주제는_공항 온실가스 배출량의 변화로 하겠습니다. 근데 이제 노선과 항공기종을 곁들인.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b7c9fcb9",
   "metadata": {},
   "source": [
    "# 공항 온실가스 배출량 변화 요인 산출 및 제언-항공 노선 운항 편수 및 항공기종의 변화 위주"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c30384dc",
   "metadata": {},
   "source": [
    "# 1. 서론"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a4b2ea8",
   "metadata": {},
   "source": [
    "지구온난화에 대해 수송 분야에서의 온실가스 감축의 필요성이 점차 대두되고 있습니다. 지구온난화는 해수면 상승, 기후 이상, 생태계 파괴 등의 문제를 가지기 때문에 이를 해결하기 위해 전반적인 온실가스 배출을 감축해야 할 필요성이 있습니다. 우리나라는 2030년까지 온실가스 배출을 전망치 대비 37% 줄이는 것을 목표로 하고 있습니다. 하지만 제조업 중심 경제 특성상 산업계 온실감축량은 전망치 대비 12% 이내로 설정했습니다. 따라서 발전, 수송 부문에서의 감축이 요구되며, 항공 수송에서의 온실가스 배출 절감이 불가피합니다. 항공기 온실가스 배출량은 전세계 온실가스 배출량의 2.5%를 차지하고 있으며 [1], 성층권에 직접 온실가스를 배출하는 항공기 특성상 지구온난화에 항공기가 미치는 영향은 무시할 수 없습니다 [2]. 이때 항공기의 온실가스 발생량이 공항 발생 온실가스의 상당 비율(약 78%, 2020년도 인천국제공항 기준)을 차지하기 때문에 항공기에서 배출되는 온실가스의 양만 감축해도 공항에서 발생하는 온실가스를 상당수 감소시킬 수 있습니다 [3]. \n",
    "\n",
    "본 코드에서는 2011년부터 2020년까지 국내 공항에서의 온실가스 배출 현황을 알아봅니다. 그리고 온실가스 변화 요인으로 추정되는 국내공항 항공기 운행 편수, 운행 거리, 그리고 연료 소모량 변화를 알아봅니다.\n",
    "  \n",
    "본론은 크게 현황 분석과 제언 두 부분으로 나뉩니다. 현황 분석에서는 2011년부터 2020년까지의 온실가스 배출 현황을 알아봅니다. 그 후 온실가스 배출에 영향을 끼칠 수 있는 요소들인 연도별 항공기 운항 편수, 운항 거리, 연료 소모량을 살펴봅니다. 마지막으로 이 네가지 변수를 조합해 종합 상관관계 분석을 통해 각각의 관계를 분석합니다.\n",
    "\n",
    "제언부분에서는 현황 분석을 바탕으로 실질적으로 항공기 온실가스를 줄이기 위한 방안을 살펴봅니다. 그 과정에서 다중 회귀 분석을 통해 온실가스 배출량에 가장 큰 영향을 주는 항공기종을 찾아내고 그 기종 비율 조정을 통해 얼마나 온실가스를 감축할 수 있는지 살펴봅니다. 또한 회귀분석을 통해 2030년 예상 노선 사용량을 예측해 온실가스 발생량 감소 대상 우선순위 노선을 산출합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01304120",
   "metadata": {},
   "source": [
    "# 2. 본론"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dbb42a1",
   "metadata": {},
   "source": [
    "## 2-1. 데이터 분석 및 시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0614454f",
   "metadata": {},
   "source": [
    "### 2-1-0. 관련 데이터 수집 및 1차 가공"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa42a0d2",
   "metadata": {},
   "source": [
    "#### 관련 패키지 설치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f1607e97",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'XML' was built under R version 3.6.3\"Warning message:\n",
      "\"package 'readxl' was built under R version 3.6.3\"Warning message:\n",
      "\"package 'dplyr' was built under R version 3.6.3\"\n",
      "Attaching package: 'dplyr'\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "Warning message:\n",
      "\"package 'gridExtra' was built under R version 3.6.3\"\n",
      "Attaching package: 'gridExtra'\n",
      "\n",
      "The following object is masked from 'package:dplyr':\n",
      "\n",
      "    combine\n",
      "\n",
      "Registered S3 method overwritten by 'GGally':\n",
      "  method from   \n",
      "  +.gg   ggplot2\n"
     ]
    }
   ],
   "source": [
    "#install.packages(\"XML\")\n",
    "#install.packages(\"readxl\")\n",
    "#install.packages(\"ggplot2\")\n",
    "#install.packages(\"dplyr\")\n",
    "#install.packages(\"gridExtra\")\n",
    "#install.packages(\"GGally\")\n",
    "library(XML)       # OpenAPI 데이터 추출용 패키지\n",
    "library(rvest)     # 웹사이트 크롤링 관련 패키지\n",
    "library(readxl)    # 엑셀 파일 관련 패키지\n",
    "library(stringr)   # 문자열 편집 패키지 (함수: str_trim 등)\n",
    "library(dplyr)     # 데이터베이스 변형 패키지\n",
    "library(ggplot2)   # 데이터베이스 시각화 패키지 (함수: ggplot)\n",
    "library(grid)      # 시각화 자료 변형 패키지 (함수: textGrob)\n",
    "library(gridExtra) # 시각화 자료 변형 패키지 (함수: grid.arrange)\n",
    "library(GGally)    # 데이터 요소 간 상관관계 그래프 시각화 관련 패키지"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d37b1ede",
   "metadata": {},
   "source": [
    "#### 데이터 수집용 변수 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9da571dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "serviceKey <- \"dGin1VVy2Rems7vf1voG21KwgnTBn2vz2UQzQcS/EVA1YpQHdTVlDcJhCHl13yNAKoQ6TMeLLlGLaUEqpv9xyQ==\"  # (개별) 인증키\n",
    "numOfRows <- 500\n",
    "\n",
    "# 공항 온실가스 데이터\n",
    "serviceURL_GG <- \"http://openapi.airport.co.kr/service/rest/EpiDataService/\"  # 서비스 URL\n",
    "op_GG <- \"getEpiGhDataService\"  # 오퍼레이션명\n",
    "parameter_GG <- paste0(\"?serviceKey=\",serviceKey,\n",
    "                       \"&numOfRows=\",numOfRows)  # 요청 변수\n",
    "\n",
    "# 수송실적통계 데이터\n",
    "serviceURL_AS <- \"http://openapi.airport.co.kr/service/rest/totalAirportStatsService/\"  # 서비스 URL\n",
    "op_AS <- \"getAirportStats\"  # 오퍼레이션명\n",
    "\n",
    "# 항공기 운항 스케줄 데이터\n",
    "serviceURL_FS <- \"http://openapi.airport.co.kr/service/rest/FlightScheduleList/\"  # 서비스 URL\n",
    "op_FSD <- \"getDflightScheduleList\"  # 오퍼레이션명(국내)\n",
    "op_FSI <- \"getIflightScheduleList\"  # 오퍼레이션명(국제)\n",
    "parameter_FS <- paste0(\"?serviceKey=\",serviceKey,\n",
    "                        \"&numOfRows=\",numOfRows)  # 요청 변수\n",
    "\n",
    "# 노선별 소요시간 및 거리 정보 데이터\n",
    "serviceURL_TD <- \"http://openapi.airport.co.kr/service/rest/serviceLine/\"  # 서비스 URL\n",
    "op_TD <- \"serviceLines\"  # 오퍼레이션명\n",
    "schStDate <- \"20110101\"  # 검색시작일\n",
    "schEdDate <- \"20201231\"  #검색종료일\n",
    "parameter_TD <- paste0(\"?serviceKey=\",serviceKey,\n",
    "                       \"&schStDate=\",schStDate,\n",
    "                       \"&schEdDate=\",schEdDate,\n",
    "                       \"&numOfRows=\",numOfRows)  # 요청 변수"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4c35c2",
   "metadata": {},
   "source": [
    "#### 데이터 수집 및 1차 가공"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7735376d",
   "metadata": {},
   "source": [
    "##### 1. 공항 온실가스 데이터 1) 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a46aafd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>yyyy</th><th scope=col>fuel1</th><th scope=col>fuel2</th><th scope=col>fuel3</th><th scope=col>totalTco2e</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>2011     </td><td>29050.5  </td><td>181051.5 </td><td> 653312.5</td><td> 775176.5</td></tr>\n",
       "\t<tr><td>2012     </td><td>29009.5  </td><td>192885.5 </td><td> 715757.5</td><td> 826851.5</td></tr>\n",
       "\t<tr><td>2013     </td><td>32791.5  </td><td>171407.5 </td><td> 755860.5</td><td> 862866.5</td></tr>\n",
       "\t<tr><td>2014     </td><td>32707.5  </td><td>172215.5 </td><td> 790155.5</td><td> 875587.5</td></tr>\n",
       "\t<tr><td>2015     </td><td>33493.5  </td><td>173108.5 </td><td> 904992.5</td><td>1028146.5</td></tr>\n",
       "\t<tr><td>2016     </td><td>35307.0  </td><td>176595.0 </td><td>1070124.0</td><td>1150826.0</td></tr>\n",
       "\t<tr><td>2017     </td><td>37483.0  </td><td>209759.0 </td><td>1131579.0</td><td>1218698.0</td></tr>\n",
       "\t<tr><td>2018     </td><td>40592.0  </td><td>254128.0 </td><td>1193121.0</td><td>1277675.0</td></tr>\n",
       "\t<tr><td>2019     </td><td>39405.0  </td><td>244654.0 </td><td>1242697.0</td><td>1326234.0</td></tr>\n",
       "\t<tr><td>2020     </td><td>28500.0  </td><td>201429.0 </td><td> 690036.0</td><td> 759794.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " yyyy & fuel1 & fuel2 & fuel3 & totalTco2e\\\\\n",
       "\\hline\n",
       "\t 2011      & 29050.5   & 181051.5  &  653312.5 &  775176.5\\\\\n",
       "\t 2012      & 29009.5   & 192885.5  &  715757.5 &  826851.5\\\\\n",
       "\t 2013      & 32791.5   & 171407.5  &  755860.5 &  862866.5\\\\\n",
       "\t 2014      & 32707.5   & 172215.5  &  790155.5 &  875587.5\\\\\n",
       "\t 2015      & 33493.5   & 173108.5  &  904992.5 & 1028146.5\\\\\n",
       "\t 2016      & 35307.0   & 176595.0  & 1070124.0 & 1150826.0\\\\\n",
       "\t 2017      & 37483.0   & 209759.0  & 1131579.0 & 1218698.0\\\\\n",
       "\t 2018      & 40592.0   & 254128.0  & 1193121.0 & 1277675.0\\\\\n",
       "\t 2019      & 39405.0   & 244654.0  & 1242697.0 & 1326234.0\\\\\n",
       "\t 2020      & 28500.0   & 201429.0  &  690036.0 &  759794.0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| yyyy | fuel1 | fuel2 | fuel3 | totalTco2e |\n",
       "|---|---|---|---|---|\n",
       "| 2011      | 29050.5   | 181051.5  |  653312.5 |  775176.5 |\n",
       "| 2012      | 29009.5   | 192885.5  |  715757.5 |  826851.5 |\n",
       "| 2013      | 32791.5   | 171407.5  |  755860.5 |  862866.5 |\n",
       "| 2014      | 32707.5   | 172215.5  |  790155.5 |  875587.5 |\n",
       "| 2015      | 33493.5   | 173108.5  |  904992.5 | 1028146.5 |\n",
       "| 2016      | 35307.0   | 176595.0  | 1070124.0 | 1150826.0 |\n",
       "| 2017      | 37483.0   | 209759.0  | 1131579.0 | 1218698.0 |\n",
       "| 2018      | 40592.0   | 254128.0  | 1193121.0 | 1277675.0 |\n",
       "| 2019      | 39405.0   | 244654.0  | 1242697.0 | 1326234.0 |\n",
       "| 2020      | 28500.0   | 201429.0  |  690036.0 |  759794.0 |\n",
       "\n"
      ],
      "text/plain": [
       "   yyyy fuel1   fuel2    fuel3     totalTco2e\n",
       "1  2011 29050.5 181051.5  653312.5  775176.5 \n",
       "2  2012 29009.5 192885.5  715757.5  826851.5 \n",
       "3  2013 32791.5 171407.5  755860.5  862866.5 \n",
       "4  2014 32707.5 172215.5  790155.5  875587.5 \n",
       "5  2015 33493.5 173108.5  904992.5 1028146.5 \n",
       "6  2016 35307.0 176595.0 1070124.0 1150826.0 \n",
       "7  2017 37483.0 209759.0 1131579.0 1218698.0 \n",
       "8  2018 40592.0 254128.0 1193121.0 1277675.0 \n",
       "9  2019 39405.0 244654.0 1242697.0 1326234.0 \n",
       "10 2020 28500.0 201429.0  690036.0  759794.0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 저장용 데이터베이스\n",
    "totalData_GG <- data.frame()  \n",
    "\n",
    "# 국내공항(인천공항 제외) 온실가스 배출량 입력\n",
    "## 데이터 추출용 URL\n",
    "url_GG <- paste0(serviceURL_GG, op_GG, parameter_GG)\n",
    "\n",
    "## Open API 호출\n",
    "xmlDocument_GG <- xmlTreeParse(url_GG, useInternalNodes = TRUE, encoding = \"UTF-8\")\n",
    "\n",
    "## XML Root Node 추출\n",
    "rootNode_GG <- xmlRoot(xmlDocument_GG)\n",
    "\n",
    "## item Node의 데이터 추출\n",
    "xmlData_GG <- xmlToDataFrame(nodes = getNodeSet(rootNode_GG,'//item'), stringsAsFactor = FALSE)\n",
    "totalData_GG <- rbind(totalData_GG, xmlData_GG)\n",
    "\n",
    "## 2021년 데이터 제거\n",
    "totalData_GG <- totalData_GG %>% filter(yyyy != 2021)\n",
    "\n",
    "## 데이터베이스 각 열의 타입 변경\n",
    "totalData_GG[,c(\"fuel1\",\"fuel2\",\"totalTco2e\")] <- sapply(totalData_GG[,c(\"fuel1\",\"fuel2\",\"totalTco2e\")], as.numeric)\n",
    "\n",
    "# 데이터베이스에 인천공항 온실가스 배출량 추가(출처: 인천국제공항 그린레포트)\n",
    "totalData_GG$fuel1 <- totalData_GG$fuel1 + c(16245,18096,20397,20964,20921,22637,23347,28131,27476,18114)\n",
    "totalData_GG$fuel2 <- totalData_GG$fuel1 + c(160356,172231,146971,147863,147970,141288,172276,213536,205249,172929)\n",
    "\n",
    "## 기타간접온실가스 추가(16-20년: 21년도 기준, 11-15년: 16년도 기준)\n",
    "totalData_GG$fuel3 <- c(988679,1051124,1091227,1125522,1240359,1070124,1131579,1193121,1242697,690036)\n",
    "\n",
    "## 데이터 산정범위 및 변동으로 인한 2016년 및 2017년 온실가스 배출량 차이 (2019년 및 2017년 책자 출처)\n",
    "fuel1gap <- c(22637-22637,23346-23347)\n",
    "fuel2gap <- c(141288-141288,172276-188987)\n",
    "fuel3gap <- c(1070124-1406103,1131579-(135741+931062+66581+332949))\n",
    "\n",
    "## 11-15년도 데이터에 배출량 차이 적용\n",
    "totalData_GG$fuel1 <- ifelse(totalData_GG$yyyy<=2015,totalData_GG$fuel1+mean(fuel1gap),totalData_GG$fuel1)\n",
    "totalData_GG$fuel2 <- ifelse(totalData_GG$yyyy<=2015,totalData_GG$fuel2+mean(fuel2gap),totalData_GG$fuel2)\n",
    "totalData_GG$fuel3 <- ifelse(totalData_GG$yyyy<=2015,totalData_GG$fuel3+mean(fuel3gap),totalData_GG$fuel3)\n",
    "\n",
    "## totalTco2e에 기타간접온실가스 배출량 추가\n",
    "totalData_GG$totalTco2e <- totalData_GG$totalTco2e + totalData_GG$fuel3\n",
    "\n",
    "# 셀 순서 변경\n",
    "totalData_GG <- totalData_GG %>% select(yyyy, fuel1, fuel2, fuel3, totalTco2e)\n",
    "\n",
    "totalData_GG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91a792cd",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다. 이때 모든 배출량의 단위는 tCO2e(이산화탄소 환산톤)입니다.\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:--- | :--- | :---\n",
    "|1|yyyy|연도(범위: 2011~2020년)\n",
    "|2|fuel1|직접 온실가스 배출량(보일러 및 차량 연료, 소각 등)\n",
    "|3|fuel2|간접 온실가스 배출량(전력, 중온수 등)\n",
    "|4|fuel3|기타 간접 온실가스 배출량(항공기 배출, 입주사 사용 전력 및 중온수, 지상조업차량, 승객 및 임직원 이동 등)\n",
    "|5|totalTco2e|fuel1~3을 합한 값\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28d54794",
   "metadata": {},
   "source": [
    "##### 2. 수송실적통계 데이터  3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "58008bfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>airport</th><th scope=col>arrcargo</th><th scope=col>arrflgt</th><th scope=col>arrpassenger</th><th scope=col>depcargo</th><th scope=col>depflgt</th><th scope=col>deppassenger</th><th scope=col>subcargo</th><th scope=col>subflgt</th><th scope=col>subpassenger</th><th scope=col>year</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>김포      </td><td> 136706219</td><td> 58169    </td><td> 8626264  </td><td> 105415089</td><td> 58233    </td><td> 8604853  </td><td> 242121308</td><td>116402    </td><td>17231117  </td><td>2011      </td></tr>\n",
       "\t<tr><td>김해      </td><td>  54300685</td><td> 30529    </td><td> 4049534  </td><td>  61864939</td><td> 30554    </td><td> 4017875  </td><td> 116165624</td><td> 61083    </td><td> 8067409  </td><td>2011      </td></tr>\n",
       "\t<tr><td>제주      </td><td> 100166471</td><td> 52315    </td><td> 8017091  </td><td> 134853977</td><td> 52329    </td><td> 8095621  </td><td> 235020448</td><td>104644    </td><td>16112712  </td><td>2011      </td></tr>\n",
       "\t<tr><td>대구      </td><td>   8076701</td><td>  3914    </td><td>  540337  </td><td>  10156241</td><td>  3909    </td><td>  546983  </td><td>  18232942</td><td>  7823    </td><td> 1087320  </td><td>2011      </td></tr>\n",
       "\t<tr><td>광주      </td><td>   5946032</td><td>  4946    </td><td>  644632  </td><td>   8207066</td><td>  4945    </td><td>  632075  </td><td>  14153098</td><td>  9891    </td><td> 1276707  </td><td>2011      </td></tr>\n",
       "\t<tr><td>무안      </td><td>    437105</td><td>   376    </td><td>   41844  </td><td>    334997</td><td>   376    </td><td>   35558  </td><td>    772102</td><td>   752    </td><td>   77402  </td><td>2011      </td></tr>\n",
       "\t<tr><td>청주      </td><td>   8120617</td><td>  4142    </td><td>  616983  </td><td>   6851047</td><td>  4142    </td><td>  606239  </td><td>  14971664</td><td>  8284    </td><td> 1223222  </td><td>2011      </td></tr>\n",
       "\t<tr><td>양양      </td><td>     29284</td><td>    36    </td><td>    2917  </td><td>     41422</td><td>    36    </td><td>    2832  </td><td>     70706</td><td>    72    </td><td>    5749  </td><td>2011      </td></tr>\n",
       "\t<tr><td>여수      </td><td>    823059</td><td>  2653    </td><td>  282703  </td><td>   1588030</td><td>  2652    </td><td>  289109  </td><td>   2411089</td><td>  5305    </td><td>  571812  </td><td>2011      </td></tr>\n",
       "\t<tr><td>울산      </td><td>   1573441</td><td>  2710    </td><td>  270017  </td><td>   1493077</td><td>  2710    </td><td>  271936  </td><td>   3066518</td><td>  5420    </td><td>  541953  </td><td>2011      </td></tr>\n",
       "\t<tr><td>진주      </td><td>    369170</td><td>   833    </td><td>   68422  </td><td>    290766</td><td>   833    </td><td>   63540  </td><td>    659936</td><td>  1666    </td><td>  131962  </td><td>2011      </td></tr>\n",
       "\t<tr><td>포항      </td><td>    418671</td><td>  1645    </td><td>  121276  </td><td>    385262</td><td>  1645    </td><td>  121081  </td><td>    803933</td><td>  3290    </td><td>  242357  </td><td>2011      </td></tr>\n",
       "\t<tr><td>군산      </td><td>    822640</td><td>   647    </td><td>   81878  </td><td>    696124</td><td>   647    </td><td>   78287  </td><td>   1518764</td><td>  1294    </td><td>  160165  </td><td>2011      </td></tr>\n",
       "\t<tr><td>원주      </td><td>    239496</td><td>   320    </td><td>   33966  </td><td>    153917</td><td>   320    </td><td>   32649  </td><td>    393413</td><td>   640    </td><td>   66615  </td><td>2011      </td></tr>\n",
       "\t<tr><td>인천      </td><td>1375657543</td><td>105116    </td><td>16118530  </td><td>1463928475</td><td>105071    </td><td>15926794  </td><td>2839586018</td><td>210187    </td><td>32045324  </td><td>2011      </td></tr>\n",
       "\t<tr><td>총계      </td><td>1693687134</td><td>268351    </td><td>39516394  </td><td>1796260429</td><td>268402    </td><td>39325432  </td><td>3489947563</td><td>536753    </td><td>78841826  </td><td>2011      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       " airport & arrcargo & arrflgt & arrpassenger & depcargo & depflgt & deppassenger & subcargo & subflgt & subpassenger & year\\\\\n",
       "\\hline\n",
       "\t 김포       &  136706219 &  58169     &  8626264   &  105415089 &  58233     &  8604853   &  242121308 & 116402     & 17231117   & 2011      \\\\\n",
       "\t 김해       &   54300685 &  30529     &  4049534   &   61864939 &  30554     &  4017875   &  116165624 &  61083     &  8067409   & 2011      \\\\\n",
       "\t 제주       &  100166471 &  52315     &  8017091   &  134853977 &  52329     &  8095621   &  235020448 & 104644     & 16112712   & 2011      \\\\\n",
       "\t 대구       &    8076701 &   3914     &   540337   &   10156241 &   3909     &   546983   &   18232942 &   7823     &  1087320   & 2011      \\\\\n",
       "\t 광주       &    5946032 &   4946     &   644632   &    8207066 &   4945     &   632075   &   14153098 &   9891     &  1276707   & 2011      \\\\\n",
       "\t 무안       &     437105 &    376     &    41844   &     334997 &    376     &    35558   &     772102 &    752     &    77402   & 2011      \\\\\n",
       "\t 청주       &    8120617 &   4142     &   616983   &    6851047 &   4142     &   606239   &   14971664 &   8284     &  1223222   & 2011      \\\\\n",
       "\t 양양       &      29284 &     36     &     2917   &      41422 &     36     &     2832   &      70706 &     72     &     5749   & 2011      \\\\\n",
       "\t 여수       &     823059 &   2653     &   282703   &    1588030 &   2652     &   289109   &    2411089 &   5305     &   571812   & 2011      \\\\\n",
       "\t 울산       &    1573441 &   2710     &   270017   &    1493077 &   2710     &   271936   &    3066518 &   5420     &   541953   & 2011      \\\\\n",
       "\t 진주       &     369170 &    833     &    68422   &     290766 &    833     &    63540   &     659936 &   1666     &   131962   & 2011      \\\\\n",
       "\t 포항       &     418671 &   1645     &   121276   &     385262 &   1645     &   121081   &     803933 &   3290     &   242357   & 2011      \\\\\n",
       "\t 군산       &     822640 &    647     &    81878   &     696124 &    647     &    78287   &    1518764 &   1294     &   160165   & 2011      \\\\\n",
       "\t 원주       &     239496 &    320     &    33966   &     153917 &    320     &    32649   &     393413 &    640     &    66615   & 2011      \\\\\n",
       "\t 인천       & 1375657543 & 105116     & 16118530   & 1463928475 & 105071     & 15926794   & 2839586018 & 210187     & 32045324   & 2011      \\\\\n",
