Incorporate IV exclusions

.gitignore

@@ -1,2 +1,3 @@
 *~
-data/*
+data/*
+*.rds

prep/transform-intovalue.R

@@ -1,10 +1,28 @@
 library(tidyverse)
 
-## Generate this CSV using the script here:
-## https://github.com/maia-sh/intovalue-data/blob/main/R/05_prepare-clinical-dashboard-data.R
-iv_umc <- read_csv(
-    "../shiny_app/data/ct-dashboard-intovalue-umc.csv"
-)
+## Get intovalue.rds from here:
+## https://github.com/maia-sh/intovalue-data/blob/main/data/processed/intovalue.rds
+## Save it in the prep/ folder in this repo
+
+intovalue <- read_rds("intovalue.rds")
+
+## Apply the IntoValue exclusion criteria
+intovalue <- intovalue %>%
+    filter(
+        iv_completion,
+        iv_status,
+        iv_interventional,
+        has_german_umc_lead,
+        ## In case of dupes, exclude IV1 version
+        !(is_dupe & iv_version == 1)
+    )
+
+iv_all <- intovalue
+
+## Un-nest the cities
+iv_umc <- intovalue %>%
+    mutate(lead_cities = strsplit(as.character(lead_cities), " ")) %>%
+    tidyr::unnest(lead_cities)
 
 ## This is the library of transformations
 transforms <- read_csv("intovalue-city-transforms.csv")
@@ -13,6 +31,9 @@ transforms <- read_csv("intovalue-city-transforms.csv")
 iv_umc <- iv_umc %>%
     left_join(transforms)
 
-## This writes the final CSV out
+## This writes the final CSVs out
+iv_all %>%
+    write_csv("../data/ct-dashboard-intovalue-all.csv")
+
 iv_umc %>%
-    write_csv("../shiny_app/data/ct-dashboard-intovalue-umc.csv")
+    write_csv("../data/ct-dashboard-intovalue-umc.csv")