Description

This is my compile markdown with r studio and this is a compile provides detailed data sales information for BMW vehicles from 2010 to 2024 across global regions. It includes attributes such as model, year, engine size, mileage, transmission type, fuel type, price, and sales volume.
Sumber Pengambilan DataSet Kaggle.com
For Compile File in word.docx go to github

Let’s load some data

library(readxl)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.1     ✔ stringr   1.5.2
## ✔ ggplot2   4.0.0     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
BMW_sales_data_2010_2024_ <- read_excel("C:/Users/Mareko/Downloads/archive (7)/BMW sales data (2010-2024).xlsx")
View(BMW_sales_data_2010_2024_)
print(BMW_sales_data_2010_2024_)
## # A tibble: 50,000 × 12
##    Model     Year Region   Color Fuel_Type Transmission Engine_Size_L Mileage_KM
##    <chr>    <dbl> <chr>    <chr> <chr>     <chr>        <chr>              <dbl>
##  1 i8        2010 South A… Black Hybrid    Automatic    4.7                16020
##  2 i8        2010 Middle … Grey  Petrol    Automatic    4.0                98514
##  3 X6        2010 Europe   Red   Hybrid    Manual       3.8               128477
##  4 i8        2010 North A… White Electric  Automatic    2.5                75457
##  5 X6        2010 Africa   Silv… Petrol    Manual       1.7               176650
##  6 M5        2010 South A… White Diesel    Manual       2.8               121393
##  7 3 Series  2010 Asia     Black Petrol    Manual       2.1               107572
##  8 5 Series  2010 Europe   Red   Petrol    Manual       1.8               194101
##  9 i3        2010 Africa   Blue  Petrol    Manual       3.6                91061
## 10 i3        2010 Africa   Blue  Diesel    Manual       1.8               120482
## # ℹ 49,990 more rows
## # ℹ 4 more variables: Price_USD <dbl>, Sales_Volume <dbl>,
## #   Sales_Classification <chr>, New_Count <dbl>
summary(BMW_sales_data_2010_2024_)
##     Model                Year         Region             Color          
##  Length:50000       Min.   :2010   Length:50000       Length:50000      
##  Class :character   1st Qu.:2013   Class :character   Class :character  
##  Mode  :character   Median :2017   Mode  :character   Mode  :character  
##                     Mean   :2017                                        
##                     3rd Qu.:2021                                        
##                     Max.   :2024                                        
##   Fuel_Type         Transmission       Engine_Size_L        Mileage_KM    
##  Length:50000       Length:50000       Length:50000       Min.   :     3  
##  Class :character   Class :character   Class :character   1st Qu.: 50178  
##  Mode  :character   Mode  :character   Mode  :character   Median :100389  
##                                                           Mean   :100307  
##                                                           3rd Qu.:150630  
##                                                           Max.   :199996  
##    Price_USD       Sales_Volume  Sales_Classification   New_Count
##  Min.   : 30000   Min.   : 100   Length:50000         Min.   :1  
##  1st Qu.: 52435   1st Qu.:2588   Class :character     1st Qu.:1  
##  Median : 75012   Median :5087   Mode  :character     Median :1  
##  Mean   : 75035   Mean   :5068                        Mean   :1  
##  3rd Qu.: 97628   3rd Qu.:7537                        3rd Qu.:1  
##  Max.   :119998   Max.   :9999                        Max.   :1



This Data is to much for us to compile so, I create or minimalize to 100 data with this code :

BMWSD = head(BMW_sales_data_2010_2024_, 100)
print(BMWSD)
## # A tibble: 100 × 12
##    Model     Year Region   Color Fuel_Type Transmission Engine_Size_L Mileage_KM
##    <chr>    <dbl> <chr>    <chr> <chr>     <chr>        <chr>              <dbl>
##  1 i8        2010 South A… Black Hybrid    Automatic    4.7                16020
##  2 i8        2010 Middle … Grey  Petrol    Automatic    4.0                98514
##  3 X6        2010 Europe   Red   Hybrid    Manual       3.8               128477
##  4 i8        2010 North A… White Electric  Automatic    2.5                75457
##  5 X6        2010 Africa   Silv… Petrol    Manual       1.7               176650
##  6 M5        2010 South A… White Diesel    Manual       2.8               121393
##  7 3 Series  2010 Asia     Black Petrol    Manual       2.1               107572
##  8 5 Series  2010 Europe   Red   Petrol    Manual       1.8               194101
##  9 i3        2010 Africa   Blue  Petrol    Manual       3.6                91061
## 10 i3        2010 Africa   Blue  Diesel    Manual       1.8               120482
## # ℹ 90 more rows
## # ℹ 4 more variables: Price_USD <dbl>, Sales_Volume <dbl>,
## #   Sales_Classification <chr>, New_Count <dbl>
summary(BMWSD)
##     Model                Year         Region             Color          
##  Length:100         Min.   :2010   Length:100         Length:100        
##  Class :character   1st Qu.:2010   Class :character   Class :character  
##  Mode  :character   Median :2010   Mode  :character   Mode  :character  
##                     Mean   :2010                                        
##                     3rd Qu.:2010                                        
##                     Max.   :2010                                        
##   Fuel_Type         Transmission       Engine_Size_L        Mileage_KM    
##  Length:100         Length:100         Length:100         Min.   :  3514  
##  Class :character   Class :character   Class :character   1st Qu.: 46205  
##  Mode  :character   Mode  :character   Mode  :character   Median :105041  
##                                                           Mean   :103430  
##                                                           3rd Qu.:152912  
##                                                           Max.   :199290  
##    Price_USD       Sales_Volume  Sales_Classification   New_Count
##  Min.   : 30946   Min.   : 184   Length:100           Min.   :1  
##  1st Qu.: 48255   1st Qu.:2706   Class :character     1st Qu.:1  
##  Median : 67198   Median :5824   Mode  :character     Median :1  
##  Mean   : 72359   Mean   :5499                        Mean   :1  
##  3rd Qu.: 99819   3rd Qu.:8095                        3rd Qu.:1  
##  Max.   :117253   Max.   :9996                        Max.   :1

