bug!: bug-fixes after testing #118 on yi pipeline

- add import tag for `purrr::transpose()` in `fit_multivar_MA()` #102 - defuse argument `outcome` in `cli()` call in `fit_boxcox_ratings_cont()` - `meta_analyse_datasets()`: - use correct argument in calls to `fit_boxcox_ratings_*()` (`data` from `.data`) - apply coalesce on list-cols only and `@import` `tidyselect::where()` (previously non-list-cols were ignored because character columns were explicitly grouped, however current approach in pipeline is ungrouped in order to allow for generalisability / scalability #87. Can't caoalesce a character vector and list, must be of same type). build!: update calls in targets `yi` pipeline
egouldo · Aug 28, 2024 · 25418a1 · 25418a1
1 parent 4b228cb
commit 25418a1
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diff --git a/NAMESPACE b/NAMESPACE
@@ -112,23 +112,14 @@ export(summarise_variable_counts)
 export(validate_predictions)
 export(validate_predictions_df_blue_tit)
 export(variance_box_cox)
-import(NatParksPalettes)
-import(broom)
-import(broom.mixed)
-import(cli)
 import(dplyr)
-import(forcats)
-import(ggforestplot)
 import(ggplot2)
 import(lme4)
 import(metafor)
-import(purrr)
-import(rlang)
-import(see)
-import(stringr)
-import(tidyr)
 importFrom(EnvStats,stat_n_text)
+importFrom(NatParksPalettes,scale_color_natparks_d)
 importFrom(betapart,beta.pair)
+importFrom(broom,tidy)
 importFrom(broom.mixed,tidy)
 importFrom(cli,cli_abort)
 importFrom(cli,cli_alert)
@@ -191,12 +182,14 @@ importFrom(purrr,exec)
 importFrom(purrr,flatten_dbl)
 importFrom(purrr,is_scalar_vector)
 importFrom(purrr,keep)
+importFrom(purrr,keep_at)
 importFrom(purrr,list_c)
 importFrom(purrr,list_flatten)
 importFrom(purrr,list_rbind)
 importFrom(purrr,map)
 importFrom(purrr,map2)
 importFrom(purrr,map_chr)
+importFrom(purrr,map_dfr)
 importFrom(purrr,map_if)
 importFrom(purrr,map_int)
 importFrom(purrr,map_lgl)
@@ -206,6 +199,8 @@ importFrom(purrr,possibly)
 importFrom(purrr,reduce)
 importFrom(purrr,reduce2)
 importFrom(purrr,set_names)
+importFrom(purrr,simplify)
+importFrom(purrr,transpose)
 importFrom(readr,read_csv)
 importFrom(recipes,juice)
 importFrom(recipes,prep)
@@ -214,18 +209,27 @@ importFrom(recipes,step_mutate)
 importFrom(recipes,step_naomit)
 importFrom(recipes,tidy)
 importFrom(recipes,update_role)
+importFrom(rlang,"!!")
+importFrom(rlang,":=")
 importFrom(rlang,as_function)
+importFrom(rlang,as_string)
 importFrom(rlang,caller_env)
 importFrom(rlang,enquo)
 importFrom(rlang,ensym)
 importFrom(rlang,exec)
 importFrom(rlang,expr)
+importFrom(rlang,exprs)
+importFrom(rlang,f_lhs)
 importFrom(rlang,inject)
+importFrom(rlang,is_call)
+importFrom(rlang,is_list)
 importFrom(rlang,is_na)
 importFrom(rlang,is_null)
 importFrom(rlang,na_chr)
 importFrom(rlang,new_formula)
 importFrom(sae,bxcx)
+importFrom(see,geom_jitter2)
+importFrom(see,scale_fill_material_d)
 importFrom(see,theme_modern)
 importFrom(stringr,str_detect)
 importFrom(stringr,str_remove)
@@ -237,14 +241,18 @@ importFrom(tibble,enframe)
 importFrom(tibble,rownames_to_column)
 importFrom(tibble,tibble)
 importFrom(tidyr,any_of)
+importFrom(tidyr,drop_na)
 importFrom(tidyr,hoist)
 importFrom(tidyr,nest)
 importFrom(tidyr,pivot_longer)
 importFrom(tidyr,pivot_wider)
+importFrom(tidyr,separate)
+importFrom(tidyr,tibble)
 importFrom(tidyr,unite)
 importFrom(tidyr,unnest)
 importFrom(tidyr,unnest_longer)
 importFrom(tidyselect,all_of)
+importFrom(tidyselect,where)
 importFrom(timetk,step_box_cox)
 importFrom(workflows,add_model)
 importFrom(workflows,add_recipe)

diff --git a/R/filt_multivar_MA.R b/R/filt_multivar_MA.R
@@ -14,7 +14,7 @@
 #' @importFrom lme4 lmer
 #' @importFrom pointblank test_col_vals_gte expect_col_exists
 #' @import dplyr
-#' @importFrom purrr list_flatten list_c
+#' @importFrom purrr list_flatten list_c transpose
 #' @importFrom tibble enframe
 #' @importFrom tidyr unite
 #' @importFrom cli cli_alert_info cli_bullets cli_h2 style_italic

diff --git a/R/fit_boxcox_ratings_cat.R b/R/fit_boxcox_ratings_cat.R
@@ -42,7 +42,7 @@ fit_boxcox_ratings_cat <- function(data, outcome, outcome_var, interceptless = F
   )
 
