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diff --git a/listings.json b/listings.json
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--- a/listings.json
+++ b/listings.json
@@ -2,8 +2,8 @@
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+      "/posts/2024-07-22_teal_app_development_pharmaverseadam/teal-app-development.html",
       "/posts/2024-07-08_unix_vs_sas_date.../unix_vs_sas_datetime.html",
-      "/posts/2024-07-12_teal_app_development_pharmaverseadam/teal-app-development.html",
       "/posts/2024-07-03_introducing_a_ne.../introducing_a_new_coursera_course_for_hands_on_clinical_data_science_using__r..html",
       "/posts/2024-06-07_admiral_1.1_is.../admiral_1.1_is_here.html",
       "/posts/2024-06-17_new_admiral_ex.../new_admiral_extension_packages_admiralpeds_coming_soon.html",
diff --git a/posts/2023-06-27__hackathon_app/index.html b/posts/2023-06-27__hackathon_app/index.html
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@@ -533,7 +533,7 @@ <h2 class="anchored" data-anchor-id="conclusion">Conclusion</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:27.561749</p>
+<p>2024-07-22 07:51:59.642791</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-06-27__hackathon_app/index.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-06-27_admiral/valuelevel/derive_param_computed.html b/posts/2023-06-27_admiral/valuelevel/derive_param_computed.html
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@@ -337,7 +337,7 @@ <h3 class="anchored" data-anchor-id="compare">Compare</h3>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:39.972777</p>
+<p>2024-07-22 07:52:11.27803</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-06-27_admiral/valuelevel/derive_param_computed.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-06-27_hackathon_writeup/index.html b/posts/2023-06-27_hackathon_writeup/index.html
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@@ -258,7 +258,7 @@ <h2 class="anchored" data-anchor-id="conclusion">Conclusion</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:20.545946</p>
+<p>2024-07-22 07:51:52.332838</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-06-27_hackathon_writeup/index.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-06-28_welcome/index.html b/posts/2023-06-28_welcome/index.html
index 3196c74b..7970b580 100644
--- a/posts/2023-06-28_welcome/index.html
+++ b/posts/2023-06-28_welcome/index.html
@@ -244,7 +244,7 @@ <h2 class="anchored" data-anchor-id="how-can-i-make-a-blog-post">How can I make
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:10.438958</p>
+<p>2024-07-22 07:51:41.977795</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-06-28_welcome/index.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-07-09_falcon/falcon.html b/posts/2023-07-09_falcon/falcon.html
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@@ -214,7 +214,7 @@ <h2 class="anchored" data-anchor-id="next-steps-vision">Next steps &amp; vision<
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:23.990611</p>
+<p>2024-07-22 07:51:55.914557</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
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diff --git a/posts/2023-07-10_blanks_and_nas/blanks_and_nas.html b/posts/2023-07-10_blanks_and_nas/blanks_and_nas.html
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+++ b/posts/2023-07-10_blanks_and_nas/blanks_and_nas.html
@@ -315,7 +315,7 @@ <h2 class="anchored" data-anchor-id="thats-it">That’s it!</h2>
 
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-<p>2024-07-22 07:39:41.926395</p>
+<p>2024-07-22 07:52:13.251377</p>
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 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-07-10_blanks_and_nas/blanks_and_nas.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-07-14_code_sections/code_sections.html b/posts/2023-07-14_code_sections/code_sections.html
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@@ -313,7 +313,7 @@ <h2 class="anchored" data-anchor-id="conclusion">Conclusion</h2>
 
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-<p>2024-07-22 07:39:25.704262</p>
+<p>2024-07-22 07:51:57.704728</p>
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diff --git a/posts/2023-07-20_cross_company_dev/cross_industry_dev.html b/posts/2023-07-20_cross_company_dev/cross_industry_dev.html
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+++ b/posts/2023-07-20_cross_company_dev/cross_industry_dev.html
@@ -280,7 +280,7 @@ <h2 class="anchored" data-anchor-id="impact">Impact</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:17.046527</p>
+<p>2024-07-22 07:51:48.658244</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-07-20_cross_company_dev/cross_industry_dev.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-07-24_rounding/rounding.html b/posts/2023-07-24_rounding/rounding.html
index cb278167..137d64c5 100644
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+++ b/posts/2023-07-24_rounding/rounding.html
@@ -480,7 +480,7 @@ <h2 class="anchored" data-anchor-id="conclusion">Conclusion</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:04.984076</p>
+<p>2024-07-22 07:51:36.488665</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-07-24_rounding/rounding.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-08-08_study_day/study_day.html b/posts/2023-08-08_study_day/study_day.html
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--- a/posts/2023-08-08_study_day/study_day.html
+++ b/posts/2023-08-08_study_day/study_day.html
@@ -276,7 +276,7 @@ <h1 class="title">It’s all relative? - Calculating Relative Days using admiral
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:43.980182</p>
+<p>2024-07-22 07:52:15.398026</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-08-08_study_day/study_day.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-08-14_rhino_submission_2/rhino_submission_2.html b/posts/2023-08-14_rhino_submission_2/rhino_submission_2.html
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+++ b/posts/2023-08-14_rhino_submission_2/rhino_submission_2.html
@@ -382,7 +382,7 @@ <h1>References</h1>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:22.158226</p>
+<p>2024-07-22 07:51:53.969462</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-08-14_rhino_submission_2/rhino_submission_2.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-09-26_date_functions_and_imputation/date_functions_and_imputation.html b/posts/2023-09-26_date_functions_and_imputation/date_functions_and_imputation.html
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--- a/posts/2023-09-26_date_functions_and_imputation/date_functions_and_imputation.html
+++ b/posts/2023-09-26_date_functions_and_imputation/date_functions_and_imputation.html
@@ -810,7 +810,7 @@ <h1>Conclusion</h1>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:36.370718</p>
+<p>2024-07-22 07:52:08.92427</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-09-26_date_functions_and_imputation/date_functions_and_imputation.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-10-10_pharmaverse_story/pharmaverse_story.html b/posts/2023-10-10_pharmaverse_story/pharmaverse_story.html
index f4971136..73bee10c 100644
--- a/posts/2023-10-10_pharmaverse_story/pharmaverse_story.html
+++ b/posts/2023-10-10_pharmaverse_story/pharmaverse_story.html
@@ -232,7 +232,7 @@ <h2 class="anchored" data-anchor-id="release-growth-and-developments">Release, g
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:11.925774</p>
+<p>2024-07-22 07:51:43.477046</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-10-10_pharmaverse_story/pharmaverse_story.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-10-30_floating_point/floating_point.html b/posts/2023-10-30_floating_point/floating_point.html
index a72eaf2e..604fc679 100644
--- a/posts/2023-10-30_floating_point/floating_point.html
+++ b/posts/2023-10-30_floating_point/floating_point.html
@@ -344,8 +344,8 @@ <h2 class="anchored" data-anchor-id="issues-arising">Issues arising</h2>
 <span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(TERM, GRADE_CRITERIA_CODE) <span class="sc">%&gt;%</span></span>
 <span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">reactable</span>(<span class="at">defaultPageSize =</span> <span class="dv">4</span>, <span class="at">highlight =</span> <span class="cn">TRUE</span>, <span class="at">bordered =</span> <span class="cn">TRUE</span>, <span class="at">striped =</span> <span class="cn">TRUE</span>, <span class="at">resizable =</span> <span class="cn">TRUE</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
-<div class="reactable html-widget html-fill-item" id="htmlwidget-ada919f8879912af62b1" style="width:auto;height:auto;"></div>
-<script type="application/json" data-for="htmlwidget-ada919f8879912af62b1">{"x":{"tag":{"name":"Reactable","attribs":{"data":{"TERM":["Amylase, High","Lipase, High"],"GRADE_CRITERIA_CODE":["case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(5*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(5*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )"]},"columns":[{"id":"TERM","name":"TERM","type":"character"},{"id":"GRADE_CRITERIA_CODE","name":"GRADE_CRITERIA_CODE","type":"character"}],"resizable":true,"defaultPageSize":4,"highlight":true,"bordered":true,"striped":true,"dataKey":"ccaa297eedbe0b2b624ea3477967a787"},"children":[]},"class":"reactR_markup"},"evals":[],"jsHooks":[]}</script>
+<div class="reactable html-widget html-fill-item" id="htmlwidget-db41c37d25e4d8bafeb7" style="width:auto;height:auto;"></div>
+<script type="application/json" data-for="htmlwidget-db41c37d25e4d8bafeb7">{"x":{"tag":{"name":"Reactable","attribs":{"data":{"TERM":["Amylase, High","Lipase, High"],"GRADE_CRITERIA_CODE":["case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(5*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(5*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )"]},"columns":[{"id":"TERM","name":"TERM","type":"character"},{"id":"GRADE_CRITERIA_CODE","name":"GRADE_CRITERIA_CODE","type":"character"}],"resizable":true,"defaultPageSize":4,"highlight":true,"bordered":true,"striped":true,"dataKey":"ccaa297eedbe0b2b624ea3477967a787"},"children":[]},"class":"reactR_markup"},"evals":[],"jsHooks":[]}</script>
 </div>
 </div>
 <p>As you can see, the data-frame contains the column <code>GRADE_CRITERIA_CODE</code> which contains comparisons of floating point values. And there was a discrepancy of what Gordon expected to see, and how R actually computed the comparison initially:</p>
@@ -602,8 +602,8 @@ <h2 class="anchored" data-anchor-id="potential-solutions">Potential solutions</h
 <span id="cb71-2"><a href="#cb71-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(TERM, GRADE_CRITERIA_CODE) <span class="sc">%&gt;%</span></span>
 <span id="cb71-3"><a href="#cb71-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">reactable</span>(<span class="at">defaultPageSize =</span> <span class="dv">4</span>, <span class="at">highlight =</span> <span class="cn">TRUE</span>, <span class="at">bordered =</span> <span class="cn">TRUE</span>, <span class="at">striped =</span> <span class="cn">TRUE</span>, <span class="at">resizable =</span> <span class="cn">TRUE</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
-<div class="reactable html-widget html-fill-item" id="htmlwidget-b9a6df8f7329e668dd8c" style="width:auto;height:auto;"></div>
-<script type="application/json" data-for="htmlwidget-b9a6df8f7329e668dd8c">{"x":{"tag":{"name":"Reactable","attribs":{"data":{"TERM":["Acidosis","Albumin, Low","Alkaline Phosphatase, High","Alkalosis","ALT, High","Amylase, High","AST, High","Bicarbonate, Low","Direct Bilirubin, High","Direct Bilirubin, High","Total Bilirubin, High","Total Bilirubin, High","Calcium, High","Calcium, High","Calcium (Ionized), High","Calcium, Low","Calcium, Low","Calcium (Ionized), Low","Creatine Kinase, High","Creatinine, High","Glucose Fasting, High","Glucose Nonfasting, High","Glucose, Low","Glucose, Low","Lactate, High","Lipase, High","Cholesterol, Fasting, High","Cholesterol, Fasting, High","LDL, Fasting, High","LDL, Fasting, High","LDL, Fasting, High","Triglycerides, Fasting, High","Magnesium, Low","Phosphate, Low","Phosphate, Low","Phosphate, Low","Potassium, High","Potassium, Low","Sodium, High","Sodium, Low","Uric Acid, High","Absolute CD4+ Count, Low","Absolute CD4+ Count, Low","Absolute Lymphocyte Count, Low","Absolute Lymphocyte Count, Low","Absolute Neutrophil Count (ANC), Low","Absolute Neutrophil Count (ANC), Low","Absolute Neutrophil Count (ANC), Low","Fibrinogen Decreased","Hemoglobin, Low","Hemoglobin, Low","Hemoglobin, Low","Hemoglobin, Low","Hemoglobin, Low","Hemoglobin, Low","Hemoglobin, Low","INR, High","Methemoglobin","PTT, High","Platelets, Decreased","PT, High","WBC, Decreased","WBC, Decreased"],"GRADE_CRITERIA_CODE":["case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) < 7.3  ~ \"4\",  is.na(ANRLO) ~ NA_character_,  signif(AVAL, signif_dig) < signif(ANRLO, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(ANRLO, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) <  20 ~ \"3\",  signif(AVAL, signif_dig) < 30 ~ \"2\",  is.na(ANRLO) ~ NA_character_,  signif(AVAL, signif_dig) < signif(ANRLO, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) >= signif(ANRLO, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(10*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(5*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(2.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.25*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.25*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) > 7.5 ~ \"4\",  is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) > signif(ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) <= signif(ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >= signif(10*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >= signif(5*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(2.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.25*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.25*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(5*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >= signif(10*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >= signif(5*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(2.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.25*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.25*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) < 8 ~ \"4\",  signif(AVAL, signif_dig) < 11 ~ \"3\",  signif(AVAL, signif_dig) < 16 ~ \"2\",  is.na(ANRLO) ~ NA_character_,  signif(AVAL, signif_dig) < signif(ANRLO, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) >= signif(ANRLO, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >  signif(ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) <= signif(ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >  2*17.1 ~ \"4\",  signif(AVAL, signif_dig) >  1.5*17.1 ~ \"3\",  signif(AVAL, signif_dig) >  17.1 ~ \"2\",  is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >= signif(ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >=  5*ANRHI ~ \"4\",  signif(AVAL, signif_dig) >=  2.6*ANRHI ~ \"3\",  signif(AVAL, signif_dig) >= 1.6*ANRHI ~ \"2\",  signif(AVAL, signif_dig) >= 1.1*ANRHI ~ \"1\",  signif(AVAL, signif_dig) < 1.1*ANRHI ~ \"0\"  )","NA_character_","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >= 3.38 ~ \"4\",  signif(AVAL, signif_dig) >= 3.13 ~ \"3\",  signif(AVAL, signif_dig) >= 2.88 ~ \"2\",  signif(AVAL, signif_dig) >= 2.65 ~ \"1\",  signif(AVAL, signif_dig) < 2.65 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >= 3.38 ~ \"4\",  signif(AVAL, signif_dig) >= 3.23 ~ \"3\",  signif(AVAL, signif_dig) >= 3.1 ~ \"2\",  signif(AVAL, signif_dig) >= 2.88 ~ \"1\",  signif(AVAL, signif_dig) < 2.88 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >= 1.8 ~ \"4\",  signif(AVAL, signif_dig) >= 1.6 ~ \"3\",  signif(AVAL, signif_dig) >= 1.5 ~ \"2\",  is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) > signif(ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) <= signif(ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 1.53 ~ \"4\",  signif(AVAL, signif_dig) < 1.75 ~ \"3\",  signif(AVAL, signif_dig) < 1.95 ~ \"2\",  signif(AVAL, signif_dig) < 2.1 ~ \"1\",  signif(AVAL, signif_dig) >= 2.1 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 1.38 ~ \"4\",  signif(AVAL, signif_dig) < 1.5 ~ \"3\",  signif(AVAL, signif_dig) < 1.63 ~ \"2\",  signif(AVAL, signif_dig) < 1.88 ~ \"1\",  signif(AVAL, signif_dig) >= 1.88 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) < 0.8 ~ \"4\",  signif(AVAL, signif_dig) < 0.9 ~ \"3\",  signif(AVAL, signif_dig) < 1 ~ \"2\",  is.na(ANRLO) ~ NA_character_,  signif(AVAL, signif_dig) < signif(ANRLO, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) >= signif(ANRLO, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(20*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(10*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(6*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(3*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(3*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  !is.na(ANRHI) & signif(AVAL, signif_dig) >=  signif(3.5*ANRHI, signif_dig) ~ \"4\",  !is.na(BASE) & signif(AVAL, signif_dig) >=  signif(2*BASE, signif_dig) ~ \"4\",  !is.na(ANRHI) & signif(AVAL, signif_dig) >  signif(1.8*ANRHI, signif_dig) ~ \"3\",  !is.na(BASE) & signif(AVAL, signif_dig) >= signif(1.5*BASE, signif_dig) ~ \"3\",  !is.na(ANRHI) & signif(AVAL, signif_dig) > signif(1.3*ANRHI, signif_dig) ~ \"2\",  !is.na(BASE) & signif(AVAL, signif_dig) >= signif(1.3*BASE, signif_dig) ~ \"2\",  is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >= 27.75 ~ \"4\",  signif(AVAL, signif_dig) >= 13.89 ~ \"3\",  signif(AVAL, signif_dig) >= 6.95 ~ \"2\",  signif(AVAL, signif_dig) >= 6.11 ~ \"1\",  signif(AVAL, signif_dig) < 6.11 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >= 27.75 ~ \"4\",  signif(AVAL, signif_dig) >= 13.89 ~ \"3\",  signif(AVAL, signif_dig) >= 8.89 ~ \"2\",  signif(AVAL, signif_dig) >= 6.44 ~ \"1\",  signif(AVAL, signif_dig) < 6.44 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 1.67 ~ \"4\",  signif(AVAL, signif_dig) < 2.22 ~ \"3\",  signif(AVAL, signif_dig) < 3.05 ~ \"2\",  signif(AVAL, signif_dig) < 3.55 ~ \"1\",  signif(AVAL, signif_dig) >= 3.55 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 1.67 ~ \"4\",  signif(AVAL, signif_dig) < 2.22 ~ \"3\",  signif(AVAL, signif_dig) < 2.78 ~ \"2\",  signif(AVAL, signif_dig) < 3 ~ \"1\",  signif(AVAL, signif_dig) >= 3 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >= signif(2*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(5*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >= 7.77 ~ \"3\",  signif(AVAL, signif_dig) >= 6.19 ~ \"2\",  signif(AVAL, signif_dig) >= 5.18 ~ \"1\",  signif(AVAL, signif_dig) < 5.18 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >= 7.77 ~ \"3\",  signif(AVAL, signif_dig) >= 5.15 ~ \"2\",  signif(AVAL, signif_dig) >= 4.4 ~ \"1\",  signif(AVAL, signif_dig) < 4.4 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >= 4.9 ~ \"3\",  signif(AVAL, signif_dig) >= 4.12 ~ \"2\",  signif(AVAL, signif_dig) >= 3.37 ~ \"1\",  signif(AVAL, signif_dig) < 3.37 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >= 4.9 ~ \"3\",  signif(AVAL, signif_dig) >= 3.34 ~ \"2\",  signif(AVAL, signif_dig) >= 