       "\t 총계       & 1693687134 & 268351     & 39516394   & 1796260429 & 268402     & 39325432   & 3489947563 & 536753     & 78841826   & 2011      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| airport | arrcargo | arrflgt | arrpassenger | depcargo | depflgt | deppassenger | subcargo | subflgt | subpassenger | year |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 김포       |  136706219 |  58169     |  8626264   |  105415089 |  58233     |  8604853   |  242121308 | 116402     | 17231117   | 2011       |\n",
       "| 김해       |   54300685 |  30529     |  4049534   |   61864939 |  30554     |  4017875   |  116165624 |  61083     |  8067409   | 2011       |\n",
       "| 제주       |  100166471 |  52315     |  8017091   |  134853977 |  52329     |  8095621   |  235020448 | 104644     | 16112712   | 2011       |\n",
       "| 대구       |    8076701 |   3914     |   540337   |   10156241 |   3909     |   546983   |   18232942 |   7823     |  1087320   | 2011       |\n",
       "| 광주       |    5946032 |   4946     |   644632   |    8207066 |   4945     |   632075   |   14153098 |   9891     |  1276707   | 2011       |\n",
       "| 무안       |     437105 |    376     |    41844   |     334997 |    376     |    35558   |     772102 |    752     |    77402   | 2011       |\n",
       "| 청주       |    8120617 |   4142     |   616983   |    6851047 |   4142     |   606239   |   14971664 |   8284     |  1223222   | 2011       |\n",
       "| 양양       |      29284 |     36     |     2917   |      41422 |     36     |     2832   |      70706 |     72     |     5749   | 2011       |\n",
       "| 여수       |     823059 |   2653     |   282703   |    1588030 |   2652     |   289109   |    2411089 |   5305     |   571812   | 2011       |\n",
       "| 울산       |    1573441 |   2710     |   270017   |    1493077 |   2710     |   271936   |    3066518 |   5420     |   541953   | 2011       |\n",
       "| 진주       |     369170 |    833     |    68422   |     290766 |    833     |    63540   |     659936 |   1666     |   131962   | 2011       |\n",
       "| 포항       |     418671 |   1645     |   121276   |     385262 |   1645     |   121081   |     803933 |   3290     |   242357   | 2011       |\n",
       "| 군산       |     822640 |    647     |    81878   |     696124 |    647     |    78287   |    1518764 |   1294     |   160165   | 2011       |\n",
       "| 원주       |     239496 |    320     |    33966   |     153917 |    320     |    32649   |     393413 |    640     |    66615   | 2011       |\n",
       "| 인천       | 1375657543 | 105116     | 16118530   | 1463928475 | 105071     | 15926794   | 2839586018 | 210187     | 32045324   | 2011       |\n",
       "| 총계       | 1693687134 | 268351     | 39516394   | 1796260429 | 268402     | 39325432   | 3489947563 | 536753     | 78841826   | 2011       |\n",
       "\n"
      ],
      "text/plain": [
       "   airport arrcargo   arrflgt arrpassenger depcargo   depflgt deppassenger\n",
       "1  김포     136706219  58169   8626264      105415089  58233   8604853    \n",
       "2  김해      54300685  30529   4049534       61864939  30554   4017875    \n",
       "3  제주     100166471  52315   8017091      134853977  52329   8095621    \n",
       "4  대구       8076701   3914    540337       10156241   3909    546983    \n",
       "5  광주       5946032   4946    644632        8207066   4945    632075    \n",
       "6  무안        437105    376     41844         334997    376     35558    \n",
       "7  청주       8120617   4142    616983        6851047   4142    606239    \n",
       "8  양양         29284     36      2917          41422     36      2832    \n",
       "9  여수        823059   2653    282703        1588030   2652    289109    \n",
       "10 울산       1573441   2710    270017        1493077   2710    271936    \n",
       "11 진주        369170    833     68422         290766    833     63540    \n",
       "12 포항        418671   1645    121276         385262   1645    121081    \n",
       "13 군산        822640    647     81878         696124    647     78287    \n",
       "14 원주        239496    320     33966         153917    320     32649    \n",
       "15 인천    1375657543 105116  16118530     1463928475 105071  15926794    \n",
       "16 총계    1693687134 268351  39516394     1796260429 268402  39325432    \n",
       "   subcargo   subflgt subpassenger year\n",
       "1   242121308 116402  17231117     2011\n",
       "2   116165624  61083   8067409     2011\n",
       "3   235020448 104644  16112712     2011\n",
       "4    18232942   7823   1087320     2011\n",
       "5    14153098   9891   1276707     2011\n",
       "6      772102    752     77402     2011\n",
       "7    14971664   8284   1223222     2011\n",
       "8       70706     72      5749     2011\n",
       "9     2411089   5305    571812     2011\n",
       "10    3066518   5420    541953     2011\n",
       "11     659936   1666    131962     2011\n",
       "12     803933   3290    242357     2011\n",
       "13    1518764   1294    160165     2011\n",
       "14     393413    640     66615     2011\n",
       "15 2839586018 210187  32045324     2011\n",
       "16 3489947563 536753  78841826     2011"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 저장용 데이터베이스\n",
    "totalData_AS <- data.frame()  \n",
    "\n",
    "for(year in 2011:2020){\n",
    "    # 날짜 설정\n",
    "    stDate <- '0101'\n",
    "    edDate <- '1231'\n",
    "    startDePd <- paste0(as.character(year),stDate)\n",
    "    endDePd <- paste0(as.character(year),edDate)\n",
    "    \n",
    "    parameter_AS <- paste0(\"?serviceKey=\",serviceKey,\n",
    "                           \"&startDePd=\", startDePd,\n",
    "                           \"&endDePd=\", endDePd)  # 요청 변수\n",
    "    # 데이터 추출용 URL\n",
    "    url_AS <- paste0(serviceURL_AS, op_AS, parameter_AS)\n",
    "\n",
    "    # Open API 호출\n",
    "    xmlDocument_AS <- xmlTreeParse(url_AS, useInternalNodes = TRUE, encoding = \"UTF-8\")\n",
    "\n",
    "    # XML Root Node 추출\n",
    "    rootNode_AS <- xmlRoot(xmlDocument_AS)\n",
    "\n",
    "    # item Node의 데이터 추출\n",
    "    xmlData_AS <- xmlToDataFrame(nodes = getNodeSet(rootNode_AS,'//item'), stringsAsFactor = FALSE)\n",
    "    xmlData_AS[,2:ncol(xmlData_AS)] <- sapply(xmlData_AS[,2:ncol(xmlData_AS)], as.numeric)\n",
    "    xmlData_AS[,\"year\"] <- rep(as.character(year),times=nrow(xmlData_AS))\n",
    "    totalData_AS <- rbind(totalData_AS, xmlData_AS)\n",
    "}\n",
    "\n",
    "totalData_AS %>% filter(year==2011)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa708cb",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다.\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:--- | :--- | :---\n",
    "|1|airport|국내 공항명\n",
    "|2|depcargo, arrcargo, subcargo|출발, 도착 화물 및 화물 소계(단위: kg)\n",
    "|3|depflgt, arrflgt, subflgt|출발, 도착 운항 편수 및 운항 편수 소계(단위: 편)\n",
    "|4|deppassenger, arrpassenger, subpassenger|출발, 도착 승객 및 승객 수 소계(단위: 명)\n",
    "|5|year|연도(범위: 2011~2020년)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1e8f2b",
   "metadata": {},
   "source": [
    "##### 3. 국내/국제 항공기 운항 스케줄 데이터  4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98fd39f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 저장용 데이터베이스\n",
    "totalData_FSD <- data.frame()\n",
    "totalData_FSI <- data.frame()\n",
    "\n",
    "# 데이터 추출용 URL\n",
    "url_FSD <- paste0(serviceURL_FS, op_FSD, parameter_FS)\n",
    "url_FSI <- paste0(serviceURL_FS, op_FSI, parameter_FS)\n",
    "\n",
    "# Open API 호출 (page수 확인용)\n",
    "xmlDocument_FSD <- xmlTreeParse(url_FSD, useInternalNodes = TRUE, encoding = \"UTF-8\")\n",
    "xmlDocument_FSI <- xmlTreeParse(url_FSI, useInternalNodes = TRUE, encoding = \"UTF-8\")\n",
    "\n",
    "# XML Root Node 추출\n",
    "rootNode_FSD <- xmlRoot(xmlDocument_FSD)\n",
    "rootNode_FSI <- xmlRoot(xmlDocument_FSI)\n",
    "\n",
    "# 오픈API 호출 결과 데이터의 개수\n",
    "numOfRows_FSD <- as.numeric(xpathSApply(rootNode_FSD, \"//numOfRows\",xmlValue))\n",
    "numOfRows_FSI <- as.numeric(xpathSApply(rootNode_FSI, \"//numOfRows\",xmlValue))\n",
    "\n",
    "# 전체 데이터의 개수 확인\n",
    "totalCount_FSD <- as.numeric(xpathSApply(rootNode_FSD, \"//totalCount\",xmlValue))\n",
    "totalCount_FSI <- as.numeric(xpathSApply(rootNode_FSI, \"//totalCount\",xmlValue))\n",
    "\n",
    "# 총 open API 호출 횟수 계산\n",
    "loopCount_FSD <- round(totalCount_FSD/numOfRows_FSD, 0)\n",
    "loopCount_FSI <- round(totalCount_FSI/numOfRows_FSI, 0)\n",
    "\n",
    "# API 호출 횟수 보정\n",
    "if(loopCount_FSD * numOfRows_FSD < totalCount_FSD){loopCount_FSD <- loopCount_FSD + 1}\n",
    "if(loopCount_FSI * numOfRows_FSI < totalCount_FSI){loopCount_FSI <- loopCount_FSI + 1}\n",
    "\n",
    "# Open API 호출(국내선)\n",
    "for(i in 1:loopCount_FSD){\n",
    "  url <- paste0(serviceURL_FS,op_FSD,parameter_FS,\n",
    "                paste0(\"&pageNo=\", i)) # 반복 실행 때마다 페이지 위치 1씩 증가\n",
    "  \n",
    "  doc <- xmlTreeParse(url, useInternalNodes = TRUE, encoding = \"UTF-8\")\n",
    "  \n",
    "  # XML Root Node 추출\n",
    "  rootNode <- xmlRoot(doc)\n",
    "  \n",
    "  # item Node의 데이터 추출\n",
    "  xmlData <- xmlToDataFrame(nodes = getNodeSet(rootNode,'//item'), stringsAsFactor = FALSE)\n",
    "  totalData_FSD <- bind_rows(totalData_FSD, xmlData)\n",
    "}\n",
    "\n",
    "# Open API 호출(국제선)\n",
    "for(i in 1:loopCount_FSI){\n",
    "  url <- paste0(serviceURL_FS,op_FSI,parameter_FS,\n",
    "                paste0(\"&pageNo=\", i)) # 반복 실행 때마다 페이지 위치 1씩 증가\n",
    "  \n",
    "  doc <- xmlTreeParse(url, useInternalNodes = TRUE, encoding = \"UTF-8\")\n",
    "  \n",
    "  # XML Root Node 추출\n",
    "  rootNode <- xmlRoot(doc)\n",
    "  \n",
    "  # item Node의 데이터 추출\n",
    "  xmlData <- xmlToDataFrame(nodes = getNodeSet(rootNode,'//item'), stringsAsFactor = FALSE)\n",
    "  totalData_FSI <- bind_rows(totalData_FSI, xmlData)\n",
    "}\n",
    "\n",
    "# 운항 시작일 및 종료일 데이터 타입 변경(chr -> Date)\n",
    "totalData_FSD$domesticStdate <- as.Date(totalData_FSD$domesticStdate)\n",
    "totalData_FSD$domesticEddate <- as.Date(totalData_FSD$domesticEddate)\n",
    "totalData_FSD$domesticStartTime <- format(strptime(totalData_FSD$domesticStartTime,\"%H%M\"),\"%H:%M\")\n",
    "totalData_FSD$domesticArrivalTime <- format(strptime(totalData_FSD$domesticArrivalTime,\"%H%M\"),\"%H:%M\")\n",
    "totalData_FSI$internationalStdate <- as.Date(totalData_FSI$internationalStdate)\n",
    "totalData_FSI$internationalEddate <- as.Date(totalData_FSI$internationalEddate)\n",
    "totalData_FSI$internationalTime <- format(strptime(totalData_FSI$internationalTime,\"%H%M\"),\"%H:%M\")\n",
    "\n",
    "# 2011~2020년의 운항 스케줄 데이터 추출 및 컬럼 순서 정돈\n",
    "totalData_FSD <- distinct(totalData_FSD) %>%\n",
    "    filter(domesticStdate <= as.Date('2020-12-31') &\n",
    "           domesticEddate >= as.Date('2011-01-01') &\n",
    "           domesticStdate <= domesticEddate) %>%  # 도착 일정이 출발 일정보다 이후인 데이터만 추출\n",
    "    arrange(domesticStdate, domesticEddate) %>%\n",
    "    select(\"domesticNum\", \"airlineEnglish\",\"startcityCode\", \"startcity\", \"arrivalcityCode\", \"arrivalcity\",\n",
    "           \"domesticStdate\", \"domesticEddate\", \"domesticStartTime\", \"domesticArrivalTime\",\"flightPurpose\",\n",
    "           \"domesticMon\",\"domesticTue\",\"domesticWed\",\"domesticThu\",\"domesticFri\",\"domesticSat\",\"domesticSun\")\n",
    "totalData_FSI <- distinct(totalData_FSI) %>%\n",
    "    filter(internationalStdate <= as.Date('2020-12-31') &\n",
    "           internationalEddate >= as.Date('2011-01-01') &\n",
    "           internationalStdate <= internationalEddate) %>%  # 도착 일정이 출발 일정보다 이후인 데이터만 추출\n",
    "    arrange(internationalStdate, internationalEddate, internationalIoType) %>%\n",
    "    select(\"internationalNum\", \"airlineEnglish\", \"airportCode\", \"airport\", \"cityCode\", \"city\",\n",
    "           \"internationalStdate\", \"internationalEddate\", \"internationalTime\", \"internationalIoType\",\"flightPurpose\",\n",
    "           \"internationalMon\",\"internationalTue\",\"internationalWed\",\"internationalThu\",\n",
    "           \"internationalFri\",\"internationalSat\",\"internationalSun\")\n",
    "\n",
    "# 컬럼명 바꾸기\n",
    "names(totalData_FSD) <- c(\"flightNum\", \"airline\",\"startCityCode\", \"startCity\", \"arrivalCityCode\", \"arrivalCity\",\n",
    "                          \"startDate\", \"endDate\", \"startTime\", \"arrivalTime\",\"flightPurpose\",\n",
    "                          \"mon\",\"tue\",\"wed\",\"thu\",\"fri\",\"sat\",\"sun\")\n",
    "names(totalData_FSI) <- c(\"flightNum\", \"airline\", \"domesticCityCode\", \"domesticCity\",\n",
    "                          \"internationalCityCode\", \"internationalCity\",\n",
    "                          \"startDate\", \"endDate\", \"recordedTime\", \"ioType\",\"flightPurpose\",\n",
    "                          \"mon\",\"tue\",\"wed\",\"thu\",\"fri\",\"sat\",\"sun\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7435c55d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>flightNum</th><th scope=col>airline</th><th scope=col>startCityCode</th><th scope=col>startCity</th><th scope=col>arrivalCityCode</th><th scope=col>arrivalCity</th><th scope=col>startDate</th><th scope=col>endDate</th><th scope=col>startTime</th><th scope=col>arrivalTime</th><th scope=col>flightPurpose</th><th scope=col>mon</th><th scope=col>tue</th><th scope=col>wed</th><th scope=col>thu</th><th scope=col>fri</th><th scope=col>sat</th><th scope=col>sun</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>OZ8903        </td><td>ASIANA AIRLINE</td><td>GMP           </td><td>서울/김포     </td><td>CJU           </td><td>제주          </td><td>2010-10-31    </td><td>2011-01-02    </td><td>06:40         </td><td>07:45         </td><td>여객기        </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td></tr>\n",
       "\t<tr><td>OZ8906        </td><td>ASIANA AIRLINE</td><td>CJU           </td><td>제주          </td><td>GMP           </td><td>서울/김포     </td><td>2010-10-31    </td><td>2011-01-02    </td><td>08:15         </td><td>09:20         </td><td>여객기        </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td></tr>\n",
       "\t<tr><td>OZ8911        </td><td>ASIANA AIRLINE</td><td>GMP           </td><td>서울/김포     </td><td>CJU           </td><td>제주          </td><td>2010-10-31    </td><td>2011-01-02    </td><td>09:50         </td><td>10:55         </td><td>여객기        </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td></tr>\n",
       "\t<tr><td>OZ8916        </td><td>ASIANA AIRLINE</td><td>CJU           </td><td>제주          </td><td>GMP           </td><td>서울/김포     </td><td>2010-10-31    </td><td>2011-01-02    </td><td>11:25         </td><td>12:30         </td><td>여객기        </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td></tr>\n",
       "\t<tr><td>OZ8919        </td><td>ASIANA AIRLINE</td><td>GMP           </td><td>서울/김포     </td><td>CJU           </td><td>제주          </td><td>2010-10-31    </td><td>2011-01-02    </td><td>12:35         </td><td>13:40         </td><td>여객기        </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td></tr>\n",
       "\t<tr><td>OZ8923        </td><td>ASIANA AIRLINE</td><td>GMP           </td><td>서울/김포     </td><td>CJU           </td><td>제주          </td><td>2010-10-31    </td><td>2011-01-02    </td><td>13:00         </td><td>14:05         </td><td>여객기        </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td><td>Y             </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       " flightNum & airline & startCityCode & startCity & arrivalCityCode & arrivalCity & startDate & endDate & startTime & arrivalTime & flightPurpose & mon & tue & wed & thu & fri & sat & sun\\\\\n",
       "\\hline\n",
       "\t OZ8903         & ASIANA AIRLINE & GMP            & 서울/김포      & CJU            & 제주           & 2010-10-31     & 2011-01-02     & 06:40          & 07:45          & 여객기         & Y              & Y              & Y              & Y              & Y              & Y              & Y             \\\\\n",
       "\t OZ8906         & ASIANA AIRLINE & CJU            & 제주           & GMP            & 서울/김포      & 2010-10-31     & 2011-01-02     & 08:15          & 09:20          & 여객기         & Y              & Y              & Y              & Y              & Y              & Y              & Y             \\\\\n",
       "\t OZ8911         & ASIANA AIRLINE & GMP            & 서울/김포      & CJU            & 제주           & 2010-10-31     & 2011-01-02     & 09:50          & 10:55          & 여객기         & Y              & Y              & Y              & Y              & Y              & Y              & Y             \\\\\n",
       "\t OZ8916         & ASIANA AIRLINE & CJU            & 제주           & GMP            & 서울/김포      & 2010-10-31     & 2011-01-02     & 11:25          & 12:30          & 여객기         & Y              & Y              & Y              & Y              & Y              & Y              & Y             \\\\\n",
       "\t OZ8919         & ASIANA AIRLINE & GMP            & 서울/김포      & CJU            & 제주           & 2010-10-31     & 2011-01-02     & 12:35          & 13:40          & 여객기         & Y              & Y              & Y              & Y              & Y              & Y              & Y             \\\\\n",
       "\t OZ8923         & ASIANA AIRLINE & GMP            & 서울/김포      & CJU            & 제주           & 2010-10-31     & 2011-01-02     & 13:00          & 14:05          & 여객기         & Y              & Y              & Y              & Y              & Y              & Y              & Y             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| flightNum | airline | startCityCode | startCity | arrivalCityCode | arrivalCity | startDate | endDate | startTime | arrivalTime | flightPurpose | mon | tue | wed | thu | fri | sat | sun |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| OZ8903         | ASIANA AIRLINE | GMP            | 서울/김포      | CJU            | 제주           | 2010-10-31     | 2011-01-02     | 06:40          | 07:45          | 여객기         | Y              | Y              | Y              | Y              | Y              | Y              | Y              |\n",
       "| OZ8906         | ASIANA AIRLINE | CJU            | 제주           | GMP            | 서울/김포      | 2010-10-31     | 2011-01-02     | 08:15          | 09:20          | 여객기         | Y              | Y              | Y              | Y              | Y              | Y              | Y              |\n",
       "| OZ8911         | ASIANA AIRLINE | GMP            | 서울/김포      | CJU            | 제주           | 2010-10-31     | 2011-01-02     | 09:50          | 10:55          | 여객기         | Y              | Y              | Y              | Y              | Y              | Y              | Y              |\n",
