{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "804ad891",
   "metadata": {},
   "source": [
    "# 히브리어 데이터를 이용한 통계적 언어 분석의 예\n",
    "\n",
    "본 노트에서는 구문 정보가 포함되어 있는 성서 데이터 베이스(Text-Fabric)와 다양한 통계 수치를 산출할 수 있는 통계 프로그램인 R을 이용하여 간단하게 성서 히브리어 데이터를 활용한 간단한 통계 분석을 해 보도록 하겠습니다. 히브리어 통계 분석을 위해서는 언어 데이터를 먼저 준비해야 합니다. (*본 내용은 테스트를 위한 예시입니다)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d1650e34",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "library(grid)\n",
    "library(dplyr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e5dad26",
   "metadata": {},
   "source": [
    "본 노트에서 통계적 언어 분석을 위한 절차는 대략 아래와 같습니다.\n",
    "\n",
    "* 샘플 텍스트 선정: 샘플 텍스트는 텍스트의 시대 구분에 있어서 학자들의 이견이 거의 없는 것으로 선정을 해야 합니다. 여기에서는 상대적으로 이른 시기의 본문으로 사무엘서/열왕기서를 택하였고, 포로기 이후의 본문으로 역대기서를 택하였습니다.\n",
    "* 시험 텍스트 선정: 샘플 텍스트 분석을 통해 어느 정도 특징을 구별해 낼 수 있으면 이 데이터를 토대로 시대를 추측할 대상인 텍스트를 선정해야 합니다. 여기에서는 레위기와 에스더를 선정했습니다. 이 책들이 사무엘서/열왕기서 혹은 역대기서에 가까운지 혹은 과도기적인 특징을 보이는 지를 살펴볼 것입니다.\n",
    "* 분류 기준 선정: 어느 특징에 따라 텍스트를 분류할 것인지를 결정해야 합니다. 정확한 분석을 위해서는 다양한 언어적인 특징들을 적용해야겠지만, 저는 프랑크 폴락(Frank Polak)이 제시한 기준 가운데 명사절 비율[1]과 아비 후르비츠(Avi Hurvitz)가 제시한 여러 기준 가운데 אל과 על 전치사 사용 비율[2] 등을 기준으로 텍스트의 특징을 파악해 볼 것입니다.\n",
    "\n",
    "\n",
    "-------\n",
    "\n",
    "[1] 명사절 비율이 높을수록 구어체보다는 문어체에 가까우며 이는 포로 후기 페르시아 서기관 문화의 영향을 반영하는 것이다.\n",
    "\n",
    "[2] 포로 후기 문헌으로 갈수록 아람어의 영향으로 אל 보다는 על을 선호하는 경향이 강하다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34ff52b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 역대기서\n",
    "chr1 <- read.csv(\"1_Chronicles.csv\", header = TRUE)\n",
    "chr2 <- read.csv(\"2_Chronicles.csv\", header = TRUE)\n",
    "chr <- rbind(chr1, chr2)\n",
    "\n",
    "#사무엘서+열왕기서 (편의상 영어 약어는 sam으로 표기함)\n",
    "sam1 <- read.csv(\"1_Samuel.csv\", header = TRUE)\n",
    "sam2 <- read.csv(\"2_Samuel.csv\", header = TRUE)\n",
    "sam3 <- read.csv(\"1_Kings.csv\", header = TRUE)\n",
    "sam4 <- read.csv(\"2_Kings.csv\", header = TRUE)\n",
    "sam <- rbind(sam1, sam2, sam3, sam4)\n",
    "\n",
    "#레위기\n",
    "lev <- read.csv(\"Leviticus.csv\", header = TRUE)\n",
    "\n",
    "#에스더\n",
    "esth <- read.csv(\"Esther.csv\", header = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "97708647",
   "metadata": {},
   "outputs": [
    {
     "data": {
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JB0UCQb062OehMvFCkEQJFsy4zfEvWUA4oUCqBItuXr6I+1oSGPfCX1Lq0IA0VC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\nANF/i7VeJ0bIAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clause type\n",
    "sam_ctyp = as.data.frame(table(sam$ctyp, exclude=\"\"))\n",
    "sam_ctyp <- cbind(sam_ctyp, prop.table(sam_ctyp$Freq)*100)\n",
    "names(sam_ctyp) <- c(\"ctyp\", \"freq\", \"prop\")\n",
    "#sam_ctyp <- sam_ctyp[!(sam_ctyp$sam < 200),]\n",
    "sam_ctyp <- sam_ctyp[order(sam_ctyp$freq,decreasing=T)[1:5],]\n",
    "sam_ctyp <- arrange(sam_ctyp, desc(freq))\n",
    "\n",
    "chr_ctyp = as.data.frame(table(chr$ctyp, exclude=\"\"))\n",
