- docs!: update @imports #102

- style: use comments to structure function. rearrange condition checks, and helper functions. update indentation. #97

R/make_viz.R

@@ -5,29 +5,34 @@
 #'
 #' @return a nested dataframe grouped by variables `exclusion_set`, `dataset`, `estimate_type`, `publishable_subset`, `expertise_subset`, `collinearity_subset` containing model summaries, tidy model summaries, model fit stats, funnel plots and forest plots
 #' @export
-#' @family targets-pipeline functions
 #' @family Multi-dataset Wrapper Functions
 #' @import dplyr
 #' @importFrom purrr map_if map2 pmap possibly
 #' @importFrom stringr str_detect
-#' @importFrom broom.mixed tidy
+#' @importFrom broom tidy
 #' @importFrom performance performance
 #' @importFrom metaviz viz_funnel
 #' @importFrom ggplot2 ggplot
 #' @importFrom parameters parameters
 #' @import metafor
 #' @import lme4
-#' @importFrom tidyr pivot_longer
-#' @importFrom tidyr unnest
+#' @importFrom tidyr unnest pivot_longer
 #' @importFrom rlang is_na
 make_viz <- function(data) {
-  # targets wrapper function
-  # define map helper fun
+
+  # ---- Define Helper Functions ----
+
   tidy_mod <- function(mod) {
-    broom.mixed::tidy(mod, conf.int = TRUE)
+    broom::tidy(mod, conf.int = TRUE)
   }
-  # remove unnecessary inputs, create summary tables and visualisations
-  # repeat for yi and Zr
+
+  viz_funnel_2 <- function(x) {
+    metaviz::viz_funnel(x, y_axis = "precision")
+  }
+
+  poss_viz_funnel <- possibly(viz_funnel_2, otherwise = NA)
+
+  # ---- Prepare Data for Visualisation ----
   if (any(str_detect(unique(data$estimate_type), pattern = "Zr"))) {
     data_Zr <- data %>%
       filter(estimate_type == "Zr") %>%
@@ -56,7 +61,7 @@ make_viz <- function(data) {
         -effects_analysis
       )
   }
-
+  
   if (any(str_detect(unique(data$estimate_type), "y"))) {
     data_yi <- data %>%
       filter(estimate_type %in% c("yi", "y25", "y50", "y75")) %>%
@@ -83,79 +88,83 @@ make_viz <- function(data) {
       ) %>%
       mutate(publishable_subset = NA)
   }
-
+  
   if (exists("data_Zr") & exists("data_yi")) {
     all_data <- bind_rows(data_Zr, data_yi)
   } else if (exists("data_Zr")) {
     all_data <- data_Zr
   } else {
     all_data <- data_yi
   }
-
-  viz_funnel_2 <- function(x) {
-    metaviz::viz_funnel(x, y_axis = "precision")
-  }
-
-  poss_viz_funnel <- possibly(viz_funnel_2, otherwise = NA)
-
+
+  # ----- Extract Model Metrics and Apply Visualisation Functions -----
+
   viz_out <-
     all_data %>%
     mutate(
-      mod_summary = map_if(
-        .x = model,
-        .p = ~ !rlang::is_na(.x),
-        .f = summary,
-        .else = ~ return(NA)
-      ),
-      tidy_mod_summary = map_if(
-        .x = model,
-        .p = ~ !rlang::is_na(.x),
-        .f = purrr::possibly(tidy_mod,
-          otherwise = NA,
-          quiet = TRUE
-        ),
-        .else = ~ return(NA)
-      ),
-      mod_fit_stats = map_if(
-        .x = model,
-        .p = ~ !rlang::is_na(.x),
-        .f = purrr::possibly(performance::performance, # switch to performance from glance
-          otherwise = NA,
-          quiet = FALSE
+      mod_summary = 
+        map_if(
+          .x = model,
+          .p = ~ !rlang::is_na(.x),
+          .f = summary,
+          .else = ~ return(NA)
         ),
-        .else = ~ return(NA)
-      ),
-      funnel_plot = purrr::map_if(
-        .x = model,
-        .p = ~ any(class(.x) == "rma.uni"),
-        .f = poss_viz_funnel,
-        # metafor::funnel(., yaxis = "seinv") #alternative plot fun
-        .else = ~NA
-      ),
-      forest_plot = ifelse(!rlang::is_na(model) & model_name == "MA_mod",
-        purrr::pmap(
-          .l =
-            list(model, estimate_type, dataset),
-          .f = purrr::possibly(gg_forest, otherwise = NA)
+      tidy_mod_summary = 
+        map_if(
+          .x = model,
+          .p = ~ !rlang::is_na(.x),
+          .f = purrr::possibly(tidy_mod,
+                               otherwise = NA,
+                               quiet = TRUE
+          ),
+          .else = ~ return(NA)
         ),
-        NA
-      ),
-      MA_fit_stats = ifelse(model_name == "MA_mod" & !rlang::is_na(model),
+      mod_fit_stats = 
         map_if(
           .x = model,
-          .p = ~ "rma.mv" %in% class(.x),
-          .f = purrr::possibly(get_MA_fit_stats, otherwise = NA),
+          .p = ~ !rlang::is_na(.x),
+          .f = purrr::possibly(performance::performance, # switch to performance from glance
+                               otherwise = NA,
+                               quiet = FALSE
+          ),
           .else = ~ return(NA)
         ),
-        NA
-      ),
+      funnel_plot = 
+        purrr::map_if(
+          .x = model,
+          .p = ~ any(class(.x) == "rma.uni"),
+          .f = poss_viz_funnel,
+          # metafor::funnel(., yaxis = "seinv") #alternative plot fun
+          .else = ~NA
+        ),
+      forest_plot = 
+        ifelse(!rlang::is_na(model) & model_name == "MA_mod",
+               purrr::pmap(
+                 .l =
+                   list(model, estimate_type, dataset),
+                 .f = purrr::possibly(gg_forest, otherwise = NA)
+               ),
+               NA
+        ),
+      MA_fit_stats = 
+        ifelse(model_name == "MA_mod" & !rlang::is_na(model),
+               map_if(
+                 .x = model,
+                 .p = ~ "rma.mv" %in% class(.x),
+                 .f = purrr::possibly(get_MA_fit_stats, otherwise = NA),
+                 .else = ~ return(NA)
+               ),
+               NA
+        ),
       model_params = map_if(
         .x = model,
-        .p = possibly(\(x) class(x) %in% parameters::supported_models() %>% any(), otherwise = NA),
+        .p = possibly(\(x) class(x) %in% parameters::supported_models() %>% 
+                        any(), 
+                      otherwise = NA),
         .f = possibly(parameters::parameters, NA),
         .else = ~ return(NA),
       )
     )
-
+  
   return(viz_out)
 }