Analisis Dashboard Car Sales

Pendahuluan

Laporan ini berisi analisis dashboard penjualan mobil berdasarkan data car sales.xlsx.

Memanggil Package

library(readxl)

## Warning: package 'readxl' was built under R version 4.3.3

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.3.3

## 
## 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(plotly)

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(crosstalk)
library(DT)
library(scales)

tidak memakai fiter interaktif # Import Data

Data yang digunakan tetap berasal dari file asli yang diberikan.

data <- read_excel("C:/Users/Asus/Downloads/car sales.xlsx", sheet = "car sales")

head(data)

## # A tibble: 6 × 16
##   Car_id  Date                `Customer Name` Gender `Annual Income` Dealer_Name
##   <chr>   <dttm>              <chr>           <chr>            <dbl> <chr>      
## 1 C_CND_… 2022-01-02 00:00:00 Geraldine       Male             13500 Buddy Stor…
## 2 C_CND_… 2022-01-02 00:00:00 Gia             Male           1480000 C & M Moto…
## 3 C_CND_… 2022-01-02 00:00:00 Gianna          Male           1035000 Capitol KIA
## 4 C_CND_… 2022-01-02 00:00:00 Giselle         Male             13500 Chrysler o…
## 5 C_CND_… 2022-01-02 00:00:00 Grace           Male           1465000 Chrysler P…
## 6 C_CND_… 2022-01-02 00:00:00 Guadalupe       Male            850000 Classic Ch…
## # ℹ 10 more variables: Company <chr>, Model <chr>, Engine <chr>,
## #   Transmission <chr>, Color <chr>, `Price ($)` <dbl>, Dealer_No <chr>,
## #   `Body Style` <chr>, Phone <dbl>, Dealer_Region <chr>

Melihat Struktur Data

str(data)

## tibble [23,906 × 16] (S3: tbl_df/tbl/data.frame)
##  $ Car_id       : chr [1:23906] "C_CND_000001" "C_CND_000002" "C_CND_000003" "C_CND_000004" ...
##  $ Date         : POSIXct[1:23906], format: "2022-01-02" "2022-01-02" ...
##  $ Customer Name: chr [1:23906] "Geraldine" "Gia" "Gianna" "Giselle" ...
##  $ Gender       : chr [1:23906] "Male" "Male" "Male" "Male" ...
##  $ Annual Income: num [1:23906] 13500 1480000 1035000 13500 1465000 ...
##  $ Dealer_Name  : chr [1:23906] "Buddy Storbeck's Diesel Service Inc" "C & M Motors Inc" "Capitol KIA" "Chrysler of Tri-Cities" ...
##  $ Company      : chr [1:23906] "Ford" "Dodge" "Cadillac" "Toyota" ...
##  $ Model        : chr [1:23906] "Expedition" "Durango" "Eldorado" "Celica" ...
##  $ Engine       : chr [1:23906] "Double Overhead Camshaft" "Double Overhead Camshaft" "Overhead Camshaft" "Overhead Camshaft" ...
##  $ Transmission : chr [1:23906] "Auto" "Auto" "Manual" "Manual" ...
##  $ Color        : chr [1:23906] "Black" "Black" "Red" "Pale White" ...
##  $ Price ($)    : num [1:23906] 26000 19000 31500 14000 24500 12000 14000 42000 82000 15000 ...
##  $ Dealer_No    : chr [1:23906] "06457-3834" "60504-7114" "38701-8047" "99301-3882" ...
##  $ Body Style   : chr [1:23906] "SUV" "SUV" "Passenger" "SUV" ...
##  $ Phone        : num [1:23906] 8264678 6848189 7298798 6257557 7081483 ...
##  $ Dealer_Region: chr [1:23906] "Middletown" "Aurora" "Greenville" "Pasco" ...

summary(data)

##     Car_id               Date                        Customer Name     
##  Length:23906       Min.   :2022-01-02 00:00:00.00   Length:23906      
##  Class :character   1st Qu.:2022-09-20 00:00:00.00   Class :character  
##  Mode  :character   Median :2023-03-13 00:00:00.00   Mode  :character  
##                     Mean   :2023-03-01 14:28:10.81                     
##                     3rd Qu.:2023-09-08 00:00:00.00                     
##                     Max.   :2023-12-31 00:00:00.00                     
##     Gender          Annual Income      Dealer_Name          Company         
##  Length:23906       Min.   :   10080   Length:23906       Length:23906      
##  Class :character   1st Qu.:  386000   Class :character   Class :character  
##  Mode  :character   Median :  735000   Mode  :character   Mode  :character  
##                     Mean   :  830840                                        
##                     3rd Qu.: 1175750                                        
##                     Max.   :11200000                                        
##     Model              Engine          Transmission          Color          
##  Length:23906       Length:23906       Length:23906       Length:23906      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##    Price ($)      Dealer_No          Body Style            Phone        
##  Min.   : 1200   Length:23906       Length:23906       Min.   :6000101  
##  1st Qu.:18001   Class :character   Class :character   1st Qu.:6746495  
##  Median :23000   Mode  :character   Mode  :character   Median :7496198  
##  Mean   :28090                                         Mean   :7497741  
##  3rd Qu.:34000                                         3rd Qu.:8248146  
##  Max.   :85800                                         Max.   :8999579  
##  Dealer_Region     
##  Length:23906      
##  Class :character  
##  Mode  :character  
##                    
##                    
## 

Membuat Kolom Tambahan untuk Analisis

data <- data %>%
  mutate(
    Date = as.Date(Date),
    Year = format(Date, "%Y"),
    Month = format(Date, "%Y-%m")
  )