    "chr_ctyp <- cbind(chr_ctyp, prop.table(chr_ctyp$Freq)*100)\n",
    "names(chr_ctyp) <- c(\"ctyp\", \"freq\", \"prop\")\n",
    "#chr_ctyp <- chr_ctyp[!(chr_ctyp$chr < 200),]\n",
    "chr_ctyp <- chr_ctyp[order(chr_ctyp$freq,decreasing=T)[1:5],]\n",
    "chr_ctyp <- arrange(chr_ctyp, desc(freq))\n",
    "\n",
    "lev_ctyp = as.data.frame(table(lev$ctyp, exclude=\"\"))\n",
    "lev_ctyp <- cbind(lev_ctyp, prop.table(lev_ctyp$Freq)*100)\n",
    "names(lev_ctyp) <- c(\"ctyp\", \"freq\", \"prop\")\n",
    "#lev_ctyp <- lev_ctyp[!(lev_ctyp$lev < 200),]\n",
    "lev_ctyp <- lev_ctyp[order(lev_ctyp$freq,decreasing=T)[1:5],]\n",
    "lev_ctyp <- arrange(lev_ctyp, desc(freq))\n",
    "\n",
    "esth_ctyp = as.data.frame(table(esth$ctyp, exclude=\"\"))\n",
    "esth_ctyp <- cbind(esth_ctyp, prop.table(esth_ctyp$Freq)*100)\n",
    "names(esth_ctyp) <- c(\"ctyp\", \"freq\", \"prop\")\n",
    "esth_ctyp <- esth_ctyp[order(esth_ctyp$freq,decreasing=T)[1:5],]\n",
    "esth_ctyp <- arrange(esth_ctyp, desc(freq))\n",
    "\n",
    "ctyp1 <- ggplot(data=sam_ctyp, aes(x=ctyp, y=prop)) + \n",
    "#  geom_bar(stat=\"identity\", width=1) +\n",
    "  geom_bar(stat=\"identity\") +\n",
    "  ggtitle(\"Clause Type of Samuel/Kings\")\n",
    "\n",
    "ctyp2 <- ggplot(data=chr_ctyp, aes(x=ctyp, y=prop)) + \n",
    "#  geom_bar(stat=\"identity\", width=1) +\n",
    "  geom_bar(stat=\"identity\") +\n",
    "  ggtitle(\"Clause Type of Chronicles\")\n",
    "\n",
    "ctyp3 <- ggplot(data=lev_ctyp, aes(x=ctyp, y=prop)) + \n",
    "  #  geom_bar(stat=\"identity\", width=1) +\n",
    "  geom_bar(stat=\"identity\") +\n",
    "  ggtitle(\"Clause Type of Leviticus\")\n",
    "\n",
    "ctyp4 <- ggplot(data=esth_ctyp, aes(x=ctyp, y=prop)) + \n",
    "  geom_bar(stat=\"identity\") +\n",
    "  ggtitle(\"Clause Type of Esther\")\n",
    "\n",
    "\n",
    "grid.newpage()\n",
    "grid.draw(rbind(ggplotGrob(ctyp1), ggplotGrob(ctyp2), ggplotGrob(ctyp3), ggplotGrob(ctyp4), size = \"last\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c25f664e",
   "metadata": {},
   "source": [
    "위 그래프는 책 별로 가장 많이 사용된 상위 5개의 절 유형을 분석하고 있는 그래프입니다. 각 지표는 아래와 같습니다.\n",
    "\n",
    "* InfC: 구문형 부정사 절\n",
    "* NmCl: 명사절\n",
    "* Ptcp: 분사절\n",
    "* Way0, WayX, WQtX: 봐브+동사로 이루어진 다양한 유형의 동사절\n",
    "\n",
    "위 유형들 가운데 동사절을 제외한 구문형 부정사 절, 명사절, 분사절 등은 모두 명사적인 요소로 이루어진 절이라고 볼 수 있습니다. 위의 지표를 살펴보면 역대기에서 명사절의 비율이 상당히 높게 나타나는 것을 발견할 수 있습니다. 반면 사무엘서는 동사절의 비율이 매우 높습니다. 즉 후기의 책일수록 명사절의 비율이 높다는 폴락의 이론을 어느 정도 뒷받침해주는 지표라고 볼 수 있습니다.\n",
    "\n",
    "그리고 레위기와 에스더서의 경우를 보면 레위기는 명사절보다는 동사절의 비율이 크게 나타나는 것을 볼 수 있습니다. 명사절의 비율만 놓고 본다면 레위기는 포로 후기의 문체를 보이고 있지는 않은 것 같습니다. 반면 에스더서는 명사절 비율이 높게 나타나는데 이는 에스더서가 포로 후기의 문체를 전형적으로 나타내 보이고 있는 것 같습니다. 좀 더 그 차이를 확실히 하기 위해 각 책의 명사절 요소들의 비율을 모두 합산하여 점 그래프로 아래와 같이 만들어 보았습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9bc01576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZKNkMQIyYWQRAhJjJBshCRGSC6EJEJI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\nJEZINkISI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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#proportion of nominal clause of each book\n",