Basicly I just take a 2010 sample for my presentation, why? because the minimal data is 2010 in year section on the max side is also 2010


The Main Focus

MILEAGE(KM), PRICE(USD), and SALES VOLUME

MILEAGE KILOMETER

This will shows you the mean, median, and mode

summary(BMWSD$Mileage_KM)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3514   46205  105041  103430  152912  199290
mean(BMWSD$Mileage_KM)
## [1] 103430
median(BMWSD$Mileage_KM)
## [1] 105041
names(sort(-table(BMWSD$Mileage_KM)))[1]
## [1] "191728"

PRICE USD

This will shows you the mean, median, and mode

summary(BMWSD$Price_USD)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   30946   48255   67198   72359   99819  117253
mean(BMWSD$Price_USD)
## [1] 72358.59
median(BMWSD$Price_USD)
## [1] 67198
names(sort(-table(BMWSD$Price_USD)))[1]
## [1] "30946"

SALES VOLUME

This will shows you the mean, median, and mode

summary(BMWSD$Sales_Volume)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     184    2706    5824    5499    8095    9996
mean(BMWSD$Sales_Volume)
## [1] 5499.05
median(BMWSD$Sales_Volume)
## [1] 5824
names(sort(-table(BMWSD$Sales_Volume)))[1]
## [1] "184"

The Influence of car mileage on car price

Bar Chart and Scatterplot showing the relationship between car price and the km on the car.

price_car <- c(BMWSD$Price_USD)
cars <- c(BMWSD$Model)
mileage <- c(BMWSD$Mileage_KM)

barplot(price_car, names.arg = cars, col = "blue",main = "BMW Sales 2010") 

plot(mileage, price_car,col = "blue", main = "BMW Sales 2010")


plot(mileage, price_car, col = "blue", main = "BMW Sales 2010")

model <- lm(price_car ~ mileage)
abline(model, col = "red", lwd = 2)

Analisis Hubungan Mileage dan Harga Mobil BMW (2010)

Hasil analisis dari scatterplot yang sudah dibuat menunjukkan korelasi yang hampir tidak memiliki korelasinya pada data. \(R^2 = 0,0164\) menunjukkan korelasinya tidak ada, itu disebabkan penjualan produk dari series BMW tahun 2010 bukan sebab bagaimana kondisinya tetapi langka dan popularitasnya menjadikan perusahaan BMW menampilkan performa yang baik dengan menciptakan mobil yang dapat penggunanya merasa nyaman, bukan sekedar melihat mileage(km) oleh karena itu hasilnya bahkan tidak memiliki korelasi yang kuat

PIE CHART

PIVOT TABLE

This code will show the frequencies transmission appearing on 2010 sales BMW’s Cars.

library(pivottabler)

pivot <- qpvt(BMWSD,"Model","Transmission","sum(New_Count)")
print(pivot)
##           Automatic  Manual  Total  
## 3 Series          3       2      5  
## 5 Series          4       7     11  
## 7 Series          6       4     10  
## M3                6       4     10  
## M5                5       5     10  
## X1                5       9     14  
## X3                5       2      7  
## X5                6       4     10  
## X6                2       5      7  
## i3                2       4      6  
## i8                9       1     10  
## Total            53      47    100
Automatic = c(3,4,6,2,9,6,5,5,5,6,2)
Model_Series = c("3 Series","5 Series","7 Series","i3","i8","M3","M5","X1","X3","X5","X6")
pct <- round(Automatic/sum(Automatic)*100)
Model_Series <- paste(Model_Series,"=", pct)

