使用 xml2 从 TEI XML 创建数据框

Create data frame from TEI XML using xml2

我正在尝试使用 Hadley Wickham 的 xml2 包创建 TEI-XML 版 Moby Dick 的数据框。我希望数据框最终看起来像这样(对于小说中的所有单词):

df <- data.frame(
chapter = c("1", "1", "1"),
words = c("call", "me", "ishmael"))

我能得到碎片,但不是全部。到目前为止,这是我得到的:

library("xml2")

# Read file
melville <- read_xml("data/melville.xml")

# Get chapter divs (remember, doesn't include epilogue)
chap_frames <- xml_find_all(melville, "//d1:div1[@type='chapter']", xml_ns(melville))

这给了我们一个长度为 134 的列表(即每一章)。我们可以获得特定元素的章节编号,如下所示:

xml_attr(chap_frames[[1]], "n")

我们可以得到特定章节的段落(即减去章节标题)如下:

words <- xml_find_all(chap_frames[[1]], ".//d1:p", xml_ns(melville)) %>%  # remember doesn't include epilogue
xml_text()

并且我们可以得到章节的单词如下:

# Split words function
split_words <- function (ll) {
  result <- unlist(strsplit(ll, "\W+"))
  result <- result[result != ""]
  tolower(result)
}

# Apply function
words <- split_words(words)

我想不通的是如何获取每个单词的章节编号。我有一个有用的玩具示例:

mini <- read_xml(
'
<div1 type="chapter" n="1" id="_75784">
<head>Loomings</head>
    <p rend="fiction">Call me Ishmael.</p>
    <p rend="fiction">There now is your insular city of the Manhattoes, belted round by wharves as Indian isles by coral reefs- commerce surrounds it with her surf.</p> 
</div1>
')

# Function
process_chap <- function(div){
chapter <- xml_attr(div, "n")
words <- xml_find_all(div, "//p") %>%
    xml_text()
data.frame(chapter = chapter,
           word = split_words(words))
}

process_chap(mini)

但它不适用于更长的例子

 process_chap2 <- function(div){
 chapter <- xml_attr(div, "n")
 words <- xml_find_all(div, ".//d1:p", xml_ns(melville)) %>%  # remember doesn't include epilogue
 xml_text()
 data.frame(chapter = chapter,
           word = split_words(words))
}

# Fails because there are more words than chapter names
df <- process_chap2(chap_frames)

# Gives all the words p (not chapters), chapter numbers are `NULL`. 
df2 <- process_chap2(melville)

(我知道为什么 toy example 有效但 Melville 的无效,但我想包括它以展示我正在尝试做的事情)。我猜我可能需要某种循环,但我不确定从哪里开始。有什么建议吗?

PS:我不完全确定我是否应该 link 到我在 Github 上找到的 xml 版本的 Moby Dick,但你可以找到它很容易搜索 melville1.xml

方法是一次绘制每一章的数据。然后将一章的单词连同章节编号组合成一个数据框。 R 将根据需要重复章节编号的单个值:

words <- letters[1:3]
n     <- 1

df <- data.frame(words, n)

df
##  words n
## 1     a 1
## 2     b 1
## 3     c 1

将所有章节的信息收集到整齐的数据框中后,您就可以使用 rbind() 将整体组合成一个数据框。

这就是您数据的前两章的样子....

library(xml2)
library(dplyr)
library(stringr)


# Read file
url <- "https://raw.githubusercontent.com/reganna/TextAnalysisWithR/master/data/XML1/melville1.xml"
melville <- read_xml(url)


# get chapter frame and number
chap_frames <- xml_find_all(melville, "//d1:div1[@type='chapter']", xml_ns(melville))
chap_n <- xml_attr(chap_frames, "n")


# get the date for first chapter
words1 <- 
  xml_find_all(chap_frames[[1]], ".//d1:p", xml_ns(melville))  %>% 
    xml_text() %>% 
    unlist() %>% 
    str_split("\W+") %>% 
    unlist()  %>% 
    tolower()

n1 <- xml_attr(chap_frames[[1]], "n")


# get the data for the second chapter
words2 <- 
  xml_find_all(chap_frames[[2]], ".//d1:p", xml_ns(melville))  %>% 
  xml_text() %>% 
  unlist() %>% 
  str_split("\W+") %>% 
  unlist()  %>% 
  tolower()

n2 <- xml_attr(chap_frames[[2]], "n")


# put it together
df <- 
  rbind(
    data_frame(words=words1, chapter=n1),
    data_frame(words=words2, chapter=n2)
  )
df


## Source: local data frame [3,719 x 2]
## 
##      words chapter
## 1     call       1
## 2       me       1
## 3  ishmael       1
## 4     some       1
## 5    years       1
## 6      ago       1
## 7    never       1
## 8     mind       1
## 9      how       1
## 10    long       1
## ..     ...     ...

为了对所有章节更有效地执行此操作,您可以构建一个循环,为所有章节重复这些步骤,或者您可以考虑一个执行提取的函数,将其应用于所有章节,然后通过 rbind()稍后。

...我可能会那样做:

# building function
extract_data <- function(chapter_frame){
  words <- 
    xml_find_all(chapter_frame, ".//d1:p", xml_ns(melville))  %>% 
    xml_text() %>% 
    unlist() %>% 
    str_split("\W+") %>% 
    unlist()  %>% 
    tolower()
  n   <- xml_attr(chapter_frame, "n")
  pos <- seq_along(words)
  data_frame(words, chapter=n, paragraph=pos)
}

# using function
chapter_words <- 
  lapply(chap_frames, extract_data) 

# `rbind()`ing data
chapter_words <- do.call(rbind, chapter_words)

chapter_words
## Source: local data frame [216,669 x 3]
## 
##      words chapter  paragraph
## 1     call       1          1
## 2       me       1          2
## 3  ishmael       1          3
## 4     some       1          4
## 5    years       1          5        
## 6      ago       1          6
## 7    never       1          7
## 8     mind       1          8
## 9      how       1          9
## 10    long       1         10 
## ..     ...     ...        ...