Online S011
## 2 2 2024-11-25 00:00:00 Food 10 1 e6 Online S011
## 3 3 2024-06-18 00:00:00 Home & Gar… 4 1.5e5 Online S018
## 4 4 2024-03-13 00:00:00 Beauty 2 2.5e5 <NA> S018
## 5 5 2023-04-24 00:00:00 Electronics 7 1.5e5 <NA> S011
## 6 6 2023-04-24 00:00:00 Beauty 6 1 e5 Online S012Struktur Dataset
str(sales)## tibble [1,000 × 7] (S3: tbl_df/tbl/data.frame)
## $ transaction_id: num [1:1000] 1 2 3 4 5 6 7 8 9 10 ...
## $ date : POSIXct[1:1000], format: "2023-10-01" "2024-11-25" ...
## $ product : chr [1:1000] "Sports" "Food" "Home & Garden" "Beauty" ...
## $ quantity : num [1:1000] 7 10 4 2 7 6 3 6 6 5 ...
## $ price : num [1:1000] 500000 1000000 150000 250000 150000 100000 1000000 150000 100000 50000 ...
## $ channel : chr [1:1000] "Online" "Online" "Online" NA ...
## $ store_id : chr [1:1000] "S011" "S011" "S018" "S018" ...summary(sales)## transaction_id date product
## Min. : 1.0 Min. :2023-01-04 00:00:00 Length:1000
## 1st Qu.: 250.8 1st Qu.:2023-06-22 00:00:00 Class :character
## Median : 500.5 Median :2023-12-29 00:00:00 Mode :character
## Mean : 500.5 Mean :2023-12-24 08:42:43
## 3rd Qu.: 750.2 3rd Qu.:2024-06-27 06:00:00
## Max. :1000.0 Max. :2024-12-30 00:00:00
##
## quantity price channel store_id
## Min. : 1.000 Min. : 50000 Length:1000 Length:1000
## 1st Qu.: 3.000 1st Qu.: 100000 Class :character Class :character
## Median : 5.000 Median : 150000 Mode :character Mode :character
## Mean : 6.888 Mean : 505000
## 3rd Qu.: 8.000 3rd Qu.: 500000
## Max. :97.000 Max. :2000000
## NA's :30Data Cleaning
-Mengecek Missing Value
colSums(is.na(sales))## transaction_id date product quantity price
## 0 0 0 0 30
## channel store_id
## 20 0-Menampilkan Data yang Memiliki Missing Value
sales %>% filter(is.na(price) | is.na(channel))## # A tibble: 50 × 7
## transaction_id date product quantity price channel store_id
## <dbl> <dttm> <chr> <dbl> <dbl> <chr> <chr>
## 1 4 2024-03-13 00:00:00 Beauty 2 250000 <NA> S018
## 2 5 2023-04-24 00:00:00 Electron… 7 150000 <NA> S011
## 3 49 2024-02-04 00:00:00 Sports 2 NA Online S020
## 4 77 2024-07-17 00:00:00 Electron… 9 NA Online S017
## 5 86 2023-08-26 00:00:00 Food 5 500000 <NA> S012
## 6 91 2023-03-29 00:00:00 Food 6 100000 <NA> S010
## 7 102 2024-04-09 00:00:00 Electron… 5 NA Offline S008
## 8 124 2023-03-22 00:00:00 Electron… 1 NA Online S003
## 9 138 2024-05-28 00:00:00 electron… 9 NA Online S008
## 10 139 2023-09-23 00:00:00 Fashion 4 NA Offline S017
## # ℹ 40 more rows-Menangani Missing Value
Channel
sales$channel[is.na(sales$channel)] <- "Offline"Price
sales$price[is.na(sales$price)] <- mean(sales$price, na.rm = TRUE)-Cleaning Duplikasi Kategori Product
sales$product <- tools::toTitleCase(tolower(sales$product))Mengecek Hasil Cleaning
unique(sales$product)## [1] "Sports" "Food" "Home & Garden" "Beauty"
## [5] "Electronics" "Fashion"table(sales$product)##
## Beauty Electronics Fashion Food Home & Garden
## 174 167 176 163 149
## Sports
## 171Cek Ulang
colSums(is.na(sales))## transaction_id date product quantity price
## 0 0 0 0 0
## channel store_id
## 0 0sum(duplicated(sales))## [1] 0Transformasi Data
sales <- sales %>%
mutate(
total_sales = quantity * price,
month = month(date, label = TRUE),
year = year(date)
)Insight Dataset
-Total Penjualan Keseluruhan
sum(sales$total_sales)## [1] 3756170000-Produk Paling Banyak Terjual
product_summary <- sales %>%
group_by(product) %>%
summarise(total_quantity = sum(quantity)) %>%
arrange(desc(total_quantity))
product_summary## # A tibble: 6 × 2
## product total_quantity
## <chr> <dbl>
## 1 Beauty 1398
## 2 Fashion 1240
## 3 Home & Garden 1223
## 4 Electronics 1065
## 5 Sports 1023
## 6 Food 939-Channel Penjualan Terbanyak
channel_summary <- sales %>%
group_by(channel) %>%
summarise(total_sales = sum(total_sales))
channel_summary## # A tibble: 2 × 2
## channel total_sales
## <chr> <dbl>
## 1 Offline 1333095000
## 2 Online 2423075000-Store dengan Penjualan Tertinggi
store_summary <- sales %>%
group_by(store_id) %>%
summarise(total_sales = sum(total_sales)) %>%
arrange(desc(total_sales))
head(store_summary)## # A tibble: 6 × 2
## store_id total_sales
## <chr> <dbl>
## 1 S016 371305000
## 2 S015 311400000
## 3 S007 309125000
## 4 S013 255465000
## 5 S009 250940000
## 6 S014 238110000Visualisasi Data
-Bar Chart Produk Terlaris
ggplot(product_summary,
aes(x = reorder(product, total_quantity),
y = total_quantity,
fill = product)) +
geom_col() +
coord_flip() +
labs(
title = "Produk Terlaris",
x = "Produk",
y = "Total Quantity"
) +
theme_minimal()
Insight: Produk dengan jumlah pembelian tertinggi menunjukkan kategori yang paling diminati pelanggan.
