It may not be as verbose as the XML package but xml2 doesn’t have the memory leaks and is laser-focused on data extraction. I use trimws which is a really recent addition to R core.
library(xml2)
pg <- read_xml("http://www.ggobi.org/book/data/olive.xml")
# get all the <record>s
recs <- xml_find_all(pg, "//record")
# extract and clean all the columns
vals <- trimws(xml_text(recs))
# extract and clean (if needed) the area names
labs <- trimws(xml_attr(recs, "label"))
# mine the column names from the two variable descriptions
# this XPath construct lets us grab either the <categ…> or <real…> tags
# and then grabs the 'name' attribute of them
cols <- xml_attr(xml_find_all(pg, "//data/variables/*[self::categoricalvariable or
self::realvariable]"), "name")
# this converts each set of <record> columns to a data frame
# after first converting each row to numeric and assigning
# names to each column (making it easier to do the matrix to data frame conv)
dat <- do.call(rbind, lapply(strsplit(vals, "\ +"),
function(x) {
data.frame(rbind(setNames(as.numeric(x),cols)))
}))
# then assign the area name column to the data frame
dat$area_name <- labs
head(dat)
## region area palmitic palmitoleic stearic oleic linoleic linolenic
## 1 1 1 1075 75 226 7823 672 NA
## 2 1 1 1088 73 224 7709 781 31
## 3 1 1 911 54 246 8113 549 31
## 4 1 1 966 57 240 7952 619 50
## 5 1 1 1051 67 259 7771 672 50
## 6 1 1 911 49 268 7924 678 51
## arachidic eicosenoic area_name
## 1 60 29 North-Apulia
## 2 61 29 North-Apulia
## 3 63 29 North-Apulia
## 4 78 35 North-Apulia
## 5 80 46 North-Apulia
## 6 70 44 North-Apulia
UPDATE
I’d prbly do the last bit this way now:
library(tidyverse)
strsplit(vals, "[[:space:]]+") %>%
map_df(~as_data_frame(as.list(setNames(., cols)))) %>%
mutate(area_name=labs)