Ch5.rmd

---
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
library(knitr)
library(rmdformats)

## Global options
options(max.print="100")
opts_chunk$set(# echo=TRUE,
	             cache = TRUE,
               prompt = FALSE,
               tidy = FALSE,
               comment = NA,
               message = FALSE,
               warning = FALSE,
	             fig.align = "center",
	             fig.height = 5,
	             fig.width = 5)
opts_knit$set(width=100)
```

### 5.2 데이터 전처리

#### 5.2.1 데이터 및 패키지 불러오기

```{r}
library(stringr)
library(dplyr)
library(ggplot2)
library(ggsci)

DIR = "D:\\CSV\\UsedCar\\"
List_Files = list.files(DIR)
File_Length = length(List_Files)

Total_Data = data.frame()

for(k in 1:File_Length){
  
  if(!str_detect(str = List_Files[k],pattern = "unclean")){
    
    DF = read.csv(paste0(DIR, List_Files[k]))
    DF$Brand = gsub(".csv","",List_Files[k])
    
    Total_Data = Total_Data %>%
      bind_rows(DF)
    
  }
  
}

```


#### 5.2.2 데이터 전처리

```{r}
Total_Data$model = gsub(" ","",Total_Data$model)

set.seed(1234)
SL = sample(1:nrow(Total_Data), nrow(Total_Data) * 0.8, replace = FALSE)

Total_Data_Train = Total_Data[SL,]
Total_Data_Test = Total_Data[-SL,]
```

### 5.3 현재 시세 정보 서비스 구현   

```{r}
Total_Summary = Total_Data_Train %>%
  group_by(Brand, model, year) %>%
  summarise(Min_price = min(price),
            Mean_price = mean(price),
            Max_price = max(price))

Total_Summary
```

#### 5.3.1 서비스 구현을 위한 함수 정의

- 브랜드 모델명 출력   

```{r}
Model_Print = function(DF, B){
  
  Brand_List = unique(DF$Brand)
  
  if(B %in% Brand_List){
      
    DF2 = DF %>%
      filter(Brand == B)
    
    print(unique(DF2$model))
    
  }else{
    
    print("브랜드명을 다시 입력하세요.")
    
  }
  
}

Model_Print(DF = Total_Summary,
            B = "audi")

Model_Print(DF = Total_Summary,
            B = "audi2")
```
- 선택 조건에 따른 통계량 추출   

```{r}
Summary_Extraction = function(DF, B, M, Y){
  
  Error = "N"
  
  Summary_DATA = DF %>%
    filter(Brand == B) %>%
    filter(model == M) %>%
    filter(year == Y) 
  
  Summary_DATA2 = DF %>%
    filter(Brand == B) %>%
    filter(model == M) %>%
    mutate(Group =  "N")
  
  Summary_DATA2$Group[Summary_DATA2$year == Y] = "Y"
  
  if(nrow(Summary_DATA) == 0){
    
    Error = "E"

  }
  
  Result = list()
  Result[['Summary_DATA']] = Summary_DATA
  Result[['Summary_DATA2']] = Summary_DATA2
  Result[['Error']] = Error
  
  return(Result)
  
}
```

```{r}
Process1 = Summary_Extraction(DF = Total_Summary,
                              	B = "audi",
                                 M = "A1",
                                 Y = 2019)

Process1

Process1_2 = Summary_Extraction(DF = Total_Summary,
                              B = "audi",
                              M = "A1",
                              Y = 2021) 

Process1_2
```

```{r}
Information_Print = function(Process1){
  
  Error = Process1[["Error"]]
  Summary_DATA = Process1[['Summary_DATA']]
  Summary_DATA2 = Process1[['Summary_DATA2']]
  
  if(Error == "N"){

    print(paste0("브랜드(모델): ",
                 Summary_DATA$Brand,"(",Summary_DATA$model,")"))
    print(paste0("연식: ",
                 Summary_DATA$year))
    print(paste0("평균시세: ",
                 round(Summary_DATA$Mean_price,2)))
    print(paste0("가격범위: ",
                  Summary_DATA$Min_price,"~",Summary_DATA$Max_price))
    