   # ----- Fit model -----
-  cli::cli_h2(c("Fitting {.fn lmer} with categorical ratings predictor {.arg PublishableAsIs} on Box-Cox transformed outcome: outcome: {.val {outcome}}"))
+  cli::cli_h2(c("Fitting {.fn lmer} with categorical ratings predictor {.arg PublishableAsIs} on Box-Cox transformed {.arg outcome}:  {.arg {rlang::enexpr(outcome)}}"))
 
   data_tbl <-
     data %>%

diff --git a/R/fit_boxcox_ratings_cont.R b/R/fit_boxcox_ratings_cont.R
@@ -29,7 +29,7 @@ fit_boxcox_ratings_cont <- function(data, outcome, outcome_var, ..., env = rlang
   )
 
   # ----- Fit model -----
-  cli::cli_h2(c("Fitting {.fn lmer} with continuous ratings predictor {.arg RateAnalysis} on Box-Cox transformed outcome: {.val {outcome}}"))
+  cli::cli_h2(c("Fitting {.fn lmer} with continuous ratings predictor {.arg RateAnalysis} on Box-Cox transformed {.arg outcome}: {.arg {rlang::enexpr(outcome)}}"))
   data_tbl <- # TODO, consider extracting unnesting outside of this fn.
     data %>%
     select(

diff --git a/R/meta_analyse_datasets.R b/R/meta_analyse_datasets.R
@@ -19,6 +19,7 @@
 #' @importFrom stringr str_detect
 #' @importFrom purrr map_chr map2 map possibly pmap list_c map_lgl 
 #' @import dplyr
+#' @importFrom tidyselect where
 #' @importFrom cli cli_h1 cli_abort
 #' @importFrom rlang na_chr is_null na_chr is_list is_call exprs f_lhs as_string
 #' @importFrom pointblank expect_col_exists
@@ -197,7 +198,7 @@ meta_analyse_datasets <- function(data, outcome_variable = NULL, outcome_SE = NU
         purrr::map(
           .x = effects_analysis,
           .f = ~ fit_boxcox_ratings_cont(
-            .data = .x,
+            data = .x,
             outcome = box_cox_abs_deviation_score_estimate,
             outcome_var = box_cox_var
           )
@@ -206,7 +207,7 @@ meta_analyse_datasets <- function(data, outcome_variable = NULL, outcome_SE = NU
         purrr::map(
           .x = effects_analysis,
           .f = ~ poss_fit_boxcox_ratings_cat(
-            .data = .x,
+            data = .x,
             outcome = box_cox_abs_deviation_score_estimate,
             outcome_var = box_cox_var,
             interceptless = FALSE
@@ -216,7 +217,7 @@ meta_analyse_datasets <- function(data, outcome_variable = NULL, outcome_SE = NU
         purrr::map(
           .x = effects_analysis,
           .f = ~ poss_fit_boxcox_ratings_cat(
-            .data = .x,
+            data = .x,
             outcome = box_cox_abs_deviation_score_estimate,
             outcome_var = box_cox_var,
             interceptless = TRUE
@@ -268,9 +269,10 @@ meta_analyse_datasets <- function(data, outcome_variable = NULL, outcome_SE = NU
 
   out <-
     out %>% # replace any NULL values with NA
-    mutate(across(everything(),
-                  .fns = ~ coalesce(.x, list(NA))
-    ))
+    mutate(
+      across(where(is.list), 
+             .fns = ~ coalesce(.x, list(NA)))
+    )
 
   return(out)
 }
diff --git a/_targets.R b/_targets.R
@@ -260,18 +260,27 @@ list(tarchetypes::tar_file_read(name = euc_reviews,
                              dataset_log_transform = "eucalyptus") %>%
                            generate_yi_subsets() %>% #TODO: must be run after prepare_response_variables??
                            apply_VZ_exclusions(
-                             VZ_colname = list("eucalyptus" = "std.error_log", 
+                             VZ_colname = list("eucalyptus" = "se_log", 
                                                "blue tit" = "VZ"), 
                              VZ_cutoff = 3) %>%
-                           generate_exclusion_subsets() %>% #TODO: runs on ManyEcoEvo that contains Zr and yi results.
-                           generate_outlier_subsets(outcome_variable = 
-                                                      list(dataset = list("eucalyptus" = "mean_log", 
-                                                                          "blue tit" = "Z")), 
-                                                    n_min = -3, 
-                                                    n_max = -3, 
-                                                    ignore_subsets = NULL) %>%
+                           generate_exclusion_subsets() %>% #TODO: runs on ManyEcoEvo that contains Zr and yi results; DELETE, not needed
+                           generate_outlier_subsets(
+                             outcome_variable = 
+                               list(dataset = 
+                                      list("eucalyptus" = "mean_log", 
+                                           "blue tit" = "Z")), 
+                             n_min = -3, 
+                             n_max = -3, 
+                             ignore_subsets = NULL) %>%
                            compute_MA_inputs() %>%  
-                           meta_analyse_datasets(filter_vars = NULL) #TODO requires col exclusion_set from generate_exclusion_subsets() but don't need that fun in this pipeline anymore
+                           meta_analyse_datasets(
+                             outcome_variable = 
+                               list(dataset = 
+                                      list("eucalyptus" = "mean_log", "blue tit" = "Z")), 
+                             outcome_SE = 
+                               list(dataset = 
+                                      list("eucalyptus" = "se_log", "blue tit" = "VZ")),
+                             filter_vars = rlang::exprs(exclusion_set == "complete")) #TODO requires col exclusion_set from generate_exclusion_subsets() but don't need that fun in this pipeline anymore
      ),
      targets::tar_target(name = ManyEcoEvo_yi_viz,
                          command = make_viz(ManyEcoEvo_yi_results)),