v0.10.0: Rename @render.data_frame method .input_cell_selection() to .cell_selection()

.github/workflows/deploy-docs.yml

@@ -4,7 +4,6 @@ on:
   push:
     branches: [main]
   pull_request:
-    branches: [main]
 
 jobs:
   build:
@@ -50,7 +49,7 @@ jobs:
       # =====================================================
       - uses: quarto-dev/quarto-actions/setup@v2
         with:
-          version: 1.4.549
+          version: 1.4.557
 
       - name: Build site
         run: |

_quarto.yml

@@ -62,6 +62,9 @@ website:
     image-height: 630
     card-style: "summary_large_image"
 
+  repo-url: https://github.com/posit-dev/py-shiny-site
+  repo-actions: [issue, edit]
+
   navbar:
     background: primary
     foreground: light

components/_metadata.yml

@@ -10,3 +10,4 @@ filters:
   - quarto
   - line-highlight
   - shinylive
+repo-actions: false

components/outputs/data-grid/app-variation-select-rows-core.py

@@ -17,7 +17,7 @@ def penguins_df():
 
     @render.ui
     def rows():
-        rows = penguins_df.input_cell_selection()["rows"]  # <<
+        rows = penguins_df.cell_selection()["rows"]  # <<
         selected = ", ".join(str(i) for i in sorted(rows)) if rows else "None"
         return f"Rows selected: {selected}"
 

components/outputs/data-grid/app-variation-select-rows-express.py

@@ -9,7 +9,7 @@
 
 @render.ui()
 def rows():
-    rows = penguins_df.input_cell_selection()["rows"]  # <<
+    rows = penguins_df.cell_selection()["rows"]  # <<
     selected = ", ".join(str(i) for i in sorted(rows)) if rows else "None"
     return f"Rows selected: {selected}"
 

components/outputs/data-grid/index.qmd

@@ -47,7 +47,7 @@ listing:
   - title: Select Rows
     description: Set `selection_mode` in `render.DataGrid()` to `"row"` to allow the
       user to select one row at a time. Set it to `"rows"` to allow the user to select
-      multiple rows at a time. Access the selection(s) as `<id>.input_cell_selection()["rows"]`.
+      multiple rows at a time. Access the selection(s) as `<id>.cell_selection()["rows"]`.
     apps:
     - title: Preview
       file: app-variation-select-rows-core.py
@@ -130,9 +130,9 @@ To make a reactive Data Grid, follow three steps:
 
 A Data Grid can also collect input from the user. To allow this, set `render.DataGrid(selection_mode="row")` or `render.DataGrid(selection_mode="rows")` to allow the user to select one or more rows of the Data Grid.
 
-The indices of the selected rows will be accessible within the server function as a reactive variable returned by `<name>.input_cell_selection()["rows"]`, where <name> is the name of the function decorated with `@render.data_frame`.
+The indices of the selected rows will be accessible within the server function as a reactive variable returned by `<name>.cell_selection()["rows"]`, where <name> is the name of the function decorated with `@render.data_frame`.
 
-The value returned will be an empty tuple if no rows are selected, or a tuple of integers representing the indices of the selected rows. To filter a pandas data frame down to the selected rows, use `df.iloc[list(<name>.input_cell_selection()["rows"])]`.
+The value returned will be an empty tuple if no rows are selected, or a tuple of integers representing the indices of the selected rows. To filter a pandas data frame down to the selected rows, use `df.iloc[list(<name>.cell_selection()["rows"])]`.
 