    
    G = ggplot(Summary_DATA2) +
      geom_errorbarh(aes(xmin = Min_price, xmax = Max_price, y = year,
                         col = Group),
                     alpha = 0.8) +
      geom_point(aes(x = Mean_price, y = year, col = Group), size = 4) +
      scale_color_nejm() +
      scale_y_continuous(breaks = 2000:2030) +
      ggtitle(paste0(Summary_DATA2$Brand[1],
                     "(model : ",Summary_DATA2$model[1],")")) + 
      theme_bw() +
      theme(text = element_text(face = "bold", size = 14),
            legend.position = "bottom") +
      xlab("중고차 시세") + ylab("연식") +
      guides(col = FALSE)
    
    print(G)
    
  }else{
    
    print(paste0("브랜드(모델): ",
                 Summary_DATA2$Brand[1],"(",Summary_DATA2$model[1],")"))
    print("연식: 정보없음")
    print("평균시세: 정보없음")
    print("가격범위: 정보없음")
    
    G = ggplot(Summary_DATA2) +
      geom_errorbarh(aes(xmin = Min_price, xmax = Max_price, y = year,
                         col = Group),
                     alpha = 0.8,height = 0.5) +
      geom_point(aes(x = Mean_price, y = year, col = Group), size = 4) +
      scale_color_nejm() +
      scale_y_continuous(breaks = 2000:2030) +
      ggtitle(paste0(Summary_DATA2$Brand[1],
                     "(model : ",Summary_DATA2$model[1],")")) + 
      theme_bw() +
      theme(text = element_text(face = "bold", size = 14),
            legend.position = "bottom") +
      xlab("중고차 시세") + ylab("연식") +
      guides(col = FALSE)
    
    print(G)
  }
  
}
```

```{r}
Information_Print(Process1 = Process1)
```


```{r}
Information_Print(Process1 = Process1_2)
```

### 5.4 신규 서비스 구현  

#### 5.4.1 신규 서비스 구현을 위한 함수 정의

- 모델 학습     

```{r}
library(mgcv)

Regression_Modeling = function(DF){
  
  Regression = list()
  
  Group1 = Total_Data %>%
    filter(!Brand %in% c("cclass","focus"))

  Group1_BRAND = unique(Group1$Brand)

  for(B in 1:length(Group1_BRAND)){
    
    DF_Brand = Group1%>%
      filter(Brand == Group1_BRAND[B])
  
    GAM = gam(log(price) ~ model + s(mileage) + mpg + engineSize + fuelType,
              data = DF_Brand)
  
    Regression[[paste0(Group1_BRAND[B])]] = GAM
      
  }
  
  Group2 = Total_Data %>%
    filter(Brand %in% c("cclass","focus")) 
  
  Group2_BRAND = unique(Group2$Brand)
  
  for(B in 1:length(Group2_BRAND)){
    
    DF_Brand = Group2%>%
      filter(Brand == Group2_BRAND[B])

    GAM = gam(log(price) ~ s(mileage) + engineSize + fuelType,
              data = DF_Brand)
  
    Regression[[paste0(Group2_BRAND[B])]] = GAM
    
  }
  
  return(Regression)

}
```

```{r}
Regression_list = Regression_Modeling(DF = Total_Data_Train)

rsq_V = c()

for(S in 1:length(Regression_list)){
  
  sm = summary(Regression_list[[S]])
  rsq = sm$r.sq
  rsq_V[S] = rsq
  
}

ggplot(NULL) +
  geom_bar(aes(x = names(Regression_list), y = rsq_V), stat = 'identity') +
  geom_label(aes(x = names(Regression_list), y = rsq_V, label = round(rsq_V,2))) +
  xlab("브랜드") + ylab("R square") +
  scale_y_continuous(breaks = seq(0,1, by = 0.1),
                     limits = c(0,1.05), expand = c(0,0)) +
  theme_bw() +
  theme(text = element_text(size = 15, face = "bold")) +
  coord_flip()

```
- 중고차 시세 예측

