Reading data from an XML File Using R

Tag: xml,r

From reading through, then trying a few previous examples on StackOverflow related to reading an XML file in R, it seems that due to the “jagged” nature of the following file I can’t use XPath related methods.

https://www.dropbox.com/s/jz8sj2fifuobkva/Data.xml?oref=e&n=305307914

Therefore, it seems I need to use a combination of xmlToList() and ldply() to read data from the following file.

Specifically, for all 20 events in the file (ie. event.1, event.2, … event.20), I am wanting to get the following variables (structured as)

$movements$movement$clips$clip$data$event$begin (vector)
$movements$movement$clips$clip$data$event$end (vector)
$movements$movement$clips$clip$data$event$max$cells (data frame)
As per above but $rollover$data$quant$cells where there are several samples within an event (n data frames)

Based on other StackOverflow examples the code (using R v3.1.2) I have tried to read the “begin” data is as follows :-

library(XML)
library(plyr)

datfile <- "D:/Data.xml"
xmlfile <- xmlTreeParse(datfile,useInternal = TRUE)
sampledata <- xmlToList(xmlfile)
startdata <- ldply(sampledata$movements$movement$clips$clip$data$event$begin)

When I do this I only get the first variable (0.240) in event.1. I have now got to the point where I am stuck, and have exhausted my investigations on how to do this.

Best How To :

If you're willing to give xml2 a go, you can get to begin in a few lines:

library(xml2)
library(magrittr)

# get a vector

doc <- read_xml("~/Dropbox/Data.xml")

doc %>%
  xml_find_all("//d1:event/d1:begin", ns=xml_ns(doc)) %>%
  xml_text() %>%
  as.numeric()

##  [1] 0.24 0.73 1.25 1.75 2.24 2.75 3.27 3.76 4.30 4.77 5.28 5.78 6.32 6.82
## [15] 7.34 7.85 8.37 8.86 9.39 9.89

# get data frames

library(stringr)

make_df <- function(txt) {

  txt %>%
    str_split("\n") %>% extract2(1) %>%
    str_trim() %>%
    textConnection() -> con

  dat <- read.table(con)
  close(con)

  dat

}

doc %>%
  xml_find_all("//d1:max/d1:cells", ns=xml_ns(doc)) %>%
  xml_text() %>%
  lapply(make_df) -> df_list

df_list[[1]]

##     V1   V2   V3   V4   V5   V6   V7   V8   V9 V10 V11 V12
## 1  0.0  0.0  1.5  3.5  3.0  1.5  0.0  0.0  0.0 0.0 0.0   0
## 2  0.0  1.0  5.5  8.5  7.0  3.5  2.0  2.0  1.0 0.0 0.0   0
## 3  0.0  3.0  9.0 13.0  9.0  4.0  3.0  3.5  2.5 1.0 0.0   0
## 4  0.0  4.5 11.0 14.0  9.0  4.0  3.0  4.0  4.0 2.0 0.0   0
## 5  0.0  4.0 10.5 12.0  7.5  4.0  3.0  4.0  4.5 3.0 0.0   0
## 6  0.0  4.5  8.5 10.0  8.0  7.5  6.5  4.5  4.0 2.5 0.0   0
## 7  2.0  8.0 14.5 16.0 14.0 13.5 13.0  9.5  5.5 2.5 0.0   0
## 8  3.5 12.0 20.0 20.5 18.0 18.0 18.0 14.5  9.0 4.0 1.5   0
## 9  4.5 12.5 20.5 21.0 18.0 18.0 18.5 16.0 11.5 6.5 2.5   0
## 10 4.5 12.0 19.0 20.0 17.5 17.5 18.0 16.5 12.5 7.5 3.5   0
## 11 3.5  9.5 15.5 16.5 15.0 14.5 14.5 14.0 11.5 8.0 4.0   1
## 12 2.0  6.5 10.0 12.0 11.0 11.0 12.0 12.0 10.5 7.5 4.0   0
## 13 1.5  4.5  6.5  7.0  7.0  7.0  8.0  9.0  8.0 6.5 3.5   0
## 14 1.0  4.0  5.5  5.5  5.5  5.5  6.0  6.0  6.0 4.5 2.5   0
## 15 1.5  4.5  6.0  5.5  5.5  5.5  5.5  5.5  5.5 4.0 2.0   0
## 16 2.0  5.0  7.0  7.0  6.0  6.0  6.0  6.0  5.5 4.0 1.5   0
## 17 2.5  5.5  7.5  7.5  7.0  7.0  6.5  6.5  5.5 4.0 1.5   0
## 18 2.0  5.5  7.0  7.5  7.5  7.5  7.5  6.5  5.5 3.5 0.0   0
## 19 2.5  5.5  7.5  8.0  7.5  8.0  7.5  6.5  5.0 2.5 0.0   0
## 20 2.0  5.0  6.5  7.5  7.5  8.0  7.5  6.5  4.5 2.0 0.0   0
## 21 1.5  4.0  6.0  7.5  8.5  8.5  8.0  6.0  3.5 1.0 0.0   0
## 22 1.0  3.5  6.5  8.5  9.5  9.5  8.0  5.5  3.0 0.0 0.0   0
## 23 0.0  4.0  8.0 11.0 12.5 11.0  8.5  5.5  2.5 0.0 0.0   0
## 24 0.0  4.5  9.5 13.5 14.5 12.0  8.5  5.5  2.0 0.0 0.0   0
## 25 0.0  5.5 13.0 17.5 17.0 14.5  9.5  5.5  1.5 0.0 0.0   0
## 26 0.0  6.5 16.0 21.0 19.5 15.5 10.0  5.0  1.0 0.0 0.0   0
## 27 0.0  7.0 17.0 22.5 21.0 16.0 10.0  5.0  0.0 0.0 0.0   0
## 28 0.0  7.0 17.5 22.5 20.5 15.5  9.0  3.5  0.0 0.0 0.0   0
## 29 0.0  5.5 14.5 20.5 18.5 14.0  8.0  2.5  0.0 0.0 0.0   0
## 30 0.0  3.5 10.0 14.5 14.0 10.0  5.0  1.0  0.0 0.0 0.0   0
## 31 0.0  1.5  5.5  8.5  8.0  5.5  2.5  0.0  0.0 0.0 0.0   0
## 32 0.0  0.0  0.0  2.5  2.5  0.0  0.0  0.0  0.0 0.0 0.0   0