2.85 ~ \"1\",  signif(AVAL, signif_dig) < 2.85 ~ \"0\"  )","NA_character_","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) > 11.4 ~ \"4\",  signif(AVAL, signif_dig) > 5.7 ~ \"3\",  signif(AVAL, signif_dig) > 3.42 ~ \"2\",  signif(AVAL, signif_dig) >= 1.71 ~ \"1\",  signif(AVAL, signif_dig) < 1.71 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) < 0.3 ~ \"4\",  signif(AVAL, signif_dig) < 0.45 ~ \"3\",  signif(AVAL, signif_dig) < 0.6 ~ \"2\",  signif(AVAL, signif_dig) < 0.7 ~ \"1\",  signif(AVAL, signif_dig) >= 0.7 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.32 ~ \"4\",  signif(AVAL, signif_dig) < 0.45 ~ \"3\",  signif(AVAL, signif_dig) < 0.65 ~ \"2\",  is.na(ANRLO) ~ NA_character_,  signif(AVAL, signif_dig) < signif(ANRLO, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) >= signif(ANRLO, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.48 ~ \"4\",  signif(AVAL, signif_dig) < 0.81 ~ \"3\",  signif(AVAL, signif_dig) < 0.97 ~ \"2\",  signif(AVAL, signif_dig) < 1.13 ~ \"1\",  signif(AVAL, signif_dig) >= 1.13 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.48 ~ \"4\",  signif(AVAL, signif_dig) < 0.81 ~ \"3\",  signif(AVAL, signif_dig) < 1.13 ~ \"2\",  signif(AVAL, signif_dig) < 1.45 ~ \"1\",  signif(AVAL, signif_dig) >= 1.45 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >= 7 ~ \"4\",  signif(AVAL, signif_dig) >= 6.5 ~ \"3\",  signif(AVAL, signif_dig) >= 6 ~ \"2\",  signif(AVAL, signif_dig) >= 5.6 ~ \"1\",  signif(AVAL, signif_dig) < 5.6 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) < 2 ~ \"4\",  signif(AVAL, signif_dig) < 2.5 ~ \"3\",  signif(AVAL, signif_dig) < 3 ~ \"2\",  signif(AVAL, signif_dig) < 3.4 ~ \"1\",  signif(AVAL, signif_dig) >= 3.4 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >= 160 ~ \"4\",  signif(AVAL, signif_dig) >= 154 ~ \"3\",  signif(AVAL, signif_dig) >= 150 ~ \"2\",  signif(AVAL, signif_dig) >= 146 ~ \"1\",  signif(AVAL, signif_dig) < 146 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) <= 120 ~ \"4\",  signif(AVAL, signif_dig) < 125 ~ \"3\",  signif(AVAL, signif_dig) < 130 ~ \"2\",  signif(AVAL, signif_dig) < 135 ~ \"1\",  signif(AVAL, signif_dig) >= 135 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >= 890 ~ \"4\",  signif(AVAL, signif_dig) >= 710 ~ \"3\",  signif(AVAL, signif_dig) >= 590 ~ \"2\",  signif(AVAL, signif_dig) >= 450 ~ \"1\",  signif(AVAL, signif_dig) < 450 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.1 ~ \"4\",  signif(AVAL, signif_dig) < 0.2 ~ \"3\",  signif(AVAL, signif_dig) < 0.3 ~ \"2\",  signif(AVAL, signif_dig) < 0.4 ~ \"1\",  signif(AVAL, signif_dig) >= 0.4 ~ \"0\"  )","NA_character_","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.35 ~ \"4\",  signif(AVAL, signif_dig) < 0.5 ~ \"3\",  signif(AVAL, signif_dig) < 0.6 ~ \"2\",  signif(AVAL, signif_dig) < 0.65 ~ \"1\",  signif(AVAL, signif_dig) >= 0.65 ~ \"0\"  )","NA_character_","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.4 ~ \"4\",  signif(AVAL, signif_dig) < 0.6 ~ \"3\",  signif(AVAL, signif_dig) < 0.8 ~ \"2\",  signif(AVAL, signif_dig) <= 1 ~ \"1\",  signif(AVAL, signif_dig) > 1 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.75 ~ \"4\",  signif(AVAL, signif_dig) < 1 ~ \"3\",  signif(AVAL, signif_dig) < 1.25 ~ \"2\",  signif(AVAL, signif_dig) <= 1.5 ~ \"1\",  signif(AVAL, signif_dig) > 1.5 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 1.5 ~ \"4\",  signif(AVAL, signif_dig) < 3 ~ \"3\",  signif(AVAL, signif_dig) < 4 ~ \"2\",  signif(AVAL, signif_dig) <= 5 ~ \"1\",  signif(AVAL, signif_dig) > 5 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) <  0.5 ~ \"4\",  is.na(ANRLO) ~ NA_character_,  signif(AVAL, signif_dig) <  signif(0.25*ANRLO, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) < 0.75 ~ \"3\",  signif(AVAL, signif_dig) <  signif(0.5*ANRLO, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) <  1 ~ \"2\",  signif(AVAL, signif_dig) <  signif(0.75*ANRLO, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) <  2 ~ \"1\",  signif(AVAL, signif_dig) <  signif(ANRLO, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) >= signif(ANRLO, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(SEX) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 70 ~ \"4\",  signif(AVAL, signif_dig) < 90 ~ \"3\",  signif(AVAL, signif_dig) < 100 ~ \"2\",  signif(AVAL, signif_dig) <= 109 ~ \"1\",  signif(AVAL, signif_dig) > 109 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(SEX)  | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 65 ~ \"4\",  signif(AVAL, signif_dig) < 85 ~ \"3\",  signif(AVAL, signif_dig) < 95 ~ \"2\",  signif(AVAL, signif_dig) <= 104 ~ \"1\",  signif(AVAL, signif_dig) > 104 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 65 ~ \"4\",  signif(AVAL, signif_dig) < 85 ~ \"3\",  signif(AVAL, signif_dig) < 95 ~ \"2\",  signif(AVAL, signif_dig) <= 104 ~ \"1\",  signif(AVAL, signif_dig) > 104 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 60 ~ \"4\",  signif(AVAL, signif_dig) < 70 ~ \"3\",  signif(AVAL, signif_dig) < 85 ~ \"2\",  signif(AVAL, signif_dig) <= 96 ~ \"1\",  signif(AVAL, signif_dig) > 96 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 67 ~ \"4\",  signif(AVAL, signif_dig) < 80 ~ \"3\",  signif(AVAL, signif_dig) < 95 ~ \"2\",  signif(AVAL, signif_dig) <= 110 ~ \"1\",  signif(AVAL, signif_dig) > 110 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 80 ~ \"4\",  signif(AVAL, signif_dig) < 90 ~ \"3\",  signif(AVAL, signif_dig) < 110 ~ \"2\",  signif(AVAL, signif_dig) <= 130 ~ \"1\",  signif(AVAL, signif_dig) > 130 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 90 ~ \"4\",  signif(AVAL, signif_dig) < 100 ~ \"3\",  signif(AVAL, signif_dig) < 130 ~ \"2\",  signif(AVAL, signif_dig) <= 140 ~ \"1\",  signif(AVAL, signif_dig) > 140 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(2*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >=  20 ~ \"4\",  signif(AVAL, signif_dig) >=  15 ~ \"3\",  signif(AVAL, signif_dig) >= 10 ~ \"2\",  signif(AVAL, signif_dig) >= 5 ~ \"1\",  signif(AVAL, signif_dig) < 5 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(2.33*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.66*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) < 25 ~ \"4\",  signif(AVAL, signif_dig) < 50 ~ \"3\",  signif(AVAL, signif_dig) < 100 ~ \"2\",  signif(AVAL, signif_dig) < 125 ~ \"1\",  signif(AVAL, signif_dig) >= 125 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(1.5*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.25*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 1 ~ \"4\",  signif(AVAL, signif_dig) < 1.5 ~ \"3\",  signif(AVAL, signif_dig) < 2 ~ \"2\",  signif(AVAL, signif_dig) < 2.5 ~ \"1\",  signif(AVAL, signif_dig) >= 2.5 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 2.5 ~ \"4\",  signif(AVAL, signif_dig) < 4 ~ \"3\",  signif(AVAL, signif_dig) < 5.5 ~ \"2\",  signif(AVAL, signif_dig) < 7 ~ \"1\",  signif(AVAL, signif_dig) >= 7 ~ \"0\"  )"]},"columns":[{"id":"TERM","name":"TERM","type":"character"},{"id":"GRADE_CRITERIA_CODE","name":"GRADE_CRITERIA_CODE","type":"character"}],"resizable":true,"defaultPageSize":4,"highlight":true,"bordered":true,"striped":true,"dataKey":"8fa319e59eefe4722d1000fa5630ae3a"},"children":[]},"class":"reactR_markup"},"evals":[],"jsHooks":[]}</script>
+<div class="reactable html-widget html-fill-item" id="htmlwidget-b3aea4d646bf86a810cf" style="width:auto;height:auto;"></div>
+<script type="application/json" data-for="htmlwidget-b3aea4d646bf86a810cf">{"x":{"tag":{"name":"Reactable","attribs":{"data":{"TERM":["Acidosis","Albumin, Low","Alkaline Phosphatase, High","Alkalosis","ALT, High","Amylase, High","AST, High","Bicarbonate, Low","Direct Bilirubin, High","Direct Bilirubin, High","Total Bilirubin, High","Total Bilirubin, High","Calcium, High","Calcium, High","Calcium (Ionized), High","Calcium, Low","Calcium, Low","Calcium (Ionized), Low","Creatine Kinase, High","Creatinine, High","Glucose Fasting, High","Glucose Nonfasting, High","Glucose, Low","Glucose, Low","Lactate, High","Lipase, High","Cholesterol, Fasting, High","Cholesterol, Fasting, High","LDL, Fasting, High","LDL, Fasting, High","LDL, Fasting, High","Triglycerides, Fasting, High","Magnesium, Low","Phosphate, Low","Phosphate, Low","Phosphate, Low","Potassium, High","Potassium, Low","Sodium, High","Sodium, Low","Uric Acid, High","Absolute CD4+ Count, Low","Absolute CD4+ Count, Low","Absolute Lymphocyte Count, Low","Absolute Lymphocyte Count, Low","Absolute Neutrophil Count (ANC), Low","Absolute Neutrophil Count (ANC), Low","Absolute Neutrophil Count (ANC), Low","Fibrinogen Decreased","Hemoglobin, Low","Hemoglobin, Low","Hemoglobin, Low","Hemoglobin, Low","Hemoglobin, Low","Hemoglobin, Low","Hemoglobin, Low","INR, High","Methemoglobin","PTT, High","Platelets, Decreased","PT, High","WBC, Decreased","WBC, Decreased"],"GRADE_CRITERIA_CODE":["case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) < 7.3  ~ \"4\",  is.na(ANRLO) ~ NA_character_,  signif(AVAL, signif_dig) < signif(ANRLO, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(ANRLO, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) <  20 ~ \"3\",  signif(AVAL, signif_dig) < 30 ~ \"2\",  is.na(ANRLO) ~ NA_character_,  signif(AVAL, signif_dig) < signif(ANRLO, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) >= signif(ANRLO, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(10*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(5*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(2.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.25*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.25*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) > 7.5 ~ \"4\",  is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) > signif(ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) <= signif(ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >= signif(10*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >= signif(5*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(2.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.25*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.25*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(5*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >= signif(10*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >= signif(5*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(2.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.25*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.25*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) < 8 ~ \"4\",  signif(AVAL, signif_dig) < 11 ~ \"3\",  signif(AVAL, signif_dig) < 16 ~ \"2\",  is.na(ANRLO) ~ NA_character_,  signif(AVAL, signif_dig) < signif(ANRLO, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) >= signif(ANRLO, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >  signif(ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) <= signif(ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >  2*17.1 ~ \"4\",  signif(AVAL, signif_dig) >  1.5*17.1 ~ \"3\",  signif(AVAL, signif_dig) >  17.1 ~ \"2\",  is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >= signif(ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >=  5*ANRHI ~ \"4\",  signif(AVAL, signif_dig) >=  2.6*ANRHI ~ \"3\",  signif(AVAL, signif_dig) >= 1.6*ANRHI ~ \"2\",  signif(AVAL, signif_dig) >= 1.1*ANRHI ~ \"1\",  signif(AVAL, signif_dig) < 1.1*ANRHI ~ \"0\"  )","NA_character_","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >= 3.38 ~ \"4\",  signif(AVAL, signif_dig) >= 3.13 ~ \"3\",  signif(AVAL, signif_dig) >= 2.88 ~ \"2\",  signif(AVAL, signif_dig) >= 2.65 ~ \"1\",  signif(AVAL, signif_dig) < 2.65 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >= 3.38 ~ \"4\",  signif(AVAL, signif_dig) >= 3.23 ~ \"3\",  signif(AVAL, signif_dig) >= 3.1 ~ \"2\",  signif(AVAL, signif_dig) >= 2.88 ~ \"1\",  signif(AVAL, signif_dig) < 2.88 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >= 1.8 ~ \"4\",  signif(AVAL, signif_dig) >= 1.6 ~ \"3\",  signif(AVAL, signif_dig) >= 1.5 ~ \"2\",  is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) > signif(ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) <= signif(ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 1.53 ~ \"4\",  signif(AVAL, signif_dig) < 1.75 ~ \"3\",  signif(AVAL, signif_dig) < 1.95 ~ \"2\",  signif(AVAL, signif_dig) < 2.1 ~ \"1\",  signif(AVAL, signif_dig) >= 2.1 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 1.38 ~ \"4\",  signif(AVAL, signif_dig) < 1.5 ~ \"3\",  signif(AVAL, signif_dig) < 1.63 ~ \"2\",  signif(AVAL, signif_dig) < 1.88 ~ \"1\",  signif(AVAL, signif_dig) >= 1.88 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) < 0.8 ~ \"4\",  signif(AVAL, signif_dig) < 0.9 ~ \"3\",  signif(AVAL, signif_dig) < 1 ~ \"2\",  is.na(ANRLO) ~ NA_character_,  signif(AVAL, signif_dig) < signif(ANRLO, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) >= signif(ANRLO, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(20*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(10*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(6*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(3*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(3*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  !is.na(ANRHI) & signif(AVAL, signif_dig) >=  signif(3.5*ANRHI, signif_dig) ~ \"4\",  !is.na(BASE) & signif(AVAL, signif_dig) >=  signif(2*BASE, signif_dig) ~ \"4\",  !is.na(ANRHI) & signif(AVAL, signif_dig) >  signif(1.8*ANRHI, signif_dig) ~ \"3\",  !is.na(BASE) & signif(AVAL, signif_dig) >= signif(1.5*BASE, signif_dig) ~ \"3\",  !is.na(ANRHI) & signif(AVAL, signif_dig) > signif(1.3*ANRHI, signif_dig) ~ \"2\",  !is.na(BASE) & signif(AVAL, signif_dig) >= signif(1.3*BASE, signif_dig) ~ \"2\",  is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >= 27.75 ~ \"4\",  signif(AVAL, signif_dig) >= 13.89 ~ \"3\",  signif(AVAL, signif_dig) >= 6.95 ~ \"2\",  signif(AVAL, signif_dig) >= 6.11 ~ \"1\",  signif(AVAL, signif_dig) < 6.11 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >= 27.75 ~ \"4\",  signif(AVAL, signif_dig) >= 13.89 ~ \"3\",  signif(AVAL, signif_dig) >= 8.89 ~ \"2\",  signif(AVAL, signif_dig) >= 6.44 ~ \"1\",  signif(AVAL, signif_dig) < 6.44 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 1.67 ~ \"4\",  signif(AVAL, signif_dig) < 2.22 ~ \"3\",  signif(AVAL, signif_dig) < 3.05 ~ \"2\",  signif(AVAL, signif_dig) < 3.55 ~ \"1\",  signif(AVAL, signif_dig) >= 3.55 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 1.67 ~ \"4\",  signif(AVAL, signif_dig) < 2.22 ~ \"3\",  signif(AVAL, signif_dig) < 2.78 ~ \"2\",  signif(AVAL, signif_dig) < 3 ~ \"1\",  signif(AVAL, signif_dig) >= 3 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >= signif(2*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(5*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >= 7.77 ~ \"3\",  signif(AVAL, signif_dig) >= 6.19 ~ \"2\",  signif(AVAL, signif_dig) >= 5.18 ~ \"1\",  signif(AVAL, signif_dig) < 5.18 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >= 7.77 ~ \"3\",  signif(AVAL, signif_dig) >= 5.15 ~ \"2\",  signif(AVAL, signif_dig) >= 4.4 ~ \"1\",  signif(AVAL, signif_dig) < 4.4 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >= 4.9 ~ \"3\",  signif(AVAL, signif_dig) >= 4.12 ~ \"2\",  signif(AVAL, signif_dig) >= 3.37 ~ \"1\",  signif(AVAL, signif_dig) < 3.37 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) >= 4.9 ~ \"3\",  signif(AVAL, signif_dig) >= 3.34 ~ \"2\",  signif(AVAL, signif_dig) >= 2.85 ~ \"1\",  signif(AVAL, signif_dig) < 2.85 ~ \"0\"  )","NA_character_","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) > 11.4 ~ \"4\",  signif(AVAL, signif_dig) > 5.7 ~ \"3\",  signif(AVAL, signif_dig) > 3.42 ~ \"2\",  signif(AVAL, signif_dig) >= 1.71 ~ \"1\",  signif(AVAL, signif_dig) < 1.71 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) < 0.3 ~ \"4\",  signif(AVAL, signif_dig) < 0.45 ~ \"3\",  signif(AVAL, signif_dig) < 0.6 ~ \"2\",  signif(AVAL, signif_dig) < 0.7 ~ \"1\",  signif(AVAL, signif_dig) >= 0.7 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.32 ~ \"4\",  signif(AVAL, signif_dig) < 0.45 ~ \"3\",  signif(AVAL, signif_dig) < 0.65 ~ \"2\",  is.na(ANRLO) ~ NA_character_,  signif(AVAL, signif_dig) < signif(ANRLO, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) >= signif(ANRLO, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.48 ~ \"4\",  signif(AVAL, signif_dig) < 0.81 ~ \"3\",  signif(AVAL, signif_dig) < 0.97 ~ \"2\",  signif(AVAL, signif_dig) < 1.13 ~ \"1\",  signif(AVAL, signif_dig) >= 1.13 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.48 ~ \"4\",  signif(AVAL, signif_dig) < 0.81 ~ \"3\",  signif(AVAL, signif_dig) < 1.13 ~ \"2\",  signif(AVAL, signif_dig) < 1.45 ~ \"1\",  signif(AVAL, signif_dig) >= 1.45 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >= 7 ~ \"4\",  signif(AVAL, signif_dig) >= 6.5 ~ \"3\",  signif(AVAL, signif_dig) >= 6 ~ \"2\",  signif(AVAL, signif_dig) >= 5.6 ~ \"1\",  signif(AVAL, signif_dig) < 5.6 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) < 2 ~ \"4\",  signif(AVAL, signif_dig) < 2.5 ~ \"3\",  signif(AVAL, signif_dig) < 3 ~ \"2\",  signif(AVAL, signif_dig) < 3.4 ~ \"1\",  signif(AVAL, signif_dig) >= 3.4 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >= 160 ~ \"4\",  signif(AVAL, signif_dig) >= 154 ~ \"3\",  signif(AVAL, signif_dig) >= 150 ~ \"2\",  signif(AVAL, signif_dig) >= 146 ~ \"1\",  signif(AVAL, signif_dig) < 146 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) <= 120 ~ \"4\",  signif(AVAL, signif_dig) < 125 ~ \"3\",  signif(AVAL, signif_dig) < 130 ~ \"2\",  signif(AVAL, signif_dig) < 135 ~ \"1\",  signif(AVAL, signif_dig) >= 135 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >= 890 ~ \"4\",  signif(AVAL, signif_dig) >= 710 ~ \"3\",  signif(AVAL, signif_dig) >= 590 ~ \"2\",  signif(AVAL, signif_dig) >= 450 ~ \"1\",  signif(AVAL, signif_dig) < 450 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.1 ~ \"4\",  signif(AVAL, signif_dig) < 0.2 ~ \"3\",  signif(AVAL, signif_dig) < 0.3 ~ \"2\",  signif(AVAL, signif_dig) < 0.4 ~ \"1\",  signif(AVAL, signif_dig) >= 0.4 ~ \"0\"  )","NA_character_","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.35 ~ \"4\",  signif(AVAL, signif_dig) < 0.5 ~ \"3\",  signif(AVAL, signif_dig) < 0.6 ~ \"2\",  signif(AVAL, signif_dig) < 0.65 ~ \"1\",  signif(AVAL, signif_dig) >= 0.65 ~ \"0\"  )","NA_character_","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.4 ~ \"4\",  signif(AVAL, signif_dig) < 0.6 ~ \"3\",  signif(AVAL, signif_dig) < 0.8 ~ \"2\",  signif(AVAL, signif_dig) <= 1 ~ \"1\",  signif(AVAL, signif_dig) > 1 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 0.75 ~ \"4\",  signif(AVAL, signif_dig) < 1 ~ \"3\",  signif(AVAL, signif_dig) < 1.25 ~ \"2\",  signif(AVAL, signif_dig) <= 1.5 ~ \"1\",  signif(AVAL, signif_dig) > 1.5 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 1.5 ~ \"4\",  signif(AVAL, signif_dig) < 3 ~ \"3\",  signif(AVAL, signif_dig) < 4 ~ \"2\",  signif(AVAL, signif_dig) <= 5 ~ \"1\",  signif(AVAL, signif_dig) > 5 ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) <  0.5 ~ \"4\",  is.na(ANRLO) ~ NA_character_,  signif(AVAL, signif_dig) <  signif(0.25*ANRLO, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) < 0.75 ~ \"3\",  signif(AVAL, signif_dig) <  signif(0.5*ANRLO, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) <  1 ~ \"2\",  signif(AVAL, signif_dig) <  signif(0.75*ANRLO, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) <  