1. Import Data

setwd("C:/Users/georg/Downloads")
cars_data <- read.csv("Large Cars Dataset.csv", stringsAsFactors = FALSE)

2. Eksplorasi Awal

str(cars_data)
## 'data.frame':    428 obs. of  15 variables:
##  $ Brand       : chr  "Acura" "Acura" "Acura" "Acura" ...
##  $ Model       : chr  "MDX" "RSX Type S 2dr" "TSX 4dr" "TL 4dr" ...
##  $ VehicleClass: chr  "SUV" "Sedan" "Sedan" "Sedan" ...
##  $ Region      : chr  "Asia" "Asia" "Asia" "Asia" ...
##  $ DriveTrain  : chr  "All" "Front" "Front" "Front" ...
##  $ MSRP        : chr  "$36,945 " "$23,820 " "$26,990 " "$33,195 " ...
##  $ DealerCost  : chr  "$33,337 " "$21,761 " "$24,647 " "$30,299 " ...
##  $ EngineSize  : num  3.5 2 2.4 3.2 3.5 3.5 3.2 1.8 1.8 3 ...
##  $ Cylinders   : int  6 4 4 6 6 6 6 4 4 6 ...
##  $ HorsePower  : int  265 200 200 270 225 225 290 170 170 220 ...
##  $ MPG_City    : int  17 24 22 20 18 18 17 22 23 20 ...
##  $ MPG_Highway : int  23 31 29 28 24 24 24 31 30 28 ...
##  $ Weight      : int  4451 2778 3230 3575 3880 3893 3153 3252 3638 3462 ...
##  $ Wheelbase   : int  106 101 105 108 115 115 100 104 105 104 ...
##  $ Length      : int  189 172 183 186 197 197 174 179 180 179 ...
head(cars_data)
##   Brand                   Model VehicleClass Region DriveTrain     MSRP
## 1 Acura                     MDX          SUV   Asia        All $36,945 
## 2 Acura          RSX Type S 2dr        Sedan   Asia      Front $23,820 
## 3 Acura                 TSX 4dr        Sedan   Asia      Front $26,990 
## 4 Acura                  TL 4dr        Sedan   Asia      Front $33,195 
## 5 Acura              3.5 RL 4dr        Sedan   Asia      Front $43,755 
## 6 Acura 3.5 RL w/Navigation 4dr        Sedan   Asia      Front $46,100 
##   DealerCost EngineSize Cylinders HorsePower MPG_City MPG_Highway Weight
## 1   $33,337         3.5         6        265       17          23   4451
## 2   $21,761         2.0         4        200       24          31   2778
## 3   $24,647         2.4         4        200       22          29   3230
## 4   $30,299         3.2         6        270       20          28   3575
## 5   $39,014         3.5         6        225       18          24   3880
## 6   $41,100         3.5         6        225       18          24   3893
##   Wheelbase Length
## 1       106    189
## 2       101    172
## 3       105    183
## 4       108    186
## 5       115    197
## 6       115    197
summary(cars_data)
##     Brand              Model           VehicleClass          Region         
##  Length:428         Length:428         Length:428         Length:428        
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##   DriveTrain            MSRP            DealerCost          EngineSize   
##  Length:428         Length:428         Length:428         Min.   :1.300  
##  Class :character   Class :character   Class :character   1st Qu.:2.375  
##  Mode  :character   Mode  :character   Mode  :character   Median :3.000  
##                                                           Mean   :3.197  
##                                                           3rd Qu.:3.900  
##                                                           Max.   :8.300  
##                                                                          
##    Cylinders        HorsePower       MPG_City      MPG_Highway   
##  Min.   : 3.000   Min.   : 73.0   Min.   :10.00   Min.   :12.00  
##  1st Qu.: 4.000   1st Qu.:165.0   1st Qu.:17.00   1st Qu.:24.00  
##  Median : 6.000   Median :210.0   Median :19.00   Median :26.00  
##  Mean   : 5.808   Mean   :215.9   Mean   :20.06   Mean   :26.84  
##  3rd Qu.: 6.000   3rd Qu.:255.0   3rd Qu.:21.25   3rd Qu.:29.00  
##  Max.   :12.000   Max.   :500.0   Max.   :60.00   Max.   :66.00  
##  NA's   :2                                                       
##      Weight       Wheelbase         Length     
##  Min.   :1850   Min.   : 89.0   Min.   :143.0  
##  1st Qu.:3104   1st Qu.:103.0   1st Qu.:178.0  
##  Median :3474   Median :107.0   Median :187.0  
##  Mean   :3578   Mean   :108.2   Mean   :186.4  
##  3rd Qu.:3978   3rd Qu.:112.0   3rd Qu.:194.0  
##  Max.   :7190   Max.   :144.0   Max.   :238.0  
##

Insight: Dataset ini berisi informasi tentang spesifikasi mobil seperti MSRP, dealer cost, efisiensi bahan bakar, horsepower, engine size, dan lainnya. Data perlu dibersihkan karena format MSRP dan DealerCost menggunakan simbol dolar dan koma.

