clean up general run script

data-raw/configuration.R

@@ -1,42 +1,28 @@
-# TODO list
-# * Only pull data once per species and then combine the pull results with the
-#   configuration matrix again
-# * Fix how format_data returns an object without the nwfscSurvey class instead
-#   it is class(data) > [1] "tbl_df" "tbl" "data.frame"
-# * for vessel_year, might want a different level scaling things might not have
-#   to give this to grid
+# Read in the configuration file within data-raw that define what model set-up
+# to apply by species
 configuration <- tibble::as_tibble(read.csv(
   file.path("data-raw", "configuration.csv")
 ))
 
-configuration <- configuration[39:40,]
-configuration$spatiotemporal1 <- "off"
-configuration$spatiotemporal2 <- "off"
-
-data <- configuration %>%
+# Download the data and filter the data based upon species-specific
+# depths and latitudes in the configuration file
+data <- configuration |>
   # Row by row ... do stuff then ungroup
-  dplyr::rowwise() %>%
+  dplyr::rowwise() |>
   # Pull the data based on the function found in fxn column
   dplyr::mutate(
     data_raw = list(format_data(eval(parse(text = fxn)))),
-    data_filtered = list(data_raw %>%
+    data_filtered = list(data_raw |>
       dplyr::filter(
         depth <= min_depth, depth >= max_depth,
         latitude >= min_latitude, latitude <= max_latitude,
         year >= min_year, year <= max_year
-      ) |>
-        dplyr::mutate(region = ifelse(latitude > 40.1666667, "N", "S")))
-  ) %>%
+      ))
+  ) |>
   dplyr::ungroup()
 
-# Confirm no data in the south in 2007:
-dplyr::filter(data$data_filtered[[2]], catch_weight > 0) |>
-  dplyr::group_by(region, year) |>
-  dplyr::summarise(n = n())
-
-
-
-best <- data %>%
+# Run the model across all species in the configuration file
+best <- data |>
   dplyr::mutate(
     # Evaluate the call in family
     family = purrr::map(family, .f = ~ eval(parse(text = .x))),
@@ -48,56 +34,18 @@ best <- data %>%
         family = family,
         anisotropy = anisotropy,
         n_knots = knots,
+        share_range = share_range,
         spatiotemporal = purrr::map2(spatiotemporal1, spatiotemporal2, list)
       ),
       .f = indexwc::run_sdmtmb
     )
   )
 
-indices <- dir(
-  file.path("canary_rockfish", "wcgbts"),
-  pattern = "sdmTMB_save",
-  recursive = TRUE,
-  full.names = TRUE
-) %>%
-  unlist() %>%
-  purrr::map_df(
-    .f = function(x) {
-      load(x)
-      if (exists("index_areas")) {
-        return(data.frame(i = x, index_areas))
-      } else {
-        return(NULL)
-      }
-    }
-  )
-gg <- ggplot2::ggplot(
-  data = indices %>%
-    dplyr::mutate(
-      dist = basename(dirname(dirname(i)))
-    ) %>%
-    dplyr::filter(area == "coastwide"),
-  ggplot2::aes(x = year, y = est, lty = dist, col = dist, group = i)
-) +
-  ggplot2::geom_line() +
-  ggplot2::theme_bw()
-ggsave(gg, filename = "indexwc_copper_rockfish.png")
-
+# TODO list
+# * Only pull data once per species and then combine the pull results with the
+#   configuration matrix again
+# * Fix how format_data returns an object without the nwfscSurvey class instead
+#   it is class(data) > [1] "tbl_df" "tbl" "data.frame"
+# * for vessel_year, might want a different level scaling things might not have
+#   to give this to grid
 
-best <- data[2, ] %>%
-  dplyr::mutate(
-    # Evaluate the call in family
-    family = purrr::map(family, .f = ~ eval(parse(text = .x))),
-    # Run the model on each row in data
-    results = purrr::pmap(
-      .l = list(
-        data = data_filtered,
-        formula = formula,
-        family = family,
-        anisotropy = anisotropy
-      ),
-      spatiotemporal = list("iid", "off"),
-      n_knots = 200,
-      .f = indexwc::run_sdmtmb
-    )
-  )