Model_Series <- paste(Model_Series, "%",sep = "")
pie(Automatic, labels = Model_Series,col = rainbow(length(Model_Series)), main = "Presentage Automatic/Matic")

Automatic = c(2,7,4,4,1,4,5,9,2,4,5)
Model_Series = c("3 Series","5 Series","7 Series","i3","i8","M3","M5","X1","X3","X5","X6")
pct <- round(Automatic/sum(Automatic)*100)
Model_Series <- paste(Model_Series,"=", pct)

Model_Series <- paste(Model_Series, "%",sep = "")
pie(Automatic, labels = Model_Series,col = rainbow(length(Model_Series)), main = "Presentage Manual")

Transmisi Automatic/ Matic

Dari diagram “Presentase Automatic/Matic”, tiga model dengan proporsi penjualan tertinggi adalah:

  1. i8 (17%) – Menjadi model paling populer pada kategori transmisi matic. Hal ini menunjukkan bahwa varian i8 dengan sistem transmisi otomatis sangat diminati, mungkin karena performa tinggi dan kenyamanan berkendara khas model ini.

  2. 7 Series (11%) dan X6 (11%) – Keduanya berbagi posisi kedua, menggambarkan preferensi konsumen terhadap seri premium dan SUV besar dengan transmisi otomatis.

  3. X1 dan M5 (masing-masing 9%) – Menempati posisi ketiga, memperlihatkan bahwa mobil sport dan crossover juga memiliki pangsa signifikan di segmen matic.


Transmisi Manual

Sementara pada diagram “Presentase Manual”, tiga model dengan penjualan tertinggi adalah:

  1. X1 (19%) – Mendominasi segmen manual, menunjukkan bahwa SUV kompak ini populer di kalangan pengemudi yang lebih menyukai kendali manual.

  2. 5 Series (15%) – Berada di posisi kedua, memperlihatkan bahwa sedan eksekutif dengan transmisi manual masih memiliki peminat setia.

  3. X6 (11%) – Menempati posisi ketiga, menandakan SUV besar dengan transmisi manual juga cukup diminati, meski tidak sebanyak versi matic-nya.

PIVOT TABLE

This code will show you the frequencies of the fuel type in each series/model of BMW’s Cars on 2010 sales.

library(pivottabler)

pivot <- qpvt(BMWSD,"Model","Fuel_Type","sum(New_Count)")
print(pivot)
##           Diesel  Electric  Hybrid  Petrol  Total  
## 3 Series       3                 1       1      5  
## 5 Series       3         3       3       2     11  
## 7 Series       2         2       2       4     10  
## M3             1         3       3       3     10  
## M5             3         4       2       1     10  
## X1             7         3       2       2     14  
## X3             2         3       2              7  
## X5             6                 2       2     10  
## X6                       1       2       4      7  
## i3             1                 2       3      6  
## i8             2         2       3       3     10  
## Total         30        21      24      25    100
Diesel = c(3,3,2,1,2,1,3,7,2,6,0)
Model_Series = c("3 Series","5 Series","7 Series","i3","i8","M3","M5","X1","X3","X5","X6")
pct <- round(Diesel/sum(Diesel)*100)
Model_Series <- paste(Model_Series,"=", pct)

Model_Series <- paste(Model_Series, "%",sep = "")
pie(Automatic, labels = Model_Series,col = rainbow(length(Model_Series)), main = "Diesel")

Hybd = c(1,3,2,2,3,3,2,2,2,2,2)
Model_Series = c("3 Series","5 Series","7 Series","i3","i8","M3","M5","X1","X3","X5","X6")
pct <- round(Hybd/sum(Hybd)*100)
Model_Series <- paste(Model_Series,"=", pct)

Model_Series <- paste(Model_Series, "%",sep = "")
pie(Automatic, labels = Model_Series,col = rainbow(length(Model_Series)), main = "Hybrid")

Petrol = c(1,2,4,3,3,3,1,2,0,2,4)
Model_Series = c("3 Series","5 Series","7 Series","i3","i8","M3","M5","X1","X3","X5","X6")
pct <- round(Petrol/sum(Petrol)*100)
Model_Series <- paste(Model_Series,"=", pct)

Model_Series <- paste(Model_Series, "%",sep = "")
pie(Automatic, labels = Model_Series,col = rainbow(length(Model_Series)), main = "Petrol")

Electric = c(0,3,2,0,2,3,4,3,3,0,1)
Model_Series = c("3 Series","5 Series","7 Series","i3","i8","M3","M5","X1","X3","X5","X6")
pct <- round(Electric/sum(Electric)*100)
Model_Series <- paste(Model_Series,"=", pct)

Model_Series <- paste(Model_Series, "%",sep = "")
pie(Automatic, labels = Model_Series,col = rainbow(length(Model_Series)), main = "Electric")