       "| OZ8916         | ASIANA AIRLINE | CJU            | 제주           | GMP            | 서울/김포      | 2010-10-31     | 2011-01-02     | 11:25          | 12:30          | 여객기         | Y              | Y              | Y              | Y              | Y              | Y              | Y              |\n",
       "| OZ8919         | ASIANA AIRLINE | GMP            | 서울/김포      | CJU            | 제주           | 2010-10-31     | 2011-01-02     | 12:35          | 13:40          | 여객기         | Y              | Y              | Y              | Y              | Y              | Y              | Y              |\n",
       "| OZ8923         | ASIANA AIRLINE | GMP            | 서울/김포      | CJU            | 제주           | 2010-10-31     | 2011-01-02     | 13:00          | 14:05          | 여객기         | Y              | Y              | Y              | Y              | Y              | Y              | Y              |\n",
       "\n"
      ],
      "text/plain": [
       "  flightNum airline        startCityCode startCity arrivalCityCode arrivalCity\n",
       "1 OZ8903    ASIANA AIRLINE GMP           서울/김포 CJU             제주       \n",
       "2 OZ8906    ASIANA AIRLINE CJU           제주      GMP             서울/김포  \n",
       "3 OZ8911    ASIANA AIRLINE GMP           서울/김포 CJU             제주       \n",
       "4 OZ8916    ASIANA AIRLINE CJU           제주      GMP             서울/김포  \n",
       "5 OZ8919    ASIANA AIRLINE GMP           서울/김포 CJU             제주       \n",
       "6 OZ8923    ASIANA AIRLINE GMP           서울/김포 CJU             제주       \n",
       "  startDate  endDate    startTime arrivalTime flightPurpose mon tue wed thu fri\n",
       "1 2010-10-31 2011-01-02 06:40     07:45       여객기        Y   Y   Y   Y   Y  \n",
       "2 2010-10-31 2011-01-02 08:15     09:20       여객기        Y   Y   Y   Y   Y  \n",
       "3 2010-10-31 2011-01-02 09:50     10:55       여객기        Y   Y   Y   Y   Y  \n",
       "4 2010-10-31 2011-01-02 11:25     12:30       여객기        Y   Y   Y   Y   Y  \n",
       "5 2010-10-31 2011-01-02 12:35     13:40       여객기        Y   Y   Y   Y   Y  \n",
       "6 2010-10-31 2011-01-02 13:00     14:05       여객기        Y   Y   Y   Y   Y  \n",
       "  sat sun\n",
       "1 Y   Y  \n",
       "2 Y   Y  \n",
       "3 Y   Y  \n",
       "4 Y   Y  \n",
       "5 Y   Y  \n",
       "6 Y   Y  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(totalData_FSD)  # 국내 항공기 운항 스케줄 데이터"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54c86ad5",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다.\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:--- | :--- | :---\n",
    "|1|flightNum|항공편명\n",
    "|2|airline|항공사\n",
    "|3|startCityCode, startCity|출발 공항 코드(IATA) 및 출발 공항명\n",
    "|4|arrivalCityCode, arrivalCity|도착 공항 코드(IATA) 및 도착 공항명\n",
    "|5|startDate|운항 스케줄 시행 시작일\n",
    "|6|endDate|운항 스케줄 시행 종료일\n",
    "|7|startTime, arrivalTime|항공기 출발 시각 및 도착 시각\n",
    "|8|flightPurpose|항공기 사용 목적(여객기 등)\n",
    "|9|mon~sun|운항 스케줄 시행 요일 여부(Y, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52053e67",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>flightNum</th><th scope=col>airline</th><th scope=col>domesticCityCode</th><th scope=col>domesticCity</th><th scope=col>internationalCityCode</th><th scope=col>internationalCity</th><th scope=col>startDate</th><th scope=col>endDate</th><th scope=col>recordedTime</th><th scope=col>ioType</th><th scope=col>flightPurpose</th><th scope=col>mon</th><th scope=col>tue</th><th scope=col>wed</th><th scope=col>thu</th><th scope=col>fri</th><th scope=col>sat</th><th scope=col>sun</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>KE830                 </td><td>KOREAN AIR            </td><td>PUS                   </td><td>부산/김해             </td><td>PEK                   </td><td>북경/베이징           </td><td>2010-10-31            </td><td>2011-01-01            </td><td>13:05                 </td><td>IN                    </td><td>여객기                </td><td>Y                     </td><td>Y                     </td><td>Y                     </td><td>Y                     </td><td>N                     </td><td>Y                     </td><td>N                     </td></tr>\n",
       "\t<tr><td>KE772                 </td><td>KOREAN AIR            </td><td>PUS                   </td><td>부산/김해             </td><td>CTS                   </td><td>삿포로                </td><td>2010-10-31            </td><td>2011-01-01            </td><td>20:05                 </td><td>IN                    </td><td>여객기                </td><td>N                     </td><td>Y                     </td><td>N                     </td><td>Y                     </td><td>N                     </td><td>Y                     </td><td>N                     </td></tr>\n",
       "\t<tr><td>KE829                 </td><td>KOREAN AIR            </td><td>PUS                   </td><td>부산/김해             </td><td>PEK                   </td><td>북경/베이징           </td><td>2010-10-31            </td><td>2011-01-01            </td><td>07:20                 </td><td>OUT                   </td><td>여객기                </td><td>Y                     </td><td>Y                     </td><td>Y                     </td><td>Y                     </td><td>N                     </td><td>Y                     </td><td>N                     </td></tr>\n",
       "\t<tr><td>MU273                 </td><td>CHINA EASTERN AIRLINES</td><td>CJU                   </td><td>제주                  </td><td>PEK                   </td><td>북경/베이징           </td><td>2010-10-31            </td><td>2011-01-10            </td><td>20:40                 </td><td>IN                    </td><td>여객기                </td><td>Y                     </td><td>N                     </td><td>N                     </td><td>N                     </td><td>N                     </td><td>N                     </td><td>N                     </td></tr>\n",
       "\t<tr><td>MU274                 </td><td>CHINA EASTERN AIRLINES</td><td>CJU                   </td><td>제주                  </td><td>PEK                   </td><td>북경/베이징           </td><td>2010-10-31            </td><td>2011-01-10            </td><td>21:30                 </td><td>OUT                   </td><td>여객기                </td><td>Y                     </td><td>N                     </td><td>N                     </td><td>N                     </td><td>N                     </td><td>N                     </td><td>N                     </td></tr>\n",
       "\t<tr><td>KE715                 </td><td>KOREAN AIR            </td><td>PUS                   </td><td>부산/김해             </td><td>NRT                   </td><td>동경/도쿄/나리타      </td><td>2010-10-31            </td><td>2011-02-05            </td><td>10:50                 </td><td>OUT                   </td><td>여객기                </td><td>Y                     </td><td>Y                     </td><td>Y                     </td><td>Y                     </td><td>Y                     </td><td>Y                     </td><td>Y                     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       " flightNum & airline & domesticCityCode & domesticCity & internationalCityCode & internationalCity & startDate & endDate & recordedTime & ioType & flightPurpose & mon & tue & wed & thu & fri & sat & sun\\\\\n",
       "\\hline\n",
       "\t KE830                  & KOREAN AIR             & PUS                    & 부산/김해              & PEK                    & 북경/베이징            & 2010-10-31             & 2011-01-01             & 13:05                  & IN                     & 여객기                 & Y                      & Y                      & Y                      & Y                      & N                      & Y                      & N                     \\\\\n",
       "\t KE772                  & KOREAN AIR             & PUS                    & 부산/김해              & CTS                    & 삿포로                 & 2010-10-31             & 2011-01-01             & 20:05                  & IN                     & 여객기                 & N                      & Y                      & N                      & Y                      & N                      & Y                      & N                     \\\\\n",
       "\t KE829                  & KOREAN AIR             & PUS                    & 부산/김해              & PEK                    & 북경/베이징            & 2010-10-31             & 2011-01-01             & 07:20                  & OUT                    & 여객기                 & Y                      & Y                      & Y                      & Y                      & N                      & Y                      & N                     \\\\\n",
       "\t MU273                  & CHINA EASTERN AIRLINES & CJU                    & 제주                   & PEK                    & 북경/베이징            & 2010-10-31             & 2011-01-10             & 20:40                  & IN                     & 여객기                 & Y                      & N                      & N                      & N                      & N                      & N                      & N                     \\\\\n",
       "\t MU274                  & CHINA EASTERN AIRLINES & CJU                    & 제주                   & PEK                    & 북경/베이징            & 2010-10-31             & 2011-01-10             & 21:30                  & OUT                    & 여객기                 & Y                      & N                      & N                      & N                      & N                      & N                      & N                     \\\\\n",
       "\t KE715                  & KOREAN AIR             & PUS                    & 부산/김해              & NRT                    & 동경/도쿄/나리타       & 2010-10-31             & 2011-02-05             & 10:50                  & OUT                    & 여객기                 & Y                      & Y                      & Y                      & Y                      & Y                      & Y                      & Y                     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| flightNum | airline | domesticCityCode | domesticCity | internationalCityCode | internationalCity | startDate | endDate | recordedTime | ioType | flightPurpose | mon | tue | wed | thu | fri | sat | sun |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| KE830                  | KOREAN AIR             | PUS                    | 부산/김해              | PEK                    | 북경/베이징            | 2010-10-31             | 2011-01-01             | 13:05                  | IN                     | 여객기                 | Y                      | Y                      | Y                      | Y                      | N                      | Y                      | N                      |\n",
       "| KE772                  | KOREAN AIR             | PUS                    | 부산/김해              | CTS                    | 삿포로                 | 2010-10-31             | 2011-01-01             | 20:05                  | IN                     | 여객기                 | N                      | Y                      | N                      | Y                      | N                      | Y                      | N                      |\n",
       "| KE829                  | KOREAN AIR             | PUS                    | 부산/김해              | PEK                    | 북경/베이징            | 2010-10-31             | 2011-01-01             | 07:20                  | OUT                    | 여객기                 | Y                      | Y                      | Y                      | Y                      | N                      | Y                      | N                      |\n",
       "| MU273                  | CHINA EASTERN AIRLINES | CJU                    | 제주                   | PEK                    | 북경/베이징            | 2010-10-31             | 2011-01-10             | 20:40                  | IN                     | 여객기                 | Y                      | N                      | N                      | N                      | N                      | N                      | N                      |\n",
       "| MU274                  | CHINA EASTERN AIRLINES | CJU                    | 제주                   | PEK                    | 북경/베이징            | 2010-10-31             | 2011-01-10             | 21:30                  | OUT                    | 여객기                 | Y                      | N                      | N                      | N                      | N                      | N                      | N                      |\n",
       "| KE715                  | KOREAN AIR             | PUS                    | 부산/김해              | NRT                    | 동경/도쿄/나리타       | 2010-10-31             | 2011-02-05             | 10:50                  | OUT                    | 여객기                 | Y                      | Y                      | Y                      | Y                      | Y                      | Y                      | Y                      |\n",
       "\n"
      ],
      "text/plain": [
       "  flightNum airline                domesticCityCode domesticCity\n",
       "1 KE830     KOREAN AIR             PUS              부산/김해   \n",
       "2 KE772     KOREAN AIR             PUS              부산/김해   \n",
       "3 KE829     KOREAN AIR             PUS              부산/김해   \n",
       "4 MU273     CHINA EASTERN AIRLINES CJU              제주        \n",
       "5 MU274     CHINA EASTERN AIRLINES CJU              제주        \n",
       "6 KE715     KOREAN AIR             PUS              부산/김해   \n",
       "  internationalCityCode internationalCity startDate  endDate    recordedTime\n",
       "1 PEK                   북경/베이징       2010-10-31 2011-01-01 13:05       \n",
       "2 CTS                   삿포로            2010-10-31 2011-01-01 20:05       \n",
       "3 PEK                   북경/베이징       2010-10-31 2011-01-01 07:20       \n",
       "4 PEK                   북경/베이징       2010-10-31 2011-01-10 20:40       \n",
       "5 PEK                   북경/베이징       2010-10-31 2011-01-10 21:30       \n",
       "6 NRT                   동경/도쿄/나리타  2010-10-31 2011-02-05 10:50       \n",
       "  ioType flightPurpose mon tue wed thu fri sat sun\n",
       "1 IN     여객기        Y   Y   Y   Y   N   Y   N  \n",
       "2 IN     여객기        N   Y   N   Y   N   Y   N  \n",
       "3 OUT    여객기        Y   Y   Y   Y   N   Y   N  \n",
       "4 IN     여객기        Y   N   N   N   N   N   N  \n",
       "5 OUT    여객기        Y   N   N   N   N   N   N  \n",
       "6 OUT    여객기        Y   Y   Y   Y   Y   Y   Y  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(totalData_FSI)  # 국제 항공기 운항 스케줄 데이터"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "644b0fa9",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다.\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:--- | :--- | :---\n",
    "|1|flightNum|항공편명\n",
    "|2|airline|항공사\n",
    "|3|domesticCityCode, domesticCity|국내 공항 코드(IATA) 및 국내 공항명\n",
    "|4|internationalCityCode, internationalCity|해외 공항 코드(IATA) 및 해외 공항명\n",
    "|5|startDate|운항 스케줄 시행 시작일\n",
    "|6|endDate|운항 스케줄 시행 종료일\n",
    "|7|recordedTime|국내 출발 또는 도착 기록 시각\n",
    "|8|ioType|입국 또는 출국 구분(IN, OUT)\n",
    "|9|flightPurpose|항공기 사용 목적(여객기 등)\n",
    "|10|mon~sun|운항 스케줄 시행 요일 여부(Y, N)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924311b7",
   "metadata": {},
   "source": [
    "##### 4. 노선별 소요시간 및 거리 정보 데이터  5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f528acd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>diType</th><th scope=col>cityCode1</th><th scope=col>cityCode2</th><th scope=col>distance.km</th><th scope=col>time.m</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>D  </td><td>CJJ</td><td>CJU</td><td>367</td><td>42 </td></tr>\n",
       "\t<tr><td>D  </td><td>CJU</td><td>CJJ</td><td>367</td><td>42 </td></tr>\n",
       "\t<tr><td>D  </td><td>CJU</td><td>GMP</td><td>449</td><td>48 </td></tr>\n",
       "\t<tr><td>D  </td><td>CJU</td><td>HIN</td><td>227</td><td>32 </td></tr>\n",
       "\t<tr><td>D  </td><td>CJU</td><td>ICN</td><td>438</td><td>47 </td></tr>\n",
       "\t<tr><td>D  </td><td>CJU</td><td>KPO</td><td>383</td><td>43 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " diType & cityCode1 & cityCode2 & distance.km & time.m\\\\\n",
       "\\hline\n",
       "\t D   & CJJ & CJU & 367 & 42 \\\\\n",
       "\t D   & CJU & CJJ & 367 & 42 \\\\\n",
       "\t D   & CJU & GMP & 449 & 48 \\\\\n",
       "\t D   & CJU & HIN & 227 & 32 \\\\\n",
       "\t D   & CJU & ICN & 438 & 47 \\\\\n",
       "\t D   & CJU & KPO & 383 & 43 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| diType | cityCode1 | cityCode2 | distance.km | time.m |\n",
       "|---|---|---|---|---|\n",
       "| D   | CJJ | CJU | 367 | 42  |\n",
       "| D   | CJU | CJJ | 367 | 42  |\n",
       "| D   | CJU | GMP | 449 | 48  |\n",
       "| D   | CJU | HIN | 227 | 32  |\n",
       "| D   | CJU | ICN | 438 | 47  |\n",
       "| D   | CJU | KPO | 383 | 43  |\n",
       "\n"
      ],
      "text/plain": [
       "  diType cityCode1 cityCode2 distance.km time.m\n",
       "1 D      CJJ       CJU       367         42    \n",
       "2 D      CJU       CJJ       367         42    \n",
       "3 D      CJU       GMP       449         48    \n",
       "4 D      CJU       HIN       227         32    \n",
       "5 D      CJU       ICN       438         47    \n",
       "6 D      CJU       KPO       383         43    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 저장용 데이터베이스\n",
    "totalData_TD <- data.frame()  \n",
    "\n",
    "# 데이터 추출용 URL\n",
    "url_TD <- paste0(serviceURL_TD, op_TD, parameter_TD)\n",
    "\n",
    "# Open API 호출 (page수 확인용)\n",
    "xmlDocument_TD <- xmlTreeParse(url_TD, useInternalNodes = TRUE, encoding = \"UTF-8\")\n",
    "\n",
    "# XML Root Node 추출\n",
    "rootNode_TD <- xmlRoot(xmlDocument_TD)\n",
    "\n",
    "# 오픈API 호출 결과 데이터의 개수\n",
    "numOfRows_TD <- as.numeric(xpathSApply(rootNode_TD, \"//numOfRows\",xmlValue))\n",
    "\n",
    "# 전체 데이터의 개수 확인\n",
    "totalCount_TD <- as.numeric(xpathSApply(rootNode_TD, \"//totalCount\",xmlValue))\n",
    "\n",
    "# 총 open API 호출 횟수 계산\n",
    "loopCount_TD <- round(totalCount_TD/numOfRows_TD, 0)\n",
    "\n",
    "# API 호출 횟수 보정\n",
    "if(loopCount_TD * numOfRows_TD < totalCount_TD){loopCount_TD <- loopCount_TD + 1}\n",
    "\n",
    "# Open API 호출\n",
    "for(i in 1:loopCount_TD){\n",
    "  url <- paste0(serviceURL_TD,op_TD,parameter_TD,\n",
    "                paste0(\"&pageNo=\", i)) # 반복 실행 때마다 페이지 위치 1씩 증가\n",
    "  \n",
    "  doc <- xmlTreeParse(url, useInternalNodes = TRUE, encoding = \"UTF-8\")\n",
    "  \n",
    "  # XML Root Node 추출\n",
    "  rootNode <- xmlRoot(doc)\n",
    "  \n",
    "  # item Node의 데이터 추출\n",
    "  xmlData <- xmlToDataFrame(nodes = getNodeSet(rootNode,'//item'), stringsAsFactor = FALSE)\n",
