R: Xmleventparse with Large, Varying-Node Xml Input and Conversion to Data Frame

R: xmlEventParse with Large, Varying-node XML Input and Conversion to Data Frame

Setup a function that will create a temp storage area for our element data as well as a function that will be called every time a is found.

library(XML)

uid_traverse <- function() {

  # we'll store them as character vectors and then make a data frame out of them.
  # this is likely one of the cheapest & fastest methods despite growing a vector
  # inch by inch. You can pre-allocate space and modify this idiom accordingly
  # for another speedup.

  uids <- c() 
  refs <- c()

  REC <- function(x) {

    uid <- xpathSApply(x, "//UID", xmlValue)
    ref <- xpathSApply(x, "//reference/uid", xmlValue)

    if (length(uid) > 0) {

      if (length(ref) == 0) {

        uids <<- c(uids, uid)
        refs <<- c(refs, NA_character_)

      } else {

        uids <<- c(uids, rep(uid, length(ref)))
        refs <<- c(refs, ref)

      } 

    } 

  }

  # we return a named list with the element handler and another
  # function that turns the vectors into a data frame

  list(
    REC = REC, 
    uid_df = function() { 
      data.frame(uid = uids, ref = refs, stringsAsFactors = FALSE)
    }
  )

}

We need one instance of this function.

uid_f <- uid_traverse()

Now, we call xmlEventParse() and give it our function, using invisible() since we don’t need what xmlEventParse() returns but just want the side-effects:

invisible(
  xmlEventParse(
  file = path.expand("~/data/so.xml"), 
  branches = uid_f["REC"])
)

And, we see the results:

uid_f$uid_df()
##       uid      ref
## 1 ABCD123 ABCD2345
## 2 ABCD123 ABCD3456
## 3 ABCD123 ABCD4567
## 4 XYZ0987     <NA>

R: Memory Management during xmlEventParse of Huge (20GB) files

Here is an example, we have a launch script invoke.sh, that calls an R Script and passes the url and filename as parameters... In this case, I had previously downloaded the test file medsamp2015.xml and put in the ./data directory.

• My sense would be to create a loop in the invoke.sh script and iterate through the list of target file names. For each file you invoke an R instance, download it, process the file and move on to the next.

Caveat: I didn't check or change your function against any other download files and formats. I would turn off the printing of the output by removing the print() wrapper on line 62.

print( cbind(c(rep(v1, length(v2))), v2))

• See: runtime.txt for print out.
• The output .csv files are placed in the ./data directory.

Note: This is a derivative of a previous answer provided by me on this subject:
R memory not released in Windows. I hope it helps by way of example.

Launch Script

  1 #!/usr/local/bin/bash -x
  2
  3 R --no-save -q --slave < ./47162861.R --args "https://www.nlm.nih.gov/databases/dtd" "medsamp2015.xml"

R File - 47162861.R

# Set working directory

projectDir <- "~/dev/stackoverflow/47162861"
setwd(projectDir)

# -----------------------------------------------------------------------------
# Load required Packages...
requiredPackages <- c("XML")

ipak <- function(pkg) {
  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
  if (length(new.pkg))
    install.packages(new.pkg, dependencies = TRUE)
  sapply(pkg, require, character.only = TRUE)
}

ipak(requiredPackages)

# -----------------------------------------------------------------------------
# Load required Files
# trailingOnly=TRUE means that only your arguments are returned
args <- commandArgs(trailingOnly = TRUE)

if ( length(args) != 0 ) {
  dataDir <- file.path(projectDir,"data")
  fileUrl = args[1]
  fileName = args[2]
} else {
  dataDir <- file.path(projectDir,"data")
  fileUrl <- "https://www.nlm.nih.gov/databases/dtd"
  fileName <- "medsamp2015.xml"
}

# -----------------------------------------------------------------------------
# Download file

# Does the directory Exist? If it does'nt create it
if (!file.exists(dataDir)) {
  dir.create(dataDir)
}

# Now we check if we have downloaded the data already if not we download it

if (!file.exists(file.path(dataDir, fileName))) {
  download.file(fileUrl, file.path(dataDir, fileName), method = "wget")
}

# -----------------------------------------------------------------------------
# Now we extrat the data

tempdat <- data.frame(pmid = as.numeric(), lname = character(),
  stringsAsFactors = FALSE)
cnt <- 1

branchFunction <- function() {
  func <- function(x, ...) {
    v1 <- xpathSApply(x, path = "//PMID", xmlValue)
    v2 <- xpathSApply(x, path = "//Author/LastName", xmlValue)
    print(cbind(c(rep(v1, length(v2))), v2))

    # below is where I store/write the temp data along the way
    # but even without doing this, memory is used (even after
    # clearing)

    tempdat <<- rbind(tempdat, cbind(c(rep(v1, length(v2))),
      v2))
    if (nrow(tempdat) > 1000) {
      outname <- file.path(dataDir, paste0(cnt, ".csv")) # Create FileName
      write.csv(tempdat, outname, row.names = F) # Write File to created directory
      tempdat <<- data.frame(pmid = as.numeric(), lname = character(),
        stringsAsFactors = FALSE)
      cnt <<- cnt + 1
    }
  }
  list(MedlineCitation = func)
}

myfunctions <- branchFunction()

# -----------------------------------------------------------------------------
# RUN
xmlEventParse(file = file.path(dataDir, fileName),
              handlers = NULL,
              branches = myfunctions)

Test File and output

~/dev/stackoverflow/47162861/data/medsamp2015.xml

$ ll                                                            
total 2128
drwxr-xr-x@ 7 hidden  staff   238B Nov 10 11:05 .
drwxr-xr-x@ 9 hidden  staff   306B Nov 10 11:11 ..
-rw-r--r--@ 1 hidden  staff    32K Nov 10 11:12 1.csv
-rw-r--r--@ 1 hidden  staff    20K Nov 10 11:12 2.csv
-rw-r--r--@ 1 hidden  staff    23K Nov 10 11:12 3.csv
-rw-r--r--@ 1 hidden  staff    37K Nov 10 11:12 4.csv
-rw-r--r--@ 1 hidden  staff   942K Nov 10 11:05 medsamp2015.xml

Runtime Output

> ./invoke.sh > runtime.txt
+ R --no-save -q --slave --args https://www.nlm.nih.gov/databases/dtd medsamp2015.xml
Loading required package: XML

File: runtime.txt

Extract text from XML, but file has duplicated node-names

library(xml2)
library(tidyverse)

doc <- read_xml("file.xml")

xml_find_all(doc, ".//ArchivedIncident") %>% # iterate over each incident
  map_df(~{
    set_names(
      xml_find_all(.x, ".//value/value") %>% xml_text(), # get entry values
      xml_find_all(.x, ".//key") %>% xml_text()          # get entry keys (column names)
    ) %>% 
      as.list() %>%                                      # turn named vector to list
      flatten_df() %>%                                   # and list to df
      mutate(ID = xml_attr(.x, "ID"))                    # add id
  }) %>%
  type_convert() %>% # let R convert the values for you
  select(ID, everything()) # get it in the order you likely want
## # A tibble: 2 x 5
##      ID TEST1 TEST2 TEST3 TEST4
##   <int> <chr> <int> <chr> <chr>
## 1   100  <NA>    12     A  <NA>
## 2   101  BLAH    NA  <NA>  <NA>

create hash value for each row of data in dataframe in R

If I get what you want properly, digest will work directly with apply:

library(digest)
ssi.10q3.v1.hash <- data.frame(uid = 1:nrow(ssi.10q3.v1), hash = apply(ssi.10q3.v1, 1, digest))

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