```{r}
Price_Prediction = function(Regression, New_DF){
  
  Predicted_Price = predict(Regression_list[[New_DF$Brand[1]]], newdata = New_DF)
  Predicted_Price = exp(Predicted_Price)
  
  return(Predicted_Price)
  
}

```

- 신규 서비스 화면 구성   

```{r}
Information_Print2 = function(Process1, Predicted_price){
  
  Error = Process1[["Error"]]
  Summary_DATA = Process1[['Summary_DATA']]
  Summary_DATA2 = Process1[['Summary_DATA2']]
  
  if(Error == "N"){

    print(paste0("브랜드(모델): ",
                 Summary_DATA$Brand,"(",Summary_DATA$model,")"))
    print(paste0("연식: ",
                 Summary_DATA$year))
    print(paste0("평균시세: ",
                 round(Summary_DATA$Mean_price,2)))
    print(paste0("가격범위: ",
                  Summary_DATA$Min_price,"~",Summary_DATA$Max_price))
    print(paste0("예측시세: ",
                 round(Predicted_price,2)))
    
    
    G = ggplot() +
      geom_errorbarh(data = Summary_DATA2,
                     aes(xmin = Min_price, xmax = Max_price, y = year,
                         col = Group),
                     alpha = 0.8) +
      geom_point(data = Summary_DATA2,
                 aes(x = Mean_price, y = year, col = Group), size = 4) +
      geom_point(aes(x = Predicted_price, y = Summary_DATA$year), 
			size = 4, col = "black") +
      scale_color_nejm() +
      scale_y_continuous(breaks = 2000:2030) +
      ggtitle(paste0(Summary_DATA2$Brand[1],
                     "(model : ",Summary_DATA2$model[1],")")) + 
      theme_bw() +
      theme(text = element_text(face = "bold", size = 14),
            legend.position = "bottom") +
      xlab("중고차 시세") + ylab("연식") +
      guides(col = FALSE)
    
    print(G)
    
  }else{
    
    print(paste0("브랜드(모델): ",
                 Summary_DATA2$Brand[1],"(",Summary_DATA2$model[1],")"))
    print("연식: 정보없음")
    print("평균시세: 정보없음")
    print("가격범위: 정보없음")
    print(paste0("예측시세: ",
                 round(Predicted_price,2)))
    
    G = ggplot() +
      geom_errorbarh(data = Summary_DATA2,
                     aes(xmin = Min_price, xmax = Max_price, y = year,
                         col = Group),
                     alpha = 0.8,height = 0.5) +
      geom_point(data = Summary_DATA2,
                 aes(x = Mean_price, y = year, col = Group), size = 4) +
      geom_point(aes(x = Predicted_price, y = Summary_DATA$year), 
			size = 4, col = "black") +
      scale_color_nejm() +
      scale_y_continuous(breaks = 2000:2030) +
      ggtitle(paste0(Summary_DATA2$Brand[1],
                     "(model : ",Summary_DATA2$model[1],")")) + 
      theme_bw() +
      theme(text = element_text(face = "bold", size = 14),
            legend.position = "bottom") +
      xlab("중고차 시세") + ylab("연식") +
      guides(col = FALSE)
    
    print(G)
  }
  
}

```

- 개발 서비스 테스트   

```{r}
N_DF = Total_Data_Test[1,]
N_DF$price
```


```{r}
N_Process = Summary_Extraction(DF = Total_Summary,
                              B = N_DF$Brand[1],
                              M = N_DF$model[1],
                              Y = N_DF$year[1])

Information_Print(Process1 = N_Process)
```

### 5.5. 신규 서비스 테스트

```{r}
N_DF = Total_Data_Test[1,]
N_DF$price

N_Process = Summary_Extraction(DF = Total_Summary,
                              B = N_DF$Brand[1],
                              M = N_DF$model[1],
                              Y = N_DF$year[1])

Information_Print(Process1 = N_Process)
```


```{r}
New_DF = N_DF

Predicted = Price_Prediction(Regression = Regression_list,
                             New_DF = N_DF)

Information_Print2(Process1 = N_Process, Predicted_price = Predicted)
```