Are locally used car more in the market?

Some cars are used abroad and imported into the country while others are used newly in the country. Let’s see the percentage of locally and foreign used cars.

car_data %>% 
  group_by(isimported) %>% 
  summarise(count = n()) %>% 
  mutate(percentage = (count/sum(count))*100) %>% 
  ggplot(aes(x = isimported, 
             y = percentage, 
             fill = isimported)) +
  geom_col() +
  labs(x = "Cars",
       y = "Percentage",
       title = "Percentage of the type of used car",
       aes = "Type of Used Car") +
  guides(fill = guide_legend(title="Type of Use"))

Wow we also have new cars in the market, but they don’t seem to be much. It also seems that more foreign used cars are sold in the market.

Prices of type of used cars

We now know the kind of cars sold in the market, let’s compare the prices and see which among the types of cars are more expensive.

car_data %>% 
    ggplot(aes(x = isimported,
               y = price_millions,
               colour = isimported))+
  geom_boxplot()+
  labs(x = "Cars",
       y = "Price in millions",
       title = "Prices of various types of car",
       aes = "Type of Used Car") +
  guides(fill = guide_legend(title="Type of Use"))

New cars are very expensive, an average new car is even costlier than an expensive foreign or locally used car. There are also locally and foreign used cars that seem to be on a very high price. This means that we are having problems of outliers, extreme price values especially on locally used cars which might be possible as a result of luxury cars which are very expensive. Let’s take a look on cars above 100 million naira.

car_data %>% 
  filter(price_millions > 100)
## # A tibble: 6 x 10
##   title     odometer location isimported  engine  transmission fuel  paint  year
##   <chr>        <dbl> <chr>    <chr>       <chr>   <chr>        <chr> <chr> <dbl>
## 1 Land Rov~    13687 Lagos    Foreign Us~ 8-cyli~ automatic    petr~ Green  2019
## 2 Mercedes~       20 Lagos    New         8-cyli~ automatic    petr~ Black  2019
## 3 Mercedes~     6758 Lagos    New         12-cyl~ automatic    petr~ Black  2019
## 4 Land Rov~    18720 Lagos    Foreign Us~ 8-cyli~ automatic    petr~ Grey   2019
## 5 Lexus LX~    55530 Abuja    Foreign Us~ 8-cyli~ automatic    petr~ Black  2014
## 6 Rolls-Ro~    16069 Lagos    Locally us~ 4-cyli~ automatic    petr~ Black  2011
## # ... with 1 more variable: price_millions <dbl>

Well guessed right, they are luxury cars!. I wish I could just be the owner of the Rolls-Royce Ghost(lol). Now let’s take a critical look on foreign and locally used cars below 25 million naira since majority of them fall below this range.

car_data %>% 
  filter(isimported != "New",
         price_millions < 25) %>% 
    ggplot(aes(x = isimported,
               y = price_millions,
               colour = isimported))+
  geom_boxplot() +
  labs(x = "Cars",
       y = "Percentage",
       title = "Percentage of the type of used car",
       aes = "Type of Used Car") +
  guides(fill = guide_legend(title="Type of Use"))

Most foreign used cars are expensive than locally used cars and an average foreign used car is more expensive than an expensive locally used car.

Prices of cars in various locations

Let’s see how many locations do we have.

car_data %>% 
  group_by(location) %>% 
  summarize(count = n()) %>% 
  arrange(desc(count))
## # A tibble: 13 x 2
##    location         count
##    <chr>            <int>
##  1 Lagos             1159
##  2 Abuja              216
##  3 Ogun                34
##  4 Lagos State         21
##  5 other                5
##  6 Abia                 2
##  7 FCT                  2
##  8 Ogun State           2
##  9 Abia State           1
## 10 Accra                1
## 11 Adamawa              1
## 12 Arepo ogun state     1
## 13 Mushin               1

There is a problem, we are having Lagos state and Lagos instead of only Lagos. Let’s fix this and also limit the locations to Abuja and Lagos only.

car_data %>% 
  mutate(location = replace(location, 
                            location == "Lagos State",
                            "Lagos")) %>% 
  group_by(location) %>% 
  summarize(count = n()) %>% 
  arrange(desc(count)) %>%
  filter(location == c("Lagos", "Abuja"))
## # A tibble: 2 x 2
##   location count
##   <chr>    <int>
## 1 Lagos     1180
## 2 Abuja      216

Now let us look at the prices of cars in the above locations.

car_data %>% 
  mutate(location = replace(location, 
                            location == "Lagos State",
                            "Lagos")) %>% 
  filter(location == c("Lagos", "Abuja")) %>% 
  ggplot(aes(x = location,
             y = price_millions,
             colour = location))+
  geom_boxplot() +
  xlab("Location")+
  ylab("Price in Million")+
  ggtitle("Car price in various locations")

Well can’t say much about the data but cars with a higher price tag seems to be much more in Lagos than Abuja. Lets us filter the visualization to look at cars below 25 million naira, since the box plot above shows that most of our data lies below 25 million naira.

car_data %>% 
  mutate(location = replace(location, 
                            location == "Lagos State",
                            "Lagos")) %>% 
  filter(location == c("Lagos", "Abuja"),
         price_millions <= 25) %>% 
  ggplot(aes(x = location,
             y = price_millions,
             colour = location))+
  geom_boxplot() +
  xlab("Location")+
  ylab("Price in Million")+
  ggtitle("Car price in various locations")

Well a better conclusion will be that cars that majority of cars in both location have price below 9 million naira. Also, cars in Lagos have a much more higher price tag than cars in Abuja.

Conclusion

Well that is all for this post, there are also many questions which can be answered from the data set such as how the price of car changes yearly and so on, these are all but a few.