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xmlrectr separates structural evidence from the
analytical decision. The recommended workflow is:
proposal -> review -> profile -> rectangleThe proposal is deliberately not executable. It tells you what the sample XML contains; you decide what a row means and which values belong in the result.
The package ships with a small XML example:
file <- system.file("extdata", "orders.xml", package = "xmlrectr")
proposal <- propose_xml_profile(file)
proposal
#> XML profile proposal (review only)
#> Source: /tmp/RtmptLGIJq/Rinst148f3b47e63/xmlrectr/extdata/orders.xml
#> Row candidates: 2
#> Row used for proposal analysis: orders/order (heuristic_for_review)
#> ID candidates: 3
#> Field candidates: 11
#> Sample-based non-character type suggestions: 2
#> Nothing is executed or accepted automatically.
#> Use review_xml_proposal() and then create an explicit xml_profile().Review candidate row structures:
review_xml_proposal(proposal, "rows")
#> # A tibble: 2 × 10
#> rows namespace occurrences depth max_per_parent sample_repeated xsd_repeated
#> <chr> <chr> <int> <int> <int> <lgl> <lgl>
#> 1 order… urn:exam… 2 2 2 TRUE FALSE
#> 2 order… urn:exam… 3 3 2 TRUE FALSE
#> # ℹ 3 more variables: xsd_selector <chr>, priority <chr>, reason <chr>Review possible identifiers and fields:
review_xml_proposal(proposal, "ids")
#> # A tibble: 3 × 4
#> source value_kind priority reason
#> <chr> <chr> <chr> <chr>
#> 1 @id attribute strong Complete, unique scalar attribute conventi…
#> 2 @status attribute plausible Complete, unique scalar attribute; semanti…
#> 3 customer/name text possible Complete, unique scalar value; semantics s…
review_xml_proposal(proposal, "fields")
#> # A tibble: 11 × 11
#> source value_kind entity records_observed coverage max_per_record
#> <chr> <chr> <chr> <int> <dbl> <int>
#> 1 @id attribute order 2 1 1
#> 2 @status attribute order 2 1 1
#> 3 customer/name text order 2 1 1
#> 4 customer/postal-c… text order 1 0.5 1
#> 5 item/@sku attribute item 2 1 2
#> 6 item/@quantity attribute item 2 1 2
#> 7 item/description text item 2 1 2
#> 8 item/unit-price/@… attribute item 2 1 2
#> 9 item/unit-price text item 2 1 2
#> 10 note/@priority attribute order 1 0.5 1
#> 11 note text order 1 0.5 1
#> # ℹ 5 more variables: under_repetition <lgl>, wide_safe <lgl>,
#> # sample_type <chr>, sample_confidence <chr>, sample_reason <chr>For this XML family, one order is the desired analytical
row and its id value identifies the record:
profile <- xml_profile(
rows = "order",
id = "id"
)
profile
#> XML rectangle profile
#> Rows: order
#> ID: id
#> Fields: all values
#> Types: all character
#> Layout: safe (one atomic table)A profile is intentionally small and human-readable. It can be stored as JSON or YAML and reviewed independently from the code that executes it.
The simplest call can compile the profile against the file and apply it:
out <- rectangle_xml(file, profile)
out
#> # A tibble: 24 × 15
#> document_id record_index record_id entity entity_index field value_index
#> <chr> <int> <chr> <chr> <int> <chr> <int>
#> 1 orders.xml 1 A-001 order 1 id 1
#> 2 orders.xml 1 A-001 order 1 status 1
#> 3 orders.xml 1 A-001 order 1 customer/… 1
#> 4 orders.xml 1 A-001 order 1 customer/… 1
#> 5 orders.xml 1 A-001 item 1 sku 1
#> 6 orders.xml 1 A-001 item 1 quantity 1
#> 7 orders.xml 1 A-001 item 1 descripti… 1
#> 8 orders.xml 1 A-001 item 1 unit-pric… 1
#> 9 orders.xml 1 A-001 item 1 unit-price 1
#> 10 orders.xml 1 A-001 item 2 sku 1
#> # ℹ 14 more rows
#> # ℹ 8 more variables: value <chr>, value_kind <chr>, source <chr>,
#> # record_node_id <int>, entity_node_id <int>, parent_entity_node_id <int>,
#> # source_node_id <int>, source_path <chr>For repeated processing of files from the same XML family, compile once and reuse the specification:
The same function controls execution:
rectangle_xml(file, spec, parallel = FALSE) # exact sequential path
rectangle_xml(file, spec, parallel = TRUE) # request tuned parallel defaults
rectangle_xml(file, spec, parallel = "auto") # engine choosesparallel = "auto" is useful for ordinary work because
small record workloads stay sequential instead of paying process
startup/scheduling overhead.
If an XSD exists, it can provide additional occurrence/required/type evidence:
typed_xml <- system.file("extdata", "types.xml", package = "xmlrectr")
typed_xsd <- system.file("extdata", "types.xsd", package = "xmlrectr")
xsd_proposal <- propose_xml_profile(typed_xml, xsd = typed_xsd)
review_xml_proposal(xsd_proposal, "xsd")
#> # A tibble: 9 × 11
#> selector kind name type builtin_type analytical_type type_source min_occurs
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 measure… elem… meas… <NA> <NA> <NA> unresolved 1
#> 2 measure… elem… meas… <NA> <NA> <NA> unresolved 1
#> 3 measure… elem… count xs:i… int integer type_attri… 1
#> 4 measure… elem… ratio xs:d… double double type_attri… 1
#> 5 measure… elem… acti… xs:b… boolean logical type_attri… 1
#> 6 measure… elem… obse… xs:d… date date type_attri… 1
#> 7 measure… elem… code xs:s… string character type_attri… 1
#> 8 measure… attr… id xs:ID ID character type_attri… 0
#> 9 custom elem… cust… Loca… <NA> <NA> type_attri… 1
#> # ℹ 3 more variables: max_occurs <chr>, repeated <lgl>, required <lgl>XSD information is advisory. It does not automatically determine the
best analytical rectangle, and inspect_xsd() is not
intended as a complete XSD validator.
When you want one self-contained table quickly and do not yet need a reusable profile contract:
analyst <- rectangle_xml_analyst(file)
analyst
#> # A tibble: 6 × 26
#> xml_document_id xml_entity xml_entity_id xml_parent_entity
#> <chr> <chr> <chr> <chr>
#> 1 orders.xml orders e000000002 <NA>
#> 2 orders.xml order e000000004 orders
#> 3 orders.xml item e000000012 order
#> 4 orders.xml item e000000020 order
#> 5 orders.xml order e000000031 orders
#> 6 orders.xml item e000000037 order
#> # ℹ 22 more variables: xml_parent_entity_id <chr>, xml_occurrence <int>,
#> # xml_node_id <int>, xml_parent_node_id <int>, xml_depth <int>,
#> # xml_source_path <chr>, xml_namespace_uri <chr>, xml_key_column <chr>,
#> # xml_key_value <chr>, xml_namespaces <chr>, orders__attr_generated <chr>,
#> # order__attr_id <chr>, order__attr_status <chr>,
#> # order__customer__name <chr>, order__customer__postal_code <chr>,
#> # item__attr_sku <chr>, item__attr_quantity <int>, item__description <chr>, …The analyst table retains universal xml_*
provenance/entity columns. For production extraction across a family of
documents, prefer an explicit profile once the intended structure is
understood.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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