Question

我在刮除Transfermarket时遇到了麻烦。我想收集过去20个赛季的欧洲前5个联赛（英超，西甲，意甲，联赛1，德甲）的数据。在此，我想收集一系列详细信息-球员姓名，年龄，球员位置，球员俱乐部，剩余球员俱乐部，费用。但是，即使使用了这个非常基本的代码，我也只写了一页18/19英超联赛转会费，就收集了球队和名字（补给），但我却得到了一个我不明白的错误。我也一直在使用选择器小工具。

我的代码：

require(rvest)

page = "https://www.transfermarkt.com/premier-league/transfers/wettbewerb/GB1/plus/?saison_id=2012&s_w=&leihe=0&leihe=1&intern=0"

scraped_page <- read_html(page)

Team_html  = html_nodes(page, ".tooltipstered+ .tooltipstered") 
Team = html_text(Team_html)
Addition_html = html_nodes(page, ".table-header+ .responsive-table .spielprofil_tooltip")
Addition = html_text(Addition_html)


df <- data.frame(Team, Addition)

head(df)

R返回什么：

> page = "https://www.transfermarkt.com/premier-league/transfers/wettbewerb/GB1/plus/?saison_id=2012&s_w=&leihe=0&leihe=1&intern=0"
> 
> scraped_page <- read_html(page)
> 
> Team_html  = html_nodes(page, ".tooltipstered+ .tooltipstered") 
Error in UseMethod("xml_find_all") : 
  no applicable method for 'xml_find_all' applied to an object of class "character"
> Team = html_text(Team_html)
> Addition_html = html_nodes(page, ".table-header+ .responsive-table .spielprofil_tooltip")
Error in UseMethod("xml_find_all") : 
  no applicable method for 'xml_find_all' applied to an object of class "character"
> Addition = html_text(Addition_html)
> 
> 
> df <- data.frame(Team, Addition)
Error in data.frame(Team, Addition) : 
  arguments imply differing number of rows: 0, 922
> 
> head(df)

1 function (x, df1, df2, ncp, log = FALSE)    
2 {                                           
3     if (missing(ncp))                       
4         .Call(C_df, x, df1, df2, log)       
5     else .Call(C_dnf, x, df1, df2, ncp, log)
6 }

我当时想从这里开始，然后使用gsub和其他一些命令在一个年复一年的联赛中循环循环...

Answer 1

您遇到的主要问题是

 Team_html  = html_nodes(page, ".tooltipstered+ .tooltipstered")

应该是

 Team_html  = html_nodes(scraped_page, ".tooltipstered+ .tooltipstered")

此外，我认为您没有正确指定选择器。我想您可能想做这样的事情...

更新

潜在的解决方案1：

按组分别刮擦每个表，它们在堆栈数据之前手动添加组名。在下面的代码中，我为前5个团队这样做

in1<-html_nodes(scraped_page, '#main > div:nth-child(13) > div.large-8.columns > div:nth-child(4) > div:nth-child(2) > table') %>% html_table()
in2<-html_nodes(scraped_page, '#main > div:nth-child(13) > div.large-8.columns > div:nth-child(5) > div:nth-child(2) > table') %>% html_table()
in3<-html_nodes(scraped_page, '#main > div:nth-child(13) > div.large-8.columns > div:nth-child(6) > div:nth-child(2) > table') %>% html_table()
in4<-html_nodes(scraped_page, '#main > div:nth-child(13) > div.large-8.columns > div:nth-child(7) > div:nth-child(2) > table') %>% html_table()
in5<-html_nodes(scraped_page, '#main > div:nth-child(13) > div.large-8.columns > div:nth-child(8) > div:nth-child(2) > table') %>% html_table()

out1<-html_nodes(scraped_page, '#main > div:nth-child(13) > div.large-8.columns > div:nth-child(4) > div:nth-child(4) > table') %>% html_table()
out2<-html_nodes(scraped_page, '#main > div:nth-child(13) > div.large-8.columns > div:nth-child(5) > div:nth-child(4) > table') %>% html_table()
out3<-html_nodes(scraped_page, '#main > div:nth-child(13) > div.large-8.columns > div:nth-child(6) > div:nth-child(4) > table') %>% html_table()
out4<-html_nodes(scraped_page, '#main > div:nth-child(13) > div.large-8.columns > div:nth-child(7) > div:nth-child(4) > table') %>% html_table()
out5<-html_nodes(scraped_page, '#main > div:nth-child(13) > div.large-8.columns > div:nth-child(8) > div:nth-child(4) > table') %>% html_table()

in1<-in1[[1]]
in2<-in2[[1]]
in3<-in3[[1]]
in4<-in4[[1]]
in5<-in5[[1]]

out1<-out1[[1]]
out2<-out2[[1]]
out3<-out3[[1]]
out4<-out4[[1]]
out5<-out5[[1]]

in1$team<-"Arsenal"
in2$team<-"Man U"
in3$team<-"West Brom"
in4$team<-"Fulham"
in5$team<-"New Castle"


out1$team<-"Arsenal"
out2$team<-"Man U"
out3$team<-"West Brom"
out4$team<-"Fulham"
out5$team<-"New Castle"


ins<-rbind(in1,in2,in3,in4,in5)
outs<-rbind(out1,out2,out3,out4,out5)

潜在解决方案2：

此解决方案不保留团队名称，而是通过缝合来更有效地梳理。

tab<-html_nodes(scraped_page, ".responsive-table td") %>% html_text()
temp<-data.frame(value=tab, index=index)
df<-data.frame(x1=character(981), x2=character(981), x3=character(981), x4=character(981), x5=character(981),
               x6=character(981),x7=character(981),x8=character(981),x9=character(981))
for (i in 1:9){
df[,i]<-temp$value[temp$index==i]

}
head(df)

用Rvest刮擦Transfermarket

1 个答案: