Merge branch 'master' into floating-table

R/format_data.R

@@ -9451,14 +9451,15 @@ fmt_url <- function(
 #' Using a small portion of [`metro`] dataset, let's create a **gt** table. We
 #' will only include a few columns and rows from that table. The `lines` and
 #' `connect_rer` columns have comma-separated listings of numbers/letters
-#' (corresponding to lines served at each station). We have a directory SVG
-#' graphics for all of these lines in the package (the path for the image
-#' directory can be accessed via `system.file("metro_svg", package = "gt")`),
-#' and the filenames roughly correspond to the data in those two columns. The
-#' `fmt_image()` function can be used with these inputs since the `path` and
-#' `file_pattern` arguments allow us to compose complete and valid file
-#' locations. What you get from this are sequences of images in the table cells,
-#' taken from the referenced graphics files on disk.
+#' (corresponding to lines served at each station). We have a directory of SVG
+#' graphics for all of these lines within the package (the path for the
+#' directory containing the images can be accessed via
+#' `system.file("metro_svg", package = "gt")`), and the filenames roughly
+#' correspond to the data in those two columns. The `fmt_image()` function can
+#' be used with these inputs since the `path` and `file_pattern` arguments allow
+#' us to compose complete and valid file locations. What you get from all of
+#' this are sequences of images in the table cells, taken from the referenced
+#' graphics files on disk.
 #'
 #' ```r
 #' metro |>

R/modify_columns.R

@@ -2234,13 +2234,15 @@ cols_add <- function(
 #'
 #' @param missing_vals *Treatment of missing values*
 #'
-#'   `singl-kw:[gap|zero|remove]` // *default:* `"gap"`
+#'   `singl-kw:[gap|marker|zero|remove]` // *default:* `"gap"`
 #'
 #'   If missing values are encountered within the input data, there are three
-#'   strategies available for their handling: (1) `"gap"` will display data gaps
-#'   at the sites of missing data, where data lines will have discontinuities;
-#'   (2) `"zero"` will replace `NA` values with zero values; and (3) `"remove"`
-#'   will remove any incoming `NA` values.
+#'   strategies available for their handling: (1) `"gap"` will show data gaps
+#'   at the sites of missing data, where data lines will have discontinuities
+#'   and bar plots will have missing bars; (2) `"marker"` will behave like
+#'   `"gap"` but show prominent visual marks at the missing data locations; (3)
+#'   `"zero"` will replace `NA` values with zero values; and (4) `"remove"` will
+#'   remove any incoming `NA` values.
 #'
 #' @param autoscale *Automatically set x- and y-axis scale limits based on data*
 #'
@@ -2742,7 +2744,7 @@ cols_nanoplot <- function(
     rows = everything(),
     plot_type = c("line", "bar", "boxplot"),
     plot_height = "2em",
-    missing_vals = c("gap", "zero", "remove"),
+    missing_vals = c("gap", "marker", "zero", "remove"),
     autoscale = FALSE,
     autohide = TRUE,
     columns_x_vals = NULL,

R/utils_plots.R

@@ -29,7 +29,7 @@ generate_nanoplot <- function(
     x_vals = NULL,
     expand_x = NULL,
     expand_y = NULL,
-    missing_vals = c("gap", "zero", "remove"),
+    missing_vals = c("gap", "marker", "zero", "remove"),
     all_y_vals = NULL,
     all_single_y_vals = NULL,
     plot_type = c("line", "bar"),
@@ -895,7 +895,7 @@ generate_nanoplot <- function(
 
       if (is.na(data_y_points[i])) {
 
-        if (missing_vals == "gap") {
+        if (missing_vals == "marker") {
 
           # Create a symbol that should denote that a
           # missing value is present
@@ -905,7 +905,7 @@ generate_nanoplot <- function(
               "cx=\"", data_x_points[i], "\" ",
               "cy=\"", safe_y_d + (data_y_height / 2), "\" ",
               "r=\"", data_point_radius_i + (data_point_radius_i / 2), "\" ",
-              "stroke=\"red\" ",
+              "stroke=\"", "red", "\" ",
               "stroke-width=\"", data_point_stroke_width_i, "\" ",
               "fill=\"white\" ",
               ">",
@@ -956,7 +956,7 @@ generate_nanoplot <- function(
 
       if (is.na(data_y_points[i])) {
 
-        if (missing_vals == "gap") {
+        if (missing_vals == "marker") {
 
           # Create a symbol that should denote that a
           # missing value is present

R/utils_render_latex.R

@@ -567,7 +567,7 @@ create_body_component_l <- function(data) {
         if (!is.na(indent)) {
           row_splits_body[[x]][row_label_col] <<-
             paste0(
-              "\\hspace*{", indent_length_pt * indent, "px} ",
+              "\\hspace*{", indent_length_pt * indent, "pt} ",
               row_splits_body[[x]][row_label_col]
             )
         }