Homework1_EK
Eneja Kristof
2023-01-04
mydata <- read_excel("C:/Users/Eneja/Desktop/Electircal_Cars.xlsx")
head(mydata)## # A tibble: 6 × 9
## Brand Model `Capacity (kWh)` `Acceleration (sec)` TopSpeed (km…¹ Range…² Effic…³ Drive Price…⁴
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 Opel Ampera-e 58 7.3 150 335 173 Fron… 42990
## 2 Renault Kangoo Maxi ZE 33 31 22.4 130 160 194 Fron… 0
## 3 Nissan Leaf 36 7.9 144 220 164 Fron… 29990
## 4 Audi e-tron Sportback 55 quattro 86.5 5.7 200 375 231 All … 0
## 5 Porsche Taycan Turbo S 83.7 2.8 260 390 215 All … 186336
## 6 Nissan e-NV200 Evalia 36 14 123 165 218 Fron… 43433
## # … with abbreviated variable names ¹`TopSpeed (km/h)`, ²`Range (km)`, ³`Efficiency (Wh/km)`, ⁴`PriceinGer (€)`colnames(mydata) <- c("Brand","Model","Capacity","Acceleration","Top_Speed","Range","Efficiency","Drive", "Price_Germany")mydata$DriveF <- factor(mydata$Drive,
levels = c( "Front Wheel Drive", "Rear Wheel Drive", "All Wheel Drive"),
labels = c(1,2,3))mydata1 <- mydata %>%
replace_with_na(replace = list(Price_Germany = c(0)))mydata1 <- tidyr::drop_na(mydata1)mydata1 <- mydata1[c(-4,-5)]head(mydata1)## # A tibble: 6 × 8
## Brand Model Capacity Range Efficiency Drive Price_Germany DriveF
## <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <fct>
## 1 Opel Ampera-e 58 335 173 Front Wheel Drive 42990 1
## 2 Nissan Leaf 36 220 164 Front Wheel Drive 29990 1
## 3 Porsche Taycan Turbo S 83.7 390 215 All Wheel Drive 186336 3
## 4 Nissan e-NV200 Evalia 36 165 218 Front Wheel Drive 43433 1
## 5 Volkswagen ID.3 Pure Performance 45 275 164 Rear Wheel Drive 31960 2
## 6 BMW iX3 74 385 192 Rear Wheel Drive 66300 2str(mydata1)## tibble [168 × 8] (S3: tbl_df/tbl/data.frame)
## $ Brand : chr [1:168] "Opel" "Nissan" "Porsche" "Nissan" ...
## $ Model : chr [1:168] "Ampera-e" "Leaf" "Taycan Turbo S" "e-NV200 Evalia" ...
## $ Capacity : num [1:168] 58 36 83.7 36 45 74 56 37.9 66.5 45 ...
## $ Range : num [1:168] 335 220 390 165 275 385 325 235 355 250 ...
## $ Efficiency : num [1:168] 173 164 215 218 164 192 172 161 187 180 ...
## $ Drive : chr [1:168] "Front Wheel Drive" "Front Wheel Drive" "All Wheel Drive" "Front Wheel Drive" ...
## $ Price_Germany: num [1:168] 42990 29990 186336 43433 31960 ...
## $ DriveF : Factor w/ 3 levels "1","2","3": 1 1 3 1 2 2 1 2 1 1 ...Description:
- Brand: Vehicle manufacturer
- Model: Vehicle model
- Capacity: Battery storage capacity in kWh
- Acceleration: time to reach 100 km/h in seconds
- Top_Speed: Maximum travel speed in km/h
- Range: Nominal value of maximal traveled distance in km
- Efficiency: Ratio between capacity and range in Wh/km
- Drive: type of drive front/rear/all wheel drive
- Price_Germany: Estimated value of vehicle in Germany in € in 2022
Unit of observation: BEV car model in Germany in 2022
dim(mydata1)## [1] 168 8Sample size: 168 (without missing values)
Source of the data: Kaggle- Cheapest Electric Cars https://www.kaggle.com/datasets/kkhandekar/cheapest-electric-cars
The main goal of the data analysis is to see how variables like efficiency, drive type and maximum range of BEV vehicle affect the price of the BEV car in Germany.