length(df_list)

## [1] 20

# get the deeply nested ones

quant_cells <- function(node) {
  node %>%
    xml_find_all("./d1:data/d1:quant/d1:cells", ns=xml_ns(doc)) %>%
    xml_text() %>%
    lapply(make_df)
}

doc %>%
  xml_find_all("//d1:rollover", ns=xml_ns(doc)) %>%
  as_list() %>%
  lapply(quant_cells) -> quant_df_list

length(quant_df_list)

## [1] 20

length(quant_df_list[[1]])

## [1] 63

quant_df_list[[1]]

## [[1]]
##    V1  V2  V3  V4  V5 V6
## 1 0.0 0.0 0.0 0.0 0.0  0
## 2 0.0 0.0 0.2 0.0 0.0  0
## 3 0.0 0.5 1.7 0.5 0.0  0
## 4 0.5 2.7 3.4 2.3 0.3  0
## 5 2.3 4.3 4.4 3.0 0.4  0
## 6 3.2 4.8 4.8 3.3 0.4  0
## 7 2.2 4.1 3.8 2.3 0.3  0
## 8 0.3 1.4 1.4 0.4 0.0  0
## 
## [[2]]
##    V1  V2   V3   V4  V5  V6  V7  V8 V9
## 1 0.0 0.0  0.0  0.0 0.0 0.0 0.0 0.0  0
## 2 0.0 0.3  0.9  1.3 1.1 0.4 0.0 0.0  0
## 3 0.2 2.2  4.5  5.9 4.7 2.0 0.2 0.0  0
## 4 1.0 5.3  8.5  9.1 7.1 3.7 0.4 0.0  0
## 5 2.9 8.3 12.0 11.6 9.0 5.4 1.0 0.0  0
## 6 3.5 9.2 13.5 12.9 9.6 5.8 1.5 0.1  0
## 7 3.0 8.2 11.6 11.3 8.3 4.4 0.5 0.0  0
## 8 1.1 3.7  6.4  6.3 4.0 1.8 0.2 0.0  0
## 9 0.0 0.2  1.4  1.5 0.3 0.0 0.0 0.0  0
## ...
## (down to [[63]])

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Remove quotes to use result as dataset name

r,string

You can get the values with get or mget (for multiple objects) lst <- mget(myvector) lapply(seq_along(lst), function(i) write.csv(lst[[i]], file=paste(myvector[i], '.csv', sep='')) ...

Tagging values in HTML document for automated extraction

html,xml,html5

If you are using them as meta-documents and they are sent to the parser, then converted as HTML and as long as the converted HTMLs do not have any irrelevant tags, it is fine! So, if the following code: <requirement> THE REQUIREMENT HERE </requirement> Gets converted into something like: <!--...

XSL transformation outputting multiple times and other confusion

xml,xslt,xpath

1) Your template is applied to 3 elements, and for each of them, loops over all the parents li elements (yes, for each of them, ask all the li elements, children of the grand-father of the current content, which are all the 3 li elements, each time). 2) Because that's...

How (in a vectorized manner) to retrieve single value quantities from dataframe cells containing numeric arrays?

r,dataframes,vectorization

It looks like you're trying to grab summary functions from each entry in a list, ignoring the elements set to -999. You can do this with something like: get_scalar <- function(name, FUN=max) { sapply(mydata[,name], function(x) if(all(x == -999)) NA else FUN(as.numeric(x[x != -999]))) } Note that I've changed your function...