问题
So, I have large XML file with lots of reports. I created data example below to approximately show the size of xml and its structure:
x <- "<Report><Agreements><AgreementList /></Agreements><CIP><RecordList><Record><Date>2017-05-26T00:00:00</Date><Grade>2</Grade><ReasonsList><Reason><Code>R</Code><Description>local</Description></Reason></ReasonsList><Score>xxx</Score></Record><Record><Date>2017-04-30T00:00:00</Date><Grade>2</Grade><ReasonsList><Reason><Code>R</Code><Description/></Reason></ReasonsList><Score>xyx</Score></Record></RecordList></CIP><Individual><Contact><Email/></Contact><General><FirstName>MM</FirstName></General></Individual><Inquiries><InquiryList><Inquiry><DateOfInquiry>2017-03-19</DateOfInquiry><Reason>cc</Reason></Inquiry><Inquiry><DateOfInquiry>2016-10-14</DateOfInquiry><Reason>er</Reason></Inquiry></InquiryList><Summary><NumberOfInquiries>2</NumberOfInquiries></Summary></Inquiries></Report>"
x <- paste(rep(x, 1.5e+5), collapse = "")
x <- paste0("<R>", x, "</R>")
require(XML)
p <- xmlParse(x)
p <- xmlRoot(p)
p[[1]]
I would like to transform this data to data.frame, but the structure of XML isn't straightforward. Previously working with XMLs I created loop that for every report transforms its sub nodes to data.frame, but here (in this data) the sub node count is greater than 30 (didn't put all of them in the example), and the structure differs (List nodes can occur even 2 levels deep in XML).
So I have few questions:
1) I am sure that looping over reports isn't the best way to handle this. How should I approach this problem?
2) Can I somehow extract all the data of one report two one line of data.frame (recursively maybe)?
3) Or can I automatically create separate data.frames for each list object of XML?
Any help would be much appreciated.
Update:
Example of results could look like this:
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1 obs. of 17 variables:
$ Record.1.Date : chr "2017-05-26T00:00:00"
$ Record.1.Grade : num 2
$ Record.1.Reason.1.Code : chr "R"
$ Record.1.Reason.1.Description: chr "local"
$ Record.1.Score : chr "xxx"
$ Record.2.Date : chr "2017-05-26T00:00:00"
$ Record.2.Grade : num 2
$ Record.2.Reason.1.Code : chr "R"
$ Record.2.Reason.1.Description: chr "NA"
$ Record.2.Score : chr "xyx"
$ Email.1 : chr "NA"
$ FirstName : chr "MM"
$ Inquiry.1.DateOfInquiry : POSIXct, format: "2017-03-19"
$ Inquiry.1.Reason : chr "cc"
$ Inquiry.2.DateOfInquiry : POSIXct, format: "2016-10-14"
$ Inquiry.2.Reason : chr "er"
$ NumberOfInquiries : num 2
, but as I mentioned previously, sub lists could also be in separate tables.
回答1:
L=xmlToList(x)
str(data.frame(t(unlist(L)), stringsAsFactors=FALSE))
# 'data.frame': 1 obs. of 15 variables:
# $ CIP.RecordList.Record.Date : chr "2017-05-26T00:00:00"
# $ CIP.RecordList.Record.Grade : chr "2"
# $ CIP.RecordList.Record.ReasonsList.Reason.Code : chr "R"
# $ CIP.RecordList.Record.ReasonsList.Reason.Description: chr "local"
# $ CIP.RecordList.Record.Score : chr "xxx"
# $ CIP.RecordList.Record.Date.1 : chr "2017-04-30T00:00:00"
# $ CIP.RecordList.Record.Grade.1 : chr "2"
# $ CIP.RecordList.Record.ReasonsList.Reason.Code.1 : chr "R"
# $ CIP.RecordList.Record.Score.1 : chr "xyx"
# $ Individual.General.FirstName : chr "MM"
# $ Inquiries.InquiryList.Inquiry.DateOfInquiry : chr "2017-03-19"
# $ Inquiries.InquiryList.Inquiry.Reason : chr "cc"
# $ Inquiries.InquiryList.Inquiry.DateOfInquiry.1 : chr "2016-10-14"
# $ Inquiries.InquiryList.Inquiry.Reason.1 : chr "er"
# $ Inquiries.Summary.NumberOfInquiries : chr "2"
If you want to convert strings that have a suitable representation as numbers, assuming that df is the data frame above:
data.frame(t(lapply(df, function(x)
ifelse(is.na(y<-suppressWarnings(as.numeric(x))), x, y))))
Strings that do not have a number representation will not be converted.
Update
Motivation
A) In some comments the OP added a further request for execution speed, which is normally not a issue for one time tasks such as data import. The solution above is based on recursion, as explicitly required in the question. Of course, traversing up and down the nodes adds a lot of overhead.
B) One recent answer here proposes a complex method based on a collection of external tools. There might of course be different nice utilities to manage XML files, but IMHO much of the XPATH work can be comfortably and efficiently done in R itself.
C) The OP wonders if it is possible to "create separate data.frames for each list object of XML".
D) I noticed that in the question tags, the OP (seems to) require the newer xml2 package.
I address the points above using XPATH straight from R.
