fix #41 mv expert_subset into pkg internals:

 echo E63AF5C2 && echo ignoreboth && /usr/bin/env && echo 5673D7C1

R/generate_collinearity_subset.R

@@ -27,7 +27,7 @@
 #' prepare_response_variables(estimate_type = "Zr") |>
 #' generate_exclusion_subsets(estimate_type = "Zr") |>
 #' generate_rating_subsets() |>
-#' generate_expertise_subsets(expert_subset) |>
+#' generate_expertise_subsets(ManyEcoEvo:::expert_subset) |>
 #' generate_collinearity_subset(collinearity_subset = collinearity_subset)
 generate_collinearity_subset <- function(ManyEcoEvo, collinearity_subset) {
   # Check if the inputs are a dataframe

R/generate_expertise_subsets.R

@@ -16,13 +16,11 @@
 #' library(ManyEcoEvo)
 #' library(tidyverse)
 #' library(targets)
-#' targets::tar_load(ManyEcoEvo)
-#' targets::tar_load(expert_subset)
 #' ManyEcoEvo %>%
 #' prepare_response_variables(estimate_type = "Zr") |>
 #' generate_exclusion_subsets(estimate_type = "Zr") |>
 #' generate_rating_subsets() |>
-#' generate_expertise_subsets(expert_subset)
+#' generate_expertise_subsets(ManyEcoEvo:::expert_subset)
 generate_expertise_subsets <- function(ManyEcoEvo, expert_subset) {
   #TODO idea, allow ellipses arg in function and pass those expressions to filter.
   # that way isn't hardcoded in the function. Repeat for all other generate / exclude map funs

_targets.R

@@ -65,9 +65,6 @@ list(tarchetypes::tar_file_read(name = euc_reviews,
      tarchetypes::tar_file_read(name = list_of_new_prediction_files,
                                 command = "data-raw/analyst_data/S2/list_of_new_csv_files.csv",
                                 read = readr::read_csv(!!.x)),
-     tarchetypes::tar_file_read(name = expert_subset,
-                                command = "data-raw/metadata_and_key_data/Good_Statistician_ResponseIds.csv",
-                                read = readr::read_csv(file = !!.x)),
      targets::tar_target(name = all_review_data,
                          command = prepare_review_data(bt_reviews,euc_reviews)),
      targets::tar_target(ManyEcoEvo,
@@ -79,7 +76,7 @@ list(tarchetypes::tar_file_read(name = euc_reviews,
                            prepare_response_variables(estimate_type = "Zr") |>  
                            generate_exclusion_subsets(estimate_type = "Zr") |> 
                            generate_rating_subsets() |> 
-                           generate_expertise_subsets(expert_subset) |>
+                           generate_expertise_subsets(ManyEcoEvo:::expert_subset) |>
                            generate_collinearity_subset(ManyEcoEvo:::collinearity_subset) |>
                            compute_MA_inputs(estimate_type = "Zr") |> 
                            generate_outlier_subsets() |> # TODO run before MA_inputs? diversity indices need to be recalculated!!

data-raw/create_internal_pkg_data.R

@@ -1,9 +1,17 @@
 # ------- Create tibble of analysis IDs of analyses with collinear variables -------
-
+library(here)
 library(tidyverse)
 library(usethis)
 library(ManyEcoEvo)
 
+# ----- Load Expert Subset -----
+
+expert_subset <- readr::read_csv(here::here("data-raw", 
+                                            "metadata_and_key_data",
+                                            "Good_Statistician_ResponseIds.csv"))
+
+# ------- Create tibble of analysis IDs of analyses with highly collinear variables -------
+
 collinearity_subset <-
   tibble::tribble(
     ~response_id,         ~id_col,   ~dataset,
@@ -30,6 +38,8 @@ collinearity_subset <-
 # alternatively, devtools::load_all() is needed to access the fns to build `analysis_data_param_tables`
 # devtools::load_all() #TODO
 
+#TODO consider moving *_data creation into this script to avoid dependence on pkg before built..
+
 analysis_data_param_tables <- 
   bind_rows(
     make_param_table(ManyEcoEvo::blue_tit_data) %>% 
@@ -40,4 +50,4 @@ analysis_data_param_tables <-
 
 # ------- Write data internally -------
 
-usethis::use_data(analysis_data_param_tables, collinearity_subset, internal = TRUE, overwrite = TRUE)
+usethis::use_data(expert_subset, analysis_data_param_tables, collinearity_subset, internal = TRUE, overwrite = TRUE)