summary(mydata1[c(-1,-2,-6)])## Capacity Range Efficiency Price_Germany DriveF
## Min. : 16.70 Min. : 95.0 Min. :104.0 Min. : 18460 1:64
## 1st Qu.: 45.00 1st Qu.:258.8 1st Qu.:168.0 1st Qu.: 38000 2:44
## Median : 66.50 Median :340.0 Median :189.0 Median : 50648 3:60
## Mean : 65.57 Mean :338.4 Mean :194.8 Mean : 58725
## 3rd Qu.: 77.40 3rd Qu.:400.0 3rd Qu.:216.2 3rd Qu.: 64648
## Max. :200.00 Max. :970.0 Max. :281.0 Max. :215000Average Capacity of BEV vehicle was 65.57 kWh. Average Efficiency of BEV vehicle was 194.8 Wh/km. Average Range of BEV vehicles was 338.4 km. Average Price of BEV vehicle in Germany was 58725€.
Median for Capacity is 66.50 kWh- half of the BEV vehicles had battery storage of 65.57 kWh or less, the other 50% has more storage than 65.57 kWh. Median for Range is 340 km- half of the BEV vehicles could travel 340 km of distance or less, the other 50% could travel longer than 340 km. Median for Efficiency is 189.0- half of the BEV vehicles had efficiency of 189 Wh/km or less, the other 50% had efficiency more than 189 Wh/km. Median for Price of BEV vehicle in Germany is 50648€- half of the BEV vehicle in Germany cost 50648€ or less, the other 50% cost more than 50648€.
round(stat.desc(mydata1[c(3,4,5,7)]),2)## Capacity Range Efficiency Price_Germany
## nbr.val 168.00 168.00 168.00 1.680000e+02
## nbr.null 0.00 0.00 0.00 0.000000e+00
## nbr.na 0.00 0.00 0.00 0.000000e+00
## min 16.70 95.00 104.00 1.846000e+04
## max 200.00 970.00 281.00 2.150000e+05
## range 183.30 875.00 177.00 1.965400e+05
## sum 11015.40 56850.00 32720.00 9.865865e+06
## median 66.50 340.00 189.00 5.064750e+04
## mean 65.57 338.39 194.76 5.872539e+04
## SE.mean 1.95 9.30 2.55 2.524500e+03
## CI.mean.0.95 3.84 18.36 5.03 4.984050e+03
## var 637.03 14533.33 1090.66 1.070681e+09
## std.dev 25.24 120.55 33.03 3.272126e+04
## coef.var 0.38 0.36 0.17 5.600000e-01How is variable Price of BEV vehicles in Germany distributed.
hist(mydata1$Price_Germany,
xlab = "Price in Germany (€)",
ylab = "Frequency",
main = "Price of BEV vehicles in Germany")
This is positive asymmetric distribution, with a high concentration of observations around 50,000€ mark.
Who is the top Manufacturer of BEV vehicles
mydata1 %>%
ggplot(aes(x = Brand))+
geom_bar()+
labs(x="Brand")+
coord_flip()
We can see that Audi and Tesla are main producers of BEV vehicles.
Difference in price for different types of drive.
ggplot(data=mydata1, aes(x=Drive, y=Price_Germany)) +
geom_boxplot()+
xlab("Drive") +
ylab("Price")
We can see that mean price of a BEV vehicle in Germany for Front and Rear wheel drive isn’t much different, while mean price in the group of All Wheel Drive is much higher (therefore it is likely to be a difference between groups). What we can also see from the boxplot is how different variables are distributed. In case of All Wheel Drive and Front Wheel Drive we can see that the distribution will be positively skewed, while Rear Wheel Drive will be normally distributed.
