将不均匀的XML解析为R data.frame

Question

I am trying to parse a large XML file into an R data frame. The structure of the XML is uneven and does not always contain all elements and sometimes contains more than 1 duplicated element per node.

The XML is:

<root>
<members>
<member>
  <id>1</id>
  <educations>
    <education>
      <institution>Sydney University</institution>
      <program>Masters of Science</program>
      <start-date>2010</start-date>
      <end-date>2015</end-date>
      <description></description>
    </education>
    <education>
      <institution>UTS</institution>
      <program>Bachelor of Science</program>
      <start-date>2004</start-date>
      <end-date>2008</end-date>
    </education>
  </educations>
</member>

<member>
  <id>2</id>
 </member>

<member>
  <id>3</id>
  <educations>
    <education>
      <is-current>true</is-current>
      <institution>Monash Univeristy</institution>
      <start-date>2010</start-date>
    </education>
  </educations>
</member>
</members>
</root>

Desired output table would have duplicated IDs for each member and their education blocks. So ID 1 would have 2 rows for each education period and ID 3 would have just 1.

Using xmlToList() creates excessive columns and I can't find a way to duplicate the ID for each child node.

Answer 1

This is an admittedly clumsy solution, possibly there are far more elegant tidiverse-esque solutions. However, this seems to do the job.

library(XML)
library(plyr)

names_use <- c("institution", "program", "start-date", "end-date","description")
list_xml <- xmlToList(test)
df_use <- ldply(list_xml$member, function(x){
    if(is.null(x$educations)){
        df_edu <- data.frame(x$id,t(rep(NA,5)))
        names(df_edu) <- c("id",names_use)
        return(df_edu)
    }
    df_res <- ldply(x$educations, function(edu_tmp){
        df_edu <- as.data.frame(t(unlist(edu_tmp)),
            stringsAsFactors = F)
        for(i_names in names_use){
            if(!i_names %in% names(df_edu)){
                df_edu[,i_names] <- NA
            }
        }
        return(df_edu)
    })
    df_res$id <- x$id
    return(df_res[,c("id",names_use)])
})
df_use <- df_use[,c("id",names_use)]

df_use
  id       institution             program start-date end-date description
1  1 Sydney University  Masters of Science       2010     2015          NA
2  1               UTS Bachelor of Science       2004     2008          NA
3  2              <NA>                <NA>       <NA>     <NA>          NA
4  3 Monash Univeristy                <NA>       2010     <NA>          NA

Answer 2

An alternate approach:

library(xml2)
library(tidyverse)

I like tidy column names so we'll add in a helper function:

mgca <- function(tbl) {

  x <- colnames(tbl)
  x <- tolower(x)
  x <- gsub("[[:punct:][:space:]]+", "_", x)
  x <- gsub("_+", "_", x)
  x <- gsub("(^_|_$)", "", x)
  x <- make.unique(x, sep = "_")

  colnames(tbl) <- x

  tbl

}

doc <- read_xml("so.xml")

The idea here is to first iterate over each <member> , then extract the <id> for it.

Once inside a <member> see if we have any children. If not, just return the <id> in a data frame. If we do, then further iterate across each <education> node, identifying the children present and only pulling those out and making a data frame for each of them, including the <id> , finally smushing it all together into a final data frame after cleaning up column names and getting better column types:

xml_find_all(doc, ".//member") %>% 
  map_df(~{

    id <- (xml_find_first(.x, ".//id") %>% xml_text()) %||% NA_character_

    edus <- xml_find_all(.x, ".//educations/education")

    if (length(edus) > 0) {

      map_df(edus, ~{
        kid <- .x
        nodes <- xml_children(kid) %>% xml_name()
        map(nodes, ~xml_find_first(kid, sprintf(".//%s", .x)) %>% 
              xml_text()) %>% 
          set_names(nodes) %>% 
          append(list(id = id)) %>% 
          flatten_df() 
      })

    } else {
      data_frame(id = id)
    }

  }) %>% 
  mgca() %>% 
  type_convert()
## # A tibble: 4 x 7
##         institution             program start_date end_date description    id is_current
##               <chr>               <chr>      <int>    <int>       <chr> <int>      <chr>
## 1 Sydney University  Masters of Science       2010     2015        <NA>     1       <NA>
## 2               UTS Bachelor of Science       2004     2008        <NA>     1       <NA>
## 3              <NA>                <NA>         NA       NA        <NA>     2       <NA>
## 4 Monash Univeristy                <NA>       2010       NA        <NA>     3       true

Since type_convert() can't read minds, you'll likely have to turn is_current into a logical vector on your own.