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#' write object out as nquads
#'
#' @param x an object that can be represented as nquads
#' @param file output filename
#' @param ... additional parameters, see examples
#'
#' @export
#'
#' @examples
#' tmp <- tempfile(fileext = ".nq")
#' library(datasets)
#'
#' ## convert data.frame to nquads
#' write_nquads(iris, tmp)
#' rdf <- read_nquads(tmp)
#'
#' ## or starting a native rdf object
#' write_nquads(rdf, tempfile(fileext = ".nq"))
write_nquads <- function(x, file, ...){
UseMethod("write_nquads")
}
#' read an nquads file
#' @param file path to nquads file
#' @param ... additional arguments to [rdf_parse()]
#' @return an rdf object. See [rdf_parse()]
#'
#' @examples
#' tmp <- tempfile(fileext = ".nq")
#' library(datasets)
#' write_nquads(iris, tmp)
#' read_nquads(tmp)
#'
#' @export
read_nquads <- function(file, ...){
rdf_parse(file, "nquads", ...)
}
#' @export
write_nquads.rdf <- function(x, file, ...){
rdf_serialize(x, file, "nquads", ...)
}
#' @export
write_nquads.data.frame <- function(x,
file,
...){
df <- normalize_table(x, ...)
poor_mans_nquads(df, file, ...)
}
#' @importFrom tidyr gather
#' @importFrom dplyr left_join
normalize_table <- function(df, key_column = NULL, ...){
## gather looses col-classes, so pre-compute them (with base R)
col_classes <- data.frame(datatype =
vapply(df,
xs_class,
character(1)))
col_classes$predicate <- rownames(col_classes)
rownames(col_classes) <- NULL
## Use row names as key (subject), unless a key column is specified
## Should we verify that requested key column is indeed a unique key first?
out <- df
if (is.null(key_column)) {
out$subject <- as.character(1:dim(out)[[1]])
} else {
names(out)[names(out) == key_column] <- "subject"
}
## FIXME consider taking an already-gathered table to avoid dependency?
suppressWarnings(# Possible warnings about mixed types
out <- tidyr::gather(out,
key = "predicate",
value = "object",
-"subject"))
## merge is Slow! ~ 5 seconds for 800K triples
## (almost as much time as rdf_parse)
# merge(out, col_classes, by = "predicate")
dplyr::left_join(out, col_classes, by = "predicate")
}
## x is a data.frame with columns: subject, predicate, object, & datatype
#' @importFrom utils write.table
poor_mans_nquads <- function(x, file, prefix = NULL, ...){
if (is.null(prefix)) {
prefix <- paste0(deparse(substitute(x)), ":")
warning(paste("prefix not declared, using", prefix))
}
prefix <- uri_prefix(prefix)
## Currently written to be base-R compatible,
## but a tidyverse implementation may speed serialization.
## However, this seems to be fast enough that it is rarely the bottleneck
## NOTE: paste0 is a little slow ~ 1 s on 800K triples
## No datatype on blank (missing) nodes
blank_object <-is.na(x$object)
blank_subject <- is.na(x$subject)
x$datatype[blank_object] <- as.character(NA)
## NA needs to become a unique blank node number, could do uuid or _:r<rownum>
x$object[blank_object] <- paste0("_:r", which(blank_object))
x$subject[blank_subject] <- paste0("_:r", which(blank_subject))
## strings and URIs do not get a datatype
needs_type <- !is.na(x$datatype)
## URIs that are not blank nodes need <>
x$subject[!blank_subject] <- paste0("<", prefix, x$subject[!blank_subject], ">")
## Predicate is always a URI
x$predicate <- paste0("<", prefix, x$predicate, ">")
## Strings should be quoted
is_string <- !grepl("\\w+:\\w.*", x$object) &
!needs_type & !blank_object
x$object[is_string] <- paste0('\"', x$object[is_string] , '\"')
## URIs should be <> instead, but not blanks!
x$object[!blank_object] <- gsub("(^\\w+:\\w.*$)", "<\\1>",
x$object[!blank_object])
## assumes datatype is not empty (e.g. string)
x$object[needs_type] <- paste0('\"', x$object[needs_type],
'\"^^<', x$datatype[needs_type], ">")
## quads needs a graph column
x$graph <- "."
## write table is a little slow, ~ 1s on 800K triples,
## but readr cannot write in nquads style
## drop datatype
x <- x[c("subject", "predicate", "object", "graph")]
utils::write.table(x, file, col.names = FALSE, quote = FALSE, row.names = FALSE)
}
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