    "sam_NmCl <- sam_ctyp[(sam_ctyp$ctyp == 'NmCl' | sam_ctyp$ctyp == 'Ptcp' | sam_ctyp$ctyp == 'InfC' | sam_ctyp$ctyp == 'Voct' | sam_ctyp$ctyp == 'AjCl'),]\n",
    "sum_sam = sum(sam_NmCl[, c(3)])\n",
    "\n",
    "chr_NmCl <- chr_ctyp[(chr_ctyp$ctyp == 'NmCl' | chr_ctyp$ctyp == 'Ptcp' | chr_ctyp$ctyp == 'InfC' | chr_ctyp$ctyp == 'Voct' | chr_ctyp$ctyp == 'AjCl'),]\n",
    "sum_chr = sum(chr_NmCl[, c(3)])\n",
    "\n",
    "lev_NmCl <- lev_ctyp[(lev_ctyp$ctyp == 'NmCl' | lev_ctyp$ctyp == 'Ptcp' | lev_ctyp$ctyp == 'InfC' | lev_ctyp$ctyp == 'Voct' | lev_ctyp$ctyp == 'AjCl'),]\n",
    "sum_lev = sum(lev_NmCl[, c(3)])\n",
    "\n",
    "esth_NmCl <- esth_ctyp[(esth_ctyp$ctyp == 'NmCl' | esth_ctyp$ctyp == 'Ptcp' | esth_ctyp$ctyp == 'InfC' | esth_ctyp$ctyp == 'Voct' | esth_ctyp$ctyp == 'AjCl'),]\n",
    "sum_esth = sum(esth_NmCl[, c(3)])\n",
    "\n",
    "book <- c(\"sam/kgs\", \"chr\", \"lev\", \"esth\")\n",
    "NC <- c(sum_sam, sum_chr, sum_lev, sum_esth)\n",
    "NCtable <- data.frame(book, NC)\n",
    "\n",
    "ggplot(data=NCtable, aes(x=book, y=NC)) + \n",
    "  geom_point() +\n",
    "  ggtitle(\"proportion of Nominal Clause\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "72511b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'역대기'"
      ],
      "text/latex": [
       "'역대기'"
      ],
      "text/markdown": [
       "'역대기'"
      ],
      "text/plain": [
       "[1] \"역대기\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "36.6417290108063"
      ],
      "text/latex": [
       "36.6417290108063"
      ],
      "text/markdown": [
       "36.6417290108063"
      ],
      "text/plain": [
       "[1] 36.64173"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'에스더'"
      ],
      "text/latex": [
       "'에스더'"
      ],
      "text/markdown": [
       "'에스더'"
      ],
      "text/plain": [
       "[1] \"에스더\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "33.2484076433121"
      ],
      "text/latex": [
       "33.2484076433121"
      ],
      "text/markdown": [
       "33.2484076433121"
      ],
      "text/plain": [
       "[1] 33.24841"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'레위기'"
      ],
      "text/latex": [
       "'레위기'"
      ],
      "text/markdown": [
       "'레위기'"
      ],
      "text/plain": [
       "[1] \"레위기\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "13.3251079580506"
      ],
      "text/latex": [
       "13.3251079580506"
      ],
      "text/markdown": [
       "13.3251079580506"
      ],
      "text/plain": [
       "[1] 13.32511"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'사무엘서/열왕기서'"
      ],
      "text/latex": [
       "'사무엘서/열왕기서'"
      ],
      "text/markdown": [
       "'사무엘서/열왕기서'"
      ],
      "text/plain": [
       "[1] \"사무엘서/열왕기서\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "18.5351064562806"
      ],
      "text/latex": [