    "  totalData_TD <- rbind(totalData_TD, xmlData)\n",
    "}\n",
    "\n",
    "# 거리 및 시간 데이터 타입 변경(chr -> numeric)\n",
    "totalData_TD$km <- as.numeric(totalData_TD$km)\n",
    "totalData_TD$time <- as.numeric(totalData_TD$time)\n",
    "\n",
    "# 컬럼 순서 정돈\n",
    "totalData_TD <- totalData_TD %>% select(\"tof\", \"arp\", \"odp\", \"km\", \"time\") %>%\n",
    "                                  arrange(tof, arp, odp)\n",
    "\n",
    "# 컬럼명 바꾸기\n",
    "names(totalData_TD) <- c(\"diType\", \"cityCode1\", \"cityCode2\", \"distance.km\", \"time.m\")\n",
    "\n",
    "head(totalData_TD)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "149ab701",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다.\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:--- | :--- | :---\n",
    "|1|diType|국내선 또는 국제선 구분(D: 국내선, I: 국제선)\n",
    "|2|cityCode1, cityCode2|노선의 출도착 공항 코드(IATA)\n",
    "|3|distance.km|출도착 공항 간 거리(단위: km)\n",
    "|4|time.m|출도착 공항 간 소요 시간(단위: 분)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5c3838",
   "metadata": {},
   "source": [
    "##### 5. 기종별 연료 소모량  데이터 - \"국내 공항 연도별 항공기종 사용 현황(하단 참조)\"의 10년 간 사용량 0인 항공기는 제외 6) 7) 8) 9) 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d8bd8d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/latex": [
       "'LC\\_COLLATE=English\\_United States.1252;LC\\_CTYPE=English\\_United States.1252;LC\\_MONETARY=English\\_United States.1252;LC\\_NUMERIC=C;LC\\_TIME=English\\_United States.1252'"
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      "text/markdown": [
       "'LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252'"
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      "text/plain": [
       "[1] \"LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'LC_COLLATE=Korean_Korea.949;LC_CTYPE=Korean_Korea.949;LC_MONETARY=Korean_Korea.949;LC_NUMERIC=C;LC_TIME=Korean_Korea.949'"
      ],
      "text/latex": [
       "'LC\\_COLLATE=Korean\\_Korea.949;LC\\_CTYPE=Korean\\_Korea.949;LC\\_MONETARY=Korean\\_Korea.949;LC\\_NUMERIC=C;LC\\_TIME=Korean\\_Korea.949'"
      ],
      "text/markdown": [
       "'LC_COLLATE=Korean_Korea.949;LC_CTYPE=Korean_Korea.949;LC_MONETARY=Korean_Korea.949;LC_NUMERIC=C;LC_TIME=Korean_Korea.949'"
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      "text/plain": [
       "[1] \"LC_COLLATE=Korean_Korea.949;LC_CTYPE=Korean_Korea.949;LC_MONETARY=Korean_Korea.949;LC_NUMERIC=C;LC_TIME=Korean_Korea.949\""
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>model</th><th scope=col>ICAO</th><th scope=col>seats</th><th scope=col>fuelBurn.kg.km</th><th scope=col>fuelPerSeat.L.100km</th><th scope=col>sector.nmi</th><th scope=col>sector.km</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Boeing 737-300  </td><td>B737            </td><td>126             </td><td>3.49            </td><td>3.46            </td><td>507             </td><td> 938.964        </td></tr>\n",
       "\t<tr><td>Boeing 737-600  </td><td>B737            </td><td>110             </td><td>3.16            </td><td>3.59            </td><td>500             </td><td> 926.000        </td></tr>\n",
       "\t<tr><td>Boeing 737-700  </td><td>B737            </td><td>126             </td><td>3.21            </td><td>3.19            </td><td>500             </td><td> 926.000        </td></tr>\n",
       "\t<tr><td>Boeing 737 MAX 7</td><td>B737            </td><td>128             </td><td>2.85            </td><td>2.77            </td><td>660             </td><td>1222.320        </td></tr>\n",
       "\t<tr><td>Boeing 737 MAX 7</td><td>B737            </td><td>144             </td><td>3.39            </td><td>2.93            </td><td>600             </td><td>1111.200        </td></tr>\n",
       "\t<tr><td>Boeing 737-800  </td><td>B737            </td><td>162             </td><td>3.59            </td><td>2.77            </td><td>500             </td><td> 926.000        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " model & ICAO & seats & fuelBurn.kg.km & fuelPerSeat.L.100km & sector.nmi & sector.km\\\\\n",
       "\\hline\n",
       "\t Boeing 737-300   & B737             & 126              & 3.49             & 3.46             & 507              &  938.964        \\\\\n",
       "\t Boeing 737-600   & B737             & 110              & 3.16             & 3.59             & 500              &  926.000        \\\\\n",
       "\t Boeing 737-700   & B737             & 126              & 3.21             & 3.19             & 500              &  926.000        \\\\\n",
       "\t Boeing 737 MAX 7 & B737             & 128              & 2.85             & 2.77             & 660              & 1222.320        \\\\\n",
       "\t Boeing 737 MAX 7 & B737             & 144              & 3.39             & 2.93             & 600              & 1111.200        \\\\\n",
       "\t Boeing 737-800   & B737             & 162              & 3.59             & 2.77             & 500              &  926.000        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| model | ICAO | seats | fuelBurn.kg.km | fuelPerSeat.L.100km | sector.nmi | sector.km |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Boeing 737-300   | B737             | 126              | 3.49             | 3.46             | 507              |  938.964         |\n",
       "| Boeing 737-600   | B737             | 110              | 3.16             | 3.59             | 500              |  926.000         |\n",
       "| Boeing 737-700   | B737             | 126              | 3.21             | 3.19             | 500              |  926.000         |\n",
       "| Boeing 737 MAX 7 | B737             | 128              | 2.85             | 2.77             | 660              | 1222.320         |\n",
       "| Boeing 737 MAX 7 | B737             | 144              | 3.39             | 2.93             | 600              | 1111.200         |\n",
       "| Boeing 737-800   | B737             | 162              | 3.59             | 2.77             | 500              |  926.000         |\n",
       "\n"
      ],
      "text/plain": [
       "  model            ICAO seats fuelBurn.kg.km fuelPerSeat.L.100km sector.nmi\n",
       "1 Boeing 737-300   B737 126   3.49           3.46                507       \n",
       "2 Boeing 737-600   B737 110   3.16           3.59                500       \n",
       "3 Boeing 737-700   B737 126   3.21           3.19                500       \n",
       "4 Boeing 737 MAX 7 B737 128   2.85           2.77                660       \n",
       "5 Boeing 737 MAX 7 B737 144   3.39           2.93                600       \n",
       "6 Boeing 737-800   B737 162   3.59           2.77                500       \n",
       "  sector.km\n",
       "1  938.964 \n",
       "2  926.000 \n",
       "3  926.000 \n",
       "4 1222.320 \n",
       "5 1111.200 \n",
       "6  926.000 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 연료 소모량 데이터베이스 종합\n",
    "tempTotalData_FC <- data.frame()\n",
    "\n",
    "# 위키백과에서 기종별 연료 소모량 데이터베이스 크롤링\n",
    "Sys.setlocale(\"LC_ALL\", \"English\")\n",
    "tdist <- read_html(\"https://en.wikipedia.org/wiki/Fuel_economy_in_aircraft\")\n",
    "table <- tdist %>%\n",
    "  html_nodes(\".wikitable\") %>%\n",
    "  html_table(header = TRUE)\n",
    "Sys.setlocale(\"LC_ALL\", \"Korean\")\n",
    "\n",
    "# 크롤링한 데이터 가공\n",
    "for(i in 1:5){\n",
    "    tempTable <- table[i][[1]]\n",
    "    if(i == 1 | i == 3){\n",
    "        tempTable <- tempTable[,c(1,3,4,5)]\n",
    "        if(i == 1){tempTable$sector.nmi <- '300'}\n",
    "        else{tempTable$sector.nmi <- '1000'}\n",
    "    }\n",
    "    else{tempTable <- tempTable[,c(1,3,5,6,4)]}\n",
    "    colnames(tempTable) <- c(\"model\", \"seats\",\"fuelBurn.kg.km\",\"fuelPerSeat.L.100km\",\"sector.nmi\")\n",
    "    tempTotalData_FC <- rbind(tempTotalData_FC, tempTable)\n",
    "}\n",
    "\n",
    "tempTotalData_FC$model <- str_replace_all(tempTotalData_FC$model, '\\u00A0', \" \")\n",
    "tempTotalData_FC$model <- str_trim(tempTotalData_FC$model)\n",
    "tempTotalData_FC$fuelBurn.kg.km <- str_replace_all(tempTotalData_FC$fuelBurn.kg.km, '\\u00A0', \" \")\n",
    "tempTotalData_FC$fuelBurn.kg.km <- substr(tempTotalData_FC$fuelBurn.kg.km, 1,\n",
    "                                        str_locate(tempTotalData_FC$fuelBurn.kg.km, \" \")[,1])\n",
    "tempTotalData_FC$fuelPerSeat.L.100km <- str_replace_all( tempTotalData_FC$fuelPerSeat.L.100km,'\\u00A0', \" \")\n",
    "tempTotalData_FC$fuelPerSeat.L.100km <- substr(tempTotalData_FC$fuelPerSeat.L.100km, 1,\n",
    "                                             str_locate(tempTotalData_FC$fuelPerSeat.L.100km, \" \")[,1])\n",
    "tempTotalData_FC$sector.nmi <- str_replace_all(tempTotalData_FC$sector.nmi, '\\u00A0', \" \")\n",
    "tempTotalData_FC$sector.nmi <- str_replace_all(tempTotalData_FC$sector.nmi, ',', \"\")\n",
    "tempTotalData_FC$sector.nmi <- ifelse(str_detect(tempTotalData_FC$sector.nmi, \" \"),\n",
    "                                    substr(tempTotalData_FC$sector.nmi, 1, str_locate(tempTotalData_FC$sector.nmi, \" \")[,1]),\n",
    "                                    tempTotalData_FC$sector.nmi)\n",
    "\n",
    "# totalData_FC에 한국공항공사의 기종별 통계에 나온 항공기 종류만 추출\n",
    "totalData_FC <- tempTotalData_FC %>% filter(str_detect(model, \"Boeing 737\")) %>% mutate(ICAO = \"B737\")\n",
    "totalData_FC <- rbind(totalData_FC,\n",
    "                        tempTotalData_FC %>% filter(str_detect(model, \"Boeing 747\")) %>% mutate(ICAO = \"B747\"))\n",
    "totalData_FC <- rbind(totalData_FC,\n",
    "                        tempTotalData_FC %>% filter(str_detect(model, \"Boeing 767\")) %>% mutate(ICAO = \"B767\"))\n",
    "totalData_FC <- rbind(totalData_FC,\n",
    "                        tempTotalData_FC %>% filter(str_detect(model, \"Boeing 777\")) %>% mutate(ICAO = \"B777\"))\n",
    "totalData_FC <- rbind(totalData_FC,\n",
    "                        tempTotalData_FC %>% filter(str_detect(model, \"A330\")) %>% mutate(ICAO = \"A330\"))\n",
    "\n",
    "## 열 순서 변경\n",
    "totalData_FC <- totalData_FC %>% select(model, ICAO, seats, fuelBurn.kg.km, fuelPerSeat.L.100km, sector.nmi)\n",
    "\n",
    "# A300 항공기 정보 추가\n",
    "totalData_FC <- rbind(totalData_FC,c(\"Airbus A300-600\",\"A300\",289,round(62000/6852,2),NA,3700,6852))\n",
    "totalData_FC <- rbind(totalData_FC,c(\"Airbus A300-600R\",\"A300\",289,round(68165/7500,2),NA,4050,7500))\n",
    "\n",
    "# MD82 항공기 정보 추가\n",
    "## 연료는 Jet A-1(밀도: 0.8075(0.775와 0.840의 평균))을 사용한다고 가정\n",
    "totalData_FC <- rbind(totalData_FC,c(\"McDonnell Douglas/Boeing MD-82\",\"MD82\",172,round(22150*0.8075/3800,2),NA,2050,3800))\n",
    "\n",
    "# IL62 항공기 정보 추가\n",
    "## 연료는 Jet A-1(밀도: 0.8075(0.775와 0.840의 평균))을 사용한다고 가정\n",
    "totalData_FC <- rbind(totalData_FC,c(\"Ilyushin Il-62\",\"IL62\", 186, round(105300*0.8075/10000,2),NA,5400,10000))\n",
    "\n",
    "# 실수형 데이터 타입 변경 (chr -> numeric)\n",
    "totalData_FC$seats <- as.numeric(totalData_FC$seats)\n",
    "totalData_FC$fuelBurn.kg.km <- as.numeric(totalData_FC$fuelBurn.kg.km)\n",
    "totalData_FC$fuelPerSeat.L.100km <- as.numeric(totalData_FC$fuelPerSeat.L.100km)\n",
    "totalData_FC$sector.nmi <- as.numeric(totalData_FC$sector.nmi)\n",
    "\n",
    "## 운항거리 킬로미터(km) 환산치 추가\n",
    "totalData_FC$sector.km <- totalData_FC$sector.nmi * 1.852\n",
    "\n",
    "head(totalData_FC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112a5b01",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다.\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:--- | :--- | :---\n",
    "|1|model|항공기 모델\n",
    "|2|ICAO|항공기종 코드\n",
    "|3|seats|기내 좌석 수\n",
    "|4|fuelBurn.kg.km|연비(단위: kg/km)\n",
    "|5|fuelPerSeat.L.100km|100km 주행 시 좌석 1칸 당 소요되는 연료의 부피(단위: L/100km)\n",
    "|6|sector.nmi|최대 운항 거리(단위: nmi(해리))\n",
    "|7|sector.km|최대 운항 거리(단위: km)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ca82cd",
   "metadata": {},
   "source": [
    "##### 6. 국내 공항 연도별 항공기종 사용 현황  데이터 - 인천국제공항 제외 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b7d7f263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>공항명</th><th scope=col>B737</th><th scope=col>B747</th><th scope=col>B767</th><th scope=col>B777</th><th scope=col>DC8</th><th scope=col>DC10</th><th scope=col>MD11</th><th scope=col>MD82</th><th scope=col>A300</th><th scope=col>A330</th><th scope=col>IL62</th><th scope=col>TU154</th><th scope=col>F100</th><th scope=col>기타</th><th scope=col>계</th><th scope=col>연도</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>김포  </td><td> 79184</td><td>2231  </td><td>2821  </td><td>1976  </td><td>0     </td><td>0     </td><td>0     </td><td> 1    </td><td> 33408</td><td> 5766 </td><td>0     </td><td>0     </td><td>0     </td><td> 728  </td><td>126115</td><td>2011  </td></tr>\n",
       "\t<tr><td>김해  </td><td> 42411</td><td>   6  </td><td> 236  </td><td>  30  </td><td>0     </td><td>0     </td><td>0     </td><td>15    </td><td> 19134</td><td> 3799 </td><td>0     </td><td>0     </td><td>0     </td><td> 894  </td><td> 66525</td><td>2011  </td></tr>\n",
       "\t<tr><td>제주  </td><td> 70571</td><td> 388  </td><td> 367  </td><td>  69  </td><td>0     </td><td>0     </td><td>0     </td><td> 0    </td><td> 37769</td><td> 3530 </td><td>0     </td><td>0     </td><td>0     </td><td>   2  </td><td>112696</td><td>2011  </td></tr>\n",
       "\t<tr><td>대구  </td><td>  4924</td><td>   0  </td><td> 132  </td><td>   0  </td><td>0     </td><td>0     </td><td>0     </td><td> 0    </td><td>  3427</td><td>    0 </td><td>0     </td><td>0     </td><td>0     </td><td>   6  </td><td>  8489</td><td>2011  </td></tr>\n",
       "\t<tr><td>광주  </td><td>  5007</td><td>   0  </td><td> 346  </td><td>   0  </td><td>0     </td><td>0     </td><td>0     </td><td> 0    </td><td>  5428</td><td>    0 </td><td>0     </td><td>0     </td><td>0     </td><td>   0  </td><td> 10781</td><td>2011  </td></tr>\n",
       "\t<tr><td>무안  </td><td>    44</td><td>   0  </td><td>   1  </td><td>   1  </td><td>0     </td><td>0     </td><td>0     </td><td> 5    </td><td>   822</td><td>    2 </td><td>0     </td><td>0     </td><td>0     </td><td>   0  </td><td>   875</td><td>2011  </td></tr>\n",
       "\t<tr><td>청주  </td><td>  5777</td><td>  44  </td><td>   0  </td><td>   0  </td><td>0     </td><td>0     </td><td>0     </td><td> 0    </td><td>  3261</td><td>    0 </td><td>0     </td><td>0     </td><td>0     </td><td>   0  </td><td>  9082</td><td>2011  </td></tr>\n",
       "\t<tr><td>양양  </td><td>     1</td><td>   0  </td><td>   0  </td><td>   0  </td><td>0     </td><td>0     </td><td>0     </td><td> 0    </td><td>    71</td><td>    0 </td><td>0     </td><td>0     </td><td>0     </td><td>   0  </td><td>    72</td><td>2011  </td></tr>\n",
       "\t<tr><td>여수  </td><td>  3742</td><td>   0  </td><td>   0  </td><td>   0  </td><td>0     </td><td>0     </td><td>0     </td><td> 0    </td><td>  2061</td><td>    0 </td><td>0     </td><td>0     </td><td>0     </td><td>   0  </td><td>  5803</td><td>2011  </td></tr>\n",
       "\t<tr><td>울산  </td><td>  5510</td><td>   0  </td><td>   0  </td><td>   0  </td><td>0     </td><td>0     </td><td>0     </td><td> 0    </td><td>   605</td><td>    0 </td><td>0     </td><td>0     </td><td>0     </td><td>   0  </td><td>  6115</td><td>2011  </td></tr>\n",
       "\t<tr><td>사천  </td><td>  1626</td><td>   0  </td><td>   0  </td><td>   0  </td><td>0     </td><td>0     </td><td>0     </td><td> 0    </td><td>   200</td><td>    0 </td><td>0     </td><td>0     </td><td>0     </td><td>   0  </td><td>  1826</td><td>2011  </td></tr>\n",
       "\t<tr><td>포항  </td><td>  2860</td><td>   0  </td><td>   0  </td><td>   0  </td><td>0     </td><td>0     </td><td>0     </td><td> 0    </td><td>   682</td><td>    0 </td><td>0     </td><td>0     </td><td>0     </td><td>   0  </td><td>  3542</td><td>2011  </td></tr>\n",
       "\t<tr><td>군산  </td><td>  1398</td><td>   0  </td><td>   0  </td><td>   0  </td><td>0     </td><td>0     </td><td>0     </td><td> 0    </td><td>     0</td><td>    0 </td><td>0     </td><td>0     </td><td>0     </td><td>   0  </td><td>  1398</td><td>2011  </td></tr>\n",
       "\t<tr><td>원주  </td><td>   696</td><td>   0  </td><td>   0  </td><td>   0  </td><td>0     </td><td>0     </td><td>0     </td><td> 0    </td><td>     0</td><td>    0 </td><td>0     </td><td>0     </td><td>0     </td><td>   0  </td><td>   696</td><td>2011  </td></tr>\n",
       "\t<tr><td>합계  </td><td>223751</td><td>2669  </td><td>3903  </td><td>2076  </td><td>0     </td><td>0     </td><td>0     </td><td>21    </td><td>106868</td><td>13097 </td><td>0     </td><td>0     </td><td>0     </td><td>1630  </td><td>354015</td><td>2011  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllll}\n",
       " 공항명 & B737 & B747 & B767 & B777 & DC8 & DC10 & MD11 & MD82 & A300 & A330 & IL62 & TU154 & F100 & 기타 & 계 & 연도\\\\\n",
       "\\hline\n",
       "\t 김포   &  79184 & 2231   & 2821   & 1976   & 0      & 0      & 0      &  1     &  33408 &  5766  & 0      & 0      & 0      &  728   & 126115 & 2011  \\\\\n",
       "\t 김해   &  42411 &    6   &  236   &   30   & 0      & 0      & 0      & 15     &  19134 &  3799  & 0      & 0      & 0      &  894   &  66525 & 2011  \\\\\n",
       "\t 제주   &  70571 &  388   &  367   &   69   & 0      & 0      & 0      &  0     &  37769 &  3530  & 0      & 0      & 0      &    2   & 112696 & 2011  \\\\\n",
       "\t 대구   &   4924 &    0   &  132   &    0   & 0      & 0      & 0      &  0     &   3427 &     0  & 0      & 0      & 0      &    6   &   8489 & 2011  \\\\\n",
       "\t 광주   &   5007 &    0   &  346   &    0   & 0      & 0      & 0      &  0     &   5428 &     0  & 0      & 0      & 0      &    0   &  10781 & 2011  \\\\\n",
       "\t 무안   &     44 &    0   &    1   &    1   & 0      & 0      & 0      &  5     &    822 &     2  & 0      & 0      & 0      &    0   &    875 & 2011  \\\\\n",
       "\t 청주   &   5777 &   44   &    0   &    0   & 0      & 0      & 0      &  0     &   3261 &     0  & 0      & 0      & 0      &    0   &   9082 & 2011  \\\\\n",
       "\t 양양   &      1 &    0   &    0   &    0   & 0      & 0      & 0      &  0     &     71 &     0  & 0      & 0      & 0      &    0   &     72 & 2011  \\\\\n",
       "\t 여수   &   3742 &    0   &    0   &    0   & 0      & 0      & 0      &  0     &   2061 &     0  & 0      & 0      & 0      &    0   &   5803 & 2011  \\\\\n",
       "\t 울산   &   5510 &    0   &    0   &    0   & 0      & 0      & 0      &  0     &    605 &     0  & 0      & 0      & 0      &    0   &   6115 & 2011  \\\\\n",
       "\t 사천   &   1626 &    0   &    0   &    0   & 0      & 0      & 0      &  0     &    200 &     0  & 0      & 0      & 0      &    0   &   1826 & 2011  \\\\\n",
       "\t 포항   &   2860 &    0   &    0   &    0   & 0      & 0      & 0      &  0     &    682 &     0  & 0      & 0      & 0      &    0   &   3542 & 2011  \\\\\n",
