pacotes <- c("readxl", "dplyr")
lapply(pacotes, library, character.only = TRUE)
cars_train <- read_excel("cars_train.xlsx")
Usando table
para olhar as marcas disponíveis:
table(cars_train$marca)
##
## ALFA ROMEO AUDI BMW BRM CHERY
## 9 1698 1784 1 153
## CHEVROLET CHRYSLER CITROËN DODGE EFFA
## 3020 30 194 37 1
## FERRARI FIAT FORD HONDA HYUNDAI
## 1 1918 1060 1586 2043
## IVECO JAC JAGUAR JEEP KIA
## 2 3 148 2000 408
## LAND ROVER LEXUS LIFAN MASERATI MERCEDES-BENZ
## 760 75 8 7 1125
## MINI MITSUBISHI NISSAN PEUGEOT PORSCHE
## 137 862 438 1675 349
## RAM RENAULT SMART SSANGYONG SUBARU
## 168 538 12 14 41
## SUZUKI TOYOTA TROLLER VOLKSWAGEN VOLVO
## 41 2180 177 4594 287
luxo <- filter(cars_train, marca %in% c("ALFA ROMEO", "AUDI", "BMW", "CHRYSLER", "FERRARI", "JAGUAR", "LAND ROVER", "LEXUS", "MASERATI", "MERCEDES-BENZ", "PORSCHE"))
vendas_por_estado <- luxo %>%
group_by(estado_vendedor) %>%
summarise(total_vendas = n()) %>%
arrange(desc(total_vendas))
top_10_estados <- head(vendas_por_estado, 10)
top_10_estados
## # A tibble: 10 × 2
## estado_vendedor total_vendas
## <chr> <int>
## 1 São Paulo (SP) 3296
## 2 Paraná (PR) 586
## 3 Rio de Janeiro (RJ) 530
## 4 Santa Catarina (SC) 477
## 5 Rio Grande do Sul (RS) 402
## 6 Minas Gerais (MG) 372
## 7 Goiás (GO) 159
## 8 Bahia (BA) 90
## 9 Pernambuco (PE) 42
## 10 Rio Grande do Norte (RN) 8
luxo_top_estados <- filter(luxo, estado_vendedor %in% top_10_estados$estado_vendedor)
media_preco_por_estado <- luxo_top_estados %>%
group_by(estado_vendedor) %>%
summarize(media_preco = mean(preco)) %>%
arrange(desc(media_preco))
media_preco_por_estado
## # A tibble: 10 × 2
## estado_vendedor media_preco
## <chr> <dbl>
## 1 Goiás (GO) 213640.
## 2 Paraná (PR) 203967.
## 3 Santa Catarina (SC) 197541.
## 4 Bahia (BA) 193282.
## 5 São Paulo (SP) 192419.
## 6 Rio de Janeiro (RJ) 191248.
## 7 Rio Grande do Sul (RS) 190896.
## 8 Rio Grande do Norte (RN) 190682.
## 9 Minas Gerais (MG) 183592.
## 10 Pernambuco (PE) 180353.
vendas_por_marca_estado <- luxo %>%
group_by(estado_vendedor, marca) %>%
count() %>%
arrange(estado_vendedor, desc(n))
marca_mais_vendida_por_estado <- vendas_por_marca_estado %>%
group_by(estado_vendedor) %>%
slice(1) %>%
ungroup() %>%
arrange(desc(n))
marca_mais_vendida_por_estado
## # A tibble: 18 × 3
## estado_vendedor marca n
## <chr> <chr> <int>
## 1 São Paulo (SP) AUDI 977
## 2 Paraná (PR) BMW 208
## 3 Rio de Janeiro (RJ) BMW 159
## 4 Santa Catarina (SC) AUDI 145
## 5 Rio Grande do Sul (RS) BMW 122
## 6 Minas Gerais (MG) BMW 112
## 7 Goiás (GO) BMW 62
## 8 Bahia (BA) AUDI 37
## 9 Pernambuco (PE) AUDI 18
## 10 Sergipe (SE) BMW 6
## 11 Ceará (CE) AUDI 4
## 12 Rio Grande do Norte (RN) AUDI 4
## 13 Pará (PA) AUDI 3
## 14 Mato Grosso do Sul (MS) BMW 2
## 15 Acre (AC) BMW 1
## 16 Alagoas (AL) LEXUS 1
## 17 Amazonas (AM) MERCEDES-BENZ 1
## 18 Mato Grosso (MT) BMW 1
Analyzing the “cars_train” dataset, we observe that the state of Paraná has the second highest volume of observations and the second highest average price for luxury cars. Knowing that the state of São Paulo is not being considered, we can conclude that, based on this data, the state of Paraná would be a favorable choice to open the dealership.
The BMW brand stands out as the most sold in the state of Paraná, demonstrating that a luxury dealership opened in this state should focus on selling BMW brand cars.
It is important to note that we did not specifically evaluate the average price of BMW brand cars, but rather the average price of luxury cars in general. Thus, as Paraná has the highest average price for luxury cars, we can infer that the population of this state has a good acceptance to pay higher values for luxury cars. In addition, the preference of Paraná residents for the BMW brand provides another indication for choosing this brand at the dealership.
These combined indicators provide a solid basis for making the decision to open a luxury dealership in the state of Paraná focused on the BMW brand.