2 ~ \"1\",  signif(AVAL, signif_dig) <  signif(ANRLO, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) >= signif(ANRLO, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(SEX) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 70 ~ \"4\",  signif(AVAL, signif_dig) < 90 ~ \"3\",  signif(AVAL, signif_dig) < 100 ~ \"2\",  signif(AVAL, signif_dig) <= 109 ~ \"1\",  signif(AVAL, signif_dig) > 109 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(SEX)  | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 65 ~ \"4\",  signif(AVAL, signif_dig) < 85 ~ \"3\",  signif(AVAL, signif_dig) < 95 ~ \"2\",  signif(AVAL, signif_dig) <= 104 ~ \"1\",  signif(AVAL, signif_dig) > 104 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 65 ~ \"4\",  signif(AVAL, signif_dig) < 85 ~ \"3\",  signif(AVAL, signif_dig) < 95 ~ \"2\",  signif(AVAL, signif_dig) <= 104 ~ \"1\",  signif(AVAL, signif_dig) > 104 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 60 ~ \"4\",  signif(AVAL, signif_dig) < 70 ~ \"3\",  signif(AVAL, signif_dig) < 85 ~ \"2\",  signif(AVAL, signif_dig) <= 96 ~ \"1\",  signif(AVAL, signif_dig) > 96 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 67 ~ \"4\",  signif(AVAL, signif_dig) < 80 ~ \"3\",  signif(AVAL, signif_dig) < 95 ~ \"2\",  signif(AVAL, signif_dig) <= 110 ~ \"1\",  signif(AVAL, signif_dig) > 110 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 80 ~ \"4\",  signif(AVAL, signif_dig) < 90 ~ \"3\",  signif(AVAL, signif_dig) < 110 ~ \"2\",  signif(AVAL, signif_dig) <= 130 ~ \"1\",  signif(AVAL, signif_dig) > 130 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 90 ~ \"4\",  signif(AVAL, signif_dig) < 100 ~ \"3\",  signif(AVAL, signif_dig) < 130 ~ \"2\",  signif(AVAL, signif_dig) <= 140 ~ \"1\",  signif(AVAL, signif_dig) > 140 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(2*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.5*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) >=  20 ~ \"4\",  signif(AVAL, signif_dig) >=  15 ~ \"3\",  signif(AVAL, signif_dig) >= 10 ~ \"2\",  signif(AVAL, signif_dig) >= 5 ~ \"1\",  signif(AVAL, signif_dig) < 5 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(2.33*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.66*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) ~ NA_character_,  signif(AVAL, signif_dig) < 25 ~ \"4\",  signif(AVAL, signif_dig) < 50 ~ \"3\",  signif(AVAL, signif_dig) < 100 ~ \"2\",  signif(AVAL, signif_dig) < 125 ~ \"1\",  signif(AVAL, signif_dig) >= 125 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(ANRHI) ~ NA_character_,  signif(AVAL, signif_dig) >=  signif(3*ANRHI, signif_dig) ~ \"4\",  signif(AVAL, signif_dig) >=  signif(1.5*ANRHI, signif_dig) ~ \"3\",  signif(AVAL, signif_dig) >= signif(1.25*ANRHI, signif_dig) ~ \"2\",  signif(AVAL, signif_dig) >= signif(1.1*ANRHI, signif_dig) ~ \"1\",  signif(AVAL, signif_dig) < signif(1.1*ANRHI, signif_dig) ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 1 ~ \"4\",  signif(AVAL, signif_dig) < 1.5 ~ \"3\",  signif(AVAL, signif_dig) < 2 ~ \"2\",  signif(AVAL, signif_dig) < 2.5 ~ \"1\",  signif(AVAL, signif_dig) >= 2.5 ~ \"0\"  )","case_when(  is.na(AVAL) | is.na(BRTHDT) | is.na(LBDT) ~ NA_character_,  signif(AVAL, signif_dig) < 2.5 ~ \"4\",  signif(AVAL, signif_dig) < 4 ~ \"3\",  signif(AVAL, signif_dig) < 5.5 ~ \"2\",  signif(AVAL, signif_dig) < 7 ~ \"1\",  signif(AVAL, signif_dig) >= 7 ~ \"0\"  )"]},"columns":[{"id":"TERM","name":"TERM","type":"character"},{"id":"GRADE_CRITERIA_CODE","name":"GRADE_CRITERIA_CODE","type":"character"}],"resizable":true,"defaultPageSize":4,"highlight":true,"bordered":true,"striped":true,"dataKey":"8fa319e59eefe4722d1000fa5630ae3a"},"children":[]},"class":"reactR_markup"},"evals":[],"jsHooks":[]}</script>
 </div>
 </div>
 </section>
@@ -645,7 +645,7 @@ <h2 class="anchored" data-anchor-id="conclusion">Conclusion</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:38:55.173352</p>
+<p>2024-07-22 07:51:26.768352</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-10-30_floating_point/floating_point.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-11-27_higher_order/higher_order.html b/posts/2023-11-27_higher_order/higher_order.html
index a8f268f2..aa377a0d 100644
--- a/posts/2023-11-27_higher_order/higher_order.html
+++ b/posts/2023-11-27_higher_order/higher_order.html
@@ -807,7 +807,7 @@ <h1>Conclusion</h1>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:15.051281</p>
+<p>2024-07-22 07:51:46.610844</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-11-27_higher_order/higher_order.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-12-18_admiral_1_0/admiral_1_0.html b/posts/2023-12-18_admiral_1_0/admiral_1_0.html
index 93ecc3f4..162970c8 100644
--- a/posts/2023-12-18_admiral_1_0/admiral_1_0.html
+++ b/posts/2023-12-18_admiral_1_0/admiral_1_0.html
@@ -514,7 +514,7 @@ <h2 class="anchored" data-anchor-id="way-back-machine">Way Back Machine</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:38:58.778672</p>
+<p>2024-07-22 07:51:30.465177</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-12-18_admiral_1_0/admiral_1_0.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2023-12-20_p_k__examples/p_k__examples.html b/posts/2023-12-20_p_k__examples/p_k__examples.html
index 8751fd6b..bf432064 100644
--- a/posts/2023-12-20_p_k__examples/p_k__examples.html
+++ b/posts/2023-12-20_p_k__examples/p_k__examples.html
@@ -371,7 +371,7 @@ <h3 class="anchored" data-anchor-id="derive-additional-baselines">Derive Additio
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:38:45.877119</p>
+<p>2024-07-22 07:51:17.715972</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2023-12-20_p_k__examples/p_k__examples.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-01-04_end_of__year__up.../end_of__year__update_from_the__pharmaverse__council.html b/posts/2024-01-04_end_of__year__up.../end_of__year__update_from_the__pharmaverse__council.html
index 48af61b8..2d181810 100644
--- a/posts/2024-01-04_end_of__year__up.../end_of__year__update_from_the__pharmaverse__council.html
+++ b/posts/2024-01-04_end_of__year__up.../end_of__year__update_from_the__pharmaverse__council.html
@@ -214,7 +214,7 @@ <h2 class="anchored" data-anchor-id="p.s.">P.S.</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:50.430239</p>
+<p>2024-07-22 07:52:23.740939</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-01-04_end_of__year__up.../end_of__year__update_from_the__pharmaverse__council.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-02-01_containers_webassembly_submission/containers_and_webassembly_submissions.html b/posts/2024-02-01_containers_webassembly_submission/containers_and_webassembly_submissions.html
index e84593c8..a85be1af 100644
--- a/posts/2024-02-01_containers_webassembly_submission/containers_and_webassembly_submissions.html
+++ b/posts/2024-02-01_containers_webassembly_submission/containers_and_webassembly_submissions.html
@@ -451,7 +451,7 @@ <h1>Next Steps</h1>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:45.640755</p>
+<p>2024-07-22 07:52:17.16781</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-02-01_containers_webassembly_submission/containers_and_webassembly_submissions.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-02-13_teal_on_cran/teal_on_cran.html b/posts/2024-02-13_teal_on_cran/teal_on_cran.html
index 80df5624..fe56ac98 100644
--- a/posts/2024-02-13_teal_on_cran/teal_on_cran.html
+++ b/posts/2024-02-13_teal_on_cran/teal_on_cran.html
@@ -249,7 +249,7 @@ <h1 class="title">teal is now available on CRAN 🎉</h1>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:38:35.446549</p>
+<p>2024-07-22 07:51:06.962373</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-02-13_teal_on_cran/teal_on_cran.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-02-14_iscr_conference/iscr_conference.html b/posts/2024-02-14_iscr_conference/iscr_conference.html
index 8e2daa92..39ea29ac 100644
--- a/posts/2024-02-14_iscr_conference/iscr_conference.html
+++ b/posts/2024-02-14_iscr_conference/iscr_conference.html
@@ -278,7 +278,7 @@ <h2 class="anchored" data-anchor-id="gallery">Gallery</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:08.879806</p>
+<p>2024-07-22 07:51:40.411425</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-02-14_iscr_conference/iscr_conference.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-03-01_admiral_filter_functions/admiral_filter_functions.html b/posts/2024-03-01_admiral_filter_functions/admiral_filter_functions.html
index ca09c87d..c6f82c41 100644
--- a/posts/2024-03-01_admiral_filter_functions/admiral_filter_functions.html
+++ b/posts/2024-03-01_admiral_filter_functions/admiral_filter_functions.html
@@ -465,7 +465,7 @@ <h1>Conclusion</h1>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:38:41.109422</p>
+<p>2024-07-22 07:51:12.734225</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-03-01_admiral_filter_functions/admiral_filter_functions.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-03-01_rhino_shiny_app_validation/rhino_shiny_app_validation.html b/posts/2024-03-01_rhino_shiny_app_validation/rhino_shiny_app_validation.html
index 08aaaef9..16a9ec5e 100644
--- a/posts/2024-03-01_rhino_shiny_app_validation/rhino_shiny_app_validation.html
+++ b/posts/2024-03-01_rhino_shiny_app_validation/rhino_shiny_app_validation.html
@@ -300,7 +300,7 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:33.875351</p>
+<p>2024-07-22 07:52:06.312995</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-03-01_rhino_shiny_app_validation/rhino_shiny_app_validation.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-03-04_inside_the__phar.../inside_the__pharmaverse.html b/posts/2024-03-04_inside_the__phar.../inside_the__pharmaverse.html
index 2d18bb8a..adb3423a 100644
--- a/posts/2024-03-04_inside_the__phar.../inside_the__pharmaverse.html
+++ b/posts/2024-03-04_inside_the__phar.../inside_the__pharmaverse.html
@@ -260,7 +260,7 @@ <h2 class="anchored" data-anchor-id="thank-you">Thank you</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:47.356379</p>
+<p>2024-07-22 07:52:19.019012</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-03-04_inside_the__phar.../inside_the__pharmaverse.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-03-11_tips_for__first_.../tips_for__first__time__contributors.html b/posts/2024-03-11_tips_for__first_.../tips_for__first__time__contributors.html
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--- a/posts/2024-03-11_tips_for__first_.../tips_for__first__time__contributors.html
+++ b/posts/2024-03-11_tips_for__first_.../tips_for__first__time__contributors.html
@@ -242,7 +242,7 @@ <h2 class="anchored" data-anchor-id="step-four-so-what-are-you-waiting-for">Step
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:30.770501</p>
+<p>2024-07-22 07:52:03.019613</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-03-11_tips_for__first_.../tips_for__first__time__contributors.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.html b/posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.html
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--- a/posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.html
+++ b/posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.html
@@ -362,7 +362,7 @@ <h1>Let’s Recap!</h1>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:07.101943</p>
+<p>2024-07-22 07:51:38.578882</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-04-08_tmc_cran/tmc_cran.html b/posts/2024-04-08_tmc_cran/tmc_cran.html
index 59942012..8350fe75 100644
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+++ b/posts/2024-04-08_tmc_cran/tmc_cran.html
@@ -226,7 +226,7 @@ <h2 class="anchored" data-anchor-id="acknowledgments">Acknowledgments</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:48.918629</p>
+<p>2024-07-22 07:52:22.133691</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-04-08_tmc_cran/tmc_cran.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-04-15_de-_mystifying__.../de-_mystifying__r__programming_in__clinical__trials.html b/posts/2024-04-15_de-_mystifying__.../de-_mystifying__r__programming_in__clinical__trials.html
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@@ -246,7 +246,7 @@ <h2 class="anchored" data-anchor-id="leveraging-the-right-tools-to-capture-value
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:38:42.93083</p>
+<p>2024-07-22 07:51:14.587806</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-04-15_de-_mystifying__.../de-_mystifying__r__programming_in__clinical__trials.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-04-29_appsilon_and__sa.../appsilon_and__sanofi_joins_the_pharmaverse_council!.html b/posts/2024-04-29_appsilon_and__sa.../appsilon_and__sanofi_joins_the_pharmaverse_council!.html
index 079cfc65..cf1401e7 100644
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+++ b/posts/2024-04-29_appsilon_and__sa.../appsilon_and__sanofi_joins_the_pharmaverse_council!.html
@@ -216,7 +216,7 @@ <h1>Sanofi and Open Source</h1>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:38:53.211277</p>
+<p>2024-07-22 07:51:24.78726</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-04-29_appsilon_and__sa.../appsilon_and__sanofi_joins_the_pharmaverse_council!.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-05-08_tlg_catalog_webr/tlg_catalog_webr.html b/posts/2024-05-08_tlg_catalog_webr/tlg_catalog_webr.html
index c4cfd3dc..bbc2c547 100644
--- a/posts/2024-05-08_tlg_catalog_webr/tlg_catalog_webr.html
+++ b/posts/2024-05-08_tlg_catalog_webr/tlg_catalog_webr.html
@@ -242,7 +242,7 @@ <h2 class="anchored" data-anchor-id="summary">Summary</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:38:37.219358</p>
+<p>2024-07-22 07:51:08.744858</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-05-08_tlg_catalog_webr/tlg_catalog_webr.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-05-29_our_experience_a.../our_experience_as_new_admiral_developers.html b/posts/2024-05-29_our_experience_a.../our_experience_as_new_admiral_developers.html
index 10548c20..576b142e 100644
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+++ b/posts/2024-05-29_our_experience_a.../our_experience_as_new_admiral_developers.html
@@ -240,7 +240,7 @@ <h1>What have we enjoyed</h1>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:38:38.746511</p>
+<p>2024-07-22 07:51:10.314369</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-05-21_our_experience_a.../our_experience_as_new_admiral_developers.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-05-31_diversity_&__inc.../diversity_&__inclusion_in_pharmaverse.html b/posts/2024-05-31_diversity_&__inc.../diversity_&__inclusion_in_pharmaverse.html
index 8ec3b88c..d32a162c 100644
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+++ b/posts/2024-05-31_diversity_&__inc.../diversity_&__inclusion_in_pharmaverse.html
@@ -193,7 +193,7 @@ <h1 class="title">Diversity &amp; Inclusion in pharmaverse</h1>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:29.304344</p>
+<p>2024-07-22 07:52:01.479645</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-05-31_diversity_&amp;__inc.../code_sections.qmd">source code</a>, <a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-05-31_diversity_&amp;__inc.../renv.lock">R environment</a></p>
diff --git a/posts/2024-06-07_admiral_1.1_is.../admiral_1.1_is_here.html b/posts/2024-06-07_admiral_1.1_is.../admiral_1.1_is_here.html
index e3c2d014..65ab754b 100644
--- a/posts/2024-06-07_admiral_1.1_is.../admiral_1.1_is_here.html
+++ b/posts/2024-06-07_admiral_1.1_is.../admiral_1.1_is_here.html
@@ -470,7 +470,7 @@ <h1>What’s next for admiral and the admiral family?</h1>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:38:51.254</p>
+<p>2024-07-22 07:51:22.840771</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-06-07_%7Badmiral%7D_1.1_is.../admiral_1.1_is_here.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-06-17_new_admiral_ex.../new_admiral_extension_packages_admiralpeds_coming_soon.html b/posts/2024-06-17_new_admiral_ex.../new_admiral_extension_packages_admiralpeds_coming_soon.html
index c8f0d4c2..56385617 100644
--- a/posts/2024-06-17_new_admiral_ex.../new_admiral_extension_packages_admiralpeds_coming_soon.html
+++ b/posts/2024-06-17_new_admiral_ex.../new_admiral_extension_packages_admiralpeds_coming_soon.html
@@ -234,7 +234,7 @@ <h1>What else?</h1>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:38:33.356289</p>
+<p>2024-07-22 07:51:04.795724</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-05-10_new_admiral_ex.../new_admiral_extension_packages_admiralpeds_coming_soon.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-07-03_introducing_a_ne.../introducing_a_new_coursera_course_for_hands_on_clinical_data_science_using__r..html b/posts/2024-07-03_introducing_a_ne.../introducing_a_new_coursera_course_for_hands_on_clinical_data_science_using__r..html
index 8e05b6be..3c8037a8 100644
--- a/posts/2024-07-03_introducing_a_ne.../introducing_a_new_coursera_course_for_hands_on_clinical_data_science_using__r..html
+++ b/posts/2024-07-03_introducing_a_ne.../introducing_a_new_coursera_course_for_hands_on_clinical_data_science_using__r..html
@@ -219,7 +219,7 @@ <h1 class="title">Introducing a <a href="https://www.coursera.org/learn/hands-on
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:03.288501</p>
+<p>2024-07-22 07:51:34.745964</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-07-03_introducing_a_ne.../introducing_a_new_coursera_course_for_hands_on_clinical_data_science_using__r..qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-07-08_unix_vs_sas_date.../unix_vs_sas_datetime.html b/posts/2024-07-08_unix_vs_sas_date.../unix_vs_sas_datetime.html
index 29d9ec7f..88f6bcd2 100644
--- a/posts/2024-07-08_unix_vs_sas_date.../unix_vs_sas_datetime.html
+++ b/posts/2024-07-08_unix_vs_sas_date.../unix_vs_sas_datetime.html
@@ -236,7 +236,7 @@ <h2 class="anchored" data-anchor-id="conclusion">Conclusion</h2>
 
 <div id="quarto-appendix" class="default"><section id="last-updated" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Last updated</h2><div class="quarto-appendix-contents">
 
-<p>2024-07-22 07:39:32.339841</p>
+<p>2024-07-22 07:52:04.671665</p>
 </div></section><section id="details" class="level2 appendix"><h2 class="anchored quarto-appendix-heading">Details</h2><div class="quarto-appendix-contents">
 
 <p><a href="https://github.com/pharmaverse/blog/tree/main/posts/2024-07-08_unix_vs_sas_date.../unix_vs_sas_datetime.qmd">Source</a>, <a href="https://pharmaverse.github.io/blog/session_info.html">Session info</a></p>
diff --git a/posts/2024-07-12_teal_app_development_pharmaverseadam/images/Asset 1.gif b/posts/2024-07-22_teal_app_development_pharmaverseadam/images/Asset 1.gif
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similarity index 100%
rename from posts/2024-07-12_teal_app_development_pharmaverseadam/images/Asset 4.gif
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diff --git a/posts/2024-07-12_teal_app_development_pharmaverseadam/images/Asset 5.gif b/posts/2024-07-22_teal_app_development_pharmaverseadam/images/Asset 5.gif
similarity index 100%
rename from posts/2024-07-12_teal_app_development_pharmaverseadam/images/Asset 5.gif
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diff --git a/posts/2024-07-12_teal_app_development_pharmaverseadam/teal-app-development.html b/posts/2024-07-22_teal_app_development_pharmaverseadam/teal-app-development.html