       "18.5351064562806"
      ],
      "text/markdown": [
       "18.5351064562806"
      ],
      "text/plain": [
       "[1] 18.53511"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"역대기\"\n",
    "sum_chr\n",
    "\n",
    "\"에스더\"\n",
    "sum_esth\n",
    "\n",
    "\"레위기\"\n",
    "sum_lev\n",
    "\n",
    "\"사무엘서/열왕기서\"\n",
    "sum_sam"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ba09a4",
   "metadata": {},
   "source": [
    "위에서 유추한대로 역대기서와 에스더서, 그리고 레위기와 사무엘서과 명확하게 구별됨을 볼 수 있습니다. 레위기는 사무엘서보다도 명사문장의 비율이 상당히 낮게 나오는 것을 볼 수 있는데, 이는 주로 제의에 대한 명령(동사)이 주되게 나오는 레위기 책 자체의 문체적인 특징에 기인하는 것이라 볼 수 있습니다.\n",
    "\n",
    "다음으로 전치사의 사용의 경향성을 살펴 보도록 하겠습니다. 위에서 했던 대로 우선 각 책에서 나타나는 전치사 사용 비율을 아래와 같이 그래프로 표현해 보았습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "038c7005",
   "metadata": {},
   "outputs": [
    {
     "data": {
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LGQlwsTr6Ci4c/xYmUn7ftNLzaYZgsWKaDWLFLZRQ0X1X64mK0JJVLYhvlSa9p0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\n7bYaLwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#preposition\n",
    "sam_prep <- filter(sam, sp == 'prep')\n",
    "sam_prep <- select(sam_prep, lex)\n",
    "sam_prep = as.data.frame(table(sam_prep$lex))\n",
    "sam_prep <- cbind(sam_prep, prop.table(sam_prep$Freq)*100)\n",
    "names(sam_prep) <- c(\"prep\", \"freq\", \"prop\")\n",
    "sam_prep <- sam_prep[!(sam_prep$prep == 'את'),]\n",
    "sam_prep <- sam_prep[order(sam_prep$freq,decreasing=T)[1:5],]\n",
    "\n",
    "chr_prep <- filter(chr, sp == 'prep')\n",
    "chr_prep <- select(chr_prep, lex)\n",
    "chr_prep = as.data.frame(table(chr_prep$lex))\n",
    "chr_prep <- cbind(chr_prep, prop.table(chr_prep$Freq)*100)\n",
    "names(chr_prep) <- c(\"prep\", \"freq\", \"prop\")\n",
    "chr_prep <- chr_prep[!(chr_prep$prep == 'את'),]\n",
    "chr_prep <- chr_prep[order(chr_prep$freq,decreasing=T)[1:5],]\n",
    "\n",
    "lev_prep <- filter(lev, sp == 'prep')\n",
    "lev_prep <- select(lev_prep, lex)\n",
    "lev_prep = as.data.frame(table(lev_prep$lex))\n",
    "lev_prep <- cbind(lev_prep, prop.table(lev_prep$Freq)*100)\n",
    "names(lev_prep) <- c(\"prep\", \"freq\", \"prop\")\n",
    "lev_prep <- lev_prep[!(lev_prep$prep == 'את'),]\n",
    "lev_prep <- lev_prep[order(lev_prep$freq,decreasing=T)[1:5],]\n",
    "\n",
    "esth_prep <- filter(esth, sp == 'prep')\n",
    "esth_prep <- select(esth_prep, lex)\n",
    "esth_prep = as.data.frame(table(esth_prep$lex))\n",
    "esth_prep <- cbind(esth_prep, prop.table(esth_prep$Freq)*100)\n",
    "names(esth_prep) <- c(\"prep\", \"freq\", \"prop\")\n",
    "esth_prep <- esth_prep[!(esth_prep$prep == 'את'),]\n",
    "esth_prep <- esth_prep[order(esth_prep$freq,decreasing=T)[1:5],]\n",
    "\n",
    "prep1 <- ggplot(data=sam_prep, aes(x=prep, y=prop)) + \n",
    "  geom_bar(stat=\"identity\") +\n",
    "  ggtitle(\"Preposition of Samuel/Kings\")\n",
    "\n",
    "prep2 <- ggplot(data=chr_prep, aes(x=prep, y=prop)) + \n",
    "  geom_bar(stat=\"identity\") +\n",
