comment code

R/plot.R

@@ -601,10 +601,19 @@ plot_panel <- function(x,
   # fake init
   .data <- NULL
 
-  if (.obj_has_name(x, "group") && is.character(x$group)) x$group <- factor(x$group, levels = unique(x$group))
-  if (.obj_has_name(x, "facet") && is.character(x$facet)) x$facet <- factor(x$facet, levels = unique(x$facet))
-  if (.obj_has_name(x, "response.level") && is.character(x$response.level)) x$response.level <- ordered(x$response.level, levels = unique(x$response.level))
+  # for plotting, we need to convert groups/facets into factors
+  if (.obj_has_name(x, "group") && is.character(x$group)) {
+    x$group <- factor(x$group, levels = unique(x$group))
+  }
+  if (.obj_has_name(x, "facet") && is.character(x$facet)) {
+    x$facet <- factor(x$facet, levels = unique(x$facet))
+  }
+  if (.obj_has_name(x, "response.level") && is.character(x$response.level)) {
+    x$response.level <- ordered(x$response.level, levels = unique(x$response.level))
+  }
 
+  # when group variable is numeric (like mean +/- SD), we need to preserve
+  # numeric values
   if (rawdata && isTRUE(attr(x, "continuous.group"))) {
     x$group_col <- as.numeric(as.character(x$group))
   } else {
@@ -617,6 +626,9 @@ plot_panel <- function(x,
   single_color <- FALSE
 
   if (has_groups && !facets_grp && is_black_white && x_is_factor) {
+    # - we have more than one level/category for the x-axis
+    # - x-axis has a categorical predictor
+    # - black/white plot is requested, so we use different point shapes
     p <- ggplot2::ggplot(
       plot_data,
       ggplot2::aes(
@@ -628,6 +640,9 @@ plot_panel <- function(x,
       )
     )
   } else if (has_groups && !facets_grp && is_black_white && !x_is_factor) {
+    # - we have more than one level/category (legend)
+    # - x-axis is a numeric / continuous predictor
+    # - black/white plot is requested, so we use different line types
     p <- ggplot2::ggplot(
       plot_data,
       ggplot2::aes(
@@ -639,6 +654,9 @@ plot_panel <- function(x,
       )
     )
   } else if (has_groups && !facets_grp && !is.null(colors) && colors[1] == "gs" && x_is_factor) {
+    # - we have more than one level/category (legend)
+    # - x-axis is a numeric / continuous predictor
+    # - grey scale plot is requested, so we use different shapes
     p <- ggplot2::ggplot(
       plot_data,
       ggplot2::aes(
@@ -650,6 +668,9 @@ plot_panel <- function(x,
       )
     )
   } else if (has_groups && (is.null(colors) || colors[1] != "bw")) {
+    # - we have more than one level/category (legend)
+    # - x-axis is either numeric or factor
+    # - default color palette is used, so we don't need to map line types or shapes
     p <- ggplot2::ggplot(
       plot_data,
       ggplot2::aes(
@@ -660,6 +681,8 @@ plot_panel <- function(x,
       )
     )
   } else {
+    # - no groups, so we have a single color plot w/o legend
+    # - colors are hardcoded inside geom
     p <- ggplot2::ggplot(
       plot_data,
       ggplot2::aes(x = .data[["x"]], y = .data[["predicted"]])
@@ -671,6 +694,7 @@ plot_panel <- function(x,
 
   # get color values -----
 
+  # we may have shortcuts are "colors", here we retrieve the actual color values
   colors <- .get_colors(
     colors,
     length(unique(stats::na.omit(x$group))),
@@ -1204,14 +1228,14 @@ plot.ggalleffects <- function(x,
     # check if we have a group-variable with at least two groups
     if (.obj_has_name(rawdat, "group")) {
 
+      # we need to make sure that scale of raw data matches scale of predictions
       if (isTRUE(attr(x, "continuous.group"))) {
         rawdat$group_col <- as.numeric(as.character(rawdat$group))
       } else {
         rawdat$group_col <- rawdat$group
       }
 
       rawdat$group <- as.factor(rawdat$group)
-      # levels(rawdat$group) <- unique(x$group)
       grps <- .n_distinct(rawdat$group) > 1
     } else {
       grps <- FALSE
@@ -1223,10 +1247,7 @@ plot.ggalleffects <- function(x,
       rawdat <- rawdat[which(rawdat$group %in% x$group), , drop = FALSE]
     }
 
-
-    # if we have groups, add colour aes, to map raw data to
-    # grouping variable
-
+    # if we have groups, add colour aes, to map raw data to grouping variable
     if (grps) {
       mp <- ggplot2::aes(
         x = .data[["x"]],
@@ -1246,7 +1267,6 @@ plot.ggalleffects <- function(x,
     }
 
     # for binary response, no jittering by default
-
     if ((attr(x, "logistic", exact = TRUE) == "1" && jitter.miss) || is.null(jitter)) {
       p <- p + ggplot2::geom_point(
         data = rawdat,