-Pie Chart Channel Penjualan
ggplot(channel_summary,
aes(x = "", y = total_sales, fill = channel)) +
geom_col(width = 1) +
coord_polar("y") +
labs(title = "Persentase Penjualan Berdasarkan Channel") +
theme_void()
Insight: Visualisasi ini menunjukkan kontribusi penjualan dari channel Online dan Offline.
-Line Chart Penjualan Bulanan
monthly_sales <- sales %>%
group_by(month) %>%
summarise(total_sales = sum(total_sales))
monthly_sales## # A tibble: 12 × 2
## month total_sales
## <ord> <dbl>
## 1 Jan 504360000
## 2 Feb 224805000
## 3 Mar 372330000
## 4 Apr 272750000
## 5 May 254480000
## 6 Jun 257615000
## 7 Jul 409795000
## 8 Aug 289410000
## 9 Sep 286980000
## 10 Oct 182720000
## 11 Nov 475990000
## 12 Dec 224935000ggplot(monthly_sales,
aes(x = month,
y = total_sales,
group = 1)) +
geom_line(size = 1.2, color = "blue") +
geom_point(size = 3, color = "red") +
labs(
title = "Trend Penjualan Bulanan",
x = "Bulan",
y = "Total Sales"
) +
theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Insight: Grafik menunjukkan perubahan penjualan setiap bulan dan membantu mengetahui periode penjualan tertinggi.
-Boxplot Harga Produk
ggplot(sales,
aes(x = product,
y = price,
fill = product)) +
geom_boxplot() +
labs(
title = "Distribusi Harga Produk",
x = "Produk",
y = "Harga"
) +
theme_minimal()
Insight: Boxplot digunakan untuk melihat persebaran harga dan mendeteksi outlier.
-Scatter Plot Quantity vs Total Sales
ggplot(sales,
aes(x = quantity,
y = total_sales,
color = channel)) +
geom_point(size = 3, alpha = 0.7) +
labs(
title = "Hubungan Quantity dan Total Sales",
x = "Quantity",
y = "Total Sales"
) +
theme_minimal()
Insight: Semakin besar quantity pembelian maka total sales cenderung meningkat.
-Density Plot
ggplot(sales,
aes(x = total_sales,
fill = channel)) +
geom_density(alpha = 0.5) +
labs(
title = "Density Plot Total Sales",
x = "Total Sales"
) +
theme_minimal()
Insight: Membandingkan distribusi sales Online vs Offline.
Menunjukkan pola penjualan tiap channel.
-Violin Plot
ggplot(sales,
aes(x = channel,
y = total_sales,
fill = channel)) +
geom_violin(trim = FALSE) +
labs(
title = "Distribusi Total Sales per Channel",
x = "Channel",
y = "Total Sales"
) +
theme_minimal()
Insight: Menunjukkan distribusi data dan kepadatan transaksi.
Membandingkan pola transaksi tiap channel.
-produk berdasarkan channel.
ggplot(sales,
aes(x = product,
fill = channel)) +
geom_bar() +
labs(
title = "Jumlah Produk Berdasarkan Channel",
x = "Produk",
y = "Jumlah"
) +
theme_minimal()
Insight: Melihat produk lebih sering dibeli lewat Online atau Offline.
-Korelasi
menghitung korelasi dan mengubah matrix
correlation_data <- sales %>%
select(quantity, price, total_sales)
correlation_matrix <- cor(correlation_data)
correlation_df <- as.data.frame(as.table(correlation_matrix))
colnames(correlation_df) <- c("Var1", "Var2", "Correlation")
correlation_df## Var1 Var2 Correlation
## 1 quantity quantity 1.00000000
## 2 price quantity 0.04212659
## 3 total_sales quantity 0.60366996
## 4 quantity price 0.04212659
## 5 price price 1.00000000
## 6 total_sales price 0.43304145
## 7 quantity total_sales 0.60366996
## 8 price total_sales 0.43304145
## 9 total_sales total_sales 1.00000000ggplot(correlation_df,
aes(x = Var1,
y = Var2,
fill = Correlation)) +
geom_tile(color = "white") +
geom_text(aes(label = round(Correlation, 2)),
color = "black",
size = 5) +
scale_fill_gradient2(
low = "blue",
mid = "white",
high = "red",
midpoint = 0
) +
labs(
title = "Heatmap Korelasi Variabel"
) +
theme_minimal()
Warna merah artinya korelasi positif kuat
Warna biru artinya
korelasi negatif
Semakin mendekati 1 artinya hubungan semakin kuat
total_sales biasanya sangat berkorelasi dengan quantity
Kesimpulan
Berdasarkan hasil EDA:
1. Dataset memiliki sedikit missing value
pada kolom channel.
2. Setelah cleaning, data siap digunakan untuk
analisis.
3. Produk dengan quantity tertinggi menjadi kategori
paling diminati.
4. Channel dengan total sales terbesar menunjukkan
metode belanja favorit pelanggan.
5. Trend penjualan bulanan dapat
digunakan untuk strategi bisnis dan promosi.
6. Visualisasi
membantu memahami pola data dengan lebih mudah.