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Quarto GHA Workflow Runner committed Jul 9, 2024
1 parent 37aa78b commit 7286fac
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2 changes: 1 addition & 1 deletion .nojekyll
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10 changes: 5 additions & 5 deletions exercises/2-basic-ui.html
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Expand Up @@ -7101,7 +7101,7 @@ <h2 class="anchored" data-anchor-id="cards-switch">2.3 Cards Switch</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
account_subset = df[df["account"] == input.account()]
if input.metric() == "ROC Curve":
return plot_auc_curve(account_subset, "is_electronics", "training_score")
Expand Down Expand Up @@ -9376,7 +9376,7 @@ <h2 class="anchored" data-anchor-id="cards-switch">2.3 Cards Switch</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
account_subset = df[df["account"] == input.account()]
if input.metric() == "ROC Curve":
return plot_auc_curve(account_subset, "is_electronics", "training_score")
Expand Down Expand Up @@ -10529,7 +10529,7 @@ <h2 class="anchored" data-anchor-id="layout-columns">2.4 Layout Columns</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
account_subset = df[df["account"] == input.account()]
if input.metric() == "ROC Curve":
return plot_auc_curve(
Expand Down Expand Up @@ -11674,7 +11674,7 @@ <h2 class="anchored" data-anchor-id="layout-columns">2.4 Layout Columns</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
account_subset = df[df["account"] == input.account()]
if input.metric() == "ROC Curve":
return plot_auc_curve(account_subset, "is_electronics", "training_score")
Expand Down Expand Up @@ -12823,7 +12823,7 @@ <h2 class="anchored" data-anchor-id="layout-columns">2.4 Layout Columns</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
account_subset = df[df["account"] == input.account()]
if input.metric() == "ROC Curve":
return plot_auc_curve(
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18 changes: 9 additions & 9 deletions exercises/3-reactivity.html
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Expand Up @@ -263,7 +263,7 @@ <h2 class="anchored" data-anchor-id="reactive-calc">3.1 Reactive Calc</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
# `account_data` is called similar to an input.
if input.metric() == "ROC Curve":
return plot_auc_curve(
Expand Down Expand Up @@ -1411,7 +1411,7 @@ <h2 class="anchored" data-anchor-id="reactive-calc">3.1 Reactive Calc</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
account_subset = df[df["account"] == input.account()]
if input.metric() == "ROC Curve":
return plot_auc_curve(
Expand Down Expand Up @@ -2569,7 +2569,7 @@ <h2 class="anchored" data-anchor-id="reactive-calc">3.1 Reactive Calc</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
# `account_data` is called similar to an input.
if input.metric() == "ROC Curve":
return plot_auc_curve(
Expand Down Expand Up @@ -3698,7 +3698,7 @@ <h2 class="anchored" data-anchor-id="stacking-reactives">3.2 Stacking Reactives<
<div id="callout-4" class="callout-4-contents callout-collapse collapse show">
<div class="callout-body-container callout-body">
<p>We have a second sidebar input which allows the user to filter the dataset by the number of characters in the <code>text</code> field. Add a second reactive calculation to the app which filters the <code>account_data()</code> reactive.</p>
<p>For reference <code>input.chars()</code> returns a tuple with the lower and upper range of a value, and you can filter the data frame with: <code>df[df["text"].str.len().between(*input.chars()]</code>.</p>
<p>For reference <code>input.chars()</code> returns a tuple with the lower and upper range of a value, and you can filter the data frame with: <code>df[df["text"].str.len().between(*input.chars())]</code>.</p>
</div>
</div>
</div>
Expand Down Expand Up @@ -3753,7 +3753,7 @@ <h2 class="anchored" data-anchor-id="stacking-reactives">3.2 Stacking Reactives<
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
if input.metric() == "ROC Curve":
return plot_auc_curve(
character_filter(), "is_electronics", "training_score"
Expand Down Expand Up @@ -4867,7 +4867,7 @@ <h2 class="anchored" data-anchor-id="stacking-reactives">3.2 Stacking Reactives<
<div id="callout-5" class="callout-5-contents callout-collapse collapse show">
<div class="callout-body-container callout-body">
<p>We have a second sidebar input which allows the user to filter the dataset by the number of characters in the <code>text</code> field. Add a second reactive calculation to the app which filters the <code>account_data()</code> reactive.</p>
<p>For reference <code>input.chars()</code> returns a tuple with the lower and upper range of a value, and you can filter the data frame with: <code>df[df["text"].str.len().between(*input.chars()]</code>.</p>