XPATH approach
Below I extract in a separate data frame the Record node. One can use the same approach for other (sub)nodes too.
library(xml2)
xx=read_xml(x)
xx=(xml_find_all(xx, "//Record"))
system.time(
xx <- xml_find_all(xx, ".//descendant::*[not(*)]"))
# user system elapsed
# 38.00 0.36 38.35
system.time(xx <- xml_text(xx))
# user system elapsed
# 68.39 0.05 68.53
head(data.frame(t(matrix(xx, 5))))
# X1 X2 X3 X4 X5
# 1 2017-05-26T00:00:00 2 R local xxx
# 2 2017-04-30T00:00:00 2 R xyx
# 3 2017-05-26T00:00:00 2 R local xxx
# 4 2017-04-30T00:00:00 2 R xyx
# 5 2017-05-26T00:00:00 2 R local xxx
# 6 2017-04-30T00:00:00 2 R xyx
(You might want to add further code to name data frame columns)
Time is referred to my average laptop.
Explanations
The core of the solutions lies in the XPATH .//descendant::*[not(*)].
.//descendant:: extracts all descendants of the current context (the Record node); adding [not(*)] further flattens the layout. This allows to linearize a tree structure, making it more for suitable for data science modeling.
The flexibility of * comes at a price in terms of computation. However, the computational burden does no lie on R, which is an interpreted language, but comes at the expenses of the highly efficient external C library libxml2. Results should be equal or better than those of other utilities and libraries.
回答2:
Because you mention, I would like to transform this data, consider XSLT, the special-purpose transformation language designed to restructure complex XML to various end-use structures. And in your case flattening any text holding nodes in XML which then can easily be imported with xmlToDataFrame(). While below uses xsltproc and .NET Xsl class, any external processor or language module (e.g., Python, Java, C#, VB, PHP) that supports XSLT 1.0 can work:
XSLT (save as .xsl file)
<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
<xsl:output method="xml" indent="yes"/>
<xsl:strip-space elements="*"/>
<xsl:template match="/R">
<xsl:copy>
<xsl:apply-templates select="Report"/>
</xsl:copy>
</xsl:template>
<xsl:template match="Report">
<xsl:copy>
<xsl:apply-templates select="descendant::*[string-length(text())>0]"/>
</xsl:copy>
</xsl:template>
<xsl:template match="*">
<xsl:element name="{concat(local-name(), position())}">
<xsl:value-of select="." />
</xsl:element>
</xsl:template>
</xsl:stylesheet>
XML Output (with numbered suffixes to avoid repeated column error)
<?xml version="1.0"?>
<R>
<Report>
<Date1>2017-05-26T00:00:00</Date1>
<Grade2>2</Grade2>
<Code3>R</Code3>
<Description4>local</Description4>
<Score5>xxx</Score5>
<Date6>2017-04-30T00:00:00</Date6>
<Grade7>2</Grade7>
<Code8>R</Code8>
<Score9>xyx</Score9>
<FirstName10>MM</FirstName10>
<DateOfInquiry11>2017-03-19</DateOfInquiry11>
<Reason12>cc</Reason12>
<DateOfInquiry13>2016-10-14</DateOfInquiry13>
<Reason14>er</Reason14>
<NumberOfInquiries15>2</NumberOfInquiries15>
</Report>
</R>
R Mac/Linux Script (calling xsltproc, available package on unix machines)
library(XML)
setwd("/path/to/working/folder")
# COMMAND LINE CALL (INSTALL xsltproc IN TERMINAL)
system(paste("cd", getwd(), " && xsltproc -o Output.xml XSLTScript.xsl Input.xml"))
# PARSE AND LOAD TO DF
doc <- xmlParse('Output.xml')
df <- xmlToDataFrame(nodes = getNodeSet(doc, "//Report"))
str(df)
# 'data.frame': 6 obs. of 15 variables:
# $ Date1 : chr "2017-05-26T00:00:00" "2017-05-26T00:00:00" "2017-05-26T00:00:00" "2017-05-26T00:00:00" ...
# $ Grade2 : chr "2" "2" "2" "2" ...
# $ Code3 : chr "R" "R" "R" "R" ...
# $ Description4 : chr "local" "local" "local" "local" ...
# $ Score5 : chr "xxx" "xxx" "xxx" "xxx" ...
# $ Date6 : chr "2017-04-30T00:00:00" "2017-04-30T00:00:00" "2017-04-30T00:00:00" "2017-04-30T00:00:00" ...
# $ Grade7 : chr "2" "2" "2" "2" ...
# $ Code8 : chr "R" "R" "R" "R" ...
# $ Score9 : chr "xyx" "xyx" "xyx" "xyx" ...
# $ FirstName10 : chr "MM" "MM" "MM" "MM" ...
# $ DateOfInquiry11 : chr "2017-03-19" "2017-03-19" "2017-03-19" "2017-03-19" ...
# $ Reason12 : chr "cc" "cc" "cc" "cc" ...
# $ DateOfInquiry13 : chr "2016-10-14" "2016-10-14" "2016-10-14" "2016-10-14" ...
# $ Reason14 : chr "er" "er" "er" "er" ...
# $ NumberOfInquiries15: chr "2" "2" "2" "2" ...
R Windows (using Powershell xsl script calling .NET Xsl class, see here)
library(XML)
# COMMAND LINE CALL (NO INSTALLS NEEDED)
system(paste0('Powershell.exe -File',
' "C:\\Path\\To\\PowerShell\\Script.ps1"',
' "C:\\Path\\To\\Input.xml"',
' "C:\\Path\\To\\XSLT\\Script.xsl"',
' "C:\\Path\\To\\Output.xml"'))
# PARSE AND LOAD TO DF
doc <- xmlParse('Output.xml')
df <- xmlToDataFrame(nodes = getNodeSet(doc, "//Report"))
来源：https://stackoverflow.com/questions/44257890/parsing-large-and-complicated-xml-file-to-data-frame