From 483ffee652b3d5c5cc38c983fe1c0692e2e09c8d Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Wed, 29 Jan 2025 16:15:11 +0100
Subject: [PATCH] `summary.estimate_slopes()` no longer working. Fixes #345

---
 R/summary.R | 63 ++++++++---------------------------------------------
 1 file changed, 9 insertions(+), 54 deletions(-)

diff --git a/R/summary.R b/R/summary.R
index c0c2bf99f..2b137e353 100644
--- a/R/summary.R
+++ b/R/summary.R
@@ -1,35 +1,15 @@
 #' @export
 summary.estimate_slopes <- function(object, ...) {
-  my_data <- as.data.frame(object)
-  trend <- attributes(object)$trend
+  out <- as.data.frame(object)
 
   # Add "Confidence" col based on the sig index present in the data
-  my_data$Confidence <- .estimate_slopes_sig(my_data, ...)
+  out$Confidence <- .estimate_slopes_significance(out, ...)
+  out$Direction <- .estimate_slopes_direction(out, ...)
 
-  # Grouping variables
-  vars <- attributes(object)$at
-  vars <- vars[!vars %in% trend]
-
-  # If no grouping variables, summarize all
-  if (length(vars) == 0) {
-    out <- .estimate_slopes_summarize(my_data, trend = trend)
-  } else {
-    out <- data.frame()
-    # Create vizmatrix of grouping variables
-    groups <- as.data.frame(insight::get_datagrid(my_data[vars], factors = "all", numerics = "all"))
-    # Summarize all of the chunks
-    for (i in seq_len(nrow(groups))) {
-      g <- datawizard::data_match(my_data, groups[i, , drop = FALSE])
-      out <- rbind(out, .estimate_slopes_summarize(g, trend = trend))
-    }
-    out <- datawizard::data_relocate(out, vars)
-  }
-
-  # Clean and sanitize
-  out$Confidence <- NULL # Drop significance col
   attributes(out) <- utils::modifyList(attributes(object), attributes(out))
-  class(out) <- c("estimate_slopes", class(out))
+  class(out) <- c("estimate_slopes", "data.frame")
   attr(out, "table_title") <- c("Average Marginal Effects", "blue")
+
   out
 }
 
@@ -45,43 +25,18 @@ summary.reshape_grouplevel <- function(object, ...) {
 # Utilities ===============================================================
 
 
-.estimate_slopes_summarize <- function(data, trend, ...) {
-  # Find beginnings and ends -----------------------
-  # First row - starting point
+.estimate_slopes_direction <- function(data, ...) {
   centrality_columns <- datawizard::extract_column_names(
     data,
     c("Coefficient", "Slope", "Median", "Mean", "MAP_Estimate"),
     verbose = FALSE
   )
-  centrality_signs <- sign(data[[centrality_columns]])
-  centrality_sign <- centrality_signs[1]
-  sig <- data$Confidence[1]
-  starts <- 1
-  ends <- nrow(data)
-  # Iterate through all rows to find blocks
-  for (i in 2:nrow(data)) {
-    if ((data$Confidence[i] != sig) || ((centrality_signs[i] != centrality_sign) && data$Confidence[i] == "Uncertain")) {
-      centrality_sign <- centrality_signs[i]
-      sig <- data$Confidence[i]
-      starts <- c(starts, i)
-      ends <- c(ends, i - 1)
-    }
-  }
-  ends <- sort(ends)
-
-  # Summarize these groups -----------------------
-  out <- data.frame()
-  for (g in seq_len(length(starts))) {
-    dat <- data[starts[g]:ends[g], ]
-    dat <- as.data.frame(insight::get_datagrid(dat, by = NULL, factors = "mode"))
-    dat <- cbind(data.frame(Start = data[starts[g], trend], End = data[ends[g], trend]), dat)
-    out <- rbind(out, dat)
-  }
-  out
+  centrality_signs <- as.character(sign(data[[centrality_columns]]))
+  datawizard::recode_into(centrality_signs == -1 ~ "negative", default = "positive")
 }
 
 
-.estimate_slopes_sig <- function(x, confidence = "auto", ...) {
+.estimate_slopes_significance <- function(x, confidence = "auto", ...) {
   insight::check_if_installed("effectsize")
 
   if (confidence == "auto") {