{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一个R语言实现的爬虫，爬取拉手网美食信息"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "点击下边的cell，点击上方工具栏里的执行图标，即可执行代码块，看到输出结果。代码块左边的In[]出现In[*]表示代码正在执行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "library(XML)\n",
    "\n",
    "giveNames = function(rootNode){\n",
    "  names <- xpathSApply(rootNode,\"//h3/a[@class='goods-name']\",xmlValue)\n",
    "  names\n",
    "}\n",
    "\n",
    "givesevices = function(rootNode){\n",
    "  sevices <- xpathSApply(rootNode,\"//h3/a[@class='goods-text']\",xmlValue)\n",
    "  sevices\n",
    "}\n",
    "\n",
    "\n",
    "giveprices = function(rootNode){\n",
    "  prices <- xpathSApply(rootNode,\"//div/span[@class='price']\",xmlValue)\n",
    "  prices\n",
    "}\n",
    "\n",
    "\n",
    "givemoney = function(rootNode){\n",
    "  money <- xpathSApply(rootNode,\"//div/span[@class='money']\",xmlValue)\n",
    "  money\n",
    "}\n",
    "\n",
    "\n",
    "giveplaces = function(rootNode){\n",
    "  places <- xpathSApply(rootNode,\"//a/span[@class='goods-place']\",xmlValue)\n",
    "  places\n",
    "}\n",
    "\n",
    "\n",
    "getmeituan = function(URL){\n",
    "  Sys.sleep(runif(1,1,2))\n",
    "  doc<-htmlParse(URL[1],encoding=\"UTF-8\")\n",
    "  rootNode<-xmlRoot(doc)\n",
    "  data.frame(\n",
    "    Names=giveNames(rootNode), #店名\n",
    "    services=givesevices(rootNode), #服务\n",
    "    prices=giveprices(rootNode),  #现价\n",
    "    money=givemoney(rootNode),  #原价\n",
    "    places=giveplaces(rootNode)  #地点\n",
    "    \n",
    "  )\n",
    "}\n",
    "\n",
    "\n",
    "URL = paste0(\"http://shenzhen.lashou.com/cate/meishi/page\",1:10)\n",
    "\n",
    "mainfunction = function(URL){\n",
    "  data = rbind(\n",
    "    getmeituan (URL[1]),\n",
    "    getmeituan (URL[2]),\n",
    "    getmeituan (URL[3]),\n",
    "    getmeituan (URL[4]),\n",
    "    getmeituan (URL[5])\n",
    "  )\n",
    "  \n",
    "  \n",
    "}\n",
    "ll=mainfunction(URL)\n",
    "write.table(ll,\"result.txt\",row.names=FALSE)\n",
    "ll\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# R语言的线性回归实例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#读入数据\n",
    "x<- c(0.10,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.20,0.21,0.23)\n",
    "y<-c(42.0,43.5,45.0,45.5,45.0,47.5,49,53,50,55,55,60)\n",
    "#绘出 x 与 y 的散列图\n",
    "plot(y~x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "执行该段代码，即可看到输出图形；从图中我们可以看出 y 和 x 存在线性相关性，可以进行线性回归分析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model<-lm(y~x)\n",
    "summary(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们通过 P 值(就是上面的 pr 那一列)来查看对应的解释变量 x 的显著性，通过将 p 值与 0.05 进行比较，若改值小于 0.05，就可以说该变量与被解释变量存在显著的相关性。\n",
    "\n",
    "Multiple R-squared 和 Adjusted R-squared 这两个值，就是我们常称为”拟合优度“和”修正的拟合优度“，是指回归方程对样本的拟合程度，这里我们可以看到，修正的拟合优度为 0.9429，表示拟合程度超过五成，这个值越高越好。\n",
    "\n",
    "最后，看下 F-statistic，也就是常说的 F 统计量，也称为 F 检验，常用语判断方程整体的显著性实验，其 p 值为 9.505e-08，显然小于 0.05，我们可以认为方程在 P=0.05 的水平上是通过显著性检验的。\n",
    "\n",
    "从上面我们看出我们的线性回归效果不错，那么我们可以利用拟合方程进行分类，或者预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "newX<-data.frame(x=0.16)\n",
    "predict(model,newdata=newX,interval=\"prediction\",level=0.95)#interval=”prediction“ level指定预测的置信区间"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# R语言的逻辑回归示例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "counts <- c(18,17,15,20,10,20,25,13,12)\n",
    "outcome <- gl(3,1,9)\n",
    "treatment <- gl(3,3)\n",
    "print(d.AD <- data.frame(treatment, outcome, counts))\n",
    "glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())\n",
    "anova(glm.D93)\n",
    "summary(glm.D93)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.2.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}