edit processing to use SB data csv

2_process.R

@@ -3,14 +3,15 @@ source('2_process/src/data_utils.R')
 p2_targets <- list(
   # Confirming raw data matches `p1_unc_stats` from SB
   tar_target(p2_unc_agg_summary,
-             p1_unc_agg |>
+             p1_unc_stats |>
                group_by(dimension, determinant) |>
                summarize(across(c(contains('related'),
                                   contains('unknown'),
-                                  contains('significant')),
+                                  contains('significant'),
+                                  contains('direction')),
                                 list(total = ~sum(.x, na.rm=TRUE)))) |> 
-               mutate(evidence_val = positively_related_total + negatively_related_total +
-                        unrelated_total + unknown_direction_total)
+               mutate(evidence_val = pos_related_total + neg_related_total +
+                        unrelated_total + unk_direction_total)
              ),
   # Based on metadata:
   # Amt of evidence: Small = total_studies < 5; Medium = total_studies 5-9; Large,total_studies = > 9
@@ -18,22 +19,22 @@ p2_targets <- list(
   tar_target(p2_top_trend_stats,
              p2_unc_agg_summary |>
                dplyr::select(dimension, determinant, #indicator, 
-                      positively_related_total, negatively_related_total, unrelated_total, 
-                      unknown_direction_total) |>
+                             pos_related_total, neg_related_total, unrelated_total, 
+                             unk_direction_total) |>
                pivot_longer(!c(dimension,determinant)) |>
                group_by(dimension, determinant) |>
-               # for each indicator find the maximum % of studies in agreement 
+               # for each determinant find the maximum % of studies in agreement 
                # across the significance categories. 
                slice_max(value) |>
                rename(sig_name = name, sig_value = value)
              ),
 # Join `p2_unc_agg_summary` to top trends to get percentages of agreement and evidence
-  tar_target(p2_unc_agg_summary_csv,
+  tar_target(`p2_unc_agg_summary_csv`,
              p2_unc_agg_summary |>
                left_join(p2_top_trend_stats) |>
                # level of agreement is the max percent of studies in agreement
                dplyr::mutate(level_agreement = 100*(sig_value/evidence_val)) |>
-               readr::write_csv('2_process/out/indicator_uncertainty.csv')
+               readr::write_csv('2_process/out/determinant_uncertainty.csv')
              ),
 
   tar_target(p2_indicators,