R语言从小木虫网页批量提取考研调剂信息

        h <- basicTextGatherer( )   # 查看服务器返回的头信息
        txt <- getURL(url, headerfunction = h$update,.encoding="UTF-8...")  # 返回字符串形式html

  htmlParse(file,asText=T,encoding="UTF-8"...) #参数file 即为XML或者HTML文件名或者text，asText参数是T指定file是text,encoding指定网页编码方式。
       这里我们需要读取网页，并且拿到该网页的html树内容
        自定义函数download,输入strURL，strURL为网址，返回html树内容
            download <- function(strURL){
              h <- basicTextGatherer( )# 查看服务器返回的头信息
              txt <- getURL(strURL, headerfunction = h$update,.encoding="gbk") ## 字符串形式
               htmlParse(txt,asText=T,encoding="gbk")      #选择gbk进行网页的解析
             }

 getHTMLLinks(doc,  xpQuery = "//a/@href"...) #doc为解析后的HTML树文件，xpQuery指定想匹配的Xpath元素

           greg <- function(pattern,istring){
                gregout <- gregexpr(pattern,istring)   #pattern为匹配模式，istring为待匹配的字符串
                substr(istring,gregout[[1]],gregout[[1]]+attr(gregout[[1]],'match.length')-1)
             }

            extradress <- function(strURL){
                 prefix <- "http://muchong.com/"
                 pattern <- "html/[0-9/]+.html"
                 links <- getHTMLLinks(strURL)
                 needlinks <- gregexpr(pattern,links)
                 needlinkslist <- list()
                for (i in which(unlist(needlinks)>0)){
                    preadress <- substr(links[i],needlinks[[i]],needlinks[[i]]+attr(needlinks[[i]],'match.length')-1)
                    needlinkslist<- c(needlinkslist,list(preadress))
                   adresses <- lapply(needlinkslist,function(x)paste(prefix,x,sep=""))
                 }
                return (adresses)
                 }

  getNodeSet(doc, path...) #doc 

      getNodeSet(doc,'//p')

  xmlValue(getNodeSet(a,'//p')[[2]]) 得到我们所要的内容

library(RCurl)
library(XML)

download <- function(strURL){
    h <- basicTextGatherer()# 查看服务器返回的头信息
    txt <- getURL(strURL, headerfunction = h$update,.encoding="gbk") ## 字符串形式
    htmlParse(txt,asText=T,encoding="gbk")      #选择gbk进行网页的解析
}

extradress <- function(strURL){
  prefix <- "http://muchong.com/"
  pattern <- "html/[0-9/]+.html"
  links <- getHTMLLinks(strURL)
  needlinks <- gregexpr(pattern,links)
  needlinkslist <- list()
  for (i in which(unlist(needlinks)>0)){
    preadress <- substr(links[i],needlinks[[i]],needlinks[[i]]+attr(needlinks[[i]],'match.length')-1)
    needlinkslist<- c(needlinkslist,list(preadress))
    adresses <- lapply(needlinkslist,function(x)paste(prefix,x,sep=""))
  }
  return (adresses)
}

gettopic <- function(doc){
    xmlValue(getNodeSet(doc,'//p')[[2]])
}

greg <- function(pattern,istring){
    gregout <- gregexpr(pattern,istring)
    substr(istring,gregout[[1]],gregout[[1]]+attr(gregout[[1]],'match.length')-1)
}

getinf <- function(topic){
pattern1 <- "招[\u4E00-\u9FA5]+[0-9-]*[\u4E00-\u9FA5]*[：、；，,;]*[\u4E00-\u9FA5]*[：、；，,;]*[\u4E00-\u9FA5]*[：、；，,;]*[\u4E00-\u9FA5]*[：、；，,;]*[\u4E00-\u9FA5]*(研究生)|(调剂)"
pattern2 <- "([\u4E00-\u9FA5]*课题组|[\u4E00-\u9FA5]*团队)"  
pattern21 <- "[\u4E00-\u9FA5]*[：、；，,;]*(教授|博士)"
pattern3 <- "[\u4E00-\u9FA5]*[：、；，,;]*[-A-Za-z0-9_.%]+@[-A-Za-z0-9_.%]+\\.[A-Za-z]+[.A-Za-z]*"
    #匹配@163.com类或者@abc.edu.cn两类邮箱
pattern4 <- "[\u4E00-\u9FA5]+老师"  #匹配某老师
pattern5 <- "[\u4E00-\u9FA5]*[：:]*1[3,5,8]{1}[0-9]{1}[0-9]{8}|0[0-9]{2,3}-[0-9]{7,8}(-[0-9]{1,4})?" #匹配联系人和号码
pattern6 <- "(主|从事)*[\u4E00-\u9FA5]*(的研究|方向)为*[：、；，,;]*[\u4E00-\u9FA5]*"
pattern7 <- "[\u4E00-\u9FA5]+(大学|学院|研究院|研究所)"
pattern8 <-"[-A-Za-z0-9_.%]+@[-A-Za-z0-9_.%]+\\.[A-Za-z]+[.A-Za-z]*" #精确匹配邮箱


cate <- greg(pattern1,topic)
proj <- greg(pattern2,topic)
PI <- greg(pattern21,topic)
email <- greg(pattern3,topic)
man <- greg(pattern4,topic)
phone <- greg(pattern5,topic)
direc <- greg(pattern6,topic)
univ <- greg(pattern7,topic)
print(cate)
if (greg("(分子|生物|植物|细胞|医学|动物|水)+",topic) !=""){
    if (man =="" && proj != ""){
        man <- unlist(strsplit(proj,"课题组")[1])
    }
 
    if (email != ""){
      email <- greg(pattern10,email)
    }
    
    data.frame("类别"=cate,"大学"=univ,"课题"=proj,"PI"=PI,"联系人"=man,"邮箱"=email,"方向"=direc,"电话"=phone)
}
else{
  return("")
}
}

strURLs="http://muchong.com/html/f430.html"
n=50
dat <- data.frame("URL"="URL","类别"="类别","大学"="大学","课题"="课题","PI"="PI","联系人"="联系人","邮箱"="邮箱","方向"="方向","电话"="电话")
strURLs <- c(strURLs,paste(rep("http://muchong.com/html/f430_",n),c(2:n),".html",sep=""))
output1 <- "a2017.2.21.txt" #未处理数据，用于进一步处理
output2 <- "b2017.2.21.txt" #进一步筛选的数据，用于查看

for ( strURL in strURLs){
    adresses <- extradress(strURL)
    for (adress in adresses){
      message(adress)
      doc <- download(adress)
      topic <- gettopic(doc)
      inf <- getinf(topic)
      if (inf != ""){
        URL <- data.frame("URL"=adress)
        inf <- cbind(URL,inf)
        dat<- rbind(dat,inf)
      }
    }
}

write.table(dat, file = output1, row.names = F, col.names=F,quote = F, sep="\t")  # tab 分隔的文件
message("完成！")

dat <- read.table(output1,sep="\t",header=T)
dat <- dat[dat$邮箱, ] #去除没有邮箱数据
dat <- dat[!duplicated(dat$邮箱), ]  #去除重复邮箱数据
dat$index <- as.numeric(rownames(dat))
dat <- dat[order(dat$index,decreasing=F),] #将乱序后的数据重新按照index排序
dat$index <- NULL
write.table(dat, file = output2, row.names = F, col.names=F,quote = F, sep="\t")  # tab 分隔的文件
message("完成！")