<p>For reference <code>input.chars()</code> returns a tuple with the lower and upper range of a value, and you can filter the data frame with: <code>df[df["text"].str.len().between(*input.chars())]</code>.</p>
</div>
</div>
</div>
Expand Down Expand Up @@ -4914,7 +4914,7 @@ <h2 class="anchored" data-anchor-id="stacking-reactives">3.2 Stacking Reactives<
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
if input.metric() == "ROC Curve":
return plot_auc_curve(account_data, "is_electronics", "training_score")
else:
Expand Down Expand Up @@ -6026,7 +6026,7 @@ <h2 class="anchored" data-anchor-id="stacking-reactives">3.2 Stacking Reactives<
<div id="callout-6" class="callout-6-contents callout-collapse collapse show">
<div class="callout-body-container callout-body">
<p>We have a second sidebar input which allows the user to filter the dataset by the number of characters in the <code>text</code> field. Add a second reactive calculation to the app which filters the <code>account_data()</code> reactive.</p>
<p>For reference <code>input.chars()</code> returns a tuple with the lower and upper range of a value, and you can filter the data frame with: <code>df[df["text"].str.len().between(*input.chars()]</code>.</p>
<p>For reference <code>input.chars()</code> returns a tuple with the lower and upper range of a value, and you can filter the data frame with: <code>df[df["text"].str.len().between(*input.chars())]</code>.</p>
</div>
</div>
</div>
Expand Down Expand Up @@ -6081,7 +6081,7 @@ <h2 class="anchored" data-anchor-id="stacking-reactives">3.2 Stacking Reactives<
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
if input.metric() == "ROC Curve":
return plot_auc_curve(
character_filter(), "is_electronics", "training_score"
Expand Down
6 changes: 3 additions & 3 deletions exercises/4-dynamic-ui.html
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Expand Up @@ -283,7 +283,7 @@ <h2 class="anchored" data-anchor-id="render-express">4.1 Render Express</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
if input.metric() == "ROC Curve":
return plot_auc_curve(
character_filter(), "is_electronics", "training_score"
Expand Down Expand Up @@ -1448,7 +1448,7 @@ <h2 class="anchored" data-anchor-id="render-express">4.1 Render Express</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
if input.metric() == "ROC Curve":
return plot_auc_curve(
character_filter(), "is_electronics", "training_score"
Expand Down Expand Up @@ -2625,7 +2625,7 @@ <h2 class="anchored" data-anchor-id="render-express">4.1 Render Express</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
if input.metric() == "ROC Curve":
return plot_auc_curve(
character_filter(), "is_electronics", "training_score"
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25 changes: 12 additions & 13 deletions exercises/5-reactive-effect.html
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Expand Up @@ -220,7 +220,7 @@ <h2 class="anchored" data-anchor-id="reactive-event">5.1 Reactive Event</h2>
</div>
<div id="callout-1" class="callout-1-contents callout-collapse collapse show">
<div class="callout-body-container callout-body">
<p>Add a <code>ui.input_action_button</code> and a <code>@reactive.event</code> decorator to make app only update when the button is clicked.</p>
<p>Add a <code>ui.input_action_button</code> and a <code>@reactive.event</code> decorator to make the app only update when the button is clicked.</p>
</div>
</div>
</div>
Expand Down Expand Up @@ -290,7 +290,7 @@ <h2 class="anchored" data-anchor-id="reactive-event">5.1 Reactive Event</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
if input.metric() == "ROC Curve":
return plot_auc_curve(
character_filter(), "is_electronics", "training_score"
Expand Down Expand Up @@ -1403,7 +1403,7 @@ <h2 class="anchored" data-anchor-id="reactive-event">5.1 Reactive Event</h2>
</div>
<div id="callout-2" class="callout-2-contents callout-collapse collapse show">
<div class="callout-body-container callout-body">
<p>Add a <code>ui.input_action_button</code> and a <code>@reactive.event</code> decorator to make app only update when the button is clicked.</p>
<p>Add a <code>ui.input_action_button</code> and a <code>@reactive.event</code> decorator to make the app only update when the button is clicked.</p>
</div>
</div>
</div>
Expand Down Expand Up @@ -1464,8 +1464,7 @@ <h2 class="anchored" data-anchor-id="reactive-event">5.1 Reactive Event</h2>
with ui.card():
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
if input.metric() == "ROC Curve":
return plot_auc_curve(
character_filter(), "is_electronics", "training_score"
Expand Down Expand Up @@ -2578,7 +2577,7 @@ <h2 class="anchored" data-anchor-id="reactive-event">5.1 Reactive Event</h2>
</div>