       "\t 군산   &   1398 &    0   &    0   &    0   & 0      & 0      & 0      &  0     &      0 &     0  & 0      & 0      & 0      &    0   &   1398 & 2011  \\\\\n",
       "\t 원주   &    696 &    0   &    0   &    0   & 0      & 0      & 0      &  0     &      0 &     0  & 0      & 0      & 0      &    0   &    696 & 2011  \\\\\n",
       "\t 합계   & 223751 & 2669   & 3903   & 2076   & 0      & 0      & 0      & 21     & 106868 & 13097  & 0      & 0      & 0      & 1630   & 354015 & 2011  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 공항명 | B737 | B747 | B767 | B777 | DC8 | DC10 | MD11 | MD82 | A300 | A330 | IL62 | TU154 | F100 | 기타 | 계 | 연도 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 김포   |  79184 | 2231   | 2821   | 1976   | 0      | 0      | 0      |  1     |  33408 |  5766  | 0      | 0      | 0      |  728   | 126115 | 2011   |\n",
       "| 김해   |  42411 |    6   |  236   |   30   | 0      | 0      | 0      | 15     |  19134 |  3799  | 0      | 0      | 0      |  894   |  66525 | 2011   |\n",
       "| 제주   |  70571 |  388   |  367   |   69   | 0      | 0      | 0      |  0     |  37769 |  3530  | 0      | 0      | 0      |    2   | 112696 | 2011   |\n",
       "| 대구   |   4924 |    0   |  132   |    0   | 0      | 0      | 0      |  0     |   3427 |     0  | 0      | 0      | 0      |    6   |   8489 | 2011   |\n",
       "| 광주   |   5007 |    0   |  346   |    0   | 0      | 0      | 0      |  0     |   5428 |     0  | 0      | 0      | 0      |    0   |  10781 | 2011   |\n",
       "| 무안   |     44 |    0   |    1   |    1   | 0      | 0      | 0      |  5     |    822 |     2  | 0      | 0      | 0      |    0   |    875 | 2011   |\n",
       "| 청주   |   5777 |   44   |    0   |    0   | 0      | 0      | 0      |  0     |   3261 |     0  | 0      | 0      | 0      |    0   |   9082 | 2011   |\n",
       "| 양양   |      1 |    0   |    0   |    0   | 0      | 0      | 0      |  0     |     71 |     0  | 0      | 0      | 0      |    0   |     72 | 2011   |\n",
       "| 여수   |   3742 |    0   |    0   |    0   | 0      | 0      | 0      |  0     |   2061 |     0  | 0      | 0      | 0      |    0   |   5803 | 2011   |\n",
       "| 울산   |   5510 |    0   |    0   |    0   | 0      | 0      | 0      |  0     |    605 |     0  | 0      | 0      | 0      |    0   |   6115 | 2011   |\n",
       "| 사천   |   1626 |    0   |    0   |    0   | 0      | 0      | 0      |  0     |    200 |     0  | 0      | 0      | 0      |    0   |   1826 | 2011   |\n",
       "| 포항   |   2860 |    0   |    0   |    0   | 0      | 0      | 0      |  0     |    682 |     0  | 0      | 0      | 0      |    0   |   3542 | 2011   |\n",
       "| 군산   |   1398 |    0   |    0   |    0   | 0      | 0      | 0      |  0     |      0 |     0  | 0      | 0      | 0      |    0   |   1398 | 2011   |\n",
       "| 원주   |    696 |    0   |    0   |    0   | 0      | 0      | 0      |  0     |      0 |     0  | 0      | 0      | 0      |    0   |    696 | 2011   |\n",
       "| 합계   | 223751 | 2669   | 3903   | 2076   | 0      | 0      | 0      | 21     | 106868 | 13097  | 0      | 0      | 0      | 1630   | 354015 | 2011   |\n",
       "\n"
      ],
      "text/plain": [
       "   공항명 B737   B747 B767 B777 DC8 DC10 MD11 MD82 A300   A330  IL62 TU154 F100\n",
       "1  김포    79184 2231 2821 1976 0   0    0     1    33408  5766 0    0     0   \n",
       "2  김해    42411    6  236   30 0   0    0    15    19134  3799 0    0     0   \n",
       "3  제주    70571  388  367   69 0   0    0     0    37769  3530 0    0     0   \n",
       "4  대구     4924    0  132    0 0   0    0     0     3427     0 0    0     0   \n",
       "5  광주     5007    0  346    0 0   0    0     0     5428     0 0    0     0   \n",
       "6  무안       44    0    1    1 0   0    0     5      822     2 0    0     0   \n",
       "7  청주     5777   44    0    0 0   0    0     0     3261     0 0    0     0   \n",
       "8  양양        1    0    0    0 0   0    0     0       71     0 0    0     0   \n",
       "9  여수     3742    0    0    0 0   0    0     0     2061     0 0    0     0   \n",
       "10 울산     5510    0    0    0 0   0    0     0      605     0 0    0     0   \n",
       "11 사천     1626    0    0    0 0   0    0     0      200     0 0    0     0   \n",
       "12 포항     2860    0    0    0 0   0    0     0      682     0 0    0     0   \n",
       "13 군산     1398    0    0    0 0   0    0     0        0     0 0    0     0   \n",
       "14 원주      696    0    0    0 0   0    0     0        0     0 0    0     0   \n",
       "15 합계   223751 2669 3903 2076 0   0    0    21   106868 13097 0    0     0   \n",
       "   기타 계     연도\n",
       "1   728 126115 2011\n",
       "2   894  66525 2011\n",
       "3     2 112696 2011\n",
       "4     6   8489 2011\n",
       "5     0  10781 2011\n",
       "6     0    875 2011\n",
       "7     0   9082 2011\n",
       "8     0     72 2011\n",
       "9     0   5803 2011\n",
       "10    0   6115 2011\n",
       "11    0   1826 2011\n",
       "12    0   3542 2011\n",
       "13    0   1398 2011\n",
       "14    0    696 2011\n",
       "15 1630 354015 2011"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 파일 읽기\n",
    "totalData_UA <- as.data.frame(read_excel(\"기종별 통계.xlsx\",\n",
    "                                 sheet = \"2011\",\n",
    "                                 range=\"A7:P22\"))\n",
    "totalData_UA$연도 <- rep(2011,times=nrow(totalData_UA))\n",
    "\n",
    "for(i in 2012:2020){\n",
    "    data <- as.data.frame(read_excel(\"기종별 통계.xlsx\",\n",
    "                                 sheet = as.character(i),\n",
    "                                 range=\"A7:P22\"))\n",
    "    data$연도 <- rep(i,times=nrow(data))\n",
    "    totalData_UA <- bind_rows(totalData_UA, data)\n",
    "}\n",
    "\n",
    "totalData_UA %>% filter(연도==\"2011\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c059783e",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다.\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:--- | :--- | :---\n",
    "1|공항명|공항명\n",
    "2|B737 ~ F100, 기타|각 항공기종의 사용량(단위: 회)\n",
    "3|계|모든 항공기종 사용량의 총합\n",
    "4|연도|연도(범위: 2011~2020년)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27dca4e7",
   "metadata": {},
   "source": [
    "### 2-1-1. 국내 공항 총 운항편수 및 연간 온실가스 배출량 변화"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efcf17c6",
   "metadata": {},
   "source": [
    "해당 시각화에서는 **1. 공항 온실가스 데이터** 및 **2. 수송실적통계 데이터**를 사용했습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51e61814",
   "metadata": {},
   "source": [
    "#### 데이터 가공"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9324c8b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>year</th><th scope=col>stats</th><th scope=col>fuelType</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>2011            </td><td> 653312.5       </td><td>기타간접온실가스</td></tr>\n",
       "\t<tr><td>2012            </td><td> 715757.5       </td><td>기타간접온실가스</td></tr>\n",
       "\t<tr><td>2013            </td><td> 755860.5       </td><td>기타간접온실가스</td></tr>\n",
       "\t<tr><td>2014            </td><td> 790155.5       </td><td>기타간접온실가스</td></tr>\n",
       "\t<tr><td>2015            </td><td> 904992.5       </td><td>기타간접온실가스</td></tr>\n",
       "\t<tr><td>2016            </td><td>1070124.0       </td><td>기타간접온실가스</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " year & stats & fuelType\\\\\n",
       "\\hline\n",
       "\t 2011             &  653312.5        & 기타간접온실가스\\\\\n",
       "\t 2012             &  715757.5        & 기타간접온실가스\\\\\n",
       "\t 2013             &  755860.5        & 기타간접온실가스\\\\\n",
       "\t 2014             &  790155.5        & 기타간접온실가스\\\\\n",
       "\t 2015             &  904992.5        & 기타간접온실가스\\\\\n",
       "\t 2016             & 1070124.0        & 기타간접온실가스\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| year | stats | fuelType |\n",
       "|---|---|---|\n",
       "| 2011             |  653312.5        | 기타간접온실가스 |\n",
       "| 2012             |  715757.5        | 기타간접온실가스 |\n",
       "| 2013             |  755860.5        | 기타간접온실가스 |\n",
       "| 2014             |  790155.5        | 기타간접온실가스 |\n",
       "| 2015             |  904992.5        | 기타간접온실가스 |\n",
       "| 2016             | 1070124.0        | 기타간접온실가스 |\n",
       "\n"
      ],
      "text/plain": [
       "  year stats     fuelType        \n",
       "1 2011  653312.5 기타간접온실가스\n",
       "2 2012  715757.5 기타간접온실가스\n",
       "3 2013  755860.5 기타간접온실가스\n",
       "4 2014  790155.5 기타간접온실가스\n",
       "5 2015  904992.5 기타간접온실가스\n",
       "6 2016 1070124.0 기타간접온실가스"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 데이터 시각화를 위해 온실가스 데이터 형태 변형\n",
    "fuel1Data <- totalData_GG[,c(\"yyyy\", \"fuel3\")]\n",
    "fuel1Data <- cbind(fuel1Data, rep(\"기타간접온실가스\", times=nrow(fuel1Data)))\n",
    "colnames(fuel1Data) <- c(\"year\", \"stats\", \"fuelType\")\n",
    "fuel2Data <- totalData_GG[,c(\"yyyy\", \"fuel2\")]\n",
    "fuel2Data <- cbind(fuel2Data, rep(\"간접온실가스\", times=nrow(fuel2Data)))\n",
    "colnames(fuel2Data) <- c(\"year\", \"stats\", \"fuelType\")\n",
    "fuel3Data <- totalData_GG[,c(\"yyyy\", \"fuel1\")]\n",
    "fuel3Data <- cbind(fuel3Data, rep(\"직접온실가스\", times=nrow(fuel3Data)))\n",
    "colnames(fuel3Data) <- c(\"year\", \"stats\", \"fuelType\")\n",
    "totalFuelData <- totalData_GG[,c(\"yyyy\",\"totalTco2e\")]\n",
    "totalFuelData <- cbind(totalFuelData, rep(\"온실가스총합\", times=nrow(totalFuelData)))\n",
    "colnames(totalFuelData) <- c(\"year\", \"stats\", \"fuelType\")\n",
    "fuelData <- rbind(fuel1Data,fuel2Data)\n",
    "fuelData <- rbind(fuelData,fuel3Data)\n",
    "fuelData <- rbind(fuelData, totalFuelData)\n",
    "\n",
    "# 연도별 총 국내 공항(인천 포함) 출발+도착 건수 추출\n",
    "subflgtData <- totalData_AS %>% select(\"subflgt\", \"year\", \"airport\") %>% filter(airport==\"총계\")\n",
    "\n",
    "head(fuelData)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33302b7c",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다.\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:--- | :--- | :---\n",
    "1|year|연도(범위: 2011~2020년)\n",
    "2|stats|유형별 온실가스 발생량(단위: tCO2e)\n",
    "3|fuelType|온실가스 유형(직접온실가스, 간접온실가스, 기타간접온실가스, 온실가스총합)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "366eb04b",
   "metadata": {},
   "source": [
    "#### 데이터 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4f0259bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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32cvd0OSFNjoMHAENbRMPqmK5DaARIN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\nEIUAiRCFAIkQhQCJEIUAiRCF/H+OXIY/xgUmiQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 시각화용 데이터 제작\n",
    "fig1Data <- inner_join(fuelData, subflgtData, by=\"year\") %>% filter(fuelType == \"온실가스총합\")\n",
    "\n",
    "# 보정값\n",
    "ratio <- mean(fig1Data$subflgt)/mean(fig1Data$stats)*0.001\n",
    "\n",
    "ggplot() + theme_bw() + \n",
    "           geom_bar(data=fig1Data,aes(x=year,y=subflgt/1000,color=subflgt, fill=subflgt),stat=\"identity\",width=0.7,alpha=0.2) +\n",
    "           geom_line(data=fig1Data,aes(x=year,y=stats*ratio),color=\"cadetblue4\", size=0.7,group=1) +\n",
    "           coord_cartesian(ylim=c(0,1000)) +\n",
    "           scale_color_gradient(low=\"#CC66FF\", high=\"#FF9999\")+\n",
    "           scale_fill_gradient(low=\"#CC66FF\", high=\"#FF9999\")+\n",
    "           labs(title=\"연도별 국내 공항 총 운항 편수(막대그래프) 및 총 온실가스 배출량(꺾은선그래프)\", x=\"연도\",\n",
    "                fill=\"운항 편수   \", color=\"운항 편수   \") + \n",
    "           scale_y_continuous(name = \"운항 편수\\n(출발 및 도착 합계, 단위: 천 편)\",\n",
    "                              sec.axis = sec_axis(~./ratio/1000, name=\"온실가스 배출량 (단위: ktCO2e)\"))+\n",
    "           theme(legend.position = \"none\",\n",
    "                 title = element_text(size=12,face='bold'),\n",
    "                 axis.title.x =  element_text(size=12,face='bold'),\n",
    "                 axis.title.y = element_text(size=12,face='bold')\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b7fab7",
   "metadata": {},
   "source": [
    "2011년부터 2019년까지 국내 항공기 온실가스 배출량이 증가하고 있음을 알 수 있습니다. 그러나 2020년 항공기 운항량의 감소로 온실가스 배출량이 급격히 하락하였습니다.\n",
    "\n",
    "위 그래프에서 볼 수 있듯 2011년부터 2019년까지 국내 항공기 운항 편수 역시 증가했습니다. 다만 2020년 코로나19 범유행으로 운항 편수가 눈에 띄게 감소했습니다. 대체적으로 온실가스 배출량과 **양의 상관관계**에 있음을 알 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b13b70",
   "metadata": {},
   "source": [
    "### 2-1-2. 국내/국제별 총 운항 거리 (운항 편수 * 노선 거리) 변화"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0627f9f7",
   "metadata": {},
   "source": [
    "해당 시각화에서는 **1. 공항 온실가스 데이터**, **3. 국내/국제 항공기 운항 스케줄 데이터** 및 **4. 노선별 소요시간 및 거리 정보 데이터**를 사용했습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a935af7f",
   "metadata": {},
   "source": [
    "#### 데이터 가공"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1bb8ece6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>flightYear</th><th scope=col>cityCode1</th><th scope=col>cityCode2</th><th scope=col>diType</th><th scope=col>distance.km</th><th scope=col>time.m</th><th scope=col>totalFlight</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>2011  </td><td>CJU   </td><td>GMP   </td><td>D     </td><td>449   </td><td>48    </td><td>233483</td></tr>\n",
       "\t<tr><td>2011  </td><td>GMP   </td><td>PUS   </td><td>D     </td><td>327   </td><td>39    </td><td> 54955</td></tr>\n",
       "\t<tr><td>2011  </td><td>CJU   </td><td>PUS   </td><td>D     </td><td>291   </td><td>36    </td><td> 53532</td></tr>\n",
       "\t<tr><td>2011  </td><td>CJJ   </td><td>CJU   </td><td>D     </td><td>367   </td><td>42    </td><td> 21390</td></tr>\n",
       "\t<tr><td>2011  </td><td>GMP   </td><td>RSU   </td><td>D     </td><td>311   </td><td>38    </td><td> 16380</td></tr>\n",
       "\t<tr><td>2011  </td><td>CJU   </td><td>KWJ   </td><td>D     </td><td>182   </td><td>28    </td><td> 16365</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " flightYear & cityCode1 & cityCode2 & diType & distance.km & time.m & totalFlight\\\\\n",
       "\\hline\n",
       "\t 2011   & CJU    & GMP    & D      & 449    & 48     & 233483\\\\\n",
       "\t 2011   & GMP    & PUS    & D      & 327    & 39     &  54955\\\\\n",
       "\t 2011   & CJU    & PUS    & D      & 291    & 36     &  53532\\\\\n",
       "\t 2011   & CJJ    & CJU    & D      & 367    & 42     &  21390\\\\\n",
       "\t 2011   & GMP    & RSU    & D      & 311    & 38     &  16380\\\\\n",
       "\t 2011   & CJU    & KWJ    & D      & 182    & 28     &  16365\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| flightYear | cityCode1 | cityCode2 | diType | distance.km | time.m | totalFlight |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 2011   | CJU    | GMP    | D      | 449    | 48     | 233483 |\n",
       "| 2011   | GMP    | PUS    | D      | 327    | 39     |  54955 |\n",
       "| 2011   | CJU    | PUS    | D      | 291    | 36     |  53532 |\n",
       "| 2011   | CJJ    | CJU    | D      | 367    | 42     |  21390 |\n",
       "| 2011   | GMP    | RSU    | D      | 311    | 38     |  16380 |\n",
       "| 2011   | CJU    | KWJ    | D      | 182    | 28     |  16365 |\n",
       "\n"
      ],
      "text/plain": [
       "  flightYear cityCode1 cityCode2 diType distance.km time.m totalFlight\n",
       "1 2011       CJU       GMP       D      449         48     233483     \n",
       "2 2011       GMP       PUS       D      327         39      54955     \n",
       "3 2011       CJU       PUS       D      291         36      53532     \n",
       "4 2011       CJJ       CJU       D      367         42      21390     \n",
       "5 2011       GMP       RSU       D      311         38      16380     \n",
       "6 2011       CJU       KWJ       D      182         28      16365     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 국내선 데이터 가공\n",
    "regData_D <- data.frame()\n",
    "\n",
    "# 운항 일정 및 운항 요일 기반 추정운항일수 추출\n",
    "for(k in 2011:2020){\n",
    "    subData_FSD <- totalData_FSD %>% select(\"flightNum\", \"startCityCode\", \"arrivalCityCode\", \"startDate\", \"endDate\",\n",
    "                                                     \"mon\", \"tue\", \"wed\", \"thu\", \"fri\", \"sat\", \"sun\") %>%\n",
    "                                                filter(format(endDate, \"%Y\")==as.character(k) | format(startDate, \"%Y\")==as.character(k))\n",
    "    # 정확한 운항 일수 추출 위해 연도별 날짜 조정\n",
    "    subData_FSD$revisedStartDate <- as.Date(ifelse(subData_FSD$startDate < as.Date(paste0(as.character(i),'-01-01')),\n",
    "                                                format(as.Date(paste0(as.character(k),'-01-01')),\"%Y-%m-%d\"),\n",
    "                                                format(subData_FSD$startDate, \"%Y-%m-%d\")))\n",
    "    subData_FSD$revisedEndDate <- as.Date(ifelse(subData_FSD$endDate > paste0(as.character(i),'-12-31'),\n",
    "                                              format(as.Date(paste0(as.character(k),'-12-31')),\"%Y-%m-%d\"),\n",
    "                                              format(subData_FSD$endDate, \"%Y-%m-%d\")))\n",
    "\n",
    "    # 요일별 운항일수 데이터 제작 (flightDay_FSD)\n",
    "    dateInterval_FSD <- subData_FSD$revisedEndDate - subData_FSD$revisedStartDate  # 운행일수\n",
    "    flightPerDay_FSD <- floor(dateInterval_FSD/7)  # 요일 당 최소운항편수 리스트\n",
    "    flightAddDay_FSD <- floor((dateInterval_FSD/7 - flightPerDay_FSD)*7)  # 보정용 요일별 운항편수 리스트\n",
    "    flightDay_FSD <- flightPerDay_FSD  # 요일별 운항일수 데이터\n",
    "    for(i in 1:6){\n",
    "        flightDay_FSD <- cbind(flightDay_FSD, flightPerDay_FSD)\n",
    "    }\n",
    "    colnames(flightDay_FSD) <- list(1,2,3,4,5,6,7)  # 컬럼명을 요일로 수정(1 = 월요일)\n",
    "    revisedStartDate_FSD <- as.numeric(factor(weekdays(subData_FSD$revisedStartDate)))  # 항공편별 시작 요일 숫자 환산값\n",
    "    for(i in 1:length(flightAddDay_FSD)){\n",
    "        for(j in 0:6){\n",
    "            if(j <= flightAddDay_FSD[i]){\n",
    "                flightDay_FSD[i,(j+revisedStartDate_FSD[i]-1)%%7+1] <- flightDay_FSD[i,(j+revisedStartDate_FSD[i]-1)%%7+1]+1\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    # 기간 내 요일별 추정 운행일수 합산\n",
    "    totalFlight_FSD <- rep(0, times=nrow(subData_FSD))\n",
    "    flightWeekdays_FSD <- subData_FSD %>% select(\"mon\",\"tue\",\"wed\",\"thu\",\"fri\",\"sat\",\"sun\")\n",
    "    for(i in 1:nrow(subData_FSD)){\n",
    "        for(j in 1:7){\n",
    "            if(flightWeekdays_FSD[i,j] != \"N\"){\n",
    "                totalFlight_FSD[i] <- totalFlight_FSD[i]+flightDay_FSD[i,j]\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    # 총 추정 운행일수 데이터를 subData_FSD에 합산\n",
    "    subData_FSD$totalFlight <- totalFlight_FSD\n",
    "\n",
    "    # 공항별 총 운항 편수(출발+도착) 추출\n",
    "    ## 임시로 startCityCode가 arrivalCityCode보다 뒤면 역순, 아니면 정순으로 컬럼 새로 지정\n",
    "    subData_FSD$cityCode1 <- ifelse(as.character(subData_FSD$startCityCode) <= as.character(subData_FSD$arrivalCityCode),\n",