similarity index 99%
rename from posts/2024-07-12_teal_app_development_pharmaverseadam/teal-app-development.html
rename to posts/2024-07-22_teal_app_development_pharmaverseadam/teal-app-development.html
index 8e83ff74..2faadc89 100644
--- a/posts/2024-07-12_teal_app_development_pharmaverseadam/teal-app-development.html
+++ b/posts/2024-07-22_teal_app_development_pharmaverseadam/teal-app-development.html
@@ -9,7 +9,7 @@
 <meta name="author" content="Pavel Demin">
 <meta name="author" content="Dror Berel">
 <meta name="author" content="APPSILON">
-<meta name="dcterms.date" content="2024-07-12">
+<meta name="dcterms.date" content="2024-07-22">
 <meta name="description" content="Learn how to quickly develop interactive and reproducible clinical data dashboards using {teal} and {pharmaverseadam}.">
 
 <title>Simplifying Clinical Data Dashboards with {teal} and {pharmaverseadam} – pharmaverse blog</title>
@@ -186,7 +186,7 @@ <h1 class="title">Simplifying Clinical Data Dashboards with {teal} and {pharmave
       <div>
       <div class="quarto-title-meta-heading">Published</div>
       <div class="quarto-title-meta-contents">
-        <p class="date">July 12, 2024</p>
+        <p class="date">July 22, 2024</p>
       </div>
     </div>
     
@@ -370,14 +370,14 @@ <h2 class="anchored" data-anchor-id="conclusion"><strong>Conclusion</strong>&nbs
   author = {Demin, Pavel and Berel, Dror and , APPSILON},
   title = {Simplifying {Clinical} {Data} {Dashboards} with \{Teal\} and
     \{Pharmaverseadam\}},
-  date = {2024-07-12},
-  url = {https://pharmaverse.github.io/blog/posts/2024-07-12_teal_app_development_pharmaverseadam/teal-app-development.html},
+  date = {2024-07-22},
+  url = {https://pharmaverse.github.io/blog/posts/2024-07-22_teal_app_development_pharmaverseadam/teal-app-development.html},
   langid = {en}
 }
 </code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre><div class="quarto-appendix-secondary-label">For attribution, please cite this work as:</div><div id="ref-demin2024" class="csl-entry quarto-appendix-citeas" role="listitem">
 Demin, Pavel, Dror Berel, and APPSILON. 2024. <span>“Simplifying
 Clinical Data Dashboards with {Teal} and {Pharmaverseadam}.”</span> July
-12, 2024. <a href="https://pharmaverse.github.io/blog/posts/2024-07-12_teal_app_development_pharmaverseadam/teal-app-development.html">https://pharmaverse.github.io/blog/posts/2024-07-12_teal_app_development_pharmaverseadam/teal-app-development.html</a>.
+22, 2024. <a href="https://pharmaverse.github.io/blog/posts/2024-07-22_teal_app_development_pharmaverseadam/teal-app-development.html">https://pharmaverse.github.io/blog/posts/2024-07-22_teal_app_development_pharmaverseadam/teal-app-development.html</a>.
 </div></div></section></div></main> <!-- /main -->
 <script id="quarto-html-after-body" type="application/javascript">
 window.document.addEventListener("DOMContentLoaded", function (event) {
diff --git a/search.json b/search.json
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     "title": "teal.modules.clinical v0.9.0 is now on CRAN!",
     "section": "Last updated",
-    "text": "Last updated\n\n2024-07-22 07:39:48.918629"
+    "text": "Last updated\n\n2024-07-22 07:52:22.133691"
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-    "title": "Testing Containers and WebAssembly in Submissions to the FDA",
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+    "title": "Inside the pharmaverse",
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-    "text": "The R Consortium Submission Working Group has now successfully made two pilot submissions to the FDA. All the submissions done by the group are focused on improving practices for R-based clinical trial regulatory submissions. Now, the R submission Working Groups, in collaboration with Appsilon and Posit, are exploring new technologies such as Containers and WebAssembly. In this article, we dive into the details of this exploration."
+    "text": "Greetings, pharmaverse phriends!\nNow that the pharmaverse has been around for a few years and seems to be making an impact on clinical reporting (yay!), we wanted to take an opportunity to cast a light on some of the inner workings of pharmaverse and its Council (us). Fortunately, some of you (you know who you are) innovated and created this fantastic channel for pharmaverse blogs. We can’t think of a better way to communicate than by the channel that the pharmaverse community itself created."
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-    "title": "Testing Containers and WebAssembly in Submissions to the FDA",
-    "section": "How Everything Started",
-    "text": "How Everything Started\n\nPilot 1\nThis pilot was initially submitted on November 22, 2021. This submission was the first publicly available R-based submission to the FDA. This was a test submission that aimed to explore the submission of an R package to the FDA following the eCTD specifications. The submission included an R package, R scripts for analysis, R-based analysis data reviewed guide (ADRG), and other important components. The final response letter from the FDA was received on March 14, 2022."
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+    "href": "posts/2024-03-04_inside_the__phar.../inside_the__pharmaverse.html#pharmaverse-inception",
+    "title": "Inside the pharmaverse",
+    "section": "Pharmaverse Inception",
+    "text": "Pharmaverse Inception\nThe pharmaverse Council was formed in 2020 as a coming together of 4 like-minded individuals embedded in the industry-wide efforts to increase the use of the R language for clinical trial analysis and reporting. These individuals included Ross Farrugia (Roche), Sumesh Kalappurakal (Johnson & Johnson), Michael Rimler (GSK), and Mike Stackhouse (Atorus). Each had historical experience with R adoption, through their primary roles within their respective organizations.\nCollectively, these 4 individuals saw the value in\n\nreducing the choice set of R packages for users,\nidentifying gaps in available R packages for delivering the clinical data pipeline, and\nencouraging the development of new packages and/or features to close those gaps.\n\nThe result would be a subset of permissively licensed and open-sourced packages that anyone could use (or modify) to suit their specific use cases, thereby reducing the incidence of organizations privately solving typical problems in isolation. For more background, please refer to the pharmaverse Charter.\nThe current pharmaverse Council companies include GSK, Roche, Johnson & Johnson, Atorus, and Novo Nordisk, represented by the original four founders and Ari Siggaard Knoph from Novo Nordisk.\nIn March 2023, the PHUSE Board of Directors approached the pharmaverse Council with a proposal offering support to pharmaverse developers and leadership to advance the pharmaverse mission. The PHUSE proposal aligned with its mission to promote “[s]haring ideas, tools and standards around data, statistical and reporting technologies to advance the future of life sciences.” PHUSE is currently a supporter of the pharmaverse and its objectives, evident through activities such as:\n\nthe provision of access to PHUSE’s communication and file-sharing platforms to package teams,\nthe provision of project management support to package teams (if needed),\nenablement and promotion of pharmaverse innovations and activities, and\nenablement of face-to-face community connections at PHUSE events."
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-    "title": "Testing Containers and WebAssembly in Submissions to the FDA",
-    "section": "Pilot 2",
-    "text": "Pilot 2\nThis was one of the first submission packages containing a Shiny application. The main goal of this pilot was to test the submission of an R-based Shiny application bundled into a submission package and transfer it successfully to FDA reviewers. The submitted application was built using the datasets and analyses that were used for the R Submission Pilot 1. The deployed version of this application is available on this site. Alternatively, a Rhino-based version of the application can be found here.\nThe final response letter from the FDA was reviewed on September 27, 2023.\n\n\n\n\n\nIn this submission, there were many open-source R packages that were used to create and execute the Shiny application. A very well-known shiny-based interactive exploration framework {teal} was used mainly for analyzing the clinical trial data; this package is included in the pharmaverse package repository. The full list of open-source and proprietary R analysis packages is available on this Analysis Data Reviewer’s Guide prepared by the R Consortium R Submissions Working Group for the Pilot 2."
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+    "title": "Inside the pharmaverse",
+    "section": "Pharmaverse Objective",
+    "text": "Pharmaverse Objective\nPharmaverse aims to promote the collaborative development of curated open-source R packages for clinical reporting in pharma. Indirectly, we also strive to encourage and increase R adoption within the industry and facilitate communication and collaboration among both R users and R developers."
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-    "section": "Pilot 3",
-    "text": "Pilot 3\nThis pilot was successfully submitted to the FDA on Aug 28, 2023. This was the first publicly available R submission that included R scripts to produce ADaM datasets and TLFs. Both the ADaMs (SDTM .xpt sources from the CDISC Pilot study) and the TLFs (ADaMs .xpt sourced from the ADaMs generated in R by the Pilot 3 team) were created using R.  The next step for this pilot is to await FDA’s review and approval, which may take several months to complete."
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+    "title": "Inside the pharmaverse",
+    "section": "Becoming a Council Member",
+    "text": "Becoming a Council Member\nMembership to the pharmaverse Council is granted to the organization and is open to any type of organization (requirements further described here). In layperson’s terms, the Council member organization must support a representative on the council and demonstrate a commitment to contributions to the pharmaverse codebase. Specifically, “Commitment to at least 2 open source packages under pharmaverse via reviews, hands-on code development, product leads, or other roles which contribute to the design, development, testing, release, and/or maintenance.” If any representative steps away from their council position, the member organization would identify the replacement. Council meeting minutes are published in the pharmaverse GitHub here."
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-    "title": "Testing Containers and WebAssembly in Submissions to the FDA",
-    "section": "Pilot 4",
-    "text": "Pilot 4\nThis pilot aims to explore using technologies such as containers and WebAssembly software to package a Shiny application into a self-contained unit, streamlining the transfer and execution process for enhanced efficiency.\nThis pilot is expected to be divided into two parallel submissions:\n(a) will investigate WebAssembly and\n(b) will investigate containers.\n\nThe Journey with WebAssembly and Containers\nOur team at Appsilon teamed up with the dynamic Pilot 4 crew to explore WebAssembly technology and containers. George Stagg and Winston Chang also joined the working group to discuss the web-assembly portion of Pilot 4. This partnership brought together our engineering prowess to contribute to these tools, injecting fresh perspectives into the ongoing pilot project.\nSome of the outcomes of the collaboration:\n\nWe were able to set up a robust container environment for this pilot project. \nWe aided the progress made on the use of both experimental technologies: containers and WebAssembly.\nWe developed a working prototype submission using Podman container technology.\nWe developed a working early-stage prototype for wrapping a small Shiny application using WebAssembly.\n\n\nWebAssembly\n\nWebAssembly allows languages like R to be executed at near-native speed directly within web browsers, providing users with the ability to run R code without having R installed locally. WebR is essentially the R programming language adapted to run in a web browser environment using WebAssembly. This project is under active development. \n\n\n\nThe Pilot 4 Shiny App Up and Running on webR!\n\n\n\n\n\nThe deployed example of the Shiny app running on webR is available here. Check out the video of the application running below.\n\nDuring this pilot, engineers at Appsilon developed a prototype of a Shiny application running on webR. The application reuses most of the code from the previous pilot apps with some tweaks and a couple of hacks/changes to get around non CRAN dependencies, specially for data loading, WebR compatibilities, and shimming some of the functionality from {teal} and other packages that are (for now) not available on CRAN.\n\nwebR Shiny App\nDuring the second iteration, which was recently held, Pedro Silva shared the process of developing this Shiny app running on webR.\n\n\nThe Process\n\nLeverage the last 2 iterations of the application\n\nReuse as much code as possible\nAvoid touching the logic part\n\nRestrict the number of dependencies to packages on CRAN\n\nReplace/shim functionality that was lost from removing dependencies\n\n\nHere is the list of dependencies to packages on CRAN; those that worked are colored green, and those that were removed are marked in orange. We ended up with just 3 problematic dependencies (bold).\n\n\n\n\n\nIssues with library(cowplot):\n\nSome issues with low-level dependencies when deployed\n\nSolution:\n\nReplace functionality with HTML\n\nIssues with library(teal):\n\nUses {shiny.widgets} (not working for webR)\n\nSolution:\n\nRedo the UI\nLoad modules directly\nRecreate filter functionality\n\nIssues with library(teal.data):\n\nUse rds exports\n\nSolution:\n\nShim functionality, load data directly\n\n\nLeverage shinylive and httpuv to export and serve the application\n\nShinylive can help streamline the export process\n\nProblems\n\nshiny.live won’t let us have non-R files in the application directory - this is an outstanding bug that George asked us to raise an issue for.\nWe wouldn’t be able to run the application as a traditional shiny app.\n\nSolution:\n\nCustom build script\n\n\n{httpuv} can help serve the application\n\n{httpuv} would run natively on a machine to serve the Shiny app\n\n\n\n\n\nApplication Structure\nThe figure below shows an overview of what we ended with:\n\nSome of the issues and solutions found at the very beginning:\n\nThe previous applications were built using golem and another one in Rhino; the support for these frameworks is not great in webR up to now.\n\nSolution\n\n{box} works out of the box (reuse the rhino version modules)\nSimplify the structure and use a simple shiny modular structure\n\n\nShinylive does not like non-R files when generating the bundle\n\nSolution\n\nKeep the app folder as clean as possible for now (www folder only)\n\n\n{teal} and {teal.data} are not on CRAN\n\nSolution\n\nShim and used functionality\nUse a simple tab system for the UI structure\n\n\n\nThe FDA was previously told that the shiny application being prepared for the Pilot 4 submission would not be a 1 to 1 mapping from the previous one submitted for the Pilot 2 due to certain constraints such as {teal} not being on CRAN; however, this didn’t represent a problem for them since they would mainly like to test the technology.\nPedro Silva, one of the engineers working on the development of this app, mentioned “While WebR is still in development, it shows tremendous promise! The loading is definitely still a pain point (over 100mb to set up the environment!) but it will only get better moving forward.”\n\n\nContainers\n\n\n\n\n\nContainerization, particularly through technologies like Docker, Podman or Singularity, offers several advantages for deploying Shiny apps.\n\nChoosing the Right Container\nChoosing the right container was a question that arose in this project. Although Docker is the most popular, we decided to move forward with Podman. \nIn our exploration of containerization tools for deploying Shiny applications, we’ve identified key distinctions between Docker and Podman that influenced our choice. \nPodman stands out for its daemonless architecture, enhancing security by eliminating the need for a central daemon process. Unlike Docker, Podman supports running containers as non-root users, a critical feature for meeting FDA reviewer requirements. Developed by Red Hat and maintained as an open-source project, Podman prioritizes security with its rootless container support, offering a robust solution for security-conscious users. \n\n\nGoals\nA Container-based method to deploy Pilot 2 Shiny App.\n\n\nWhat we did\n\nConfigurable Podman Dockerfile / docker-compose.yml\n\nR version\nRegistry / organization name / image name (differences between docker.io and ghcr.io)\n\nDocumentation on creating the container\nCI: Automated build on amd64 and arm64 platforms\n\n\n\nPodman short-demo\n\nBelow is the dockerfile (recipe) for the container:"
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+    "title": "Inside the pharmaverse",
+    "section": "Package Inclusion",
+    "text": "Package Inclusion\nApplications for a package to be included in the pharmaverse may originate from anybody and anywhere. The Council (through its Working Groups) reviews applications and either (i) approves the package into the pharmaverse or (ii) rejects the package with a rationale. When a rejection has been issued, common reasons have been ‘not now’ with feedback to enhance testing, documentation, or other aspects that enrich the package. Working Groups may also steer the applicant to other packages with similar functionality. In this sense, respective package maintainers (product owners) are encouraged to work collaboratively to reduce duplicated features across packages and improve the overall experience of the user base. The Council and Working Groups are tasked with assessing this aspect of the pharmaverse code base, but the decision on implementation ultimately resides with the individual package maintainers.\nPackage reviews are conducted in the open:\n\nExample 1: {riskassessment} from R Validation Hub working group – result successful: https://github.com/pharmaverse/pharmaverse/issues/195\nExample 2: {rhino} from Appsilon – result successful: https://github.com/pharmaverse/pharmaverse/issues/260\nExample 3: {synthetic.cdisc.data} from Roche – result unsuccessful: https://github.com/pharmaverse/pharmaverse/issues/235\n\nAt the time of publication, there were over 250 contributors to pharmaverse packages, across a network of organizations much broader than just the 5 Council member organizations. pharmaverse Council encourages diversity of individual contributors and the organizations they are affiliated with. The decision on product development team rosters is the sole discretion of the packages. Of course, Council member organizations are providing people to support the pharmaverse ecosystem (e.g., developers and product owners). However, both strategic and operational decisions on the development and maintenance of individual packages reside outside the pharmaverse Council."
