读入'；凌乱'；在R中查找XML文件

我试图将一个XML文件作为一个可以轻松处理的数据帧放入R中。我已经在网上很容易地浏览了一些示例，但是我找不到一个格式类似于XML文件的示例。这是从火车站来的

以下是特定XML文件的URL：

我想得到一个数据框，它看起来像网站上的表（date、1mo、3mo、6mo等列）。我怀疑这是因为XML文件比我所看到的示例更复杂

Ad<-'http://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=year(NEW_DATE)%20eq%202005'
XML <- xmlTreeParse(Ad)
xmltop <- xmlRoot(xmlfile)
XMLcat <- xmlSApply(xmltop, function(x) xmlSApply(x, xmlValue))
plantcat_df <- data.frame(t(XMLcat),row.names=NULL)

---

Ad您确实应该仔细阅读XML中的名称空间，以及它们在R和XPath中的工作原理。另外，
xml2
是一个较新的XML pkg，它有一些不错的特性，您应该研究一下

library(xml2)

# read the doc
doc <- read_xml("http://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=year(NEW_DATE)%20eq%202005")

# libxml2 + R == "meh" handling of default namespaces
ns <- xml_ns_rename(xml_ns(doc), d1="default")

# all the info is in the properties tag so focus on it
props <- xml_find_all(doc, "//default:entry/default:content/m:properties", ns)

# lots of ways to extract, but this data is "regular" enough to take a
# rather simplistic approach. Extract all the node values which will be 
# separated by newlines. Convert newlines to tabs, trim the whole thing
# and read it in as a table.
dat <- read.table(text=trimws(gsub("\n", "\t", unlist(lapply(props, xml_text)))), 
                  sep="\t", stringsAsFactors=FALSE)

# column names wld be good so build those from one property node
colnames(dat) <- xml_name(xml_children(props[[1]]))

# boom: done
str(dat)
## 'data.frame': 250 obs. of  14 variables:
##  $ Id              : int  3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 ...
##  $ NEW_DATE        : chr  "        2005-11-14T00:00:00" "        2005-11-10T00:00:00" "        2005-11-15T00:00:00" "        2005-11-17T00:00:00" ...
##  $ BC_1MONTH       : num  3.93 3.89 4.01 3.98 4 ...
##  $ BC_3MONTH       : num  4.02 3.97 4.01 4.01 4 ...
##  $ BC_6MONTH       : num  4.35 4.3 4.34 4.3 4.3 ...
##  $ BC_1YEAR        : num  4.4 4.34 4.38 4.32 4.34 ...
##  $ BC_2YEAR        : num  4.5 4.44 4.47 4.37 4.42 ...
##  $ BC_3YEAR        : num  4.52 4.48 4.5 4.39 4.43 ...
##  $ BC_5YEAR        : num  4.54 4.49 4.51 4.39 4.43 ...
##  $ BC_7YEAR        : num  4.57 4.51 4.52 4.42 4.45 ...
##  $ BC_10YEAR       : num  4.61 4.55 4.56 4.46 4.49 ...
##  $ BC_20YEAR       : num  4.9 4.85 4.83 4.75 4.77 ...
##  $ BC_30YEAR       : logi  NA NA NA NA NA NA ...
##  $ BC_30YEARDISPLAY: int  0 0 0 0 0 0 0 0 0 0 ...

库（xml2）
#看医生
医生
library(xml2)

# read the doc
doc <- read_xml("http://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=year(NEW_DATE)%20eq%202005")

# libxml2 + R == "meh" handling of default namespaces
ns <- xml_ns_rename(xml_ns(doc), d1="default")

# all the info is in the properties tag so focus on it
props <- xml_find_all(doc, "//default:entry/default:content/m:properties", ns)

# lots of ways to extract, but this data is "regular" enough to take a
# rather simplistic approach. Extract all the node values which will be 
# separated by newlines. Convert newlines to tabs, trim the whole thing
# and read it in as a table.
dat <- read.table(text=trimws(gsub("\n", "\t", unlist(lapply(props, xml_text)))), 
                  sep="\t", stringsAsFactors=FALSE)

# column names wld be good so build those from one property node
colnames(dat) <- xml_name(xml_children(props[[1]]))

# boom: done
str(dat)
## 'data.frame': 250 obs. of  14 variables:
##  $ Id              : int  3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 ...
##  $ NEW_DATE        : chr  "        2005-11-14T00:00:00" "        2005-11-10T00:00:00" "        2005-11-15T00:00:00" "        2005-11-17T00:00:00" ...
##  $ BC_1MONTH       : num  3.93 3.89 4.01 3.98 4 ...
##  $ BC_3MONTH       : num  4.02 3.97 4.01 4.01 4 ...
##  $ BC_6MONTH       : num  4.35 4.3 4.34 4.3 4.3 ...
##  $ BC_1YEAR        : num  4.4 4.34 4.38 4.32 4.34 ...
##  $ BC_2YEAR        : num  4.5 4.44 4.47 4.37 4.42 ...
##  $ BC_3YEAR        : num  4.52 4.48 4.5 4.39 4.43 ...
##  $ BC_5YEAR        : num  4.54 4.49 4.51 4.39 4.43 ...
##  $ BC_7YEAR        : num  4.57 4.51 4.52 4.42 4.45 ...
##  $ BC_10YEAR       : num  4.61 4.55 4.56 4.46 4.49 ...
##  $ BC_20YEAR       : num  4.9 4.85 4.83 4.75 4.77 ...
##  $ BC_30YEAR       : logi  NA NA NA NA NA NA ...
##  $ BC_30YEARDISPLAY: int  0 0 0 0 0 0 0 0 0 0 ...