    "                                             as.character(subData_FSD$startCityCode),\n",
    "                                             as.character(subData_FSD$arrivalCityCode))\n",
    "    subData_FSD$cityCode2 <- ifelse(as.character(subData_FSD$startCityCode) <= as.character(subData_FSD$arrivalCityCode),\n",
    "                                               as.character(subData_FSD$arrivalCityCode),\n",
    "                                               as.character(subData_FSD$startCityCode))\n",
    "\n",
    "    # 공항별 총 운항 편수(출발+도착) 추출\n",
    "    subData_FSD <- subData_FSD %>% group_by(cityCode1, cityCode2) %>%\n",
    "                                            summarise(totalFlight=sum(totalFlight))\n",
    "\n",
    "    # 연도 및 국내/국제 타입 컬럼 추가\n",
    "    subData_FSD$flightYear <- rep(k, times=nrow(subData_FSD))\n",
    "    subData_FSD$diType <- rep(\"D\", times=nrow(subData_FSD))\n",
    "\n",
    "    # 출발/도착지 간 노선 거리 및 소요 시간 추가\n",
    "    subData_FSD <- left_join(subData_FSD, totalData_TD, by=c(\"diType\", \"cityCode1\", \"cityCode2\"))\n",
    "    regData_D <- bind_rows(regData_D, subData_FSD)\n",
    "}\n",
    "# 순서 조정\n",
    "regData_D <- regData_D %>% select(flightYear, cityCode1, cityCode2, diType, distance.km, time.m, totalFlight)\n",
    "\n",
    "# 시각화 위해 운항 연도(flightYear) 타입 변경 (num -> chr)\n",
    "regData_D$flightYear <- as.character(regData_D$flightYear)\n",
    "\n",
    "head(regData_D %>% arrange(flightYear, desc(totalFlight)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3428ced",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다.\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:--- | :--- | :---\n",
    "|1|flightYear|운항 연도(범위: 2011~2020년)\n",
    "|2|diType|국내선 또는 국제선 구분(D: 국내선, I: 국제선)\n",
    "|3|cityCode1, cityCode2|노선의 출도착 공항 코드(IATA)\n",
    "|4|distance.km|출도착 공항 간 거리(단위: km)\n",
    "|5|time.m|출도착 공항 간 소요 시간(단위: 분)\n",
    "|**6**|**totalFlight**|**운항 일정 및 운항 요일 기반 연내 총 추정 운항 편수**(1->2 및 2->1 합계)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "62b8f7f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n",
      "`summarise()` has grouped output by 'cityCode1'. You can override using the `.groups` argument.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>flightYear</th><th scope=col>cityCode1</th><th scope=col>cityCode2</th><th scope=col>diType</th><th scope=col>distance.km</th><th scope=col>time.m</th><th scope=col>totalFlight</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>2011 </td><td>GMP  </td><td>HND  </td><td>I    </td><td>1183 </td><td>103  </td><td>22454</td></tr>\n",
       "\t<tr><td>2011 </td><td>GMP  </td><td>KIX  </td><td>I    </td><td> 837 </td><td> 77  </td><td>13717</td></tr>\n",
       "\t<tr><td>2011 </td><td>GMP  </td><td>SHA  </td><td>I    </td><td> 866 </td><td> 79  </td><td> 9012</td></tr>\n",
       "\t<tr><td>2011 </td><td>NRT  </td><td>PUS  </td><td>I    </td><td>1040 </td><td> 93  </td><td> 8974</td></tr>\n",
       "\t<tr><td>2011 </td><td>PEK  </td><td>PUS  </td><td>I    </td><td>1215 </td><td>106  </td><td> 7863</td></tr>\n",
       "\t<tr><td>2011 </td><td>CJU  </td><td>TPE  </td><td>I    </td><td>1065 </td><td> 94  </td><td> 7032</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " flightYear & cityCode1 & cityCode2 & diType & distance.km & time.m & totalFlight\\\\\n",
       "\\hline\n",
       "\t 2011  & GMP   & HND   & I     & 1183  & 103   & 22454\\\\\n",
       "\t 2011  & GMP   & KIX   & I     &  837  &  77   & 13717\\\\\n",
       "\t 2011  & GMP   & SHA   & I     &  866  &  79   &  9012\\\\\n",
       "\t 2011  & NRT   & PUS   & I     & 1040  &  93   &  8974\\\\\n",
       "\t 2011  & PEK   & PUS   & I     & 1215  & 106   &  7863\\\\\n",
       "\t 2011  & CJU   & TPE   & I     & 1065  &  94   &  7032\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| flightYear | cityCode1 | cityCode2 | diType | distance.km | time.m | totalFlight |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 2011  | GMP   | HND   | I     | 1183  | 103   | 22454 |\n",
       "| 2011  | GMP   | KIX   | I     |  837  |  77   | 13717 |\n",
       "| 2011  | GMP   | SHA   | I     |  866  |  79   |  9012 |\n",
       "| 2011  | NRT   | PUS   | I     | 1040  |  93   |  8974 |\n",
       "| 2011  | PEK   | PUS   | I     | 1215  | 106   |  7863 |\n",
       "| 2011  | CJU   | TPE   | I     | 1065  |  94   |  7032 |\n",
       "\n"
      ],
      "text/plain": [
       "  flightYear cityCode1 cityCode2 diType distance.km time.m totalFlight\n",
       "1 2011       GMP       HND       I      1183        103    22454      \n",
       "2 2011       GMP       KIX       I       837         77    13717      \n",
       "3 2011       GMP       SHA       I       866         79     9012      \n",
       "4 2011       NRT       PUS       I      1040         93     8974      \n",
       "5 2011       PEK       PUS       I      1215        106     7863      \n",
       "6 2011       CJU       TPE       I      1065         94     7032      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 국제선 데이터 가공\n",
    "regData_I <- data.frame()\n",
    "\n",
    "# 운항 일정 및 운항 요일 기반 추정운항일수 추출\n",
    "for(k in 2011:2020){\n",
    "    subData_FSI <- totalData_FSI %>% select(\"flightNum\", \"domesticCityCode\", \"internationalCityCode\", \"startDate\", \"endDate\",\n",
    "                                                     \"mon\", \"tue\", \"wed\", \"thu\", \"fri\", \"sat\", \"sun\") %>%\n",
    "                                                filter(format(endDate, \"%Y\")==as.character(k) | format(startDate, \"%Y\")==as.character(k))\n",
    "    # 정확한 운항 일수 추출 위해 연도별 날짜 조정\n",
    "    subData_FSI$revisedStartDate <- as.Date(ifelse(subData_FSI$startDate < as.Date(paste0(as.character(i),'-01-01')),\n",
    "                                                format(as.Date(paste0(as.character(k),'-01-01')),\"%Y-%m-%d\"),\n",
    "                                                format(subData_FSI$startDate, \"%Y-%m-%d\")))\n",
    "    subData_FSI$revisedEndDate <- as.Date(ifelse(subData_FSI$endDate > paste0(as.character(i),'-12-31'),\n",
    "                                              format(as.Date(paste0(as.character(k),'-12-31')),\"%Y-%m-%d\"),\n",
    "                                              format(subData_FSI$endDate, \"%Y-%m-%d\")))\n",
    "\n",
    "    # 요일별 운항일수 데이터 제작 (flightDay_FSI)\n",
    "    dateInterval_FSI <- subData_FSI$revisedEndDate - subData_FSI$revisedStartDate  # 운행일수\n",
    "    flightPerDay_FSI <- floor(dateInterval_FSI/7)  # 요일 당 최소운항편수 리스트\n",
    "    flightAddDay_FSI <- floor((dateInterval_FSI/7 - flightPerDay_FSI)*7)  # 보정용 요일별 운항편수 리스트\n",
    "    flightDay_FSI <- flightPerDay_FSI  # 요일별 운항일수 데이터\n",
    "    for(i in 1:6){\n",
    "        flightDay_FSI <- cbind(flightDay_FSI, flightPerDay_FSI)\n",
    "    }\n",
    "    colnames(flightDay_FSI) <- list(1,2,3,4,5,6,7)  # 컬럼명을 요일로 수정(1 = 월요일)\n",
    "    revisedStartDate_FSI <- as.numeric(factor(weekdays(subData_FSI$revisedStartDate)))  # 항공편별 시작 요일 숫자 환산값\n",
    "    for(i in 1:length(flightAddDay_FSI)){\n",
    "        for(j in 0:6){\n",
    "            if(j <= flightAddDay_FSI[i]){\n",
    "                flightDay_FSI[i,(j+revisedStartDate_FSI[i]-1)%%7+1] <- flightDay_FSI[i,(j+revisedStartDate_FSI[i]-1)%%7+1]+1\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    # 기간 내 요일별 추정 운행일수 합산\n",
    "    totalFlight_FSI <- rep(0, times=nrow(subData_FSI))\n",
    "    flightWeekdays_FSI <- subData_FSI %>% select(\"mon\",\"tue\",\"wed\",\"thu\",\"fri\",\"sat\",\"sun\")\n",
    "    for(i in 1:nrow(subData_FSI)){\n",
    "        for(j in 1:7){\n",
    "            if(flightWeekdays_FSI[i,j] != \"N\"){\n",
    "                totalFlight_FSI[i] <- totalFlight_FSI[i]+flightDay_FSI[i,j]\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    # 총 추정 운행일수 데이터를 subData_FSI에 합산\n",
    "    subData_FSI$totalFlight <- totalFlight_FSI\n",
    "\n",
    "    # 공항별 총 운항편수(출발+도착) 추출\n",
    "    ## 임시로 startCityCode가 arrivalCityCode보다 뒤면 역순, 아니면 정순으로 컬럼 새로 지정\n",
    "    subData_FSI$cityCode1 <- ifelse(as.character(subData_FSI$domesticCityCode) <= as.character(subData_FSI$internationalCityCode),\n",
    "                                             as.character(subData_FSI$domesticCityCode),\n",
    "                                             as.character(subData_FSI$internationalCityCode))\n",
    "    subData_FSI$cityCode2 <- ifelse(as.character(subData_FSI$domesticCityCode) <= as.character(subData_FSI$internationalCityCode),\n",
    "                                               as.character(subData_FSI$internationalCityCode),\n",
    "                                               as.character(subData_FSI$domesticCityCode))\n",
    "\n",
    "    # 공항별 총 운항편수(출발+도착) 추출\n",
    "    subData_FSI <- subData_FSI %>% group_by(cityCode1, cityCode2) %>%\n",
    "                                            summarise(totalFlight=sum(totalFlight))\n",
    "\n",
    "    # 연도 및 국내/국제 타입 컬럼 추가\n",
    "    subData_FSI$flightYear <- rep(k, times=nrow(subData_FSI))\n",
    "    subData_FSI$diType <- rep(\"I\", times=nrow(subData_FSI))\n",
    "\n",
    "    # 출발/도착지 간 노선 거리 및 소요 시간 추가\n",
    "    subData_FSI <- left_join(subData_FSI, totalData_TD, by=c(\"diType\", \"cityCode1\", \"cityCode2\"))\n",
    "    regData_I <- bind_rows(regData_I, subData_FSI)\n",
    "}\n",
    "# 순서 조정\n",
    "regData_I <- regData_I %>% select(flightYear, cityCode1, cityCode2, diType, distance.km, time.m, totalFlight)\n",
    "\n",
    "# 시각화 위해 운항 연도(flightYear) 타입 변경 (num -> chr)\n",
    "regData_I$flightYear <- as.character(regData_I$flightYear)\n",
    "\n",
    "head(regData_I %>% arrange(flightYear, desc(totalFlight)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4491006",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다.\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:--- | :--- | :---\n",
    "|1|flightYear|운항 연도(범위: 2011~2020년)\n",
    "|2|diType|국내선 또는 국제선 구분(D: 국내선, I: 국제선)\n",
    "|3|cityCode1, cityCode2|노선의 출도착 공항 코드(IATA)\n",
    "|4|distance.km|출도착 공항 간 거리(단위: km)\n",
    "|5|time.m|출도착 공항 간 소요 시간(단위: 분)\n",
    "|**6**|**totalFlight**|**운항 일정 및 운항 요일 기반 연내 총 추정 운항 편수**(1->2 및 2->1 합계)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "490cc36d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 결측(na)값 빠진 데이터 변수 생성(운항 거리 통계용)\n",
    "omRegData_D <- na.omit(regData_D) %>% arrange(desc(totalFlight*distance.km))\n",
    "omRegData_I <- na.omit(regData_I) %>% arrange(desc(totalFlight*distance.km))\n",
    "\n",
    "# 앞의 2개의 데이터를 regData에 합치기\n",
    "regData <- data.frame()\n",
    "regData <- rbind(regData, omRegData_D)\n",
    "regData <- rbind(regData, omRegData_I)\n",
    "\n",
    "# 시각화를 위해 flightYear 타입 변환(numeric -> character)\n",
    "regData$flightYear <- as.character(regData$flightYear)\n",
    "\n",
    "# 출발/도착 도시명 단일화한 컬럼 추가\n",
    "regData$flightCity <- paste0(regData$cityCode1, \"-\", regData$cityCode2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ca2358",
   "metadata": {},
   "source": [
    "#### 데이터 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5e2b7e73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TableGrob (4 x 9) \"arrange\": 5 grobs\n",
       "  z     cells    name                grob\n",
       "1 1 (2-2,1-9) arrange      gtable[layout]\n",
       "2 2 (3-3,1-4) arrange      gtable[layout]\n",
       "3 3 (3-3,5-8) arrange      gtable[layout]\n",
       "4 4 (1-1,1-9) arrange text[GRID.text.163]\n",
       "5 5 (4-4,1-9) arrange text[GRID.text.164]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YeqgPkXwo+hTnFB+gOQ3HSUCngIP1a9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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 연도별 공항 온실가스 배출량 데이터 가공\n",
    "gasData <- totalData_GG %>% select(\"yyyy\", \"totalTco2e\")\n",
    "\n",
    "# 온실가스 배출량 데이터 컬럼명 변경\n",
    "colnames(gasData) <- c(\"year\", \"totalTco2e\")\n",
    "\n",
    "# 시각화용 데이터 제작\n",
    "fig2Data <- rename(regData, \"year\" = \"flightYear\")\n",
    "fig2Data <- inner_join(fig2Data, gasData, by=\"year\")\n",
    "\n",
    "# 시각화 시 보정용 비율\n",
    "ratio <- mean(fig2Data$totalTco2e)/mean(fig2Data$totalFlight*fig2Data$distance.km)/600\n",
    "\n",
    "# 국내 및 국제선 종합 그래프(상단)\n",
    "fig2_T <- ggplot(data=fig2Data,aes(x = year, y = totalFlight*distance.km/1000000, fill=diType, color=diType)) + theme_bw() + \n",
    "           scale_fill_manual(values=c(\"orange\",\"steelblue\")) +\n",
    "           scale_color_manual(values=c(\"orange\",\"steelblue\")) +\n",
    "           geom_bar(stat=\"identity\",width=0.5,size=0.005,alpha=0.2) +\n",
    "           geom_line(data=fig2Data,aes(x=year,y=totalTco2e*ratio),color=\"cadetblue3\", size=0.7,group=1) +\n",
    "           labs(x=\"국내(황) 및 국제(청)\", fill=\"노선 유형\\n(D: 국내, I: 국제)\") + \n",
    "           coord_cartesian(ylim=c(0,600)) +\n",
    "           scale_y_continuous(name = \"총 운항 거리 (단위: 백만 km)\",\n",
    "                              sec.axis = sec_axis(~./ratio/1000,\n",
    "                                                  name=\"온실가스 배출량\\n(단위: ktCO2e)\"\n",
    "                                                 ),\n",
    "                              )+\n",
    "           theme(legend.position = \"none\",\n",
    "                 title = element_text(size=15,face='bold'),\n",
    "                 axis.title.x =  element_text(size=12,face='bold'),\n",
    "                 axis.title.y = element_text(size=12,face='bold'),\n",
    "                 axis.text.x = element_text(angle=90,vjust = 0.5, hjust=1),\n",
    "                 )\n",
    "\n",
    "# 국내선 그래프(좌하단)\n",
    "fig2_D <- ggplot(data=omRegData_D,aes(x = flightYear, y = totalFlight*distance.km/1000000, fill = totalFlight)) + theme_bw() +\n",
    "           geom_bar(stat=\"identity\",width=0.5, fill=\"orange\", color=\"orange\",size=0.005,alpha=0.2) +\n",
    "           labs(x=\"국내\", fill=\"총 운항 거리\") + \n",
    "           coord_cartesian(ylim=c(0,400)) +\n",
    "           scale_y_continuous(name = \"총 운항 거리 (단위: 백만 km)\")+\n",
    "           theme(legend.position = \"none\",\n",
    "                 title = element_text(size=15,face='bold'),\n",
    "                 axis.title.x =  element_text(size=12,face='bold'),\n",
    "                 axis.title.y = element_text(size=9,face='bold'),\n",
    "                 axis.text.x = element_text(angle=90,vjust = 0.5, hjust=1)\n",
    "                 )\n",
    "\n",
    "# 국내선 그래프(우하단)\n",
    "fig2_I <- ggplot(data=omRegData_I,aes(x = flightYear, y = totalFlight*distance.km/1000000, fill = totalFlight)) + theme_bw() +\n",
    "           geom_bar(stat=\"identity\",width=0.5, fill=\"steelblue\", color=\"steelblue\",size=0.005,alpha=0.2) +\n",
    "           labs(x=\"국제\", fill=\"총 운항 거리\") + \n",
    "           coord_cartesian(ylim=c(0,400)) +\n",
    "           theme(legend.position = \"none\",\n",
    "                 title = element_text(size=14,face='bold'),\n",
    "                 axis.title.x =  element_text(size=12,face='bold'),\n",
    "                 axis.title.y = element_blank(),\n",
    "                 axis.text.x = element_text(angle=90,vjust = 0.5, hjust=1),\n",
    "                 axis.text.y = element_blank(),\n",
    "                 )\n",
    "\n",
    "fig2 <- grid.arrange(fig2_T, fig2_D, fig2_I,\n",
    "                     nrow=2,\n",
    "                     heights=c(2, 1),\n",
    "                     layout_matrix = rbind(c(1,1,1,1,1,1,1,1,1),c(2,2,2,2,3,3,3,3,NA)),\n",
    "                     top = textGrob(paste0(\"연도별 총 운항 거리 (각 노선별 (운항 거리*연간 운항 편수)의 합, 막대그래프) 및\",\n",
    "                                           \"\\n연간 온실가스 배출량(꺾은선그래프)\"),\n",
    "                                    gp = gpar(fontsize = 15, fontface = 'bold')),\n",
    "                     bottom = textGrob(paste0(\"※ 한국공항공사의 \\\"노선별 소요시간 및 거리 정보\\\"\",\n",
    "                                              \"데이터에서 조회되는 노선만 추출하여 계산.\\n\",\n",
    "                                              \"연간 국내 평균 노선 개수(\", round(nrow(regData_D)/10),\"개 중 \",\n",
    "                                              round(nrow(omRegData_D)/10),\"개 추출) + \",\n",
    "                                              \"국제 평균 노선 개수(\", round(nrow(regData_I)/10),\"개 중 \",\n",
    "                                              round(nrow(omRegData_I)/10),\"개 추출)\\n\",\n",
    "                                              \"= 연간 평균 노선 개수(\", round((nrow(regData_D)+nrow(regData_I))/10),\"개 중 \",\n",
    "                                              round((nrow(omRegData_D)+nrow(omRegData_I))/10),\"개 추출)\\n\",\n",
    "                                              \"막대그래프 내 각각의 도수는 각 노선의 총 운항 거리를 의미함.\"),\n",
    "                                       gp = gpar(fontsize = 10), hjust=1, x=0.9))\n",
    "\n",
    "fig2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "368e850e",
   "metadata": {},
   "source": [
    "2012년부터 2019년까지 국내 항공기 총 운항거리(국내선+국제선)가 점진적으로 증가했습니다. 반면 2011년과 2012년 사이, 2019년과 2020년 사이 국내 항공기 운항거리가 감소했습니다. 특히 2020년에는 코로나19 범유행으로 운항 거리가 크게 감소했습니다.\n",
    "\n",
    "2-1-1 항목의 연도별 운항 편수-온실가스 간 관계와 마찬가지로 운항 거리와 온실가스 배출량이 대체적으로 비례하는 것을 볼 수 있습니다.\n",
    "\n",
    "세부적으로 국내선과 국제선을 나눠보면, 2012년부터 2019년까지 국제선 이용이 국내선 이용보다 빠르게 증가한 것을 볼 수 있습니다. 하지만 2020년 국제선 이용은 코로나 영향으로 크게 줄어든데 반해 국내선 이용은 오히려 소폭 증가한 모습을 보였습니다. 이는 국제선 이용 규제로 인한 반사 효과로 보입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937be1b2",
   "metadata": {},
   "source": [
    "### 2-1-3. 사용 항공기종 변화"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ac8ffa",
   "metadata": {},
   "source": [
    "해당 시각화에서는 **1. 공항 온실가스 데이터**, **5. 기종별 연료 소모량 데이터** 및 **6. 국내 공항 연도별 항공기종 사용 현황 데이터**를 사용했습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25780084",
   "metadata": {},
   "source": [
    "#### 데이터 가공"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cc3c8c2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ICAO</th><th scope=col>count</th><th scope=col>year</th><th scope=col>seats</th><th scope=col>fuelBurn.kg.km</th><th scope=col>sector.nmi</th><th scope=col>sector.km</th><th scope=col>maxFuelBurn.kt</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>B737    </td><td>223751  </td><td>2011    </td><td>151.0909</td><td>3.126818</td><td>1040.318</td><td>1926.669</td><td>1347.953</td></tr>\n",
       "\t<tr><td>B737    </td><td>249630  </td><td>2012    </td><td>151.0909</td><td>3.126818</td><td>1040.318</td><td>1926.669</td><td>1503.857</td></tr>\n",
       "\t<tr><td>B737    </td><td>253812  </td><td>2013    </td><td>151.0909</td><td>3.126818</td><td>1040.318</td><td>1926.669</td><td>1529.051</td></tr>\n",
       "\t<tr><td>B737    </td><td>256717  </td><td>2014    </td><td>151.0909</td><td>3.126818</td><td>1040.318</td><td>1926.669</td><td>1546.552</td></tr>\n",
       "\t<tr><td>B737    </td><td>278528  </td><td>2015    </td><td>151.0909</td><td>3.126818</td><td>1040.318</td><td>1926.669</td><td>1677.949</td></tr>\n",