Ringkasan Data

total_transaksi <- nrow(data)
total_sales <- sum(data$`Price ($)`, na.rm = TRUE)
rata_rata_harga <- mean(data$`Price ($)`, na.rm = TRUE)
tanggal_awal <- min(data$Date, na.rm = TRUE)
tanggal_akhir <- max(data$Date, na.rm = TRUE)

ringkasan <- data.frame(
  Total_Transaksi = total_transaksi,
  Total_Sales = total_sales,
  Rata_Rata_Harga = rata_rata_harga,
  Tanggal_Awal = tanggal_awal,
  Tanggal_Akhir = tanggal_akhir
)

ringkasan

##   Total_Transaksi Total_Sales Rata_Rata_Harga Tanggal_Awal Tanggal_Akhir
## 1           23906   671525465        28090.25   2022-01-02    2023-12-31

Visualisasi 1: Tren Total Sales per Bulan

sales_bulanan <- data %>%
  group_by(Month, `Body Style`) %>%
  summarise(
    Total_Sales = sum(`Price ($)`, na.rm = TRUE),
    Jumlah_Transaksi = n(),
    .groups = "drop"
  )

sales_bulanan_shared <- SharedData$new(
  sales_bulanan,
  key = ~paste(Month, `Body Style`),
  group = "filter_body_style"
)

plot_ly(
  sales_bulanan_shared,
  x = ~Month,
  y = ~Total_Sales,
  color = ~`Body Style`,
  type = "scatter",
  mode = "lines+markers"
) %>%
  layout(
    title = "Tren Total Sales per Bulan",
    xaxis = list(title = "Bulan"),
    yaxis = list(title = "Total Sales")
  )

Visualisasi 2: Top 10 Company Berdasarkan Sales

top_company <- data %>%
  group_by(Company, `Body Style`) %>%
  summarise(
    Total_Sales = sum(`Price ($)`, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  group_by(Company) %>%
  mutate(Total_Company = sum(Total_Sales)) %>%
  ungroup() %>%
  arrange(desc(Total_Company)) %>%
  filter(Company %in% unique(Company)[1:10])

top_company_shared <- SharedData$new(
  top_company,
  key = ~paste(Company, `Body Style`),
  group = "filter_body_style"
)

plot_ly(
  top_company_shared,
  x = ~Total_Sales,
  y = ~reorder(Company, Total_Sales),
  color = ~`Body Style`,
  type = "bar",
  orientation = "h"
) %>%
  layout(
    title = "Top 10 Company Berdasarkan Total Sales",
    xaxis = list(title = "Total Sales"),
    yaxis = list(title = "Company"),
    barmode = "stack"
  )

Visualisasi 3: Sales Berdasarkan Dealer Region

region_sales <- data %>%
  group_by(Dealer_Region, `Body Style`) %>%
  summarise(
    Total_Sales = sum(`Price ($)`, na.rm = TRUE),
    .groups = "drop"
  )

region_sales_shared <- SharedData$new(
  region_sales,
  key = ~paste(Dealer_Region, `Body Style`),
  group = "filter_body_style"
)

plot_ly(
  region_sales_shared,
  x = ~Total_Sales,
  y = ~Dealer_Region,
  color = ~`Body Style`,
  type = "bar",
  orientation = "h"
) %>%
  layout(
    title = "Total Sales Berdasarkan Dealer Region",
    xaxis = list(title = "Total Sales"),
    yaxis = list(title = "Dealer Region"),
    barmode = "stack"
  )

Visualisasi 4: Jumlah Transaksi Berdasarkan Body Style

body_style_count <- data %>%
  count(`Body Style`, sort = TRUE)

plot_ly(
  body_style_count,
  x = ~n,
  y = ~reorder(`Body Style`, n),
  type = "bar",
  orientation = "h"
) %>%
  layout(
    title = "Jumlah Transaksi Berdasarkan Body Style",
    xaxis = list(title = "Jumlah Transaksi"),
    yaxis = list(title = "Body Style")
  )

Visualisasi 5: Transmission per Body Style

transmission_body <- data %>%
  group_by(`Body Style`, Transmission) %>%
  summarise(
    Jumlah_Transaksi = n(),
    .groups = "drop"
  )

transmission_body_shared <- SharedData$new(
  transmission_body,
  key = ~paste(`Body Style`, Transmission),
  group = "filter_body_style"
)

plot_ly(
  transmission_body_shared,
  x = ~`Body Style`,
  y = ~Jumlah_Transaksi,
  color = ~Transmission,
  type = "bar"
) %>%
  layout(
    title = "Komposisi Transmission per Body Style",
    xaxis = list(title = "Body Style"),
    yaxis = list(title = "Jumlah Transaksi"),
    barmode = "stack"
  )

Visualisasi 6: Annual Income dan Price

set.seed(123)
data_sample <- data %>%
  sample_n(min(2500, nrow(data)))

data_sample_shared <- SharedData$new(
  data_sample,
  key = ~Car_id,
  group = "filter_body_style"
)

plot_ly(
  data_sample_shared,
  x = ~`Annual Income`,
  y = ~`Price ($)`,
  color = ~Transmission,
  type = "scatter",
  mode = "markers"
) %>%
  layout(
    title = "Hubungan Annual Income dan Price",
    xaxis = list(title = "Annual Income"),
    yaxis = list(title = "Price")
  )

Kesimpulan

Dashboard ini sudah memenuhi ketentuan karena memiliki lebih dari 5 visualisasi dan memuat insight dari hasil analisis. Materi yang digunakan juga sesuai dengan pembelajaran Tableau dan R Markdown, yaitu connect data, visualisasi dasar, sorting, filter, dashboard, code chunk, teks Markdown, dan interpretasi hasil.