3. Pembersihan Data

cars_data <- cars_data %>%
  mutate(
    MSRP = as.numeric(gsub("[\\$,]", "", MSRP)),
    DealerCost = as.numeric(gsub("[\\$,]", "", DealerCost)),
    Price_Category = case_when(
      MSRP > 50000 ~ "High-End",
      MSRP > 25000 ~ "Mid-Range",
      TRUE ~ "Budget"
    )
  )

Insight: MSRP dan DealerCost diubah menjadi numerik agar bisa dianalisis. Kategori harga dibuat berdasarkan MSRP.

4. Persiapan untuk Visualisasi

cars_data_long <- cars_data %>%
  mutate(
    Price_Category = factor(Price_Category, levels = c("Budget", "Mid-Range", "High-End"), ordered = TRUE),
    MPG_Group = cut(MPG_City, breaks = c(0, 15, 25, 35, 45, Inf),
                    labels = c("0-15", "15-25", "25-35", "35-45", "45+"),
                    include.lowest = TRUE)
  ) %>%
  filter(!is.na(MSRP), !is.na(MPG_City))

Insight: Kita mengelompokkan efisiensi bahan bakar (MPG_City) ke dalam 5 kelompok. Data difilter untuk menghindari nilai NA yang mengganggu visualisasi.

5. Visualisasi dan Insight

A. Hubungan Harga dan Efisiensi Bahan Bakar

ggplot(cars_data_long, aes(x = MSRP / 1000, y = MPG_City)) +
  geom_point(aes(color = MPG_Group), alpha = 0.8, size = 3) +
  geom_smooth(method = "loess", se = FALSE, linewidth = 1.5, color = "blue") +
  scale_color_manual(values = c("#1a1a1a", "#4d4d4d", "#800000", "#b30000", "#ff0000")) +
  facet_wrap(~ Price_Category, nrow = 1) +
  scale_x_continuous(labels = dollar_format(prefix = "$", suffix = "k")) +
  scale_y_continuous(limits = c(0, 50)) +
  labs(title = "Efisiensi vs Harga Mobil", x = "Harga (ribuan USD)", y = "MPG Kota") +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).

Insight: Mobil di kategori harga tinggi cenderung memiliki efisiensi bahan bakar lebih rendah, kemungkinan karena mesin yang lebih besar dan performa tinggi.

B. Efisiensi Berdasarkan Drive Type

ggplot(cars_data_long, aes(x = DriveTrain, y = MPG_City, fill = DriveTrain)) +
  geom_boxplot() +
  labs(title = "MPG Berdasarkan Drive Type", x = "Drive Type", y = "MPG") +
  theme_minimal()

Insight: Mobil dengan penggerak FWD umumnya lebih efisien dibanding AWD.

C. Horsepower Berdasarkan Drive Type

ggplot(cars_data_long, aes(x = DriveTrain, y = HorsePower, fill = DriveTrain)) +
  geom_boxplot() +
  labs(title = "Horsepower Berdasarkan Drive Type", x = "Drive Type", y = "Horsepower") +
  theme_minimal()

Insight: Mobil AWD cenderung memiliki horsepower yang lebih tinggi.

D. Harga vs Horsepower

ggplot(cars_data_long, aes(x = MSRP, y = HorsePower, color = Brand)) +
  geom_point(alpha = 0.7, size = 3) +
  labs(title = "Harga vs Horsepower", x = "Harga (USD)", y = "Horsepower") +
  theme_minimal()

Insight: Ada korelasi positif antara harga dan horsepower.

E. Harga vs Engine Size

ggplot(cars_data_long, aes(x = MSRP, y = EngineSize, color = Brand)) +
  geom_point(alpha = 0.6, size = 3) +
  geom_smooth(method = "lm", se = FALSE, color = "black") +
  labs(title = "Harga vs Ukuran Mesin", x = "Harga (USD)", y = "Engine Size (L)") +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

Insight: Mobil dengan mesin lebih besar biasanya lebih mahal.

F. Distribusi Harga dan MPG per Brand

ggplot(cars_data_long, aes(x = reorder(Brand, MSRP, FUN = median), y = MSRP, fill = Brand)) +
  geom_boxplot() +
  coord_flip() +
  labs(title = "Distribusi Harga per Brand", x = "Brand", y = "Harga (USD)") +
  theme_minimal() +
  theme(legend.position = "none")

ggplot(cars_data_long, aes(x = reorder(Brand, MPG_City, FUN = median), y = MPG_City, fill = Brand)) +
  geom_boxplot() +
  coord_flip() +
  labs(title = "Distribusi MPG per Brand", x = "Brand", y = "MPG Kota") +
  theme_minimal() +
  theme(legend.position = "none")

Insight: Beberapa brand fokus pada efisiensi, sedangkan yang lain fokus pada performa.

6. Kesimpulan