    "  ggtitle(\"Preposition of Chronicles\")\n",
    "\n",
    "prep3 <- ggplot(data=lev_prep, aes(x=prep, y=prop)) + \n",
    "  geom_bar(stat=\"identity\") +\n",
    "  ggtitle(\"Preposition of Leviticus\")\n",
    "\n",
    "\n",
    "prep4 <- ggplot(data=esth_prep, aes(x=prep, y=prop)) + \n",
    "  geom_bar(stat=\"identity\") +\n",
    "  ggtitle(\"Preposition of Esther\")\n",
    "\n",
    "grid.newpage()\n",
    "grid.draw(rbind(ggplotGrob(prep1), ggplotGrob(prep2), ggplotGrob(prep3), ggplotGrob(prep4), size = \"last\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2500ea66",
   "metadata": {},
   "source": [
    "한 눈에 봐도 ל 전치사가 가장 많이 사용되고 있는 전치사임을 볼 수 있습니다. 각각 전치사의 사용 비율을 살펴보면 그렇게 의미 있는 데이터는 눈에 띄지 않습니다. 그런데 앞서 제가 언급했듯이 여기서 중요한 것은 אל과 על의 사용 비율입니다. 사무엘서는 אל이 על보다 빈번하게 사용되고 있는 것을 볼 수 있습니다. 그런데 역대기에서는 완전히 반대입니다. על이 אל보다 훨씬 많은 비율로 사용되고 있습니다. 레위기와 에스더에서도 על이 많이는 아니지만 보다 더 많이 사용되고 있는 것 같습니다.\n",
    "\n",
    "이 데이터를 על의 사용 수 / אל의 사용 수 공식을 이용하여 사용 비율 값을 구한 뒤 아래와 같이 점 그래프로 표현해 보았습니다. 값이 크면 클 수록 על의 사용 비율이 보다 높다는 의미입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e73e2ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ClCMhGSEiF5EJIOIUkRkomQlAjJg5B0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\nDiFJEZKJk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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#proportion of על and אל of each book\n",
    "sam_al = sam_prep[sam_prep$prep == \"על\",]\n",
    "sam_al = sam_al$freq\n",
    "sam_el = sam_prep[sam_prep$prep == \"אל\",]\n",
    "sam_el = sam_el$freq\n",
    "sam_al_el = sam_al / sam_el\n",
    "\n",
    "chr_al = chr_prep[chr_prep$prep == \"על\",]\n",
    "chr_al = chr_al$freq\n",
    "chr_el = chr_prep[chr_prep$prep == \"אל\",]\n",
    "chr_el = chr_el$freq\n",
    "chr_al_el = chr_al / chr_el\n",
    "\n",
    "lev_al = lev_prep[lev_prep$prep == \"על\",]\n",
    "lev_al = lev_al$freq\n",
    "lev_el = lev_prep[lev_prep$prep == \"אל\",]\n",
    "lev_el = lev_el$freq\n",
    "lev_al_el = lev_al / lev_el\n",
    "\n",
    "esth_al = esth_prep[esth_prep$prep == \"על\",]\n",
    "esth_al = esth_al$freq\n",
    "esth_el = esth_prep[esth_prep$prep == \"אל\",]\n",
    "esth_el = esth_el$freq\n",
    "esth_al_el = esth_al / esth_el\n",
    "\n",
    "book <- c(\"sam\", \"chr\", \"lev\", \"esth\")\n",
    "alel <- c(sam_al_el, chr_al_el, lev_al_el, esth_al_el)\n",
    "aleltable <- data.frame(book, alel)\n",
    "\n",
    "ggplot(data=aleltable, aes(x=book, y=alel)) + \n",
    "  geom_point() +\n",
    "  ggtitle(\"Proportion of על and אל\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eae5207f",
   "metadata": {},
   "source": [
    "흥미로운 결과가 나왔습니다. 위의 그래프에서 볼 수 있듯이 사무엘서/열왕기서와 역대기의 특징이 명확하게 구분됩니다. 그런데 에스더서와 레위기는 거의 중간 값을 가지고 있는 것을 볼 수 있습니다. 전치사의 특징 만으로 본다면 에스더서와 레위기는 상대적으로 과도기적인 언어 특징을 보이고 있다고 볼 수 있습니다."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "r"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.2.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}