4

我花费了比预期更多的时间将 xml 传输到数据框（代码片段包含 xml 并xmlTreeParse让帖子更小，整个解决方案在此之后）：

users = xmlTreeParse(file=
'<?xml version="1.0" encoding="utf-8"?>
<users>
  <row Id="-1" Reputation="1" CreationDate="2010-07-19T06:55:26.860" DisplayName="Community" LastAccessDate="2010-07-19T06:55:26.860" Location="on the server farm" AboutMe="some text" Views="0" UpVotes="4382" DownVotes="771" EmailHash="a007be5a61f6aa8f3e85ae2fc18dd66e" />
  <row Id="2" Reputation="101" CreationDate="2010-07-19T14:01:36.697" DisplayName="Geoff Dalgas" LastAccessDate="2012-09-13T17:41:48.300" WebsiteUrl="http://stackoverflow.com" Location="Corvallis, OR" AboutMe="some text 2" Views="7" UpVotes="3" DownVotes="0" EmailHash="b437f461b3fd27387c5d8ab47a293d35" Age="36" />
  <row Id="3" Reputation="101" CreationDate="2010-07-19T15:34:50.507" DisplayName="Jarrod Dixon" LastAccessDate="2013-01-15T03:28:47.657" WebsiteUrl="http://stackoverflow.com" Location="New York, NY" AboutMe="some text 3" Views="9" UpVotes="19" DownVotes="0" EmailHash="2dfa19bf5dc5826c1fe54c2c049a1ff1" Age="34" />
  <row Id="4" Reputation="101" CreationDate="2010-07-19T19:03:27.400" DisplayName="Emmett" LastAccessDate="2013-04-16T16:51:04.780" WebsiteUrl="http://minesweeperonline.com" Location="New York, NY" AboutMe="some text 4" Views="3" UpVotes="0" DownVotes="0" EmailHash="129bc58fc3f1e3853cdd3cefc75fe1a0" Age="27" />
  <row Id="5" Reputation="6182" CreationDate="2010-07-19T19:03:57.227" DisplayName="Shane" LastAccessDate="2013-02-05T11:23:09.587" WebsiteUrl="http://www.statalgo.com" Location="New York, NY" AboutMe="some text 5" Views="605" UpVotes="659" DownVotes="5" EmailHash="0cee97ffd90277bf4ac753331d50af60" Age="34" />
  <row Id="6" Reputation="442" CreationDate="2010-07-19T19:04:07.647" DisplayName="Harlan" LastAccessDate="2013-05-09T13:11:29.027" WebsiteUrl="http://www.harlan.harris.name" Location="District of Columbia" AboutMe="some text 6" Views="30" UpVotes="42" DownVotes="0" EmailHash="9f1a68b9e623be5da422b44e733fa8bc" Age="40" />
  <row Id="7" Reputation="329" CreationDate="2010-07-19T19:04:37.257" DisplayName="Vince" LastAccessDate="2013-05-21T22:49:10.237" WebsiteUrl="http://bioinformatics.ucdavis.edu" Location="Davis, CA" AboutMe="some text 7" Views="21" UpVotes="14" DownVotes="0" EmailHash="4f7cebc8ac200d15bac5dcff51469425" Age="27" />
  <row Id="8" Reputation="6104" CreationDate="2010-07-19T19:04:52.280" DisplayName="csgillespie" LastAccessDate="2013-05-21T17:32:58.693" WebsiteUrl="http://www.mas.ncl.ac.uk/~ncsg3/" Location="Newcastle, United Kingdom" AboutMe="some text 8" Views="399" UpVotes="576" DownVotes="18" EmailHash="3c3eea4eda77ffe95ae18c78c3fc7e55" Age="35" />
  <row Id="10" Reputation="121" CreationDate="2010-07-19T19:05:40.403" DisplayName="Pierre" LastAccessDate="2012-10-04T17:17:01.430" WebsiteUrl="http://plindenbaum.blogspot.com" Location="France" AboutMe="some text 10" Views="8" UpVotes="2" DownVotes="0" EmailHash="61200477cf8983809ec152f484750204" Age="43" />
  <row Id="11" Reputation="136" CreationDate="2010-07-19T19:06:02.713" DisplayName="wahalulu" LastAccessDate="2013-05-26T20:36:24.567" WebsiteUrl="http://www.linkedin.com/in/marckvaisman" Location="Washington, DC" AboutMe="some text 11" Views="2" UpVotes="10" DownVotes="0" EmailHash="9a9a05e41ae6e3b127697967cea5f8fb" Age="39" />
  <row Id="12" Reputation="101" CreationDate="2010-07-19T19:06:34.507" DisplayName="Jin" LastAccessDate="2013-04-11T18:31:58.360" WebsiteUrl="http://www.8164.org" Location="Raleigh, NC" AboutMe="some text 12" Views="5" UpVotes="4" DownVotes="0" EmailHash="70ad2c2830eb9a7753bd6312f3811e3e" Age="37" />
  <row Id="13" Reputation="677" CreationDate="2010-07-19T19:06:49.527" DisplayName="Sharpie" LastAccessDate="2012-01-02T22:55:04.743" WebsiteUrl="http://www.sharpsteen.net" Location="United States" AboutMe="Undergraduate studying Environmental Engineering and Applied Mathematics." Views="37" UpVotes="44" DownVotes="1" EmailHash="a52001938ed33a87334447413cc5beaa" Age="27" />
  <row Id="15" Reputation="11" CreationDate="2010-07-19T19:07:32.537" DisplayName="hannes.koller" LastAccessDate="2010-08-24T14:23:18.050" WebsiteUrl="http://soma.denkt.org" Location="Vienna, Austria" AboutMe="" Views="2" UpVotes="0" DownVotes="0" EmailHash="0ecd144e2f3d05e6ee6b89404d1d4c53" Age="34" />
  <row Id="16" Reputation="101" CreationDate="2010-07-19T19:08:13.957" DisplayName="slashnick" LastAccessDate="2010-08-19T20:40:59.080" Location="London, United Kingdom" Views="2" UpVotes="7" DownVotes="0" EmailHash="5691ff74e21c78cd1563b5123254cbd6" Age="30" />
  <row Id="17" Reputation="192" CreationDate="2010-07-19T19:08:28.243" DisplayName="Random" LastAccessDate="2010-09-10T07:34:36.123" AboutMe="" Views="6" UpVotes="13" DownVotes="1" EmailHash="5a3c78de1408aae57797dffd0782b992" />
  <row Id="18" Reputation="128" CreationDate="2010-07-19T19:08:29.070" DisplayName="grokus" LastAccessDate="2012-08-09T15:02:00.600" WebsiteUrl="http://wikipedia.org" Location="United States" AboutMe="about me 18" Views="6" UpVotes="16" DownVotes="0" EmailHash="7d1f931327bfab7b214758be17627adc" Age="43" />
  <row Id="19" Reputation="101" CreationDate="2010-07-19T19:08:45.250" DisplayName="Noah Snyder" LastAccessDate="2012-06-17T15:53:43.550" WebsiteUrl="http://sbseminar.wordpress.com" Location="New York, NY" AboutMe="about me 19" Views="11" UpVotes="2" DownVotes="0" EmailHash="895385d49eb1f04c5ee1f8d7734f3a62" Age="33" />
</users>',
          asText=TRUE)