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-    "text": "Last updated\n\n2024-07-22 07:39:45.640755"
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+    "title": "Inside the pharmaverse",
+    "section": "User adoption",
+    "text": "User adoption\nPharma companies are free to choose any selection of R packages for clinical reporting and only they can justify their choices. The pharmaverse website states that “[a]nyone is free to choose any selection of pharmaverse recommended software or those from any other source.” In addition, pharmaverse does not seek to engage or get endorsement from any health authority. We are merely trying to provide a public service to help individuals and organizations involved in clinical reporting navigate a vast field of available R packages licensed as open source.\nThe pharmaverse website provides illustrative examples of how to use pharmaverse and other packages to build common deliverables."
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-    "section": "Details",
-    "text": "Details\n\nSource, Session info"
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+    "title": "Inside the pharmaverse",
+    "section": "So, what really is pharmaverse?",
+    "text": "So, what really is pharmaverse?\nPharmaverse is essentially two things:\n\nPharmaverse is a list of packages curated by the pharmaverse Council and Working Groups, primarily communicated via the pharmaverse website. Pharmaverse also maintains an R-universe build for ease of use outside GxP settings.\npharmaverse is a community of R users and R developers “working to promote collaborative development of curated open-source R packages for clinical reporting usage in pharma.”\n\nPharmaverse Council provides a Slack workspace to build community amongst all interested parties and serve as a communication platform for individuals and package teams. At present, membership to this workspace exceeds &gt;1200 members.\nPharmaverse Council also provides a GitHub organization for developers to work in, but hosting packages in the pharmaverse GitHub is not required to be part of pharmaverse.\nThese elements are supported on an all-volunteer basis, mostly with community versions of various platforms (e.g., we use the free tier on Slack)."
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-    "text": "Reading in SAS-based datasets (.sas7bdat or xpt) into R has users calling the R package haven. A typical call might invoke read_sas() or read_xpt() to bring in your source data to construct your ADaMs or SDTMs.\nUnfortunately, while using haven the character blanks (missing data) found in a typical SAS-based dataset are left as blanks. These blanks will typically prove problematic while using functions like is.na in combination with dplyr::filter() to subset data. Check out Bayer’s SAS2R catalog: handling-of-missing-values for more discussion on missing values and NAs.\nIn the admiral package, we have built a simple function called convert_blanks_to_na() to help us quickly remedy this problem. You can supply an entire dataframe to this function and it will convert any character blanks to NA_character_"
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+    "title": "Inside the pharmaverse",
+    "section": "Thank you",
+    "text": "Thank you\nThank you, each of you, for the part that you play. No matter how big or small, you are helping amplify the impact that open collaboration is having on how we deliver new medicines and vaccines to patients around the world. We hope you are finding, and continue to find, pharmaverse a valuable piece of the clinical reporting puzzle.\nAnd, if not – let us know!\nPharmaverse Council – Ari, Michael, Mike, Ross, and Sumesh\nDisclaimer: This blog contains opinions that are of the authors alone and do not necessarily reflect the strategy of their respective organizations."
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-    "section": "Loading Packages and Making Dummy Data",
-    "text": "Loading Packages and Making Dummy Data\n\nlibrary(admiral)\nlibrary(tibble)\nlibrary(dplyr)\n\ndf &lt;- tribble(\n  ~USUBJID, ~RFICDTC,\n  \"01\", \"2000-01-01\",\n  \"02\", \"2001-01-01\",\n  \"03\", \"\", # Here we have a character blank\n  \"04\", \"2001-01--\",\n  \"05\", \"2001---01\",\n  \"05\", \"\", # Here we have a character blank\n)\n\ndf\n\n# A tibble: 6 × 2\n  USUBJID RFICDTC     \n  &lt;chr&gt;   &lt;chr&gt;       \n1 01      \"2000-01-01\"\n2 02      \"2001-01-01\"\n3 03      \"\"          \n4 04      \"2001-01--\" \n5 05      \"2001---01\" \n6 05      \"\""
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+    "text": "Last updated\n\n2024-07-22 07:52:19.019012"
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-    "text": "A simple conversion\n\ndf_na &lt;- convert_blanks_to_na(df)\n\ndf_na\n\n# A tibble: 6 × 2\n  USUBJID RFICDTC   \n  &lt;chr&gt;   &lt;chr&gt;     \n1 01      2000-01-01\n2 02      2001-01-01\n3 03      &lt;NA&gt;      \n4 04      2001-01-- \n5 05      2001---01 \n6 05      &lt;NA&gt;      \n\n\n\ndf_na %&gt;% filter(is.na(RFICDTC))\n\n# A tibble: 2 × 2\n  USUBJID RFICDTC\n  &lt;chr&gt;   &lt;chr&gt;  \n1 03      &lt;NA&gt;   \n2 05      &lt;NA&gt;"
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-    "section": "That’s it!",
-    "text": "That’s it!\nA simple call to this function can make your derivation life so much easier while working in R if working with SAS-based datasets. In admiral, we make use of this function at the start of all ADaM templates for common ADaM datasets. You can use the function use_ad_template() to get the full R script for the below ADaMs.\n\nlist_all_templates()\n\nExisting ADaM templates in package 'admiral':\n• ADAE\n• ADCM\n• ADEG\n• ADEX\n• ADLB\n• ADLBHY\n• ADMH\n• ADPC\n• ADPP\n• ADPPK\n• ADSL\n• ADVS"
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+    "title": "It’s all relative? - Calculating Relative Days using admiral",
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+    "text": "Creating --DY variables for your ADaMs is super easy using derive_vars_dy() from the admiral package.\nLet’s build some dummy data with 4 subjects, a start date/time for treatment (TRTSDTM), an analysis start date/time variable (ASTDTM) and an analysis end date variable (AENDT).\nlibrary(admiral)\nlibrary(lubridate)\nlibrary(dplyr)\n\nadam &lt;- tribble(\n  ~USUBJID, ~TRTSDTM, ~ASTDTM, ~AENDT,\n  \"001\", \"2014-01-17T23:59:59\", \"2014-01-18T13:09:O9\", \"2014-01-20\",\n  \"002\", \"2014-02-25T23:59:59\", \"2014-03-18T14:09:O9\", \"2014-03-24\",\n  \"003\", \"2014-02-12T23:59:59\", \"2014-02-18T11:03:O9\", \"2014-04-17\",\n  \"004\", \"2014-03-17T23:59:59\", \"2014-03-19T13:09:O9\", \"2014-05-04\"\n) %&gt;%\n  mutate(\n    TRTSDTM = as_datetime(TRTSDTM),\n    ASTDTM = as_datetime(ASTDTM),\n    AENDT = ymd(AENDT)\n  )\nOkay! Next we run our dataset through derive_vars_dy(), specifying:\nderive_vars_dy(\n  adam,\n  reference_date = TRTSDTM,\n  source_vars = exprs(ASTDTM, AENDT)\n)\n\n# A tibble: 4 × 6\n  USUBJID TRTSDTM             ASTDTM              AENDT      ASTDY AENDY\n  &lt;chr&gt;   &lt;dttm&gt;              &lt;dttm&gt;              &lt;date&gt;     &lt;dbl&gt; &lt;dbl&gt;\n1 001     2014-01-17 23:59:59 2014-01-18 13:09:09 2014-01-20     2     4\n2 002     2014-02-25 23:59:59 2014-03-18 14:09:09 2014-03-24    22    28\n3 003     2014-02-12 23:59:59 2014-02-18 11:03:09 2014-04-17     7    65\n4 004     2014-03-17 23:59:59 2014-03-19 13:09:09 2014-05-04     3    49\nThat’s it! We got both our ASTDY and AENDY variables in only a few short lines of code!\nWhat if I want my variables to have a different naming convention?\nEasy! In the source_vars argument if you want your variables to be called DEMOADY and DEMOEDY just do DEMOADY = ASTDTM and DEMOEDY = AENDT and derive_vars_dy() will do the rest!\nderive_vars_dy(\n  adam,\n  reference_date = TRTSDTM,\n  source_vars = exprs(DEMOADY = ASTDTM, DEMOEDY = AENDT)\n)\n\n# A tibble: 4 × 6\n  USUBJID TRTSDTM             ASTDTM              AENDT      DEMOADY DEMOEDY\n  &lt;chr&gt;   &lt;dttm&gt;              &lt;dttm&gt;              &lt;date&gt;       &lt;dbl&gt;   &lt;dbl&gt;\n1 001     2014-01-17 23:59:59 2014-01-18 13:09:09 2014-01-20       2       4\n2 002     2014-02-25 23:59:59 2014-03-18 14:09:09 2014-03-24      22      28\n3 003     2014-02-12 23:59:59 2014-02-18 11:03:09 2014-04-17       7      65\n4 004     2014-03-17 23:59:59 2014-03-19 13:09:09 2014-05-04       3      49\nIf you want to get --DT or --DTM variables using admiral then check out derive_vars_dt() and derive_vars_dtm(). If things are messy in your data, e.g. partial dates, both functions have great imputation abilities, which we will cover in an upcoming blog post!"
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-    "text": "Every developer must solve two difficult problems when creating a Shiny application (in fact, any application) from the ground up: software architecture and data design. In the world of clinical data analysis, however, much development has been aimed at providing a jump-start approach to creating R/Shiny applications that would take away most of the pain caused by these two problems.\nThis blog should help you get an idea of how easy it is to get started with the pharmaverse ecosystem. We will create an interactive clinical data dashboard using {teal} and will use {pharmaverseadam} as the data source.\nFirst, you would need to install the two packages. It is recommended to use {pak} to take advantage of parallel downloads and builds (note that you can use it together with {renv} for locking dependencies)."
+    "text": "When creating ADaM Basic Data Structure (BDS) datasets, we often encounter deriving a new parameter based on the analysis values (e.g., AVAL) of other parameters.\nThe admiral function derive_param_computed() adds a parameter computed from the analysis value of other parameters.\nIt works like a calculator to derive new records without worrying about merging and combining datasets, all you need is a derivation formula, which also improves the readability of the code."
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-    "section": "Building a Simple Teal App",
-    "text": "Building a Simple Teal App\nNext, we would create a simple app - basically following the official teal guide, but we will use datasets from the {pharmaverseadam} package.\n# Step 1: import packages\nlibrary(teal)\nlibrary(pharmaverseadam)\n\n# Step 2: create a teal data object\ndata &lt;- cdisc_data(\n  ADAE = pharmaverseadam::adae,\n  ADSL = pharmaverseadam::adsl\n)\n\n# Step 3: initialize teal app\napp &lt;- init(\n  data = data,\n  modules = example_module()\n)\n\n# Step 4: run shiny app\nshinyApp(app$ui, app$server)\n\nLet’s Take a Closer Look at Each Step:\n\nIn step 1, we import libraries to use functions and datasets exported by those.\nIn step 2, we create a {teal} data object that can be used in a {teal} app. There are a couple of subtle details here: instead of the default teal_data() function, we use a special wrapper around it, designed specifically for clinical trial data - cdisc_data(). Its advantage is that it will automatically generate join_keys() for the datasets that we pass in. Please note, that the dataset names should be all caps. The resulting data object is an s4 class instance, and we can verify that the output is correct by checking data names and join_keys slots:\n\nr$&gt; data@datanames\n[1] \"ADAE\" \"ADSL\"\n\nr$&gt; data@join_keys\nA join_keys object containing foreign keys between 2 datasets:\nADSL: [STUDYID, USUBJID]\n  &lt;-- ADAE: [STUDYID, USUBJID]\nADAE: [STUDYID, USUBJID, ASTDTM, AETERM, AESEQ]\n  --&gt; ADSL: [STUDYID, USUBJID]\n\nIn step 3, we initialize the {teal} app by passing it a data object and adding an example module—it’s not much, but it will let us verify that the app works. The output app object is just a list with UI and server functions.\nFinally step 4 should be familiar to all Shiny users - we need to pass the UI and server function from the app object generated at step 3.\n\nWhen we run the app, this is what we will see in the web browser:\n\nAs you can see, with just a few lines of code, we were able to create a working app with some interesting capabilities. We have two datasets that we can switch between. For each dataset, we have keys defined (marked with a special key icon in the variable dropdown). When we filter one dataset, the other one gets filtered as well because they are connected with a key.\nThis is truly impressive, but there is just one problem… If we click the “Show R code” button, we will notice that the data used in the app is not “reproducible.” This simply means that the app currently does not have information about where the data comes from, so it cannot instruct users on how to obtain the same data.\n\nLet’s fix this. We will have to make the code slightly more verbose:\ndata &lt;- within(teal_data(), {\n  ADAE &lt;- pharmaverseadam::adae\n  ADSL &lt;- pharmaverseadam::adsl\n})\ndatanames(data) &lt;- c(\"ADAE\", \"ADSL\")\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames(data)]\nThe recommended method is to generate the data using a within function. However, this method requires manually providing databases using a helper function. We also need to provide join_keys ourselves, but given that the data names are standard ADaM names, we can take advantage of a special default_cdisc_join_keys object.\nIt is also worth noting that {teal} has its implementation of within generic.\nThis is what we will see now when running the application:\n\nNow we have reproducible data. But what about the app itself? Surely, {teal} features don’t end here. There is a collection of pre-built shiny modules that can be used in teal applications. We can install them with pak:\npak::pkg_install(\n  c(\"sparkline\", \"teal.modules.general\", \"teal.modules.clinical\")\n)\nFirst, we suggest exploring the “general” modules that are applicable to any kind of data. The only (!) thing we need to do, is to add two more modules to the app initializer:\napp &lt;- init(\n  data = data,\n  modules = modules(\n    example_module(),\n    tm_data_table(\"Table View\"),\n    tm_variable_browser(\"Variables\")\n  )\n)\nThis is possible thanks to the magic that {teal} is doing under the hood - passing the data object to each module. And now we will have access to a nice tabular view of the data, and a tool to explore each variable in greater detail.\n\nThis is great on its own, but as a bonus, we even get the ability to build a report based on some of the modules that we have. For example, we can generate some plots in the variable browser, add them to the report and preview it. Some modules would also add a block of R code showing how to get the exact same data that was used to generate a report card.\nFinally, let’s add a simple barchart module that comes from the clinical modules package. We will use an example from the {teal.modules.clinical} documentation:\nbarchart_module &lt;- tm_g_barchart_simple(\n  label = \"ADAE Analysis\",\n  x = data_extract_spec(\n    dataname = \"ADAE\",\n    select = select_spec(\n      choices = variable_choices(\n        pharmaverseadam::adae,\n        c(\"ARM\", \"ACTARM\", \"SEX\")\n      ),\n      selected = \"ACTARM\",\n      multiple = FALSE\n    )\n  )\n)\n\nThe best part about this module is that when a card is added to the report, it has R code that will reproduce exactly the same output that we see in the app.\nHere is the entire code for the application. In just 40 lines of code we were able to create a feature-rich application with the ability to interact with ADaM data, create visualizations and generate reproducible reports.\nlibrary(sparkline)\nlibrary(teal)\nlibrary(teal.data)\nlibrary(teal.modules.clinical)\nlibrary(teal.modules.general)\n\ndata &lt;- within(teal_data(), {\n  ADAE &lt;- pharmaverseadam::adae\n  ADSL &lt;- pharmaverseadam::adsl\n  # nolint end\n})\ndatanames(data) &lt;- c(\"ADAE\", \"ADSL\")\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames(data)]\n\nbarchart_module &lt;- tm_g_barchart_simple(\n  label = \"ADAE Analysis\",\n  x = data_extract_spec(\n    dataname = \"ADAE\",\n    select = select_spec(\n      choices = variable_choices(\n        pharmaverseadam::adae,\n        c(\"ARM\", \"ACTARM\", \"SEX\")\n      ),\n      selected = \"ACTARM\",\n      multiple = FALSE\n    )\n  )\n)\n\napp &lt;- init(\n  data = data,\n  modules = modules(\n    example_module(),\n    tm_data_table(\"Table View\"),\n    tm_variable_browser(\"Variables\"),\n    barchart_module\n  )\n)\n\nshinyApp(app$ui, app$server)"
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+    "text": "When creating ADaM Basic Data Structure (BDS) datasets, we often encounter deriving a new parameter based on the analysis values (e.g., AVAL) of other parameters.\nThe admiral function derive_param_computed() adds a parameter computed from the analysis value of other parameters.\nIt works like a calculator to derive new records without worrying about merging and combining datasets, all you need is a derivation formula, which also improves the readability of the code."
