crossing plumbing variables

ref #420

playgrounds/eav-playground-2.R

@@ -20,10 +20,11 @@ token <- "56F43A10D01D6578A46393394D76D88F"  # PHI-free demo: Repeating Instrume
 # forms   <- "health"
 fields  <- NULL
 forms   <- NULL
+records <- NULL #c("1")
 
 # ---- load-data ---------------------------------------------------------------
 system.time(
-  ds_expected <- REDCapR::redcap_read_oneshot(redcap_uri, token)$data
+  ds_expected <- REDCapR::redcap_read_oneshot(redcap_uri, token, records=records)$data
 )
 
 system.time({
@@ -39,7 +40,7 @@ system.time({
 
   desired_fields <- ds_metadata$field_name
 
-  ds_eav      <- REDCapR:::redcap_read_eav_oneshot(redcap_uri, token, fields = desired_fields)$data
+  ds_eav      <- REDCapR:::redcap_read_eav_oneshot(redcap_uri, token, fields = desired_fields, records=records)$data
 })
 
 testit::assert(ds_metadata$field_name == colnames(ds_expected))
@@ -48,13 +49,19 @@ testthat::expect_setequal( ds_metadata$field_name, colnames(ds_expected))
 # ---- tweak-data --------------------------------------------------------------
 if (!"event_id" %in% colnames(ds_eav)) {
   ds_eav$event_id <- "dummy_1"
+  .dummy_event <- TRUE
+} else {
+  .dummy_event <- FALSE
 }
 
+.fields_to_cross <- setdiff(ds_metadata$field_name, c("redcap_repeat_instrument", "redcap_repeat_instance"))
+.fields_to_return <- c("record", "event_id", ds_metadata$field_name)
+
 ds_eav_possible <-
   ds_eav %>%
   tidyr::expand(
     tidyr::nesting(record, event_id),
-    tidyr::crossing(field_name = ds_metadata$field_name)
+    tidyr::crossing(field_name = .fields_to_cross)
   )
 
 ds_eav_2 <-
@@ -71,23 +78,31 @@ ds_eav_2 <-
   ) %>%
   dplyr::right_join(ds_eav_possible, by = c("record", "event_id", "field_name"))
 
-# . <- NULL # For the sake of avoiding an R CMD check note.
 ds <-
   ds_eav_2 %>%
   dplyr::select(-.data$field_type, -.data$field_name_base) %>%
+  # dplyr::slice(1:46) %>%
   # dplyr::group_by(record, redcap_repeat_instrument, redcap_repeat_instance, event_id, field_name) %>%
   # dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
   # dplyr::filter(n > 1L) %>%
   # View()
   # dplyr::select(-.data$redcap_repeat_instance) %>%        # TODO: need a good fix for repeats
   # tidyr::drop_na(event_id) %>%                            # TODO: need a good fix for repeats
   tidyr::pivot_wider( # Everything else is considered an ID column
+    id_cols= c(.data$record, .data$event_id, .data$redcap_repeat_instrument, .data$redcap_repeat_instance),
     names_from  = .data$field_name,
     values_from = .data$value
   ) %>%
-  dplyr::select(!!ds_metadata$field_name) %>%
-  readr::type_convert(col_types)
+  dplyr::select(!!.fields_to_return)
   # dplyr::select(.data = ., !!intersect(variables_to_keep, colnames(.)))
 
+if (.dummy_event) {
+  ds$event_id <- NULL
+}
+
+ds <-
+  ds %>%
+  readr::type_convert(col_types)
+
 testit::assert(colnames(ds) == colnames(ds_expected))
 testthat::expect_setequal(colnames(ds), colnames(ds_expected))