XML 只是来自 stackexchange 数据转储的用户表的简单表示：

<users>
  <row Id=..... />
  <row Id=..... />
  .....
  <row Id=..... />
</users>

到数据框的映射与我映射表一样。这是为我完成工作的代码：

require(XML)
require(plyr)

# insert xmlTreeParse here

r = xmlRoot(users)

attrs = c('Id', 'Reputation', 'CreationDate', 'DisplayName', 'LastAccessDate',
          'WebsiteUrl', 'Location', 'AboutMe',  'Views', 'UpVotes', 'DownVotes', 
          'EmailHash', 'Age')

mapUserAttrs = function(x, colNames) {
  t = data.frame(as.integer(x['Id']), 
           as.integer(x['Reputation']), 
           strptime(x['CreationDate'], '%Y-%m-%dT%H:%M:%OS'), 
           as.character(x['DisplayName']), 
           strptime(x['LastAccessDate'], '%Y-%m-%dT%H:%M:%OS'), 
           as.character(x['WebsiteUrl']), 
           as.character(x['Location']), 
           as.character(x['AboutMe']),
           as.integer(x['Views']), 
           as.integer(x['UpVotes']), 
           as.integer(x['DownVotes']), 
           as.character(x['EmailHash']), 
           as.integer(x['Age']))
  names(t) = colNames
  return(t)
}

result = ldply(lapply(xmlChildren(r), xmlAttrs), mapUserAttrs, attrs)