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-    "title": "Simplifying Clinical Data Dashboards with {teal} and {pharmaverseadam}",
-    "section": "Conclusion ",
-    "text": "Conclusion \nIn conclusion, {teal} and {pharmaverseadam} make it much easier to create interactive and reproducible clinical data dashboards. By following this guide, you can quickly build a Shiny app that not only visualizes your data but also maintains reproducibility and customization options. \nGet the latest updates from the pharmaverse delivered to your inbox. Subscribe to our newsletter today."
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+    "text": "Example\nA value level validation use case, where derive_param_computed() is used to validate a derived parameter - PARAMCD = ADPCYMG (Actual Dose per Cycle) in ADEX dataset.\n\nDerivation\nActual Dose per Cycle is derived from the Total Amount of Dose (PARAMCD = TOTDOSE) / Number of Cycles (PARAMCD = NUMCYC)\nIn this example, ADEX.AVAL when ADEX.PARAMCD = ADPCYMG can be derived as:\n\\[\nAVAL (PARAMCD = ADPCYMG) = \\frac{AVAL (PARAMCD = TOTDOSE)}{AVAL (PARAMCD = NUMCYC)}\n\\]\n\n\nLoading Packages and Creating Example Data\n\nlibrary(tibble)\nlibrary(dplyr)\nlibrary(diffdf)\nlibrary(admiral)\n\nadex &lt;- tribble(\n  ~USUBJID,  ~PARAMCD,  ~PARAM,                       ~AVAL,\n  \"101\",     \"TOTDOSE\", \"Total Amount of Dose (mg)\",  180,\n  \"101\",     \"NUMCYC\",  \"Number of Cycles\",           3\n)\n\n\n\nDerive New Parameter\n\nadex_admiral &lt;- derive_param_computed(\n  adex,\n  by_vars = exprs(USUBJID),\n  parameters = c(\"TOTDOSE\", \"NUMCYC\"),\n  set_values_to = exprs(\n    PARAMCD = \"ADPCYMG\",\n    PARAM = \"Actual Dose per Cycle (mg)\",\n    AVAL = AVAL.TOTDOSE / AVAL.NUMCYC\n  )\n)\n\n\n\n# A tibble: 3 × 4\n  USUBJID PARAMCD PARAM                       AVAL\n  &lt;chr&gt;   &lt;chr&gt;   &lt;chr&gt;                      &lt;dbl&gt;\n1 101     TOTDOSE Total Amount of Dose (mg)    180\n2 101     NUMCYC  Number of Cycles               3\n3 101     ADPCYMG Actual Dose per Cycle (mg)    60\n\n\n\n\nCompare\nFor validation purpose, the diffdf package is used below to mimic SAS proc compare.\n\nadex_expected &lt;- bind_rows(\n  adex,\n  tribble(\n    ~USUBJID,  ~PARAMCD,  ~PARAM,                       ~AVAL,\n    \"101\",     \"ADPCYMG\", \"Actual Dose per Cycle (mg)\", 60\n  )\n)\n\ndiffdf(adex_expected, adex_admiral, keys = c(\"USUBJID\", \"PARAMCD\"))\n\nNo issues were found!"
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-    "text": "Last updated\n\n2024-07-22 07:39:30.770501"
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-    "text": "Last updated\n\n2024-07-22 07:39:27.561749"
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     "title": "Admiral Hackathon 2023 Revisited",
     "section": "Last updated",
-    "text": "Last updated\n\n2024-07-22 07:39:20.545946"
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-    "text": "Last updated\n\n2024-07-22 07:39:15.051281"
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     "title": "Hello pharmaverse",
     "section": "Last updated",
-    "text": "Last updated\n\n2024-07-22 07:39:10.438958"
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-    "text": "Last updated\n\n2024-07-22 07:39:07.101943"
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-    "text": "Last updated\n\n2024-07-22 07:39:03.288501"
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     "section": "Last updated",
-    "text": "Last updated\n\n2024-07-22 07:38:42.93083"
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     "title": "Our experience as new admiral developers, coming from a CRO",
     "section": "Last updated",
-    "text": "Last updated\n\n2024-07-22 07:38:38.746511"
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     "title": "teal is now available on CRAN 🎉",
     "section": "Last updated",
-    "text": "Last updated\n\n2024-07-22 07:38:35.446549"
+    "text": "Last updated\n\n2024-07-22 07:51:06.962373"
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     "title": "PK Examples",
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     "title": "The pharmaverse (hi)story",
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-    "text": "Last updated\n\n2024-07-22 07:39:11.925774"
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     "title": "Cross-Industry Open Source Package Development",
     "section": "Last updated",
-    "text": "Last updated\n\n2024-07-22 07:39:17.046527"
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     "title": "How to use Code Sections",
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-    "text": "Last updated\n\n2024-07-22 07:39:25.704262"
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     "section": "Last updated",
-    "text": "Last updated\n\n2024-07-22 07:39:32.339841"
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-    "text": "Last updated\n\n2024-07-22 07:39:36.370718"
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-    "section": "",
-    "text": "When creating ADaM Basic Data Structure (BDS) datasets, we often encounter deriving a new parameter based on the analysis values (e.g., AVAL) of other parameters.\nThe admiral function derive_param_computed() adds a parameter computed from the analysis value of other parameters.\nIt works like a calculator to derive new records without worrying about merging and combining datasets, all you need is a derivation formula, which also improves the readability of the code."
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-    "text": "When creating ADaM Basic Data Structure (BDS) datasets, we often encounter deriving a new parameter based on the analysis values (e.g., AVAL) of other parameters.\nThe admiral function derive_param_computed() adds a parameter computed from the analysis value of other parameters.\nIt works like a calculator to derive new records without worrying about merging and combining datasets, all you need is a derivation formula, which also improves the readability of the code."
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-    "text": "Example\nA value level validation use case, where derive_param_computed() is used to validate a derived parameter - PARAMCD = ADPCYMG (Actual Dose per Cycle) in ADEX dataset.\n\nDerivation\nActual Dose per Cycle is derived from the Total Amount of Dose (PARAMCD = TOTDOSE) / Number of Cycles (PARAMCD = NUMCYC)\nIn this example, ADEX.AVAL when ADEX.PARAMCD = ADPCYMG can be derived as:\n\\[\nAVAL (PARAMCD = ADPCYMG) = \\frac{AVAL (PARAMCD = TOTDOSE)}{AVAL (PARAMCD = NUMCYC)}\n\\]\n\n\nLoading Packages and Creating Example Data\n\nlibrary(tibble)\nlibrary(dplyr)\nlibrary(diffdf)\nlibrary(admiral)\n\nadex &lt;- tribble(\n  ~USUBJID,  ~PARAMCD,  ~PARAM,                       ~AVAL,\n  \"101\",     \"TOTDOSE\", \"Total Amount of Dose (mg)\",  180,\n  \"101\",     \"NUMCYC\",  \"Number of Cycles\",           3\n)\n\n\n\nDerive New Parameter\n\nadex_admiral &lt;- derive_param_computed(\n  adex,\n  by_vars = exprs(USUBJID),\n  parameters = c(\"TOTDOSE\", \"NUMCYC\"),\n  set_values_to = exprs(\n    PARAMCD = \"ADPCYMG\",\n    PARAM = \"Actual Dose per Cycle (mg)\",\n    AVAL = AVAL.TOTDOSE / AVAL.NUMCYC\n  )\n)\n\n\n\n# A tibble: 3 × 4\n  USUBJID PARAMCD PARAM                       AVAL\n  &lt;chr&gt;   &lt;chr&gt;   &lt;chr&gt;                      &lt;dbl&gt;\n1 101     TOTDOSE Total Amount of Dose (mg)    180\n2 101     NUMCYC  Number of Cycles               3\n3 101     ADPCYMG Actual Dose per Cycle (mg)    60\n\n\n\n\nCompare\nFor validation purpose, the diffdf package is used below to mimic SAS proc compare.\n\nadex_expected &lt;- bind_rows(\n  adex,\n  tribble(\n    ~USUBJID,  ~PARAMCD,  ~PARAM,                       ~AVAL,\n    \"101\",     \"ADPCYMG\", \"Actual Dose per Cycle (mg)\", 60\n  )\n)\n\ndiffdf(adex_expected, adex_admiral, keys = c(\"USUBJID\", \"PARAMCD\"))\n\nNo issues were found!"
+    "text": "Reading in SAS-based datasets (.sas7bdat or xpt) into R has users calling the R package haven. A typical call might invoke read_sas() or read_xpt() to bring in your source data to construct your ADaMs or SDTMs.\nUnfortunately, while using haven the character blanks (missing data) found in a typical SAS-based dataset are left as blanks. These blanks will typically prove problematic while using functions like is.na in combination with dplyr::filter() to subset data. Check out Bayer’s SAS2R catalog: handling-of-missing-values for more discussion on missing values and NAs.\nIn the admiral package, we have built a simple function called convert_blanks_to_na() to help us quickly remedy this problem. You can supply an entire dataframe to this function and it will convert any character blanks to NA_character_"
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-    "title": "Derive a new parameter computed from the value of other parameters",
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+    "title": "Blanks and NAs",
+    "section": "Loading Packages and Making Dummy Data",
+    "text": "Loading Packages and Making Dummy Data\n\nlibrary(admiral)\nlibrary(tibble)\nlibrary(dplyr)\n\ndf &lt;- tribble(\n  ~USUBJID, ~RFICDTC,\n  \"01\", \"2000-01-01\",\n  \"02\", \"2001-01-01\",\n  \"03\", \"\", # Here we have a character blank\n  \"04\", \"2001-01--\",\n  \"05\", \"2001---01\",\n  \"05\", \"\", # Here we have a character blank\n)\n\ndf\n\n# A tibble: 6 × 2\n  USUBJID RFICDTC     \n  &lt;chr&gt;   &lt;chr&gt;       \n1 01      \"2000-01-01\"\n2 02      \"2001-01-01\"\n3 03      \"\"          \n4 04      \"2001-01--\" \n5 05      \"2001---01\" \n6 05      \"\""
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+    "title": "Blanks and NAs",
+    "section": "A simple conversion",
+    "text": "A simple conversion\n\ndf_na &lt;- convert_blanks_to_na(df)\n\ndf_na\n\n# A tibble: 6 × 2\n  USUBJID RFICDTC   \n  &lt;chr&gt;   &lt;chr&gt;     \n1 01      2000-01-01\n2 02      2001-01-01\n3 03      &lt;NA&gt;      \n4 04      2001-01-- \n5 05      2001---01 \n6 05      &lt;NA&gt;      \n\n\n\ndf_na %&gt;% filter(is.na(RFICDTC))\n\n# A tibble: 2 × 2\n  USUBJID RFICDTC\n  &lt;chr&gt;   &lt;chr&gt;  \n1 03      &lt;NA&gt;   \n2 05      &lt;NA&gt;"
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-    "text": "Creating --DY variables for your ADaMs is super easy using derive_vars_dy() from the admiral package.\nLet’s build some dummy data with 4 subjects, a start date/time for treatment (TRTSDTM), an analysis start date/time variable (ASTDTM) and an analysis end date variable (AENDT).\nlibrary(admiral)\nlibrary(lubridate)\nlibrary(dplyr)\n\nadam &lt;- tribble(\n  ~USUBJID, ~TRTSDTM, ~ASTDTM, ~AENDT,\n  \"001\", \"2014-01-17T23:59:59\", \"2014-01-18T13:09:O9\", \"2014-01-20\",\n  \"002\", \"2014-02-25T23:59:59\", \"2014-03-18T14:09:O9\", \"2014-03-24\",\n  \"003\", \"2014-02-12T23:59:59\", \"2014-02-18T11:03:O9\", \"2014-04-17\",\n  \"004\", \"2014-03-17T23:59:59\", \"2014-03-19T13:09:O9\", \"2014-05-04\"\n) %&gt;%\n  mutate(\n    TRTSDTM = as_datetime(TRTSDTM),\n    ASTDTM = as_datetime(ASTDTM),\n    AENDT = ymd(AENDT)\n  )\nOkay! Next we run our dataset through derive_vars_dy(), specifying:\nderive_vars_dy(\n  adam,\n  reference_date = TRTSDTM,\n  source_vars = exprs(ASTDTM, AENDT)\n)\n\n# A tibble: 4 × 6\n  USUBJID TRTSDTM             ASTDTM              AENDT      ASTDY AENDY\n  &lt;chr&gt;   &lt;dttm&gt;              &lt;dttm&gt;              &lt;date&gt;     &lt;dbl&gt; &lt;dbl&gt;\n1 001     2014-01-17 23:59:59 2014-01-18 13:09:09 2014-01-20     2     4\n2 002     2014-02-25 23:59:59 2014-03-18 14:09:09 2014-03-24    22    28\n3 003     2014-02-12 23:59:59 2014-02-18 11:03:09 2014-04-17     7    65\n4 004     2014-03-17 23:59:59 2014-03-19 13:09:09 2014-05-04     3    49\nThat’s it! We got both our ASTDY and AENDY variables in only a few short lines of code!\nWhat if I want my variables to have a different naming convention?\nEasy! In the source_vars argument if you want your variables to be called DEMOADY and DEMOEDY just do DEMOADY = ASTDTM and DEMOEDY = AENDT and derive_vars_dy() will do the rest!\nderive_vars_dy(\n  adam,\n  reference_date = TRTSDTM,\n  source_vars = exprs(DEMOADY = ASTDTM, DEMOEDY = AENDT)\n)\n\n# A tibble: 4 × 6\n  USUBJID TRTSDTM             ASTDTM              AENDT      DEMOADY DEMOEDY\n  &lt;chr&gt;   &lt;dttm&gt;              &lt;dttm&gt;              &lt;date&gt;       &lt;dbl&gt;   &lt;dbl&gt;\n1 001     2014-01-17 23:59:59 2014-01-18 13:09:09 2014-01-20       2       4\n2 002     2014-02-25 23:59:59 2014-03-18 14:09:09 2014-03-24      22      28\n3 003     2014-02-12 23:59:59 2014-02-18 11:03:09 2014-04-17       7      65\n4 004     2014-03-17 23:59:59 2014-03-19 13:09:09 2014-05-04       3      49\nIf you want to get --DT or --DTM variables using admiral then check out derive_vars_dt() and derive_vars_dtm(). If things are messy in your data, e.g. partial dates, both functions have great imputation abilities, which we will cover in an upcoming blog post!"
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+    "title": "Blanks and NAs",
+    "section": "That’s it!",
+    "text": "That’s it!\nA simple call to this function can make your derivation life so much easier while working in R if working with SAS-based datasets. In admiral, we make use of this function at the start of all ADaM templates for common ADaM datasets. You can use the function use_ad_template() to get the full R script for the below ADaMs.\n\nlist_all_templates()\n\nExisting ADaM templates in package 'admiral':\n• ADAE\n• ADCM\n• ADEG\n• ADEX\n• ADLB\n• ADLBHY\n• ADMH\n• ADPC\n• ADPP\n• ADPPK\n• ADSL\n• ADVS"
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     "section": "Last updated",
-    "text": "Last updated\n\n2024-07-22 07:39:43.980182"
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-    "text": "Greetings, pharmaverse phriends!\nNow that the pharmaverse has been around for a few years and seems to be making an impact on clinical reporting (yay!), we wanted to take an opportunity to cast a light on some of the inner workings of pharmaverse and its Council (us). Fortunately, some of you (you know who you are) innovated and created this fantastic channel for pharmaverse blogs. We can’t think of a better way to communicate than by the channel that the pharmaverse community itself created."
+    "text": "The R Consortium Submission Working Group has now successfully made two pilot submissions to the FDA. All the submissions done by the group are focused on improving practices for R-based clinical trial regulatory submissions. Now, the R submission Working Groups, in collaboration with Appsilon and Posit, are exploring new technologies such as Containers and WebAssembly. In this article, we dive into the details of this exploration."
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-    "text": "Pharmaverse Inception\nThe pharmaverse Council was formed in 2020 as a coming together of 4 like-minded individuals embedded in the industry-wide efforts to increase the use of the R language for clinical trial analysis and reporting. These individuals included Ross Farrugia (Roche), Sumesh Kalappurakal (Johnson & Johnson), Michael Rimler (GSK), and Mike Stackhouse (Atorus). Each had historical experience with R adoption, through their primary roles within their respective organizations.\nCollectively, these 4 individuals saw the value in\n\nreducing the choice set of R packages for users,\nidentifying gaps in available R packages for delivering the clinical data pipeline, and\nencouraging the development of new packages and/or features to close those gaps.\n\nThe result would be a subset of permissively licensed and open-sourced packages that anyone could use (or modify) to suit their specific use cases, thereby reducing the incidence of organizations privately solving typical problems in isolation. For more background, please refer to the pharmaverse Charter.\nThe current pharmaverse Council companies include GSK, Roche, Johnson & Johnson, Atorus, and Novo Nordisk, represented by the original four founders and Ari Siggaard Knoph from Novo Nordisk.\nIn March 2023, the PHUSE Board of Directors approached the pharmaverse Council with a proposal offering support to pharmaverse developers and leadership to advance the pharmaverse mission. The PHUSE proposal aligned with its mission to promote “[s]haring ideas, tools and standards around data, statistical and reporting technologies to advance the future of life sciences.” PHUSE is currently a supporter of the pharmaverse and its objectives, evident through activities such as:\n\nthe provision of access to PHUSE’s communication and file-sharing platforms to package teams,\nthe provision of project management support to package teams (if needed),\nenablement and promotion of pharmaverse innovations and activities, and\nenablement of face-to-face community connections at PHUSE events."
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+    "text": "How Everything Started\n\nPilot 1\nThis pilot was initially submitted on November 22, 2021. This submission was the first publicly available R-based submission to the FDA. This was a test submission that aimed to explore the submission of an R package to the FDA following the eCTD specifications. The submission included an R package, R scripts for analysis, R-based analysis data reviewed guide (ADRG), and other important components. The final response letter from the FDA was received on March 14, 2022."
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-    "text": "Pharmaverse Objective\nPharmaverse aims to promote the collaborative development of curated open-source R packages for clinical reporting in pharma. Indirectly, we also strive to encourage and increase R adoption within the industry and facilitate communication and collaboration among both R users and R developers."