 For more information about interacting with data frames, see the API documentation for [Express](../../../api/express/express.render.data_frame.qmd) or [Core](../../../api/core/render.data_frame.qmd) syntax.
 

components/outputs/datatable/app-variation-select-rows-core.py

@@ -17,7 +17,7 @@ def penguins_df():
 
     @render.ui
     def rows():
-        rows = penguins_df.input_cell_selection()["rows"]  # <<
+        rows = penguins_df.cell_selection()["rows"]  # <<
         selected = ", ".join(str(i) for i in sorted(rows)) if rows else "None"
         return f"Rows selected: {selected}"
 

components/outputs/datatable/app-variation-select-rows-express.py

@@ -9,7 +9,7 @@
 
 @render.ui
 def rows():
-    rows = penguins_df.input_cell_selection()["rows"]  # <<
+    rows = penguins_df.cell_selection()["rows"]  # <<
     selected = ", ".join(str(i) for i in sorted(rows)) if rows else "None"
     return f"Rows selected: {selected}"
 

components/outputs/datatable/index.qmd

@@ -47,7 +47,7 @@ listing:
   - title: Select Rows
     description: Set `selection_mode` in `render.DataTable()` to `"row"` to allow
       the user to select one row at a time. Set it to `"rows"` to allow the user to
-      select multiple rows at a time. Access the selection(s) as `<id>.input_cell_selection()["rows"]`.
+      select multiple rows at a time. Access the selection(s) as `<id>.cell_selection()["rows"]`.
     apps:
     - title: Preview
       file: app-variation-select-rows-core.py
@@ -130,9 +130,9 @@ To make a reactive Data Table, follow three steps:
 
 A Data Table can also collect input from the user. To allow this, set `render.DataTable(selection_mode="row")` or `render.DataTable(selection_mode="rows")` to allow the user to select one or more rows of the Data Table.
 
-The indices of the selected rows will be accessible within the server function as a reactive variable returned by `<name>.input_cell_selection()["rows"]`, where <name> is the name of the function decorated with `@render.data_frame`.
+The indices of the selected rows will be accessible within the server function as a reactive variable returned by `<name>.cell_selection()["rows"]`, where <name> is the name of the function decorated with `@render.data_frame`.
 
-The value returned will be an empty tuple if no rows are selected, or a tuple of integers representing the indices of the selected rows. To filter a pandas data frame down to the selected rows, use `df.iloc[list(<name>.input_cell_selection()["rows"])]`.
+The value returned will be an empty tuple if no rows are selected, or a tuple of integers representing the indices of the selected rows. To filter a pandas data frame down to the selected rows, use `df.iloc[list(<name>.cell_selection()["rows"])]`.
 
 For more information about interacting with data frames, see the API documentation for [Express](../../../api/express/express.render.data_frame.qmd) or [Core](../../../api/core/render.data_frame.qmd) syntax.
 

docs/helpers.py

@@ -207,43 +207,57 @@ def shinylive_app_preview(
 
 
 def express_core_preview(
-    app_express: str | None = None,
-    app_core: str | None = None,
-    files: list[str] | str | None = None,
-    div_attrs=".shiny-mode-tabset",
-    group="shiny-app-mode",
-    language="py",
-    **kwargs,
+    app_express: str,
+    app_core: str,
+    div_attrs: str = '.shiny-mode-tabset  group="shiny-app-mode"',
 ) -> None:
-    if app_express is None and app_core is None:
-        return
-
-    if files is None:
-        files = []
-    elif isinstance(files, str):
-        files = [files]
+    app_preview_code(
+        {
+            "Express": app_express,
+            "Core": app_core,
+        },
+        div_attrs=div_attrs,
+    )
 
-    header_attrs = ".panel-tabset"
-    header_attrs += " " + div_attrs if div_attrs else ""
-    header_attrs += f" group='{group}'" if group else ""
 
-    block = QuartoPrint(["::: {" + header_attrs + "}"])
+def app_preview_code(
+    app_files: str | dict[str, str],
+    files: list[str] | str | None = None,
+    language: Literal["auto", "py", "r"] = "auto",
+    div_attrs: str = "",
+) -> None:
 
-    apps = zip([app_express, app_core], ["Express", "Core"])
+    is_tabset = isinstance(app_files, dict)
 
-    for app_file, tab_name in apps:
-        if app_file is None:
-            continue
+    if is_tabset:
+        div_attrs = ".panel-tabset " + div_attrs
 
-        sl_app = ShinyliveApp.from_local(app_file, files, language)
-        lang = "python" if language == "py" else "r"
+    block = QuartoPrint(["::: {" + div_attrs + "}"])
 