它看起来对我来说太忙了——但我发现没有更好的方法来使用 XML 包和我找到的大量示例和文档来完成任务。

我想知道是否有更简单（或更短）的方法来完成相同的任务？

4

1 回答 1

3

您可以使用包xmlToList中的函数执行此操作XML，因为您的某些节点包含其他节点不包含的选项，因此您还需要包中的rbind.fill函数plyr。

下面的代码行将您的 XML 转换为列表，循环遍历节点并将字符串转换为数据帧，然后将所有这些数据帧 rbind 在一起。

require(xml)
require(plyr)

out <- do.call("rbind.fill",
  lapply(xmlToList(users), 
    function(x) as.data.frame(as.list(x), stringsAsFactors = FALSE)))


head(out)
  Id Reputation            CreationDate  DisplayName          LastAccessDate             Location     AboutMe Views UpVotes DownVotes
1 -1          1 2010-07-19T06:55:26.860    Community 2010-07-19T06:55:26.860   on the server farm   some text     0    4382       771
2  2        101 2010-07-19T14:01:36.697 Geoff Dalgas 2012-09-13T17:41:48.300        Corvallis, OR some text 2     7       3         0
3  3        101 2010-07-19T15:34:50.507 Jarrod Dixon 2013-01-15T03:28:47.657         New York, NY some text 3     9      19         0
4  4        101 2010-07-19T19:03:27.400       Emmett 2013-04-16T16:51:04.780         New York, NY some text 4     3       0         0
5  5       6182 2010-07-19T19:03:57.227        Shane 2013-02-05T11:23:09.587         New York, NY some text 5   605     659         5
6  6        442 2010-07-19T19:04:07.647       Harlan 2013-05-09T13:11:29.027 District of Columbia some text 6    30      42         0
                         EmailHash                    WebsiteUrl  Age
1 a007be5a61f6aa8f3e85ae2fc18dd66e                          <NA> <NA>
2 b437f461b3fd27387c5d8ab47a293d35      http://stackoverflow.com   36
3 2dfa19bf5dc5826c1fe54c2c049a1ff1      http://stackoverflow.com   34
4 129bc58fc3f1e3853cdd3cefc75fe1a0  http://minesweeperonline.com   27
5 0cee97ffd90277bf4ac753331d50af60       http://www.statalgo.com   34
6 9f1a68b9e623be5da422b44e733fa8bc http://www.harlan.harris.name   40

编辑

生成的数据帧将完全由字符向量组成。如果您想将这些向量转换为日期、日期时间、数字等，您可以一个接一个地进行，或者您可以编写一个函数来指定应该将哪些类分配给具有特定名称的列，或者您可以编写一个函数来尝试从数据中推断出正确的类。下面是最后一个选项的示例：

giveClasses <- function(df, threshold = 0.1) {
  df_classes <- sapply(df, class)

  df_alpha <- sapply(df, function(x) {
    mean(grepl("[[:alpha:]]", x)) >= threshold}) &
    df_classes == "character"

  df_digits <- sapply(df, function(x) mean(grepl("\\d", x))) >= threshold &
    df_classes == "character" &
    !df_alpha

  df_percent <- sapply(df, function(x) mean(grepl("%", x))) >= threshold &
    df_classes == "character" &
    !df_alpha &
    df_digits

  df_digits[df_percent] <- FALSE

  df_decimal <- sapply(df, function(x) mean(grepl("\\.", x))) >= threshold &
    df_classes == "character" &
    !df_percent &
    df_digits &
    !df_alpha

  df_dates <- sapply(df, function(x) {
    mean(grepl(
      "^\\d{2,4}[[:punct:]]\\d{2}[[:punct:]]\\d{2,4}$", x)) >= threshold}) &
    df_classes == "character"

  df_datetime <- sapply(df, function(x) {
    mean(grepl(
      "^\\d{2,4}[[:punct:]]\\d{2}[[:punct:]]\\d{2,4}\\D\\d{2}:\\d{2}(:\\d{2})?(\\.\\d{1,})?$", x)) >= threshold}) &
    df_classes == "character"