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+    "text": "Pilot 2\nThis was one of the first submission packages containing a Shiny application. The main goal of this pilot was to test the submission of an R-based Shiny application bundled into a submission package and transfer it successfully to FDA reviewers. The submitted application was built using the datasets and analyses that were used for the R Submission Pilot 1. The deployed version of this application is available on this site. Alternatively, a Rhino-based version of the application can be found here.\nThe final response letter from the FDA was reviewed on September 27, 2023.\n\n\n\n\n\nIn this submission, there were many open-source R packages that were used to create and execute the Shiny application. A very well-known shiny-based interactive exploration framework {teal} was used mainly for analyzing the clinical trial data; this package is included in the pharmaverse package repository. The full list of open-source and proprietary R analysis packages is available on this Analysis Data Reviewer’s Guide prepared by the R Consortium R Submissions Working Group for the Pilot 2."
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-    "text": "Becoming a Council Member\nMembership to the pharmaverse Council is granted to the organization and is open to any type of organization (requirements further described here). In layperson’s terms, the Council member organization must support a representative on the council and demonstrate a commitment to contributions to the pharmaverse codebase. Specifically, “Commitment to at least 2 open source packages under pharmaverse via reviews, hands-on code development, product leads, or other roles which contribute to the design, development, testing, release, and/or maintenance.” If any representative steps away from their council position, the member organization would identify the replacement. Council meeting minutes are published in the pharmaverse GitHub here."
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+    "text": "Pilot 3\nThis pilot was successfully submitted to the FDA on Aug 28, 2023. This was the first publicly available R submission that included R scripts to produce ADaM datasets and TLFs. Both the ADaMs (SDTM .xpt sources from the CDISC Pilot study) and the TLFs (ADaMs .xpt sourced from the ADaMs generated in R by the Pilot 3 team) were created using R.  The next step for this pilot is to await FDA’s review and approval, which may take several months to complete."
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-    "text": "Package Inclusion\nApplications for a package to be included in the pharmaverse may originate from anybody and anywhere. The Council (through its Working Groups) reviews applications and either (i) approves the package into the pharmaverse or (ii) rejects the package with a rationale. When a rejection has been issued, common reasons have been ‘not now’ with feedback to enhance testing, documentation, or other aspects that enrich the package. Working Groups may also steer the applicant to other packages with similar functionality. In this sense, respective package maintainers (product owners) are encouraged to work collaboratively to reduce duplicated features across packages and improve the overall experience of the user base. The Council and Working Groups are tasked with assessing this aspect of the pharmaverse code base, but the decision on implementation ultimately resides with the individual package maintainers.\nPackage reviews are conducted in the open:\n\nExample 1: {riskassessment} from R Validation Hub working group – result successful: https://github.com/pharmaverse/pharmaverse/issues/195\nExample 2: {rhino} from Appsilon – result successful: https://github.com/pharmaverse/pharmaverse/issues/260\nExample 3: {synthetic.cdisc.data} from Roche – result unsuccessful: https://github.com/pharmaverse/pharmaverse/issues/235\n\nAt the time of publication, there were over 250 contributors to pharmaverse packages, across a network of organizations much broader than just the 5 Council member organizations. pharmaverse Council encourages diversity of individual contributors and the organizations they are affiliated with. The decision on product development team rosters is the sole discretion of the packages. Of course, Council member organizations are providing people to support the pharmaverse ecosystem (e.g., developers and product owners). However, both strategic and operational decisions on the development and maintenance of individual packages reside outside the pharmaverse Council."
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+    "text": "Pilot 4\nThis pilot aims to explore using technologies such as containers and WebAssembly software to package a Shiny application into a self-contained unit, streamlining the transfer and execution process for enhanced efficiency.\nThis pilot is expected to be divided into two parallel submissions:\n(a) will investigate WebAssembly and\n(b) will investigate containers.\n\nThe Journey with WebAssembly and Containers\nOur team at Appsilon teamed up with the dynamic Pilot 4 crew to explore WebAssembly technology and containers. George Stagg and Winston Chang also joined the working group to discuss the web-assembly portion of Pilot 4. This partnership brought together our engineering prowess to contribute to these tools, injecting fresh perspectives into the ongoing pilot project.\nSome of the outcomes of the collaboration:\n\nWe were able to set up a robust container environment for this pilot project. \nWe aided the progress made on the use of both experimental technologies: containers and WebAssembly.\nWe developed a working prototype submission using Podman container technology.\nWe developed a working early-stage prototype for wrapping a small Shiny application using WebAssembly.\n\n\nWebAssembly\n\nWebAssembly allows languages like R to be executed at near-native speed directly within web browsers, providing users with the ability to run R code without having R installed locally. WebR is essentially the R programming language adapted to run in a web browser environment using WebAssembly. This project is under active development. \n\n\n\nThe Pilot 4 Shiny App Up and Running on webR!\n\n\n\n\n\nThe deployed example of the Shiny app running on webR is available here. Check out the video of the application running below.\n\nDuring this pilot, engineers at Appsilon developed a prototype of a Shiny application running on webR. The application reuses most of the code from the previous pilot apps with some tweaks and a couple of hacks/changes to get around non CRAN dependencies, specially for data loading, WebR compatibilities, and shimming some of the functionality from {teal} and other packages that are (for now) not available on CRAN.\n\nwebR Shiny App\nDuring the second iteration, which was recently held, Pedro Silva shared the process of developing this Shiny app running on webR.\n\n\nThe Process\n\nLeverage the last 2 iterations of the application\n\nReuse as much code as possible\nAvoid touching the logic part\n\nRestrict the number of dependencies to packages on CRAN\n\nReplace/shim functionality that was lost from removing dependencies\n\n\nHere is the list of dependencies to packages on CRAN; those that worked are colored green, and those that were removed are marked in orange. We ended up with just 3 problematic dependencies (bold).\n\n\n\n\n\nIssues with library(cowplot):\n\nSome issues with low-level dependencies when deployed\n\nSolution:\n\nReplace functionality with HTML\n\nIssues with library(teal):\n\nUses {shiny.widgets} (not working for webR)\n\nSolution:\n\nRedo the UI\nLoad modules directly\nRecreate filter functionality\n\nIssues with library(teal.data):\n\nUse rds exports\n\nSolution:\n\nShim functionality, load data directly\n\n\nLeverage shinylive and httpuv to export and serve the application\n\nShinylive can help streamline the export process\n\nProblems\n\nshiny.live won’t let us have non-R files in the application directory - this is an outstanding bug that George asked us to raise an issue for.\nWe wouldn’t be able to run the application as a traditional shiny app.\n\nSolution:\n\nCustom build script\n\n\n{httpuv} can help serve the application\n\n{httpuv} would run natively on a machine to serve the Shiny app\n\n\n\n\n\nApplication Structure\nThe figure below shows an overview of what we ended with:\n\nSome of the issues and solutions found at the very beginning:\n\nThe previous applications were built using golem and another one in Rhino; the support for these frameworks is not great in webR up to now.\n\nSolution\n\n{box} works out of the box (reuse the rhino version modules)\nSimplify the structure and use a simple shiny modular structure\n\n\nShinylive does not like non-R files when generating the bundle\n\nSolution\n\nKeep the app folder as clean as possible for now (www folder only)\n\n\n{teal} and {teal.data} are not on CRAN\n\nSolution\n\nShim and used functionality\nUse a simple tab system for the UI structure\n\n\n\nThe FDA was previously told that the shiny application being prepared for the Pilot 4 submission would not be a 1 to 1 mapping from the previous one submitted for the Pilot 2 due to certain constraints such as {teal} not being on CRAN; however, this didn’t represent a problem for them since they would mainly like to test the technology.\nPedro Silva, one of the engineers working on the development of this app, mentioned “While WebR is still in development, it shows tremendous promise! The loading is definitely still a pain point (over 100mb to set up the environment!) but it will only get better moving forward.”\n\n\nContainers\n\n\n\n\n\nContainerization, particularly through technologies like Docker, Podman or Singularity, offers several advantages for deploying Shiny apps.\n\nChoosing the Right Container\nChoosing the right container was a question that arose in this project. Although Docker is the most popular, we decided to move forward with Podman. \nIn our exploration of containerization tools for deploying Shiny applications, we’ve identified key distinctions between Docker and Podman that influenced our choice. \nPodman stands out for its daemonless architecture, enhancing security by eliminating the need for a central daemon process. Unlike Docker, Podman supports running containers as non-root users, a critical feature for meeting FDA reviewer requirements. Developed by Red Hat and maintained as an open-source project, Podman prioritizes security with its rootless container support, offering a robust solution for security-conscious users. \n\n\nGoals\nA Container-based method to deploy Pilot 2 Shiny App.\n\n\nWhat we did\n\nConfigurable Podman Dockerfile / docker-compose.yml\n\nR version\nRegistry / organization name / image name (differences between docker.io and ghcr.io)\n\nDocumentation on creating the container\nCI: Automated build on amd64 and arm64 platforms\n\n\n\nPodman short-demo\n\nBelow is the dockerfile (recipe) for the container:"
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-    "text": "User adoption\nPharma companies are free to choose any selection of R packages for clinical reporting and only they can justify their choices. The pharmaverse website states that “[a]nyone is free to choose any selection of pharmaverse recommended software or those from any other source.” In addition, pharmaverse does not seek to engage or get endorsement from any health authority. We are merely trying to provide a public service to help individuals and organizations involved in clinical reporting navigate a vast field of available R packages licensed as open source.\nThe pharmaverse website provides illustrative examples of how to use pharmaverse and other packages to build common deliverables."
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+    "text": "Last updated\n\n2024-07-22 07:52:17.16781"
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-    "section": "So, what really is pharmaverse?",
-    "text": "So, what really is pharmaverse?\nPharmaverse is essentially two things:\n\nPharmaverse is a list of packages curated by the pharmaverse Council and Working Groups, primarily communicated via the pharmaverse website. Pharmaverse also maintains an R-universe build for ease of use outside GxP settings.\npharmaverse is a community of R users and R developers “working to promote collaborative development of curated open-source R packages for clinical reporting usage in pharma.”\n\nPharmaverse Council provides a Slack workspace to build community amongst all interested parties and serve as a communication platform for individuals and package teams. At present, membership to this workspace exceeds &gt;1200 members.\nPharmaverse Council also provides a GitHub organization for developers to work in, but hosting packages in the pharmaverse GitHub is not required to be part of pharmaverse.\nThese elements are supported on an all-volunteer basis, mostly with community versions of various platforms (e.g., we use the free tier on Slack)."
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-    "section": "Thank you",
-    "text": "Thank you\nThank you, each of you, for the part that you play. No matter how big or small, you are helping amplify the impact that open collaboration is having on how we deliver new medicines and vaccines to patients around the world. We hope you are finding, and continue to find, pharmaverse a valuable piece of the clinical reporting puzzle.\nAnd, if not – let us know!\nPharmaverse Council – Ari, Michael, Mike, Ross, and Sumesh\nDisclaimer: This blog contains opinions that are of the authors alone and do not necessarily reflect the strategy of their respective organizations."
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+    "text": "Every developer must solve two difficult problems when creating a Shiny application (in fact, any application) from the ground up: software architecture and data design. In the world of clinical data analysis, however, much development has been aimed at providing a jump-start approach to creating R/Shiny applications that would take away most of the pain caused by these two problems.\nThis blog should help you get an idea of how easy it is to get started with the pharmaverse ecosystem. We will create an interactive clinical data dashboard using {teal} and will use {pharmaverseadam} as the data source.\nFirst, you would need to install the two packages. It is recommended to use {pak} to take advantage of parallel downloads and builds (note that you can use it together with {renv} for locking dependencies)."
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+    "section": "Building a Simple Teal App",
+    "text": "Building a Simple Teal App\nNext, we would create a simple app - basically following the official teal guide, but we will use datasets from the {pharmaverseadam} package.\n# Step 1: import packages\nlibrary(teal)\nlibrary(pharmaverseadam)\n\n# Step 2: create a teal data object\ndata &lt;- cdisc_data(\n  ADAE = pharmaverseadam::adae,\n  ADSL = pharmaverseadam::adsl\n)\n\n# Step 3: initialize teal app\napp &lt;- init(\n  data = data,\n  modules = example_module()\n)\n\n# Step 4: run shiny app\nshinyApp(app$ui, app$server)\n\nLet’s Take a Closer Look at Each Step:\n\nIn step 1, we import libraries to use functions and datasets exported by those.\nIn step 2, we create a {teal} data object that can be used in a {teal} app. There are a couple of subtle details here: instead of the default teal_data() function, we use a special wrapper around it, designed specifically for clinical trial data - cdisc_data(). Its advantage is that it will automatically generate join_keys() for the datasets that we pass in. Please note, that the dataset names should be all caps. The resulting data object is an s4 class instance, and we can verify that the output is correct by checking data names and join_keys slots:\n\nr$&gt; data@datanames\n[1] \"ADAE\" \"ADSL\"\n\nr$&gt; data@join_keys\nA join_keys object containing foreign keys between 2 datasets:\nADSL: [STUDYID, USUBJID]\n  &lt;-- ADAE: [STUDYID, USUBJID]\nADAE: [STUDYID, USUBJID, ASTDTM, AETERM, AESEQ]\n  --&gt; ADSL: [STUDYID, USUBJID]\n\nIn step 3, we initialize the {teal} app by passing it a data object and adding an example module—it’s not much, but it will let us verify that the app works. The output app object is just a list with UI and server functions.\nFinally step 4 should be familiar to all Shiny users - we need to pass the UI and server function from the app object generated at step 3.\n\nWhen we run the app, this is what we will see in the web browser:\n\nAs you can see, with just a few lines of code, we were able to create a working app with some interesting capabilities. We have two datasets that we can switch between. For each dataset, we have keys defined (marked with a special key icon in the variable dropdown). When we filter one dataset, the other one gets filtered as well because they are connected with a key.\nThis is truly impressive, but there is just one problem… If we click the “Show R code” button, we will notice that the data used in the app is not “reproducible.” This simply means that the app currently does not have information about where the data comes from, so it cannot instruct users on how to obtain the same data.\n\nLet’s fix this. We will have to make the code slightly more verbose:\ndata &lt;- within(teal_data(), {\n  ADAE &lt;- pharmaverseadam::adae\n  ADSL &lt;- pharmaverseadam::adsl\n})\ndatanames(data) &lt;- c(\"ADAE\", \"ADSL\")\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames(data)]\nThe recommended method is to generate the data using a within function. However, this method requires manually providing databases using a helper function. We also need to provide join_keys ourselves, but given that the data names are standard ADaM names, we can take advantage of a special default_cdisc_join_keys object.\nIt is also worth noting that {teal} has its implementation of within generic.\nThis is what we will see now when running the application:\n\nNow we have reproducible data. But what about the app itself? Surely, {teal} features don’t end here. There is a collection of pre-built shiny modules that can be used in teal applications. We can install them with pak:\npak::pkg_install(\n  c(\"sparkline\", \"teal.modules.general\", \"teal.modules.clinical\")\n)\nFirst, we suggest exploring the “general” modules that are applicable to any kind of data. The only (!) thing we need to do, is to add two more modules to the app initializer:\napp &lt;- init(\n  data = data,\n  modules = modules(\n    example_module(),\n    tm_data_table(\"Table View\"),\n    tm_variable_browser(\"Variables\")\n  )\n)\nThis is possible thanks to the magic that {teal} is doing under the hood - passing the data object to each module. And now we will have access to a nice tabular view of the data, and a tool to explore each variable in greater detail.\n\nThis is great on its own, but as a bonus, we even get the ability to build a report based on some of the modules that we have. For example, we can generate some plots in the variable browser, add them to the report and preview it. Some modules would also add a block of R code showing how to get the exact same data that was used to generate a report card.\nFinally, let’s add a simple barchart module that comes from the clinical modules package. We will use an example from the {teal.modules.clinical} documentation:\nbarchart_module &lt;- tm_g_barchart_simple(\n  label = \"ADAE Analysis\",\n  x = data_extract_spec(\n    dataname = \"ADAE\",\n    select = select_spec(\n      choices = variable_choices(\n        pharmaverseadam::adae,\n        c(\"ARM\", \"ACTARM\", \"SEX\")\n      ),\n      selected = \"ACTARM\",\n      multiple = FALSE\n    )\n  )\n)\n\nThe best part about this module is that when a card is added to the report, it has R code that will reproduce exactly the same output that we see in the app.\nHere is the entire code for the application. In just 40 lines of code we were able to create a feature-rich application with the ability to interact with ADaM data, create visualizations and generate reproducible reports.\nlibrary(sparkline)\nlibrary(teal)\nlibrary(teal.data)\nlibrary(teal.modules.clinical)\nlibrary(teal.modules.general)\n\ndata &lt;- within(teal_data(), {\n  ADAE &lt;- pharmaverseadam::adae\n  ADSL &lt;- pharmaverseadam::adsl\n  # nolint end\n})\ndatanames(data) &lt;- c(\"ADAE\", \"ADSL\")\njoin_keys(data) &lt;- default_cdisc_join_keys[datanames(data)]\n\nbarchart_module &lt;- tm_g_barchart_simple(\n  label = \"ADAE Analysis\",\n  x = data_extract_spec(\n    dataname = \"ADAE\",\n    select = select_spec(\n      choices = variable_choices(\n        pharmaverseadam::adae,\n        c(\"ARM\", \"ACTARM\", \"SEX\")\n      ),\n      selected = \"ACTARM\",\n      multiple = FALSE\n    )\n  )\n)\n\napp &lt;- init(\n  data = data,\n  modules = modules(\n    example_module(),\n    tm_data_table(\"Table View\"),\n    tm_variable_browser(\"Variables\"),\n    barchart_module\n  )\n)\n\nshinyApp(app$ui, app$server)"
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+    "title": "Simplifying Clinical Data Dashboards with {teal} and {pharmaverseadam}",
+    "section": "Conclusion ",
+    "text": "Conclusion \nIn conclusion, {teal} and {pharmaverseadam} make it much easier to create interactive and reproducible clinical data dashboards. By following this guide, you can quickly build a Shiny app that not only visualizes your data but also maintains reproducibility and customization options. \nGet the latest updates from the pharmaverse delivered to your inbox. Subscribe to our newsletter today."