<div id="callout-3" class="callout-3-contents callout-collapse collapse show">
<div class="callout-body-container callout-body">
<p>Add a <code>ui.input_action_button</code> and a <code>@reactive.event</code> decorator to make app only update when the button is clicked.</p>
<p>Add a <code>ui.input_action_button</code> and a <code>@reactive.event</code> decorator to make the app only update when the button is clicked.</p>
</div>
</div>
</div>
Expand Down Expand Up @@ -2648,7 +2647,7 @@ <h2 class="anchored" data-anchor-id="reactive-event">5.1 Reactive Event</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
if input.metric() == "ROC Curve":
return plot_auc_curve(
character_filter(), "is_electronics", "training_score"
Expand Down Expand Up @@ -3774,7 +3773,7 @@ <h2 class="anchored" data-anchor-id="reactive-effect">5.2 Reactive Effect</h2>
</div>
<div id="callout-4" class="callout-4-contents callout-collapse collapse show">
<div class="callout-body-container callout-body">
<p>Use <code>render.express</code> to create a second dropdown which only contains the unique <code>sub_account</code> values of the filtered data.</p>
<p>Add a <code>ui.input_action_button</code> and a <code>@reactive.event</code> decorator to make a button that, when clicked, resets the slider to its initial values.</p>
</div>
</div>
</div>
Expand Down Expand Up @@ -3846,7 +3845,7 @@ <h2 class="anchored" data-anchor-id="reactive-effect">5.2 Reactive Effect</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
if input.metric() == "ROC Curve":
return plot_auc_curve(
character_filter(), "is_electronics", "training_score"
Expand Down Expand Up @@ -4959,7 +4958,7 @@ <h2 class="anchored" data-anchor-id="reactive-effect">5.2 Reactive Effect</h2>
</div>
<div id="callout-5" class="callout-5-contents callout-collapse collapse show">
<div class="callout-body-container callout-body">
<p>Use <code>render.express</code> to create a second dropdown which only contains the unique <code>sub_account</code> values of the filtered data.</p>
<p>Add a <code>ui.input_action_button</code> and a <code>@reactive.event</code> decorator to make a button that, when clicked, resets the slider to its initial values.</p>
</div>
</div>
</div>
Expand Down Expand Up @@ -5021,7 +5020,7 @@ <h2 class="anchored" data-anchor-id="reactive-effect">5.2 Reactive Effect</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
if input.metric() == "ROC Curve":
return plot_auc_curve(
character_filter(), "is_electronics", "training_score"
Expand Down Expand Up @@ -6134,7 +6133,7 @@ <h2 class="anchored" data-anchor-id="reactive-effect">5.2 Reactive Effect</h2>
</div>
<div id="callout-6" class="callout-6-contents callout-collapse collapse show">
<div class="callout-body-container callout-body">
<p>Use <code>render.express</code> to create a second dropdown which only contains the unique <code>sub_account</code> values of the filtered data.</p>
<p>Add a <code>ui.input_action_button</code> and a <code>@reactive.event</code> decorator to make a button that, when clicked, resets the slider to its initial values.</p>
</div>
</div>
</div>
Expand Down Expand Up @@ -6206,7 +6205,7 @@ <h2 class="anchored" data-anchor-id="reactive-effect">5.2 Reactive Effect</h2>
ui.card_header("Model Metrics")

@render_plotly
def metric():
def metric_plot():
if input.metric() == "ROC Curve":
return plot_auc_curve(
character_filter(), "is_electronics", "training_score"
Expand Down
20 changes: 10 additions & 10 deletions search.json
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12 changes: 6 additions & 6 deletions sitemap.xml
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Expand Up @@ -2,26 +2,26 @@
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<url>
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</url>
<url>
<loc>https://posit-dev.github.io/py-shiny-workshop/exercises/4-dynamic-ui.html</loc>
<lastmod>2024-07-08T22:02:31.650Z</lastmod>
<lastmod>2024-07-09T16:35:13.678Z</lastmod>
</url>
<url>
<loc>https://posit-dev.github.io/py-shiny-workshop/exercises/5-reactive-effect.html</loc>
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<lastmod>2024-07-09T16:35:13.678Z</lastmod>
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<url>
<loc>https://posit-dev.github.io/py-shiny-workshop/index.html</loc>
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</url>
<url>
<loc>https://posit-dev.github.io/py-shiny-workshop/exercises/1-hello-world.html</loc>
<lastmod>2024-07-08T22:02:31.650Z</lastmod>
<lastmod>2024-07-09T16:35:13.678Z</lastmod>
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<url>
<loc>https://posit-dev.github.io/py-shiny-workshop/exercises/2-basic-ui.html</loc>
<lastmod>2024-07-08T22:02:31.650Z</lastmod>
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</urlset>

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