  # convert character data to appropriate classes
  df_logical <- sapply(df, function(x) {
    y <- unique(na.omit(x))
    length(y) == 2 & 
      mean(grepl("^n", y, ignore.case = TRUE) |
          grepl("^y", y, ignore.case = TRUE)) == 1
  })

  df_digits[df_dates | df_datetime] <- FALSE

  df[,df_percent] <- lapply(df[,df_percent, drop = FALSE], function(x) {
    as.numeric(gsub("[^[:digit:].]", "", x)) / 100})

  df[,df_logical] <- lapply(df[,df_logical, drop = FALSE], function(x) {
    x[grep("^y", x, ignore.case = TRUE)] <- TRUE
    x[grep("^n", x, ignore.case = TRUE)] <- FALSE
    as.logical(x)
  })

  df[,df_decimal] <- lapply(df[,df_decimal, drop = FALSE], function(x) {
    as.numeric(gsub("[^[:digit:].]", "", x))})

  df[,df_digits] <- lapply(df[,df_digits, drop = FALSE], function(x) {
    as.integer(gsub("[^[:digit:]]", "", x))})

  df[,df_dates] <- lapply(df[,df_dates, drop = FALSE], function(x) {
    as.Date(x)})

  df[,df_datetime] <- lapply(df[,df_datetime, drop = FALSE], function(x) {
    strptime(x, '%Y-%m-%dT%H:%M:%OS')})

  df_ischaracter <- sapply(df, function(x) any(class(x) == "character"))

  df[,df_ischaracter] <- lapply(df[,df_ischaracter, drop = FALSE], function(x) {
    x <- gsub("^\\s+|\\s+$|(?<=\\s)\\s+", "", x, perl = TRUE)})

  df
}

如果该列中超过 90% 的值符合适合该类的模式，则上述函数将一个类分配给该列。否则，它将它们保留为字符。它解决了在您的示例数据集中找不到的模式 - 我只是从我正在处理的另一个项目中复制了代码。所以：

str(giveClasses(out))

'data.frame':   17 obs. of  13 variables:
 $ Id            : int  1 2 3 4 5 6 7 8 10 11 ...
 $ Reputation    : int  1 101 101 101 6182 442 329 6104 121 136 ...
 $ CreationDate  : POSIXlt, format: "2010-07-19 06:55:26" "2010-07-19 14:01:36" "2010-07-19 15:34:50" "2010-07-19 19:03:27" ...
 $ DisplayName   : chr  "Community" "Geoff Dalgas" "Jarrod Dixon" "Emmett" ...
 $ LastAccessDate: POSIXlt, format: "2010-07-19 06:55:26" "2012-09-13 17:41:48" "2013-01-15 03:28:47" "2013-04-16 16:51:04" ...
 $ Location      : chr  "on the server farm" "Corvallis, OR" "New York, NY" "New York, NY" ...
 $ AboutMe       : chr  "some text" "some text 2" "some text 3" "some text 4" ...
 $ Views         : int  0 7 9 3 605 30 21 399 8 2 ...
 $ UpVotes       : int  4382 3 19 0 659 42 14 576 2 10 ...
 $ DownVotes     : int  771 0 0 0 5 0 0 18 0 0 ...
 $ EmailHash     : chr  "a007be5a61f6aa8f3e85ae2fc18dd66e" "b437f461b3fd27387c5d8ab47a293d35" "2dfa19bf5dc5826c1fe54c2c049a1ff1" "129bc58fc3f1e3853cdd3cefc75fe1a0" ...
 $ WebsiteUrl    : chr  NA "http://stackoverflow.com" "http://stackoverflow.com" "http://minesweeperonline.com" ...
 $ Age           : int  NA 36 34 27 34 40 27 35 43 39 ...
于 2013-06-13T16:25:40.590 回答