-        block.append("### " + tab_name)
-        block.append(
-            f'```{{.{lang} .code-overflow-scroll shinylive="{sl_app.to_url()}"}}'
-        )
-        block.append_file(app_file)
-        block.extend(["```", ""])
+    if not is_tabset:
+        _add_code_chunk(block, app_files, files, language)
+    else:
+        for x in app_files:
+            block.append(f"## {x}")
+            _add_code_chunk(block, app_files[x], files, language)
 
     block.append(":::")
     print(block)
+
+
+def _add_code_chunk(
+    block: QuartoPrint,
+    file: str,
+    files: list[str] | str | None = None,
+    language: Literal["auto", "py", "r"] = "auto",
+):
+    if language == "auto":
+        language = "py" if file.endswith(".py") else "r"
+
+    sl_app = ShinyliveApp.from_local(file, files=files, language=language)
+
+    lang = "python" if language == "py" else "r"
+
+    block.append(f'```{{.{lang} .code-overflow-scroll shinylive="{sl_app.to_url()}"}}')
+    block.append_file(file)
+    block.append("```")

docs/overview.qmd

@@ -539,7 +539,7 @@ shiny create -t dashboard
 ## Development workflow
 
 See the [workflow](install-create-run.qmd) section for more information developing Shiny apps locally.
-Also keep in mind you can develop apps in the browser using the [playground](/playground).
+Also keep in mind you can develop apps in the browser using the [playground](https://shinylive.io/py/examples/).
 :::
 
 

docs/user-interfaces.qmd

@@ -9,7 +9,7 @@ With Shiny, you can create a wide variety of user interfaces (UI), including das
 Here, we'll use the following dashboard as motivation to learn about some important UI components (e.g., [cards](#cards), [value boxes](#value-boxes)) and layouts (e.g., [columns](#multi-column-layout)).
 
 
-![A Shiny dashboard with visuals for exploring restaurant tips (see [here](#altogether-now) for the code).](assets/tipping-dashboard.png){class="img-shadow"}
+![A Shiny dashboard with visuals for exploring restaurant tips (see [here](#all-together-now) for the code).](assets/tipping-dashboard.png){class="img-shadow"}
 
 ::: callout-tip
 ## More UI design inspiration
@@ -207,7 +207,7 @@ Did you know the app viewer above is resizable? Try resizing it to see how the l
 
 Tooltips and popovers are a useful means for both displaying and interacting with additional information in a non-obtrusive way.
 Tooltips are shown on hover, whereas popovers are shown on click, making them more suitable for interactive content like inputs.
-In the [actual dashboard](#altogether-now), we'll leverage a popover to effectively add a toolbar with additional inputs controls to card headers.
+In the [actual dashboard](#all-together-now), we'll leverage a popover to effectively add a toolbar with additional inputs controls to card headers.
 
 ```{shinylive-python}
 #| standalone: true

gallery/_metadata.yml

@@ -0,0 +1 @@
+repo-actions: false

index.qmd

@@ -11,6 +11,7 @@ format:
     smooth-scroll: true
     css:
       - index.css
+repo-actions: false
 ---
 
 ::::{.column-screen .mx-auto .pt-0 .mt-0 .pt-xl-5  style="max-width: 1500px;"}

layouts/_metadata.yml

@@ -1,7 +1,7 @@
 sidebar: layouts
 format:
   html:
-    css: 
+    css:
       - /components/_partials/components.css
       - _partials/layouts-list.css
     toc: false
@@ -10,5 +10,6 @@ format:
 filters:
   - quarto
   - line-highlight
-  - shinylive 
-
+  - shinylive
+
+repo-actions: false

templates/_metadata.yml

@@ -3,12 +3,13 @@ format:
   html:
     template: _partials/gallery-article.template
     page-layout: article
-    css: 
+    css:
       - /components/_partials/components.css
       - _partials/templates.css
     toc: false
     code-line-numbers: false
     include-after-body: ../components/_partials/componentsjs.html
 filters:
-  - shinylive 
-
+  - shinylive
+
+repo-actions: false