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#' Tidy Schema
#'
#' Construct a SPARQL query in which properties (predicates) become column headings
#' and values (objects) become entries of individual cells.
#'
#' @param ... names of the properties that should make up columns in the table
#' @param columns Alternatively, supply a vector of property names.
#' @param prefix the URI string to prefix before the property names to give
#' fully-resolved properties.
#' @param na.rm logical, default TRUE. Will not return a row for any
#' subject that does not have a object value matching the schema.
#' @examples
#'
#' sparql <- table_schema("Species", "Sepal.Length", prefix = "iris")
#' rdf_query(rdf, sparql)
#'
#' ## use columns arg for an existing vector of names.
#' columns <- names(iris)
#' sparql <- tidy_schema(columns = columns, prefix = "iris")
#' rdf_query(rdf, sparql)
#'
#' ## use na.rm = FALSE to include NA if variable is not defined for some observations
#' sparql <- tidy_schema("Species", "Sepal.Length", "Sepal.Color",
#' prefix = "iris", na.rm=FALSE)
#' rdf_query(rdf, sparql)
tidy_schema <- function(..., columns = NULL, prefix=NULL, na.rm = TRUE){
if(is.null(columns)){
columns <- c(...)
}
if(!is.null(prefix)){
attributes <- paste0("<",prefix, ":", columns, ">")
} else {
attributes <- paste0("<", columns, ">")
}
## Replace forbidden characters with "_",
## See spec: https://www.w3.org/TR/rdf-sparql-query/#rVARNAME
vars <- gsub("[^a-zA-Z1-9_]","_", basename(columns))
select <- paste0("?", vars)
where <- paste("?s", attributes, select)
if(!na.rm)
where <- paste("OPTIONAL {", where, "}")
else
where <- paste(where, ".")
where <- paste(where, collapse = "\n")
query <- paste("SELECT", paste0(select, collapse = " "),
"\nWHERE {\n", where, "\n}")
query
}
#sparql <- table_schema("Sepal.Length", "Sepal.Width",
# "Petal.Length", "Petal.Width",
# "Species", prefix="iris")
#sparql <- table_schema(columns=names(iris), prefix="iris")
#rdf_query(rdf, sparql) %>% as_tibble()
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