       "\t<tr><td>B737    </td><td>283341  </td><td>2016    </td><td>151.0909</td><td>3.126818</td><td>1040.318</td><td>1926.669</td><td>1706.944</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " ICAO & count & year & seats & fuelBurn.kg.km & sector.nmi & sector.km & maxFuelBurn.kt\\\\\n",
       "\\hline\n",
       "\t B737     & 223751   & 2011     & 151.0909 & 3.126818 & 1040.318 & 1926.669 & 1347.953\\\\\n",
       "\t B737     & 249630   & 2012     & 151.0909 & 3.126818 & 1040.318 & 1926.669 & 1503.857\\\\\n",
       "\t B737     & 253812   & 2013     & 151.0909 & 3.126818 & 1040.318 & 1926.669 & 1529.051\\\\\n",
       "\t B737     & 256717   & 2014     & 151.0909 & 3.126818 & 1040.318 & 1926.669 & 1546.552\\\\\n",
       "\t B737     & 278528   & 2015     & 151.0909 & 3.126818 & 1040.318 & 1926.669 & 1677.949\\\\\n",
       "\t B737     & 283341   & 2016     & 151.0909 & 3.126818 & 1040.318 & 1926.669 & 1706.944\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| ICAO | count | year | seats | fuelBurn.kg.km | sector.nmi | sector.km | maxFuelBurn.kt |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| B737     | 223751   | 2011     | 151.0909 | 3.126818 | 1040.318 | 1926.669 | 1347.953 |\n",
       "| B737     | 249630   | 2012     | 151.0909 | 3.126818 | 1040.318 | 1926.669 | 1503.857 |\n",
       "| B737     | 253812   | 2013     | 151.0909 | 3.126818 | 1040.318 | 1926.669 | 1529.051 |\n",
       "| B737     | 256717   | 2014     | 151.0909 | 3.126818 | 1040.318 | 1926.669 | 1546.552 |\n",
       "| B737     | 278528   | 2015     | 151.0909 | 3.126818 | 1040.318 | 1926.669 | 1677.949 |\n",
       "| B737     | 283341   | 2016     | 151.0909 | 3.126818 | 1040.318 | 1926.669 | 1706.944 |\n",
       "\n"
      ],
      "text/plain": [
       "  ICAO count  year seats    fuelBurn.kg.km sector.nmi sector.km maxFuelBurn.kt\n",
       "1 B737 223751 2011 151.0909 3.126818       1040.318   1926.669  1347.953      \n",
       "2 B737 249630 2012 151.0909 3.126818       1040.318   1926.669  1503.857      \n",
       "3 B737 253812 2013 151.0909 3.126818       1040.318   1926.669  1529.051      \n",
       "4 B737 256717 2014 151.0909 3.126818       1040.318   1926.669  1546.552      \n",
       "5 B737 278528 2015 151.0909 3.126818       1040.318   1926.669  1677.949      \n",
       "6 B737 283341 2016 151.0909 3.126818       1040.318   1926.669  1706.944      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 국내 연도별 기종 사용량 변형\n",
    "totalData_UA2 <- totalData_UA %>% filter(공항명 == '합계') %>% subset(select=c(-공항명, -기타, -계))\n",
    "\n",
    "## 데이터 형태 변형\n",
    "aircraftData <- data.frame()\n",
    "for(i in 1:(ncol(totalData_UA2)-1)){\n",
    "    subData <- data.frame(totalData_UA2[,i])\n",
    "    colnames(subData) <- c(\"count\")\n",
    "    colname <- colnames(totalData_UA2)[i]\n",
    "    subData$year <- 2011:2020\n",
    "    subData$ICAO <- rep(colname,times=nrow(subData))\n",
    "    aircraftData <- bind_rows(aircraftData, subData)\n",
    "}\n",
    "\n",
    "## 컬럼 순서 변경\n",
    "aircraftData <- aircraftData %>% select(ICAO, count, year)\n",
    "\n",
    "# 기종별 연료 사용량 변형\n",
    "## 각 수치를 평균화하여 ICAO별로 요약함. (각 ICAO별로 모델을 균일하게 사용한다고 가정함.)\n",
    "### 좌석별 연료 샤용량(fuelPerSeat.L.100km)의 경우 데이터가 기재되지 않은 항공기가 있어 사용하지 않음.\n",
    "fuelConsDataSummarize <- totalData_FC %>% group_by(ICAO) %>% summarize(seats=mean(seats),\n",
    "                                                                        fuelBurn.kg.km=mean(fuelBurn.kg.km),\n",
    "                                                                        sector.nmi=mean(sector.nmi),\n",
    "                                                                        sector.km=mean(sector.km))\n",
    "\n",
    "# 연도 타입 변경\n",
    "aircraftData$year <- as.character(aircraftData$year)\n",
    "\n",
    "# 기종별 연료 사용량 데이터와 join\n",
    "aircraftData <- left_join(aircraftData, fuelConsDataSummarize, by=\"ICAO\")\n",
    "\n",
    "# 결측치(NA)가 있는 데이터 제거\n",
    "aircraftData <- na.omit(aircraftData)\n",
    "\n",
    "# 연도별 총 항공기 연료 사용량 추가\n",
    "## 모든 항공기가 최대 거리(sector)를 운항한다 가정\n",
    "aircraftData$maxFuelBurn.kt <- aircraftData$count*aircraftData$fuelBurn.kg.km*aircraftData$sector.km/1000000\n",
    "\n",
    "head(aircraftData)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b459856b",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다. 이때 항공기종별 최대 연료 소모량 합계(maxFuelBurn.kt)은 모든 항공기가 최대 거리(sector)를 이동하는 것으로 가정한 후 계산한 값입니다.\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:--- | :--- | :---\n",
    "|1|ICAO|항공기종 코드\n",
    "|2|count|같은 항공기종 코드 내 모델의 연도별 항공기종 사용량 합계(단위: 회)\n",
    "|3|year|연도(범위: 2011~2020년)\n",
    "|3|seats|같은 항공기종 코드 내 모델의 평균 기내 좌석 수\n",
    "|4|fuelBurn.kg.km|같은 항공기종 코드 내 모델의 평균 연비(단위: kg/km)\n",
    "|6|sector.nmi|같은 항공기종 코드 내 모델 최대 운항 거리의 평균(단위: nmi(해리))\n",
    "|7|sector.km|같은 항공기종 코드 내 모델 최대 운항 거리의 평균(단위: km)\n",
    "|**8**|**maxFuelBurn.kt**|**항공기종별 최대 연료 소모량 합계**(count * fuelBurn.kg.km * sector.km, 단위: kt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f744a7c",
   "metadata": {},
   "source": [
    "#### 데이터 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5c774aef",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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FkYBanByN/+AKkVIBmysAjSrztGrwLS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\nARJCCgIkhBQESAgpCJAQUtD/B6UpZdx8TGfkAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 연도별 총 국내 공항(인천 포함) 출발+도착 건수 추출\n",
    "gasData <- totalData_GG %>% select(\"yyyy\", \"totalTco2e\")\n",
    "\n",
    "colnames(gasData) <- c(\"year\", \"totalTco2e\")\n",
    "\n",
    "# 시각화용 데이터 제작\n",
    "fig3Data <- inner_join(aircraftData, gasData, by=\"year\")\n",
    "\n",
    "ratio <- mean(fig3Data$maxFuelBurn.kt)/mean(fig3Data$totalTco2e) * 8\n",
    "\n",
    "fig3 <- ggplot() + theme_bw() + \n",
    "       geom_bar(data=fig3Data,aes(x=year,y=maxFuelBurn.kt,fill=ICAO,color=ICAO),stat=\"identity\", width=0.7,alpha=0.2) +\n",
    "       geom_line(data=fig3Data,aes(x=year,y=totalTco2e*ratio),color=\"cadetblue4\", size=0.7,group=1) +\n",
    "       labs(title=\"연도별 연료 소모량(막대그래프) 및 총 온실가스 배출량(꺾은선그래프)\", x=\"연도\", fill=\"항공기종  \", color=\"항공기종  \") + \n",
    "       scale_y_continuous(name = \"총 연료 소모량 (단위: kt)\",\n",
    "                          sec.axis = sec_axis(~./ratio/1000, name=\"온실가스 배출량 (단위: ktCO2e)\"))+\n",
    "       scale_color_brewer(palette = \"Set2\") +\n",
    "       scale_fill_brewer(palette = \"Set2\") + \n",
    "       theme(legend.position = \"bottom\",\n",
    "             title = element_text(size=13,face='bold'),\n",
    "             axis.title.x =  element_text(size=12,face='bold'),\n",
    "             axis.title.y = element_text(size=12,face='bold'),\n",
    "             legend.title = element_text(size=15,face='bold', vjust=0.5),\n",
    "             legend.text = element_text(size=12, vjust=0.5)\n",
    "             )\n",
    "fig3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "350a6b9b",
   "metadata": {},
   "source": [
    "연도별 총 연료 소모량은 **각 기종별 연료 소모량**에 **연간 각 기종의 운항 횟수**를 곱한 값을 더하여 구했습니다. 이때 각 기종의 연료 소모량은 노선별 사용 항공기종 공공 데이터가 없었으므로 임시적으로 기종의 **연비**에 **최대 이동 가능 거리(sector)**를 곱한 값을 이용했습니다. 또한 만일 모델이 여러 개인 항공기종의 경우 각 모델의 사용 비율이 같다고 가정, **연비의 평균**에 **최대 이동 가능 거리의 평균**을 곱한 값을 사용했습니다.\n",
    "\n",
    "연도별 총 연료 소모량은 2012년부터 2016년까지 증가하다가 2018년과 2019년에 정체, 2020년 감소하는 형태입니다. 이렇게 연료 소모량이 들쭉날쭉한 모습을 보이는 것은 운항횟수는 증가했지만 항공기종의 변화에 따라 엔진 효율화를 이룬 결과로 비춰집니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0116c5fd",
   "metadata": {},
   "source": [
    "### 2-1-4. 2-1-1, 2, 3 항목의 통계 결과 및 온실가스 배출량 간의 상관관계 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b804f6a",
   "metadata": {},
   "source": [
    "#### 데이터 가공 및 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a89cfb7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AugdJhmTlfTvYD0v82ny/v35vnj9df/NJcNtG2an8+v3evH2/JxCoP07Vr2qSv7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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "revisedFig1Data <- fig1Data %>% select(year,subflgt,stats) %>% rename(\"flightNum\" = \"subflgt\", \"total.tCO2e\"=\"stats\")\n",
    "revisedFig2Data <- fig2Data %>% group_by(year) %>% summarize(flightDistance=sum(totalFlight*distance.km))\n",
    "revisedFig3Data <- fig3Data %>% group_by(year) %>% summarize(fuelConsumption=sum(maxFuelBurn.kt))\n",
    "\n",
    "fig4Data <- inner_join(revisedFig1Data, revisedFig2Data, by=\"year\")\n",
    "fig4Data <- inner_join(fig4Data, revisedFig3Data, by=\"year\")\n",
    "fig4Data <- fig4Data %>% select(-year) %>% select(flightNum, flightDistance, fuelConsumption, total.tCO2e)\n",
    "fig4 <- ggpairs(fig4Data) + theme_bw()\n",
    "fig4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbf3f455",
   "metadata": {},
   "source": [
    "앞에서 사용한 시각화 자료를 바탕으로 운항 편수, 운항 거리, 연료 소비량과 온실가스량 사이의 상관관계를 분석했습니다. 온실가스와 운항 편수의 상관계수는 0.957, 온실가스와 운항 거리의 상관계수는 0.948, 온실가스와 연료 소비량의 상관계수는 0.927로 산출됐습니다. 이로써 운항횟수, 운항거리, 연료소비량은 온실가스량과 비교적 뚜렷한 양의 상관관계를 가짐을 알 수 있습니다. **운항횟수, 운항거리, 연료소비량이 온실가스량을 결정하는 변수**들이라 개연적으로 추측할 수 있습니다.\n",
    "\n",
    "이번에는 운항 편수, 운항 거리, 연료 소비량 각각의 상관관계를 살펴봅시다. 운항 편수와 운항 거리의 상관계수는 0.979, 운항 편수와 연료 소비량의 상관계수는 0.929, 운항 거리와 연료 소비량의 상관계수는 0.922입니다. 변수들 사이 상관관계가 높음을 볼 때 온실가스를 유발하는 변수들 사이의 독립성이 훼손됐다고 생각할 수 있습니다. 따라서 다중공선성이 발생해 이 세가지 변수를 이용해 **다중회귀분석을 하는 것은 적절하지 않다**는 결론이 나왔습니다.\n",
    "\n",
    " 상관관계를 분석하고 나니 변수들 사이 상관관계가 높은 것이 당연하다는 생각이 들었습니다. 운항 횟수가 늘수록 운항거리가 늘 확률이 개연적으로 높아지고, 연료 소모량은 연비에 운항거리를 곱한 값이기 때문입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51159197",
   "metadata": {},
   "source": [
    "## 2-2. 제언"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e42e30be",
   "metadata": {},
   "source": [
    "2-1 항목에서 보았듯 항공기 운항 횟수를 줄이고 거리를 줄이면 자연히 항공기 온실가스양은 줄어들 것입니다. 하지만 무작정 항공기 운행을 규제하는 것은 현실적이지 못합니다. 따라서 제언에서는 현실을 감안해 항공기 온실가스를 줄일 수 있는 현실적 방안을 제시하고자 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f9f369a",
   "metadata": {},
   "source": [
    "### 2-2-1. 온실가스 배출량에 영향을 주는 항공기 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3218741",
   "metadata": {},
   "source": [
    "해당 분석은 **1. 공항 온실가스 데이터**에서의 *연도별 온실가스 총 배출량*과, 각 항공기종의 연비(fuelBurn)를 반영하지 않는 **6. 국내 공항 연도별 항공기종 사용 현황 데이터**에서의 *각 항공기종의 연간 사용량*을 사용합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8255af7d",
   "metadata": {},
   "source": [
    "#### 데이터 가공"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0122bb05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 분석용 항공기종 데이터베이스\n",
    "analysisAircraft <- data.frame()\n",
    "\n",
    "# 연도별 총 국내 공항 총합 온실 가스 데이터 추출\n",
    "gasData <- totalData_GG %>% select(\"yyyy\", \"totalTco2e\")\n",
    "colnames(gasData) <- c(\"year\", \"totalTco2e\")\n",
    "\n",
    "# 분석용 데이터베이스에 항공기종 데이터베이스를 다중 회귀선(1차식) 형태로 변형하여 투입\n",
    "for(i in 2011:2020){\n",
    "    subData <- aircraftData %>% filter(year == as.character(i)) %>% select(ICAO,count) %>% arrange(ICAO)\n",
    "    colname <- subData$ICAO\n",
    "    subData <- data.frame(t(subData$count))\n",
    "    colnames(subData) <- colname\n",
    "    subData$year <- as.character(i)\n",
    "    analysisAircraft <- rbind(analysisAircraft,subData)\n",
    "}\n",
    "# 연도를 기준으로 gasData와 join\n",
    "analysisAircraft <- inner_join(analysisAircraft, gasData, by=\"year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9722d7a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'1. stepwise 방식으로 온실가스 배출량 예측에 적합한 변수 설정'"
      ],
      "text/latex": [
       "'1. stepwise 방식으로 온실가스 배출량 예측에 적합한 변수 설정'"
      ],
      "text/markdown": [
       "'1. stepwise 방식으로 온실가스 배출량 예측에 적합한 변수 설정'"
      ],
      "text/plain": [
       "[1] \"1. stepwise 방식으로 온실가스 배출량 예측에 적합한 변수 설정\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start:  AIC=205.08\n",
      "totalTco2e ~ A300 + A330 + B737 + B747 + B767 + B777 + IL62 + \n",
      "    MD82\n",
      "\n",
      "       Df  Sum of Sq        RSS    AIC\n",
      "- A330  1  161136583 1494206618 204.22\n",
      "<none>               1333070035 205.08\n",
      "- B747  1  449325110 1782395145 205.99\n",
      "- B777  1  776959617 2110029652 207.67\n",
      "- IL62  1 2960113917 4293183951 214.78\n",
      "- B737  1 3043815320 4376885354 214.97\n",
      "- MD82  1 3642043766 4975113801 216.25\n",
      "- A300  1 5751887612 7084957647 219.79\n",
      "- B767  1 6964534597 8297604632 221.37\n",
      "\n",
      "Step:  AIC=204.22\n",
      "totalTco2e ~ A300 + B737 + B747 + B767 + B777 + IL62 + MD82\n",
      "\n",
      "       Df  Sum of Sq        RSS    AIC\n",
      "<none>               1494206618 204.22\n",
      "- B747  1  339601176 1833807794 204.27\n",
      "+ A330  1  161136583 1333070035 205.08\n",
      "- B777  1  647838962 2142045580 205.82\n",
      "- IL62  1 3216303007 4710509624 213.71\n",
      "- B737  1 3359386236 4853592854 214.00\n",
      "- MD82  1 4240666540 5734873158 215.67\n",
      "- B767  1 6981698114 8475904731 219.58\n",
      "- A300  1 8362035324 9856241942 221.09\n"
     ]
    }
   ],
   "source": [
    "# stepwise 방식으로 온실가스 배출에 영향을 주지 않는 항공기종 제거\n",
    "sprintf(\"1. stepwise 방식으로 온실가스 배출량 예측에 적합한 변수 설정\")\n",
    "lmAircraft <- lm(formula = totalTco2e ~ A300 + A330 + B737 + B747 + B767 + B777 + IL62 + MD82, data = analysisAircraft)\n",
    "con <- lm(totalTco2e~1,data=analysisAircraft)  # 상수항만 포함된 회귀원형\n",
    "fitLmAircraft <- step(lmAircraft,scope=list(lower=con,upper=lmAircraft), direction = \"both\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4d9fc9f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'2. 불필요한 항공기종 사용량 변수(A330) 제거 이후 선형 회귀 시의 계수 요약'"
      ],
      "text/latex": [
       "'2. 불필요한 항공기종 사용량 변수(A330) 제거 이후 선형 회귀 시의 계수 요약'"
      ],
      "text/markdown": [
       "'2. 불필요한 항공기종 사용량 변수(A330) 제거 이후 선형 회귀 시의 계수 요약'"
      ],
      "text/plain": [
       "[1] \"2. 불필요한 항공기종 사용량 변수(A330) 제거 이후 선형 회귀 시의 계수 요약\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Estimate</th><th scope=col>Std. Error</th><th scope=col>t value</th><th scope=col>Pr(&gt;|t|)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>(Intercept)</th><td>-3.628600e+05</td><td>1.349073e+05 </td><td>-2.6896992   </td><td>0.1148896    </td></tr>\n",
       "\t<tr><th scope=row>A300</th><td> 5.412910e+00</td><td>1.617950e+00 </td><td> 3.3455357   </td><td>0.0789137    </td></tr>\n",
       "\t<tr><th scope=row>B737</th><td> 2.165464e+00</td><td>1.021201e+00 </td><td> 2.1205067   </td><td>0.1680479    </td></tr>\n",
       "\t<tr><th scope=row>B747</th><td> 5.508564e+00</td><td>8.170415e+00 </td><td> 0.6742086   </td><td>0.5696640    </td></tr>\n",
       "\t<tr><th scope=row>B767</th><td> 1.316052e+01</td><td>4.305098e+00 </td><td> 3.0569631   </td><td>0.0924146    </td></tr>\n",
       "\t<tr><th scope=row>B777</th><td>-5.770773e+00</td><td>6.197133e+00 </td><td>-0.9312005   </td><td>0.4500551    </td></tr>\n",
       "\t<tr><th scope=row>IL62</th><td>-8.459262e+04</td><td>4.077034e+04 </td><td>-2.0748569   </td><td>0.1736871    </td></tr>\n",
       "\t<tr><th scope=row>MD82</th><td> 6.395222e+02</td><td>2.684287e+02 </td><td> 2.3824661   </td><td>0.1400857    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & Estimate & Std. Error & t value & Pr(>\\textbar{}t\\textbar{})\\\\\n",
       "\\hline\n",
       "\t(Intercept) & -3.628600e+05 & 1.349073e+05  & -2.6896992    & 0.1148896    \\\\\n",
       "\tA300 &  5.412910e+00 & 1.617950e+00  &  3.3455357    & 0.0789137    \\\\\n",
       "\tB737 &  2.165464e+00 & 1.021201e+00  &  2.1205067    & 0.1680479    \\\\\n",
       "\tB747 &  5.508564e+00 & 8.170415e+00  &  0.6742086    & 0.5696640    \\\\\n",
       "\tB767 &  1.316052e+01 & 4.305098e+00  &  3.0569631    & 0.0924146    \\\\\n",
       "\tB777 & -5.770773e+00 & 6.197133e+00  & -0.9312005    & 0.4500551    \\\\\n",
       "\tIL62 & -8.459262e+04 & 4.077034e+04  & -2.0748569    & 0.1736871    \\\\\n",
       "\tMD82 &  6.395222e+02 & 2.684287e+02  &  2.3824661    & 0.1400857    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Estimate | Std. Error | t value | Pr(>|t|) |\n",
       "|---|---|---|---|---|\n",
       "| (Intercept) | -3.628600e+05 | 1.349073e+05  | -2.6896992    | 0.1148896     |\n",
       "| A300 |  5.412910e+00 | 1.617950e+00  |  3.3455357    | 0.0789137     |\n",
       "| B737 |  2.165464e+00 | 1.021201e+00  |  2.1205067    | 0.1680479     |\n",
       "| B747 |  5.508564e+00 | 8.170415e+00  |  0.6742086    | 0.5696640     |\n",
       "| B767 |  1.316052e+01 | 4.305098e+00  |  3.0569631    | 0.0924146     |\n",
       "| B777 | -5.770773e+00 | 6.197133e+00  | -0.9312005    | 0.4500551     |\n",
       "| IL62 | -8.459262e+04 | 4.077034e+04  | -2.0748569    | 0.1736871     |\n",
       "| MD82 |  6.395222e+02 | 2.684287e+02  |  2.3824661    | 0.1400857     |\n",
       "\n"
      ],
      "text/plain": [
       "            Estimate      Std. Error   t value    Pr(>|t|) \n",
       "(Intercept) -3.628600e+05 1.349073e+05 -2.6896992 0.1148896\n",
       "A300         5.412910e+00 1.617950e+00  3.3455357 0.0789137\n",
       "B737         2.165464e+00 1.021201e+00  2.1205067 0.1680479\n",
       "B747         5.508564e+00 8.170415e+00  0.6742086 0.5696640\n",
       "B767         1.316052e+01 4.305098e+00  3.0569631 0.0924146\n",
       "B777        -5.770773e+00 6.197133e+00 -0.9312005 0.4500551\n",
       "IL62        -8.459262e+04 4.077034e+04 -2.0748569 0.1736871\n",
       "MD82         6.395222e+02 2.684287e+02  2.3824661 0.1400857"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 제거 이후 상관관계 분석 요약\n",
    "sprintf(\"2. 불필요한 항공기종 사용량 변수(A330) 제거 이후 선형 회귀 시의 계수 요약\")\n",
    "summary(fitLmAircraft)$coefficients  # A300, B767이 확률 0.1 미만의 유의성을 보임"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dd0afcce",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'3. 확률(Pr(&gt;|t|)) 10% 이내 유의성을 보이는 항공기종 사용량 변수만 선형 회귀 시의 계수 요약'"
      ],
      "text/latex": [
       "'3. 확률(Pr(>\\textbar{}t\\textbar{})) 10\\% 이내 유의성을 보이는 항공기종 사용량 변수만 선형 회귀 시의 계수 요약'"
      ],
      "text/markdown": [
       "'3. 확률(Pr(&gt;|t|)) 10% 이내 유의성을 보이는 항공기종 사용량 변수만 선형 회귀 시의 계수 요약'"
      ],
      "text/plain": [
       "[1] \"3. 확률(Pr(>|t|)) 10% 이내 유의성을 보이는 항공기종 사용량 변수만 선형 회귀 시의 계수 요약\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Estimate</th><th scope=col>Std. Error</th><th scope=col>t value</th><th scope=col>Pr(&gt;|t|)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>(Intercept)</th><td>-57806.364474</td><td>1.498573e+05 </td><td>-0.3857427   </td><td>0.7111423110 </td></tr>\n",
       "\t<tr><th scope=row>A300</th><td>     7.709977</td><td>1.349788e+00 </td><td> 5.7119924   </td><td>0.0007262965 </td></tr>\n",
       "\t<tr><th scope=row>B767</th><td>     9.520726</td><td>4.490242e+00 </td><td> 2.1203146   </td><td>0.0716881925 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & Estimate & Std. Error & t value & Pr(>\\textbar{}t\\textbar{})\\\\\n",