mydata1 %>%
ggplot(aes(x = Range, y= Efficiency, color=Drive))+
geom_point()
Based on this figure we can see that Front Wheel Drive is focused more on Efficiency rather than Range. Based on the figure above (boxplot), BEV cars as such are on average the cheapest and their range is hardly exceeding 375 km. Rear Wheel Drive, on the other hand, is dispersed the most in terms of Range and not so much in terms of Efficiency. Lastly All Wheel Drive is considered as more premium type of BEV vehicle, based on figure above, and they offer bigger Range and Efficiency.
library(car)
scatterplot(y = mydata1$Price_Germany, x = mydata1$Efficiency,
main="Relation between Efficiency and Price",
ylab = "Price",
xlab = "Efficiency",
smooth = FALSE)
Based on Scatterplot we can see a positive correlation between Efficiency and Price, therefore we suspect that higher Efficiency means higher Price.
scatterplot(y = mydata1$Price_Germany, x = mydata1$Range,
main="Relation between Range and Price",
ylab = "Price",
xlab = "Range",
smooth = FALSE)
The effect of range is stronger than efficiency because the slope is steeper. Therefore, the price will increase much more in case of additional km of range.
scatterplot(Price_Germany ~ Efficiency | Drive,
main="Relation between Efficiency and Price",
ylab = "Price",
xlab = "Efficiency",
smooth = FALSE,
data = mydata1)
scatterplot(Price_Germany ~ Range| Drive,
main="Relation between Range and Price",
ylab = "Price",
xlab = "Range",
smooth = FALSE,
data = mydata1)
The last 2 scatterplots connect all four observed variables. Based on first scatterplot (observes relationship between Price and Efficiency for different Drive types) we can see that for Front and Rear Wheel drive type the correlation is positive, meaning that higher the price, higher the efficiency. However, in case of All Wheel Drive we see a negative relationship. This is likely due to All Wheel Drive types being more oriented to other attributes such as Acceleration since they are considered as more “luxury” type of cars. The second scatterplot observes relationship between Price and Range for different Drive types. We can see positive correlation for all Drive types. For All Wheel Drive type the Price is highly affected by higher Range, while for Front Wheel Drive we can see minimal correlation. Again we can say that Front Wheel Drive type is more focused on Efficiency than Range.
Lastly I researched which BEV car model is more efficient/ has more range compared to its price.
mydata3 <- mydata1[c(-3,-8)]
mydata3$Price_Efficiency <- round(mydata1$Price_Germany/mydata1$Efficiency, digits=2)
mydata3$Price_Range <- round(mydata1$Price_Germany/mydata1$Range, digits=2)head(mydata3[order(mydata3$Price_Efficiency),], 5)## # A tibble: 5 × 8
## Brand Model Range Efficiency Drive Price_Germany Price_Efficiency Price_Range
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 Smart EQ forfour 95 176 Rear Wheel Drive 19120 109. 201.
## 2 Smart EQ fortwo coupe 100 167 Rear Wheel Drive 18460 111. 185.
## 3 Smart EQ fortwo cabrio 95 176 Rear Wheel Drive 21720 123. 229.
## 4 Dacia Spring Electric 170 158 Front Wheel Drive 20490 130. 121.
## 5 Sono Sion 260 181 Front Wheel Drive 25500 141. 98.1When it comes to Efficiency Brand Smart is dominating.
head(mydata3[order(mydata3$Price_Range),], 5)## # A tibble: 5 × 8
## Brand Model Range Efficiency Drive Price_Germany Price_Efficiency Price_Range
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 Volkswagen ID.3 Pro S 450 171 Rear Wheel Drive 41995 246. 93.3
## 2 CUPRA Born 170 kW - 82 kWh 450 171 Rear Wheel Drive 43000 251. 95.6
## 3 Sono Sion 260 181 Front Wheel Drive 25500 141. 98.1
## 4 Volkswagen ID.3 Pro 350 166 Rear Wheel Drive 34995 211. 100.
## 5 Tesla Cybertruck Tri Motor 750 267 All Wheel Drive 75000 281. 100Volkswagen group is dominating in Price per Range dimension. With three of their models, considering that CUPRA is also a part of Volkswagen group. All of them request less than 100 € per one km.