R語言抓取廣州租房信息

要去廣州工作了，所以抓取了廣州租房信息看一下，來源是某家廣州租房網。網上爬蟲代碼很多，對於簡單的網頁實現起來也很簡單，直接上核心代碼：

require(RCurl)         ##載入包
require(XML)
rm(list = ls())
GZsource <- data.frame()
system.time(for (i in 1:100) {
  if(i==1){webside ="http://xxxxxxxx.com/zufang/"}
  else{webside = paste("http://xxxxxxxx/zufang/pg", i, "/", sep = "")}
  url = getURL(webside, .encoding = "utf-8")
  url_parse = htmlParse(url, encoding = "utf-8")
  # 標題、鏈接
  node = getNodeSet(url_parse, path = "//div[@class='list-wrap']//div[@class='info-panel']//a[@title]")
  title_name = sapply(node, function(X) xmlGetAttr(X, "title"))
  Encoding(title_name) = "UTF-8"
  link = paste("http://sh.xxxxx.com", sapply(node, function(X) xmlGetAttr(X, "href")), sep = "")
  # 小區、戶型、面積
  node = getNodeSet(url_parse, path = "//div[@class='list-wrap']//div[@class='where']//span")
  inf1 = sapply(node, xmlValue)
  xiaoqu = inf1[(1:length(title_name))*5 - 4]
  house_type = gsub("\\s+", "", inf1[(1:length(title_name))*5 - 2])
  size = as.numeric(gsub("[^0-9]*$", "", inf1[(1:length(title_name))*5 - 1]))
  # 區域、地段
  node = getNodeSet(url_parse, path = "//div[@class='list-wrap']//div[@class='other']//a")
  inf2 = sapply(node, xmlValue)
  area = inf2[(1:length(title_name))]
  #diduan = inf2[(1:length(title_name))*2 - 0]
  # 價格
  node = getNodeSet(url_parse, path = "//div[@class='list-wrap']//div[@class='price']/span[@class='num']")
  price = as.numeric(gsub("[^0-9]", "", sapply(node, xmlValue)))
  GZsource = rbind(GZsource, data.frame(title_name, link, xiaoqu, house_type, size, area, price, stringsAsFactors = FALSE))
  Sys.sleep(1)
})

爲了分地區分析，我直接按照行政區分別進行的抓取，比如天河區如下：

Tianhe <- data.frame()
system.time(for (i in 1:100) {
  if(i==1){
    webside = "http://xxxxxxxx/zufang/tianhe/"}
  else{webside = paste("http://xxxxxxxx/zufang/tianhe/pg", i, "/", sep = "")}
  url = getURL(webside, .encoding = "utf-8")
  url_parse = htmlParse(url, encoding = "utf-8")
  # 標題、鏈接
  node = getNodeSet(url_parse, path = "//div[@class='list-wrap']//div[@class='info-panel']//a[@title]")
  title_name = sapply(node, function(X) xmlGetAttr(X, "title"))
  Encoding(title_name) = "UTF-8"
  link = paste("http://sh.xxxxx.com", sapply(node, function(X) xmlGetAttr(X, "href")), sep = "")
  # 小區、戶型、面積
  node = getNodeSet(url_parse, path = "//div[@class='list-wrap']//div[@class='where']//span")
  inf1 = sapply(node, xmlValue)
  xiaoqu = inf1[(1:length(title_name))*5 - 4]
  house_type = gsub("\\s+", "", inf1[(1:length(title_name))*5 - 2])
  size = as.numeric(gsub("[^0-9]*$", "", inf1[(1:length(title_name))*5 - 1]))
  # 區域、地段
  node = getNodeSet(url_parse, path = "//div[@class='list-wrap']//div[@class='other']//a")
  inf2 = sapply(node, xmlValue)
  area = inf2[(1:length(title_name))]
  #diduan = inf2[(1:length(title_name))*2 - 0]
  # 總價
  node = getNodeSet(url_parse, path = "//div[@class='list-wrap']//div[@class='price']/span[@class='num']")
  price = as.numeric(gsub("[^0-9]", "", sapply(node, xmlValue)))
  # 單價
  # node = getNodeSet(url_parse, path = "//div[@class='list-wrap']//div[@class='price-pre']")
  # danjia = as.numeric(gsub("[^0-9]", "", sapply(node, xmlValue)))
  Tianhe = rbind(Tianhe, data.frame(title_name, link, xiaoqu, house_type, size, area, price, stringsAsFactors = FALSE))
  Sys.sleep(1)
})
Tianhe$District <- "天河區"

最後把每個區的合併起來就行：

GZhouse <- rbind(Tianhe,panyu,liwan,Yuexiu,haizhu,baiyun,luogang,huangpu,huadou,zengcheng)

最後結果如下

下一步就是在預處理後對房價進行可視化分析，看看現在不同區域的價格差異，等有時間再多找些數據以後多方位進行房價影響因素分析。
未完待續…