       "\\hline\n",
       "\t(Intercept) & -57806.364474 & 1.498573e+05  & -0.3857427    & 0.7111423110 \\\\\n",
       "\tA300 &      7.709977 & 1.349788e+00  &  5.7119924    & 0.0007262965 \\\\\n",
       "\tB767 &      9.520726 & 4.490242e+00  &  2.1203146    & 0.0716881925 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Estimate | Std. Error | t value | Pr(>|t|) |\n",
       "|---|---|---|---|---|\n",
       "| (Intercept) | -57806.364474 | 1.498573e+05  | -0.3857427    | 0.7111423110  |\n",
       "| A300 |      7.709977 | 1.349788e+00  |  5.7119924    | 0.0007262965  |\n",
       "| B767 |      9.520726 | 4.490242e+00  |  2.1203146    | 0.0716881925  |\n",
       "\n"
      ],
      "text/plain": [
       "            Estimate      Std. Error   t value    Pr(>|t|)    \n",
       "(Intercept) -57806.364474 1.498573e+05 -0.3857427 0.7111423110\n",
       "A300             7.709977 1.349788e+00  5.7119924 0.0007262965\n",
       "B767             9.520726 4.490242e+00  2.1203146 0.0716881925"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 10% 오차범위 이내 유의성을 보이는 변수(A300, B767)만 다시 상관관계 분석 요약\n",
    "sprintf(\"3. 확률(Pr(>|t|)) 10%% 이내 유의성을 보이는 항공기종 사용량 변수만 선형 회귀 시의 계수 요약\")\n",
    "summary(lm(formula = totalTco2e ~ A300 + B767, data = analysisAircraft))$coefficients  # A300이 확률 0.001 미만의 유의성을 보임"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "136ad4c2",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다.\n",
    "\n",
    "항공기종 ***????***의 사용량을 ***x<sub>????</sub>***, 온실가스 배출량을 ***y***라고 했을 때, 관계식\n",
    "\n",
    "**<center>*y = β<sub>0</sub> + β<sub>Xxyz</sub>x<sub>Xxyz</sub> + β<sub>Yabc</sub>x<sub>Yabc</sub> + ... + β<sub>Zdef</sub>x<sub>Zdef</sub>*</center>**\n",
    "\n",
    "에서\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:--|:--|:--\n",
    "|1|Estimate|각 항공기 사용량의 추정 β값 (β<sub>0</sub>의 경우 Intercept)\n",
    "|2|Std. Error|각 항공기종별 추정 β값의 표준 오차\n",
    "|3|t value|각 항공기종별 추정 β값을 t분포로 정규화한 값\n",
    "|4|Pr(>\\|t\\|)|각 항공기의 사용량이 온실가스 배출량에 **영향을 미치지 않을 확률** (β<sub>????</sub>=0일 확률)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd9bf02",
   "metadata": {},
   "source": [
    "A300, B737, B747, B767, B777, IL62, MD82 항공기종과 온실가스 배출량 사이의 다중회귀분석 결과, **온실가스 배출량에 영향을 주지 않을 확률(Pr(>|t|))이 0.1 이하**인 항공기종, 즉 온실가스 배출량과 **사실상 유의미한 상관관계**를 보이는 항공기종은 A300과 B767이었습니다. 특히 A300과 B767 두 변수를 대상으로 한 다중회귀분석을 한 결과, A300의 Pr(>|t|) 값은 약 **0.07%**로 온실가스 배출량과 상당히 높은 상관관계를 보였습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f15c198",
   "metadata": {},
   "source": [
    "### 2-2-2. 2030년 예상 노선 사용량 기반 온실가스 발생량 감소 대상 우선순위 노선 산출"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df42e25f",
   "metadata": {},
   "source": [
    "해당 시각화에서는 **2-1-2. 연도 및 국내/국제 총 운항 거리 총합 비교**에서 가공된 데이터를 응용했습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f347a6",
   "metadata": {},
   "source": [
    "#### 데이터 가공"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5f8e2278",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`summarise()` has grouped output by 'flightCity'. You can override using the `.groups` argument.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>flightCity</th><th scope=col>totalFlight</th><th scope=col>flightYear</th><th scope=col>diType</th><th scope=col>distance.km</th><th scope=col>intercept</th><th scope=col>slope</th><th scope=col>anticipatedValue</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>CJU-GMP    </td><td>233483     </td><td>2011       </td><td>D          </td><td> 449       </td><td>-4217969.0 </td><td>2211.7273  </td><td>229814.5273</td></tr>\n",
       "\t<tr><td>GMP-PUS    </td><td> 54955     </td><td>2011       </td><td>D          </td><td> 327       </td><td>-5917114.6 </td><td>2962.8970  </td><td> 41271.1636</td></tr>\n",
       "\t<tr><td>CJJ-CJU    </td><td> 21390     </td><td>2011       </td><td>D          </td><td> 367       </td><td>-5732137.6 </td><td>2858.9697  </td><td> 17250.4364</td></tr>\n",
       "\t<tr><td>CJU-TAE    </td><td> 14864     </td><td>2011       </td><td>D          </td><td> 330       </td><td>-5899342.8 </td><td>2939.7879  </td><td> 12570.6545</td></tr>\n",
       "\t<tr><td>GUM-PUS    </td><td>   388     </td><td>2011       </td><td>I          </td><td>2884       </td><td> -769768.1 </td><td> 382.9818  </td><td>   408.3818</td></tr>\n",
       "\t<tr><td>CJU-PUS    </td><td> 53532     </td><td>2011       </td><td>D          </td><td> 291       </td><td> -952924.9 </td><td> 500.0061  </td><td> 52587.2727</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " flightCity & totalFlight & flightYear & diType & distance.km & intercept & slope & anticipatedValue\\\\\n",
       "\\hline\n",
       "\t CJU-GMP     & 233483      & 2011        & D           &  449        & -4217969.0  & 2211.7273   & 229814.5273\\\\\n",
       "\t GMP-PUS     &  54955      & 2011        & D           &  327        & -5917114.6  & 2962.8970   &  41271.1636\\\\\n",
       "\t CJJ-CJU     &  21390      & 2011        & D           &  367        & -5732137.6  & 2858.9697   &  17250.4364\\\\\n",
       "\t CJU-TAE     &  14864      & 2011        & D           &  330        & -5899342.8  & 2939.7879   &  12570.6545\\\\\n",
       "\t GUM-PUS     &    388      & 2011        & I           & 2884        &  -769768.1  &  382.9818   &    408.3818\\\\\n",
       "\t CJU-PUS     &  53532      & 2011        & D           &  291        &  -952924.9  &  500.0061   &  52587.2727\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| flightCity | totalFlight | flightYear | diType | distance.km | intercept | slope | anticipatedValue |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| CJU-GMP     | 233483      | 2011        | D           |  449        | -4217969.0  | 2211.7273   | 229814.5273 |\n",
       "| GMP-PUS     |  54955      | 2011        | D           |  327        | -5917114.6  | 2962.8970   |  41271.1636 |\n",
       "| CJJ-CJU     |  21390      | 2011        | D           |  367        | -5732137.6  | 2858.9697   |  17250.4364 |\n",
       "| CJU-TAE     |  14864      | 2011        | D           |  330        | -5899342.8  | 2939.7879   |  12570.6545 |\n",
       "| GUM-PUS     |    388      | 2011        | I           | 2884        |  -769768.1  |  382.9818   |    408.3818 |\n",
       "| CJU-PUS     |  53532      | 2011        | D           |  291        |  -952924.9  |  500.0061   |  52587.2727 |\n",
       "\n"
      ],
      "text/plain": [
       "  flightCity totalFlight flightYear diType distance.km intercept  slope    \n",
       "1 CJU-GMP    233483      2011       D       449        -4217969.0 2211.7273\n",
       "2 GMP-PUS     54955      2011       D       327        -5917114.6 2962.8970\n",
       "3 CJJ-CJU     21390      2011       D       367        -5732137.6 2858.9697\n",
       "4 CJU-TAE     14864      2011       D       330        -5899342.8 2939.7879\n",
       "5 GUM-PUS       388      2011       I      2884         -769768.1  382.9818\n",
       "6 CJU-PUS     53532      2011       D       291         -952924.9  500.0061\n",
       "  anticipatedValue\n",
       "1 229814.5273     \n",
       "2  41271.1636     \n",
       "3  17250.4364     \n",
       "4  12570.6545     \n",
       "5    408.3818     \n",
       "6  52587.2727     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 각 노선당 회귀선 요소 데이터 추출(2011~2019년 데이터 집계, 2020년은 집계 제외)\n",
    "## 집계 연도가 2개 이상인 노선만 추출 (1개인 노선은 오류 발생)\n",
    "fig5CityCount <- regData %>% filter(as.numeric(flightYear)<=2019) %>%\n",
    "                 group_by(flightCity, diType) %>% summarize(count = n()) %>% filter(count>=2)\n",
    "\n",
    "fig5Data <- inner_join(regData, fig5CityCount, by=c(\"flightCity\",\"diType\"))\n",
    "\n",
    "# 추세선 산출 위해 연도 타입 변경(chr -> num)\n",
    "fig5Data$flightYear <- as.numeric(fig5Data$flightYear)\n",
    "\n",
    "# 노선당 추세선의 y절편(intercept) 및 기울기(slope) 산출\n",
    "fig5RegressionData <- fig5Data %>% group_by(flightCity) %>%\n",
    "                                    summarize(intercept = summary(lm(totalFlight~flightYear))$coefficients[1,1],\n",
    "                                              slope = summary(lm(totalFlight~flightYear))$coefficients[2,1])\n",
    "fig5RegressionData <- inner_join(fig5RegressionData, fig5Data %>% select(flightCity, diType), by=\"flightCity\")\n",
    "\n",
    "# 추세선 데이터 기반 2030년 예상값 추가\n",
    "fig5RegressionData$anticipate2030 <- ifelse(fig5RegressionData$intercept+fig5RegressionData$slope*2030<0,\n",
    "                                            0,fig5RegressionData$intercept+fig5RegressionData$slope*2030)\n",
    "\n",
    "# 시각화 데이터에 적용하기 위해 데이터 형태 변경\n",
    "fig5AnticiData <- fig5RegressionData %>% select(flightCity, anticipate2030,diType) %>% rename(totalFlight=anticipate2030)\n",
    "fig5AnticiData$flightYear <- rep(2030, times=nrow(fig5AnticiData))\n",
    "\n",
    "# 기존 데이터에서 노선, 총 운항 편수, 연도만 추출\n",
    "fig5Data <- fig5Data %>% select(flightCity, totalFlight, flightYear, diType)\n",
    "\n",
    "# 예상 데이터를 기존 데이터에 붙여넣기\n",
    "fig5Data <- rbind(fig5Data, fig5AnticiData)\n",
    "\n",
    "# 회귀에 사용하지 않은 데이터(2020년 데이터) 제거\n",
    "fig5Data <- fig5Data %>% filter(flightYear != 2020)\n",
    "\n",
    "# 노선 간 거리 데이터 추출\n",
    "fig5DistanceData <- regData %>% select(flightCity, distance.km)\n",
    "fig5DistanceData <- unique(fig5DistanceData)\n",
    "\n",
    "# 시각화 데이터에 거리 및 국내/국제 여부 데이터 추가\n",
    "fig5Data <- inner_join(fig5Data, fig5DistanceData, by = \"flightCity\") %>% arrange(flightCity, flightYear)\n",
    "\n",
    "# 2030년 예상 운항 거리 상위 8개 노선 데이터만 추출\n",
    "fig5Top <- head(fig5Data %>% filter(flightYear==2030) %>%\n",
    "                arrange(desc(totalFlight*distance.km)) %>% distinct(flightCity,diType), 8)\n",
    "fig5Top$top <- rep(\"o\", times=nrow(fig5Top))\n",
    "\n",
    "# 시각화 데이터에서 상위 8개 노선 데이터 추출\n",
    "fig5Data <- inner_join(fig5Data, fig5Top, by =  c(\"flightCity\",\"diType\")) %>% select(-top)\n",
    "\n",
    "# 노선별 추세선 작도 위해 y절편 및 기울기 데이터 추가\n",
    "fig5Data <- inner_join(fig5Data, unique(fig5RegressionData),by = c(\"flightCity\",\"diType\")) %>% select(-anticipate2030)\n",
    "fig5Data <- unique(fig5Data)\n",
    "\n",
    "# 각 연도의 운항 편수 예측값 추가\n",
    "fig5Data$anticipatedValue <- ifelse(fig5Data$intercept+fig5Data$slope*fig5Data$flightYear<0,\n",
    "                                  0,fig5Data$intercept+fig5Data$slope*fig5Data$flightYear)\n",
    "\n",
    "\n",
    "# 운항 거리별 정렬\n",
    "fig5Data$flightCity <- factor(fig5Data$flightCity, levels = fig5Top$flightCity)\n",
    "\n",
    "head(fig5Data %>% arrange(flightYear, flightCity))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0456dc56",
   "metadata": {},
   "source": [
    "각 컬럼의 의미는 다음과 같습니다\n",
    "\n",
    "|번호|컬럼명|내용\n",
    "|:---|:---|:---\n",
    "|1|flightCity|출도착 공항명\n",
    "|2|totalFlight|연도별 총 운항 편수\n",
    "|3|diType|국내선 또는 국제선 구분(D: 국내선, I: 국제선)\n",
    "|4|distance.km|출도착 공항 간 거리(단위: km)\n",
    "|5|intercept|노선별 단일선형회귀 실행 시 산출되는 **y절편**\n",
    "|6|slope|노선별 단일선형회귀 실행 시 산출되는 **기울기**\n",
    "|7|anticipatedValue|선형 회귀에 따른 예상값(노선별 추세선 그리기에 사용될 예정, y = intercept + slope * totalFlight)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "816188b8",
   "metadata": {},
   "source": [
    "#### 데이터 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e42b9c3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`geom_smooth()` using formula 'y ~ x'\n"
     ]
    },
    {
     "data": {
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SB5DABSkF4E0utnygEkjwFACtLCIFlG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\nBDHo/wP6dwohWyIrkAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig5 <- ggplot() + theme_bw() +\n",
    "        geom_point(data=fig5Data, aes(x=flightYear, y=totalFlight*distance.km/1000,color=flightCity, shape=diType)) +\n",
    "        geom_smooth(data=fig5Data, aes(x=flightYear, y=anticipatedValue*distance.km/1000,color=flightCity),\n",
    "                    size=0.5, linetype = 2, method = \"lm\")+\n",
    "        labs(title=\"2030년 예상 운항 거리 상위 8개 노선별 총 운항 거리\",\n",
    "             x=\"연도\", y=\"총 운항 거리 (단위: 천 km)\",color=\"노선\",shape=\"국내/국제선 구분\") + \n",
    "        #scale_x_continuous(breaks=c(2020,2030), labels) +\n",
    "        scale_y_continuous(name = \"총 운항 거리 (단위: 천 km)\")+\n",
    "        scale_color_brewer(palette = \"Set2\") +\n",
    "        theme(legend.position = \"right\",\n",
    "              title = element_text(size=13,face='bold'),\n",
    "              axis.title.x =  element_text(size=12,face='bold'),\n",
    "              axis.title.y = element_text(size=12,face='bold'),\n",
    "              legend.title = element_text(size=15,face='bold', vjust=0.5),\n",
    "              legend.text = element_text(size=12, vjust=0.5)\n",
    "              ) + \n",
    "        annotate(\"rect\", xmin=2019.5, xmax=2030.5, ymin=0, ymax=125000, alpha=0, color=\"gray25\", lty=\"dashed\") +\n",
    "        annotate(\"text\", x=2023, y=5000, label=\"회귀에 의한 예상치\", size=5, fontface=2)\n",
    "\n",
    "fig5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b0d9b3b",
   "metadata": {},
   "source": [
    " 2011년부터 2019년까지 국내 및 국제 상위 8개 노선의 총 운항 거리 데이터를 이용해 각 노선별로 선형회귀분석하여 2030년 연료소모량을 예측했습니다. 그 결과 **청주-김포 노선(CJU-GMP)**이 눈에 띄게 높은 총 운항 거리를 보였고, 다낭(베트남)-부산 노선(DAD-PUS), 김포-부산 노선(GMP-PUS)이 뒤를 이었습니다. 2020년의 데이터는 **코로나19 범유행의 특수성**을 고려해 반영하지 않았습니다. 해당 사태가 안정된다면 항공기 이용량이 기존 추세로 복귀할 것으로 예상되기 때문입니다.\n",
    " \n",
    " 이렇게 **예상 연료 사용량이 높은 노선 위주**로 기존 항공기를 연비가 좋은 항공기로 대체하거나 기존 항공기의 연비를 개선하도록 개조한다면 항공기 온실가스 증가를 효과적으로 억제할 수 있을 것입니다. 실제로 최근 대한항공이 좌석수 400석의 A380의 운영을 접고 좌석당 연료 효율이 20%이상 높은 B787과 같은 항공기로 대체하였습니다 [4]. 또한 인천 국제공항에서는 기존 화석 기반 항공유를 바이오 항공유로 대체함을 통해 온실가스 배출량을 약 80% 줄일 수 있을 것이라 예상했습니다 [5]. 이러한 항공기 기종, 항공기 연료 대체를 노선 사용량이 큰 항공기 우선으로 유도한다면 정책효과를 거둘 수 있을 것입니다. 이를 유도하기 위한 방안으로 지원금, 탄소배출권 규제 등이 있을 것입니다.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "841d4d36",
   "metadata": {},
   "source": [
    "# 3. 결론"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b719c3a9",
   "metadata": {},
   "source": [
    "## 3-1. 요약"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "619b7529",
   "metadata": {},
   "source": [
    " 지금까지 항공기 운항거리, 편수, 연료 소모량과 항공기 온실가스 사이의 관계, 온실가스 변화량과 가장 연관 관계가 큰 항공기, 미래 노선별 연료 소모량 예측을 시도했습니다. 그 과정에서 \n",
    "1. 운항 거리, 편수, 연료소모량과 온실가스 사이에 밀접한 **양의 상관관계**가 있음을 확인했습니다.\n",
    "\n",
    "2. 온실가스 배출량에 밀접한 관계를 가지는 항공기는 **A300**이었습니다. 이때 A300 항공기종의 비율이 증가함에 따라 온실가스 배출량 또한 비례하여 증가하는 관계를 갖고 있습니다.\n",
    "\n",
    "3. 청주 공항과 김포 공항 사이 2030년 운항 거리와 편수를 반영한 총 연료 소모량이 **타 노선 대비 월등히게 클 것**을 예상할 수 있었습니다. 따라서 청주 공항과 김포 공항을 이동하는 항공기 위주로 연료, 항공기 대체 작업을 한다면 온실가스 방출량을 줄일 수 있을 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80e530e5",
   "metadata": {},
   "source": [
    "## 3-2. 한계점"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3a3843",
   "metadata": {},
   "source": [
    "### 3-2-1. 항공기종별 운항 기록 부재"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2e32fa6",
   "metadata": {},
   "source": [
    "공공 데이터와 누구에게나 공개된 민간 데이터 중에서는 항공기종이 기록된 운항 기록이 존재하지 않아, 항공기의 정확한 연료 소모량을 산출할 수 없는 문제점이 있었습니다. 따라서 **2-1-3. 사용 항공기종 변화** 항목에서의 **연도별 연료 소모량 그래프**를 만들 때 각 항공기종의 연비에 그 기종의 최대 이동 거리(sector)와 항공기 사용 횟수를 곱한 값으로 대체하여 나타냈습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a23289",
   "metadata": {},
   "source": [
    "### 3-2-2. 인천국제공항공사 출처 기종별 운항 편수 데이터 부재"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "011984ff",
   "metadata": {},
   "source": [
    "인천국제공항을 제외한 모든 국내 공항의 기종 사용량은 한국공항공사에서 공개한 기종별 통계를 통해 얻을 수 있었던 반면, 인천국제공항 관련 공공 데이터를 얻을 수 있는 방법이 없었습니다. 그래서 한국공항공사 출처 기종별 통계와 위키피디아 자료를 기반으로 항공기 연료 소모량을 구하여 시각화를 한 이후, 온실가스 배출량과의 상관관계를 구하였습니다 (**2-1-3. 사용 항공기종 변화** 항목 및 **2-1-4. 2-1-1, 2, 3 항목의 통계 결과 및 온실가스 배출량 간의 상관관계 분석** 항목 참조). 만일 인천국제공항공사에서 기종별 통계를 제공한다면 연료 사용량과 온실가스 배출량 사이의 식을 통해 더 정확한 항공기 연료 소모량을 산출할 수 있을 것으로 사고합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "139ca5d7",
   "metadata": {},
   "source": [
    "### 3-2-3. 과거 실제 운항 데이터 부재"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8110071a",
   "metadata": {},
   "source": [
    "공공 데이터 중 **4) 한국공항공사의 항공기 운항정보**를 통해 실시간 운항 정보를 받을 수 있지만, **3-2-1. 항공기종별 운항 기록 부재** 항목과 마찬가지로 Flightradar24 또는 FlightAware 등의 상업용 항공 관련 데이터베이스 제공 사이트 외에는 과거 운항 데이터를 얻을 수 있는 방법이 없었습니다. 그래서 4)번 출처의 운항 스케줄로 노선별로 추정 운항 편수를 구하여 사용했습니다 (**2-1-2. 국내/국제별 총 운항 거리 변화** 항목 참조). 만일 실제 운항 데이터가 공공 데이터로 사용할 수 있다면 결항 등을 반영하여 더 정확한 운항 편수를 구할 수 있을 것으로 보입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e33356f",
   "metadata": {},
   "source": [
    "# 4. 참고 데이터 및 문헌"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "162322ab",
   "metadata": {},
   "source": [
    "## 4-1. 데이터 출처\n",
    "1) 한국공항공사. 공항 온실가스. OpenAPI. https://www.data.go.kr/tcs/dss/selectApiDataDetailView.do?publicDataPk=3050602\n",
    "\n",
    "2) 인천국제공항공사. 그린리포트. 간행물. https://www.airport.kr/co_cnt/ko/cyberpr/publicat/pbooks/pbooks.do\n",
    "\n",
    "3) 한국공항공사. 전국공항 수송실적통계. OpenAPI. https://www.data.go.kr/tcs/dss/selectApiDataDetailView.do?publicDataPk=3034194\n",
    "\n",
    "4) 한국공항공사. 항공기 운항정보. OpenAPI. https://www.data.go.kr/tcs/dss/selectApiDataDetailView.do?publicDataPk=15000126\n",
    "\n",
    "5) 한국공항공사. 노선별 소요시간 및 거리 정보. OpenAPI. https://www.data.go.kr/tcs/dss/selectApiDataDetailView.do?publicDataPk=15007809\n",
    "\n",
    "6) Wikipedia. \"Fuel economy in aircraft.\" https://en.wikipedia.org/wiki/Fuel_economy_in_aircraft\n",
    "\n",
    "7) 항공정보포털시스템. 항공기 정보 > A300. https://www.airportal.go.kr/upload/aircraft/%EC%83%81%EC%84%B8%EC%A0%95%EB%B3%B4%20-%20A300-600.pdf\n",
    "\n",
    "8) 항공정보포털시스템. 항공기 정보 > MD-80. https://www.airportal.go.kr/upload/aircraft/%EC%83%81%EC%84%B8%EC%A0%95%EB%B3%B4%20-%20A300-600.pdf\n",
    "\n",
    "9) 위키백과. \"일류신 Il-62.\" https://ko.wikipedia.org/wiki/%EC%9D%BC%EB%A5%98%EC%8B%A0_Il-62\n",
    "\n",
    "10) 한화토탈. 물질안전보건자료(MSDS). Jet A-1. https://www.hanwha-total.com/Dext/file_down?dir=product&fileName=628da97b8b0742b1af3d2fe1abcc37aa&oriFileName=GHSMSDS0591_JETA1_KR.pdf\n",
    "\n",
    "11) 한국공항공사. 정보공개 > 항공통계 > 기종별 통계. https://www.airport.co.kr/www/cms/frFlightStatsCon/kindStats.do?MENU_ID=1250"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9ccd18",
   "metadata": {},
   "source": [
    "## 문헌 출처\n",
    "[1] 주희진. \"국내 공항 온실가스 배출량 산정 및 저감효과 분석.\" 국내석사학위논문 세종대학교 일반대학원, 2016. 서울\n",
    "\n",
    "[2] 윤태영. \"공항에서 항공기(APU) 온실가스배출 저감에 관한 연구.\" 국내석사학위논문 한국항공대학교 대학원, 2013. 경기도\n",
    "\n",
    "[3] 인천국제공항공사 에너지환경처, *인천국제공항공사 그린리포트 2021*, 2021, pp. 21. Available: https://www.airport.kr/co_cnt/ko/cyberpr/publicat/pbooks/pbooks.do\n",
    "\n",
    "[4] msn 뉴스, “대한항공 ‘a380’ 등 대형기 운영 접는다…’ 연비 좋은 중형기로 대체, \"기름값 아끼자\" 연비좋은 중형기 도입 붐 - 매일경제 (mk.co.kr)\n",
    "\n",
    "[5] 서울신문, “인천공항 ‘친황경 공항’ 구축 속도낸다, 인천공항 ‘친환경 공항’ 구축 속도낸다 | 서울신문 (seoul.co.kr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdfc0920",
   "metadata": {},
   "source": [
    "본 저작물의 저작권은 Apache License v2.0을 따릅니다."
   ]
  }
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