Question

我正在为学校做项目，我需要收集个人NCAA足球运动员的职业统计数据。每个玩家的数据都采用这种格式。

http://www.sports-reference.com/cfb/players/ryan-aplin-1.html

我找不到所有球员的总数所以我需要逐页进行并拉出每个传球得分Rushing＆amp;的最后一排。接收等html表

每个玩家都按姓氏分类，并附上每个字母的链接。

http://www.sports-reference.com/cfb/players/

例如，每个名字为A的玩家都可以在这里找到。

http://www.sports-reference.com/cfb/players/a-index.html

这是我第一次真正进入数据抓取工作，所以我尝试用答案找到类似的问题。我找到的最接近的答案是this question

我相信我可以使用非常类似的东西，我用收集的玩家的名字切换页码。但是，我不确定如何更改它以查找播放器名称而不是页码。

塞缪尔·文图拉（Samuel L. Ventura）最近也发表了关于NFL数据数据搜集的讨论，可以找到here。

编辑：

Ben非常乐于助人并提供了一些很棒的代码。第一部分非常有效，但是当我尝试运行第二部分时，我遇到了这个问题。

> # unlist into a single character vector
> links <- unlist(links)
> # Go to each URL in the list and scrape all the data from the tables
> # this will take some time... don't interrupt it! 
> all_tables <- lapply(links, readHTMLTable, stringsAsFactors = FALSE)
Error in UseMethod("xmlNamespaceDefinitions") : 
 no applicable method for 'xmlNamespaceDefinitions' applied to an object of class "NULL"
> # Put player names in the list so we know who the data belong to
> # extract names from the URLs to their stats page...
> toMatch <- c("http://www.sports-reference.com/cfb/players/", "-1.html")
> player_names <- unique (gsub(paste(toMatch,collapse="|"), "", links))
Error: cannot allocate vector of size 512 Kb
> # assign player names to list of tables
> names(all_tables) <- player_names
Error: object 'player_names' not found
> fix(inx_page)
Error in edit(name, file, title, editor) : 
  unexpected '<' occurred on line 1
 use a command like
 x <- edit()
 to recover
In addition: Warning message:
In edit.default(name, file, title, editor = defaultEditor) :
  deparse may be incomplete

由于没有足够的内存（我目前使用的计算机只有4GB），这可能是一个错误。虽然我不明白错误

    > all_tables <- lapply(links, readHTMLTable, stringsAsFactors = FALSE)
Error in UseMethod("xmlNamespaceDefinitions") : 
 no applicable method for 'xmlNamespaceDefinitions' applied to an object of class "NULL"

通过我的其他数据集，我的玩家实际上只能追溯到2007年。如果从2007年开始有一些方法可以帮助人们缩小数据量。如果我有一个名单我想拉的人名单，我可以在

中替换lnk

 links[[i]] <- paste0("http://www.sports-reference.com", lnk)

只有我需要的球员？

Answer 1

以下是如何轻松获取所有播放器页面上所有表格中的所有数据...

首先列出所有玩家网页的网址...

require(RCurl); require(XML)
n <- length(letters) 
# pre-allocate list to fill
links <- vector("list", length = n)
for(i in 1:n){
  print(i) # keep track of what the function is up to
  # get all html on each page of the a-z index pages
  inx_page <- htmlParse(getURI(paste0("http://www.sports-reference.com/cfb/players/", letters[i], "-index.html")))
  # scrape URLs for each player from each index page
  lnk <- unname(xpathSApply(inx_page, "//a/@href"))
  # skip first 63 and last 10 links as they are constant on each page
  lnk <- lnk[-c(1:63, (length(lnk)-10):length(lnk))]
  # only keep links that go to players (exclude schools)
  lnk <- lnk[grep("players", lnk)]
  # now we have a list of all the URLs to all the players on that index page
  # but the URLs are incomplete, so let's complete them so we can use them from 
  # anywhere
  links[[i]] <- paste0("http://www.sports-reference.com", lnk)
}
# unlist into a single character vector
links <- unlist(links)

现在我们有一个大约67,000个网址的向量（看起来像很多玩家，这可能是对的吗？），所以：

其次，抓取每个URL的所有表格以获取其数据，如下所示：

# Go to each URL in the list and scrape all the data from the tables
# this will take some time... don't interrupt it!
# start edit1 here - just so you can see what's changed
    # pre-allocate list
all_tables <- vector("list", length = (length(links)))
for(i in 1:length(links)){
  print(i)
  # error handling - skips to next URL if it gets an error
  result <- try(
    all_tables[[i]] <- readHTMLTable(links[i], stringsAsFactors = FALSE)
  ); if(class(result) == "try-error") next;
}
# end edit1 here
# Put player names in the list so we know who the data belong to
# extract names from the URLs to their stats page...
toMatch <- c("http://www.sports-reference.com/cfb/players/", "-1.html")
player_names <- unique (gsub(paste(toMatch,collapse="|"), "", links))
# assign player names to list of tables
names(all_tables) <- player_names

结果如下（这只是输出的片段）：

all_tables
$`neli-aasa`
$`neli-aasa`$defense
   Year School Conf Class Pos Solo Ast Tot Loss  Sk Int Yds Avg TD PD FR Yds TD FF
1 *2007   Utah  MWC    FR  DL    2   1   3  0.0 0.0   0   0      0  0  0   0  0  0
2 *2010   Utah  MWC    SR  DL    4   4   8  2.5 1.5   0   0      0  1  0   0  0  0

$`neli-aasa`$kick_ret
   Year School Conf Class Pos Ret Yds  Avg TD Ret Yds Avg TD
1 *2007   Utah  MWC    FR  DL   0   0       0   0   0      0
2 *2010   Utah  MWC    SR  DL   2  24 12.0  0   0   0      0

$`neli-aasa`$receiving
   Year School Conf Class Pos Rec Yds  Avg TD Att Yds Avg TD Plays Yds  Avg TD
1 *2007   Utah  MWC    FR  DL   1  41 41.0  0   0   0      0     1  41 41.0  0
2 *2010   Utah  MWC    SR  DL   0   0       0   0   0      0     0   0       0

最后，假设我们只想看看传递表......

# just show passing tables
passing <- lapply(all_tables, function(i) i$passing)
# but lots of NULL in here, and not a convenient format, so...
passing <- do.call(rbind, passing)

我们最终得到了一个可供进一步分析的数据框（也只是一个片段）......

             Year             School Conf Class Pos Cmp Att  Pct  Yds Y/A AY/A TD Int  Rate
james-aaron  1978          Air Force  Ind        QB  28  56 50.0  316 5.6  3.6  1   3  92.6
jeff-aaron.1 2000 Alabama-Birmingham CUSA    JR  QB 100 182 54.9 1135 6.2  6.0  5   3 113.1
jeff-aaron.2 2001 Alabama-Birmingham CUSA    SR  QB  77 148 52.0  828 5.6  4.3  4   6  99.8

从R中的多个网页上的表格中刮取数据（足球运动员）

1 个答案: