Fix as.sir() data.frame: preserve already-<sir> columns, exclude metadata

Issue #278: two related bugs in the column-detection / type-assignment pipeline.

Bug 1 – already-<sir> columns deleted on re-run
  Line 886 excluded already-sir columns from the type assignment (they
  stayed type "") causing the result loop to do x[,col] <- NULL, deleting
  them.  Fix: drop the !is.sir() guard so all untyped columns fall through
  to type "sir" and are re-processed correctly.

Bug 2 – metadata columns treated as antibiotics
  as.ab("patient") -> OXY, as.ab("ward") -> PRU.  The column detector
  accepted any column whose name matched an antibiotic code, regardless of
  content.  Fix: for name-matched columns that do not already carry an AMR
  class, also verify content looks like AMR data (all_valid_mics, all-
  numeric, or any SIR-like string).  all_valid_disks() is intentionally
  avoided here because it strips letters from strings (as.disk("Pt_1")==1).

Also adds tools/benchmark_parallel.R: a standalone script that times
sequential vs parallel as.sir() across n=20/200/2000/20000 rows and
saves a ggplot2 PNG to tools/benchmark_parallel.png.

https://claude.ai/code/session_012DXCXbZUC54Zij1z9bFiHR

tools/benchmark_parallel.R

@@ -0,0 +1,71 @@
+# Benchmark: sequential vs parallel as.sir() across data-set sizes
+#
+# Run from the repo root with:
+#   Rscript tools/benchmark_parallel.R
+# or from inside an R session:
+#   source("tools/benchmark_parallel.R")
+#
+# Requires ggplot2 for the output plot; uses devtools::load_all() so the
+# package does not need to be installed.
+
+devtools::load_all(".", quiet = TRUE)
+
+sizes <- c(20, 200, 2000, 20000)
+n_ab  <- 6   # number of antibiotic columns
+
+make_df <- function(n) {
+  set.seed(42)
+  mics <- lapply(seq_len(n_ab), function(j) {
+    as.mic(sample(c("0.25", "0.5", "1", "2", "4", "8", "16", "32"), n, TRUE))
+  })
+  names(mics) <- c("AMC", "GEN", "CIP", "TZP", "IPM", "MEM")
+  data.frame(mo = "B_ESCHR_COLI", mics, stringsAsFactors = FALSE)
+}
+
+results <- do.call(rbind, lapply(sizes, function(n) {
+  df <- make_df(n)
+
+  t_seq <- system.time(
+    suppressMessages(as.sir(df, col_mo = "mo", info = FALSE, parallel = FALSE))
+  )[["elapsed"]]
+
+  t_par <- system.time(
+    suppressMessages(as.sir(df, col_mo = "mo", info = FALSE, parallel = TRUE))
+  )[["elapsed"]]
+
+  message(sprintf("n = %6d  seq = %.3fs  par = %.3fs  speedup = %.1fx",
+                  n, t_seq, t_par, t_seq / t_par))
+
+  data.frame(n = n, mode = c("sequential", "parallel"),
+             seconds = c(t_seq, t_par))
+}))
+
+if (requireNamespace("ggplot2", quietly = TRUE)) {
+  p <- ggplot2::ggplot(results, ggplot2::aes(x = n, y = seconds,
+                                             colour = mode, group = mode)) +
+    ggplot2::geom_line(linewidth = 1) +
+    ggplot2::geom_point(size = 3) +
+    ggplot2::scale_x_log10(
+      breaks = sizes,
+      labels = format(sizes, big.mark = ",", scientific = FALSE)
+    ) +
+    ggplot2::scale_colour_manual(
+      values = c(sequential = "#E05C5C", parallel = "#2E86AB")
+    ) +
+    ggplot2::labs(
+      title    = "as.sir() throughput: sequential vs parallel",
+      subtitle = sprintf("%d antibiotic columns, E. coli, EUCAST 2025", n_ab),
+      x        = "Number of rows (log scale)",
+      y        = "Wall-clock time (seconds)",
+      colour   = NULL
+    ) +
+    ggplot2::theme_minimal(base_size = 13) +
+    ggplot2::theme(legend.position = "top")
+
+  out_file <- "tools/benchmark_parallel.png"
+  ggplot2::ggsave(out_file, p, width = 7, height = 5, dpi = 150)
+  message("Plot saved to ", out_file)
+} else {
+  message("Install ggplot2 to get a plot; raw results:")
+  print(results)
+}