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-    "text": "Last updated\n\n2024-07-22 07:39:50.430239"
+    "text": "Last updated\n\n2024-07-22 07:52:23.740939"
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     "title": "Welcome to the pharmaverse blog!",
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-    "text": "Order By\n       Default\n         \n          Title\n        \n         \n          Date - Oldest\n        \n         \n          Date - Newest\n        \n         \n          Author\n        \n     \n  \n    \n      \n      \n    \n\n\n\n\n\n\n\n\n\n\nUnix versus SAS Time\n\n\n\n\n\n\nTechnical\n\n\n\nThis blog explores SAS and R date handling differences, focusing on epoch discrepancies and data types, and provides key tips for accurate date conversions to prevent a 10-year date shift.\n\n\n\n\n\nJul 16, 2024\n\n\nCéline Piraux\n\n\n\n\n\n\n\n\n\n\n\n\nSimplifying Clinical Data Dashboards with {teal} and {pharmaverseadam}\n\n\n\n\n\n\nTechnical\n\n\nShiny\n\n\nCommunity\n\n\n\nLearn how to quickly develop interactive and reproducible clinical data dashboards using {teal} and {pharmaverseadam}.\n\n\n\n\n\nJul 12, 2024\n\n\nPavel Demin, Dror Berel, APPSILON\n\n\n\n\n\n\n\n\n\n\n\n\nIntroducing a new Coursera course for hands-on clinical data science using R.\n\n\n\n\n\n\nCommunity\n\n\n\n“Releasing our second (hands-on) Coursera course, aimed at enhancing the skills of data scientists and shedding light on the impact of R open-source software within the industry.” \n\n\n\n\n\nJul 3, 2024\n\n\nJoel Laxamana, Stefan Thoma\n\n\n\n\n\n\n\n\n\n\n\n\n{admiral} 1.1.1 is here!\n\n\n\n\n\n\nADaMs\n\n\n\nGet to know what awaits you with the new release of the {admiral} package, including enhanced error messaging, improved documentation and much more!\n\n\n\n\n\nJun 20, 2024\n\n\nEdoardo Mancini, Ben Straub\n\n\n\n\n\n\n\n\n\n\n\n\nNew {admiral} extension packages: {admiralpeds} & more coming soon!\n\n\n\n\n\n\nMetadata\n\n\nADaM\n\n\nCommunity\n\n\n\nWe are very excited to announce that new extension packages are expected very soon in the {admiral} family: {admiralpeds}, a pediatrics extension, and another extension (name TBC) for obesity in clinical trials\n\n\n\n\n\nJun 17, 2024\n\n\nFanny Gautier, Lina Patil\n\n\n\n\n\n\n\n\n\n\n\n\nDiversity & Inclusion in pharmaverse\n\n\n\n\n\n\ncommunity\n\n\n\nAn update from pharmaverse council regarding diversity and inclusion within our community.\n\n\n\n\n\nMay 31, 2024\n\n\npharmaverse council\n\n\n\n\n\n\n\n\n\n\n\n\nOur experience as new admiral developers, coming from a CRO\n\n\n\n\n\n\nCommunity\n\n\nConferences\n\n\n\nAs the first CRO having joined the {admiral} family, we are very excited to share with you our first steps and thoughts in this new adventure\n\n\n\n\n\nMay 29, 2024\n\n\nFanny Gautier, Lina Patil\n\n\n\n\n\n\n\n\n\n\n\n\nTLG Catalog 🤝 WebR\n\n\n\n\n\n\nTLG\n\n\nShiny\n\n\n\nIntroducing WebR to TLG Catalog: A Game Changer for Interactive Learning\n\n\n\n\n\nMay 8, 2024\n\n\nPawel Rucki\n\n\n\n\n\n\n\n\n\n\n\n\nDe-Mystifying R Programming in Clinical Trials\n\n\n\n\n\n\nCommunity\n\n\n\nA blog highlighting the benefits/limitations of using R Programming and using the right tools to create value\n\n\n\n\n\nMay 2, 2024\n\n\nVenkatesan Balu\n\n\n\n\n\n\n\n\n\n\n\n\nAppsilon and Sanofi join the pharmaverse council!\n\n\n\n\n\n\nCommunity\n\n\n\nUpdates to the pharmaverse council\n\n\n\n\n\nApr 29, 2024\n\n\nAri Siggaard Knoph\n\n\n\n\n\n\n\n\n\n\n\n\nteal.modules.clinical v0.9.0 is now on CRAN!\n\n\n\n\n\n\nTLG\n\n\nShiny\n\n\n\nThis package release now completes the suite of {teal} family of packages recently released to CRAN.\n\n\n\n\n\nApr 8, 2024\n\n\nLeena Khatri\n\n\n\n\n\n\n\n\n\n\n\n\nxportr 0.4.0\n\n\n\n\n\n\nADaM\n\n\nSDTM\n\n\n\nxportr is a tool tailor-made for clinical programmers, enabling them to generate CDISC-compliant XPT files for SDTM and/or ADaM. It streamlines the preparation on xpt files, ensuring they are primed for submission to clinical data validation applications or regulatory agencies.\n\n\n\n\n\nMar 29, 2024\n\n\nSadchla Mascary\n\n\n\n\n\n\n\n\n\n\n\n\nTips for First Time Contributors\n\n\n\n\n\n\nCommunity\n\n\n\nOpen source can be daunting the first time you dive in - this blog will help you get started!\n\n\n\n\n\nMar 11, 2024\n\n\nRoss Farrugia\n\n\n\n\n\n\n\n\n\n\n\n\nInside the pharmaverse\n\n\n\n\n\n\nCommunity\n\n\n\nA short blog to help the Pharmaverse community understand how Pharmaverse is governed.\n\n\n\n\n\nMar 4, 2024\n\n\nMichael Rimler\n\n\n\n\n\n\n\n\n\n\n\n\nRhino: A Step Forward in Validating Shiny Apps\n\n\n\n\n\n\nSubmission\n\n\nCommunity\n\n\n\nIn this post, we explore the importance of validation in the pharmaceutical industry and how Rhino framework aids in ensuring reliability and accuracy of Shiny applications.\n\n\n\n\n\nMar 1, 2024\n\n\nKamil Żyła, Ege Can Taşlıçukur, APPSILON\n\n\n\n\n\n\n\n\n\n\n\n\nFilter out the noise!\n\n\n\n\n\n\nADaM\n\n\nTechnical\n\n\n\nA brief exposition of the filter_* functions in {admiral} - what they do and how to use them.\n\n\n\n\n\nFeb 26, 2024\n\n\nEdoardo Mancini\n\n\n\n\n\n\n\n\n\n\n\n\nISCR 17th Annual Conference 2024\n\n\n\n\n\n\nConferences\n\n\n\nThis blog highlights my experience of presenting at Indian Society for Clinical Research (ISCR) 17th Annual Conference 2024.\n\n\n\n\n\nFeb 26, 2024\n\n\nPooja Kumari\n\n\n\n\n\n\n\n\n\n\n\n\nteal is now available on CRAN 🎉\n\n\n\n\n\n\nTLG\n\n\nShiny\n\n\n\nAnnouncing the release of teal v0.15.0 on CRAN!\n\n\n\n\n\nFeb 14, 2024\n\n\nDony Unardi\n\n\n\n\n\n\n\n\n\n\n\n\nTesting Containers and WebAssembly in Submissions to the FDA\n\n\n\n\n\n\nSubmission\n\n\n\nIn this post, we dig into the ongoing efforts undertaken to evaluate new technologies for submissions to the Food and Drug Administration (FDA). These transformative approaches, including WebAssembly and containers, hold immense potential to transform the regulatory landscape and streamline the submission process.\n\n\n\n\n\nFeb 1, 2024\n\n\nAndré Veríssimo, Tymoteusz Makowski, Pedro Silva, Vedha Viyash, APPSILON\n\n\n\n\n\n\n\n\n\n\n\n\nPK Examples\n\n\n\n\n\n\nCommunity\n\n\nADaM\n\n\nMetadata\n\n\n\nExplore PK ADaM Examples on Pharmaverse Examples Page\n\n\n\n\n\nJan 26, 2024\n\n\nJeff Dickinson\n\n\n\n\n\n\n\n\n\n\n\n\nadmiral 1.0.0\n\n\n\n\n\n\nADaM\n\n\n\n1.0.0 brings a commitment to stability, new features, a few bug fixes, argument alignment and onboarding resources!\n\n\n\n\n\nJan 4, 2024\n\n\nBen Straub\n\n\n\n\n\n\n\n\n\n\n\n\nEnd of Year Update from the pharmaverse Council\n\n\n\n\n\n\nCommunity\n\n\n\n2023 Was a big year - let’s talk about it!\n\n\n\n\n\nJan 4, 2024\n\n\nMike Stackhouse\n\n\n\n\n\n\n\n\n\n\n\n\nBelieve in a higher order!\n\n\n\n\n\n\nADaM\n\n\nTechnical\n\n\n\nA brief foray into the higher order functions in the {admiral} package.\n\n\n\n\n\nNov 27, 2023\n\n\nEdoardo Mancini\n\n\n\n\n\n\n\n\n\n\n\n\nFloating point\n\n\n\n\n\n\nADaM\n\n\nTechnical\n\n\n\nThe untold story of how admiral deals with floating points.\n\n\n\n\n\nOct 30, 2023\n\n\nStefan Thoma\n\n\n\n\n\n\n\n\n\n\n\n\nReproducing the R Submissions Pilot 2 Shiny Application using Rhino\n\n\n\n\n\n\nSubmission\n\n\nCommunity\n\n\n\nA short blog post about a Rhino pilot submission.\n\n\n\n\n\nOct 10, 2023\n\n\nIsmael Rodriguez, Vedha Viyash, André Veríssimo, APPSILON\n\n\n\n\n\n\n\n\n\n\n\n\nDate/Time Functions and Imputation in {admiral}\n\n\n\n\n\n\nADaM\n\n\nTechnical\n\n\n\nDates, times and imputation can be a frustrating facet of any programming language. Here’s how {admiral} makes all of this easy!\n\n\n\n\n\nSep 26, 2023\n\n\nEdoardo Mancini\n\n\n\n\n\n\n\n\n\n\n\n\nThe pharmaverse (hi)story\n\n\n\n\n\n\nCommunity\n\n\n\n\n\n\n\n\n\nAug 30, 2023\n\n\nNicholas Eugenio\n\n\n\n\n\n\n\n\n\n\n\n\nRounding\n\n\n\n\n\n\nTechnical\n\n\n\nExploration of some commonly used rounding methods and their corresponding functions in SAS and R, with a focus on ‘round half up’ and reliable solutions for numerical precision challenges.\n\n\n\n\n\nAug 22, 2023\n\n\nKangjie Zhang\n\n\n\n\n\n\n\n\n\n\n\n\nIt’s all relative? - Calculating Relative Days using admiral\n\n\n\n\n\n\nADaM\n\n\n\n\n\n\n\n\n\nAug 8, 2023\n\n\nBen Straub\n\n\n\n\n\n\n\n\n\n\n\n\nCross-Industry Open Source Package Development\n\n\n\n\n\n\nCommunity\n\n\nConferences\n\n\nADaM\n\n\n\nThis post is based on a talk given at the regional useR! conference on July 21st 2023 in Basel.\n\n\n\n\n\nJul 25, 2023\n\n\nStefan Thoma\n\n\n\n\n\n\n\n\n\n\n\n\nHow to use Code Sections\n\n\n\n\n\n\nADaM\n\n\nTechnical\n\n\n\nCode sections - are you using them right?\n\n\n\n\n\nJul 14, 2023\n\n\nEdoardo Mancini\n\n\n\n\n\n\n\n\n\n\n\n\nBlanks and NAs\n\n\n\n\n\n\nADaM\n\n\nTechnical\n\n\n\nReading SAS datasets into R. The data is not always as it seems!\n\n\n\n\n\nJul 10, 2023\n\n\nBen Straub\n\n\n\n\n\n\n\n\n\n\n\n\nfalcon\n\n\n\n\n\n\nTLG\n\n\n\nThe {falcon} initiative is an industry collaborative effort under pharmaversewith the aspiration of building and open-sourcing a comprehensive catalog of harmonized TLGs for clinical study reporting.\n\n\n\n\n\nJul 9, 2023\n\n\nVincent Shen\n\n\n\n\n\n\n\n\n\n\n\n\nHello pharmaverse\n\n\n\n\n\n\nCommunity\n\n\n\nShort, fun and user-driven content around the pharmaverse.\n\n\n\n\n\nJun 28, 2023\n\n\nBen Straub\n\n\n\n\n\n\n\n\n\n\n\n\nAdmiral Hackathon 2023 Revisited\n\n\n\n\n\n\nCommunity\n\n\nADaM\n\n\n\nLet’s have a look at the Admiral Hackathon 2023 together.\n\n\n\n\n\nJun 27, 2023\n\n\nStefan Thoma\n\n\n\n\n\n\n\n\n\n\n\n\nHackathon Feedback Application\n\n\n\n\n\n\nShiny\n\n\nCommunity\n\n\nADaM\n\n\n\nGoing through the process of creating a shiny app for the admiral hackathon. The shiny app allows users to check their solutions autonomously, gives feedback, and rates their results.\n\n\n\n\n\nJun 27, 2023\n\n\nStefan Thoma\n\n\n\n\n\n\n\n\n\n\n\n\nDerive a new parameter computed from the value of other parameters\n\n\n\n\n\n\nADaM\n\n\n\nUse admiral::derive_param_computed() like a calculator to derive new parameters/rows based on existing ones\n\n\n\n\n\nJun 27, 2023\n\n\nKangjie Zhang\n\n\n\n\n\n\nNo matching items"
+    "text": "Order By\n       Default\n         \n          Title\n        \n         \n          Date - Oldest\n        \n         \n          Date - Newest\n        \n         \n          Author\n        \n     \n  \n    \n      \n      \n    \n\n\n\n\n\n\n\n\n\n\nSimplifying Clinical Data Dashboards with {teal} and {pharmaverseadam}\n\n\n\n\n\n\nTechnical\n\n\nShiny\n\n\nCommunity\n\n\n\nLearn how to quickly develop interactive and reproducible clinical data dashboards using {teal} and {pharmaverseadam}.\n\n\n\n\n\nJul 22, 2024\n\n\nPavel Demin, Dror Berel, APPSILON\n\n\n\n\n\n\n\n\n\n\n\n\nUnix versus SAS Time\n\n\n\n\n\n\nTechnical\n\n\n\nThis blog explores SAS and R date handling differences, focusing on epoch discrepancies and data types, and provides key tips for accurate date conversions to prevent a 10-year date shift.\n\n\n\n\n\nJul 16, 2024\n\n\nCéline Piraux\n\n\n\n\n\n\n\n\n\n\n\n\nIntroducing a new Coursera course for hands-on clinical data science using R.\n\n\n\n\n\n\nCommunity\n\n\n\n“Releasing our second (hands-on) Coursera course, aimed at enhancing the skills of data scientists and shedding light on the impact of R open-source software within the industry.” \n\n\n\n\n\nJul 3, 2024\n\n\nJoel Laxamana, Stefan Thoma\n\n\n\n\n\n\n\n\n\n\n\n\n{admiral} 1.1.1 is here!\n\n\n\n\n\n\nADaMs\n\n\n\nGet to know what awaits you with the new release of the {admiral} package, including enhanced error messaging, improved documentation and much more!\n\n\n\n\n\nJun 20, 2024\n\n\nEdoardo Mancini, Ben Straub\n\n\n\n\n\n\n\n\n\n\n\n\nNew {admiral} extension packages: {admiralpeds} & more coming soon!\n\n\n\n\n\n\nMetadata\n\n\nADaM\n\n\nCommunity\n\n\n\nWe are very excited to announce that new extension packages are expected very soon in the {admiral} family: {admiralpeds}, a pediatrics extension, and another extension (name TBC) for obesity in clinical trials\n\n\n\n\n\nJun 17, 2024\n\n\nFanny Gautier, Lina Patil\n\n\n\n\n\n\n\n\n\n\n\n\nDiversity & Inclusion in pharmaverse\n\n\n\n\n\n\ncommunity\n\n\n\nAn update from pharmaverse council regarding diversity and inclusion within our community.\n\n\n\n\n\nMay 31, 2024\n\n\npharmaverse council\n\n\n\n\n\n\n\n\n\n\n\n\nOur experience as new admiral developers, coming from a CRO\n\n\n\n\n\n\nCommunity\n\n\nConferences\n\n\n\nAs the first CRO having joined the {admiral} family, we are very excited to share with you our first steps and thoughts in this new adventure\n\n\n\n\n\nMay 29, 2024\n\n\nFanny Gautier, Lina Patil\n\n\n\n\n\n\n\n\n\n\n\n\nTLG Catalog 🤝 WebR\n\n\n\n\n\n\nTLG\n\n\nShiny\n\n\n\nIntroducing WebR to TLG Catalog: A Game Changer for Interactive Learning\n\n\n\n\n\nMay 8, 2024\n\n\nPawel Rucki\n\n\n\n\n\n\n\n\n\n\n\n\nDe-Mystifying R Programming in Clinical Trials\n\n\n\n\n\n\nCommunity\n\n\n\nA blog highlighting the benefits/limitations of using R Programming and using the right tools to create value\n\n\n\n\n\nMay 2, 2024\n\n\nVenkatesan Balu\n\n\n\n\n\n\n\n\n\n\n\n\nAppsilon and Sanofi join the pharmaverse council!\n\n\n\n\n\n\nCommunity\n\n\n\nUpdates to the pharmaverse council\n\n\n\n\n\nApr 29, 2024\n\n\nAri Siggaard Knoph\n\n\n\n\n\n\n\n\n\n\n\n\nteal.modules.clinical v0.9.0 is now on CRAN!\n\n\n\n\n\n\nTLG\n\n\nShiny\n\n\n\nThis package release now completes the suite of {teal} family of packages recently released to CRAN.\n\n\n\n\n\nApr 8, 2024\n\n\nLeena Khatri\n\n\n\n\n\n\n\n\n\n\n\n\nxportr 0.4.0\n\n\n\n\n\n\nADaM\n\n\nSDTM\n\n\n\nxportr is a tool tailor-made for clinical programmers, enabling them to generate CDISC-compliant XPT files for SDTM and/or ADaM. It streamlines the preparation on xpt files, ensuring they are primed for submission to clinical data validation applications or regulatory agencies.\n\n\n\n\n\nMar 29, 2024\n\n\nSadchla Mascary\n\n\n\n\n\n\n\n\n\n\n\n\nTips for First Time Contributors\n\n\n\n\n\n\nCommunity\n\n\n\nOpen source can be daunting the first time you dive in - this blog will help you get started!\n\n\n\n\n\nMar 11, 2024\n\n\nRoss Farrugia\n\n\n\n\n\n\n\n\n\n\n\n\nInside the pharmaverse\n\n\n\n\n\n\nCommunity\n\n\n\nA short blog to help the Pharmaverse community understand how Pharmaverse is governed.\n\n\n\n\n\nMar 4, 2024\n\n\nMichael Rimler\n\n\n\n\n\n\n\n\n\n\n\n\nRhino: A Step Forward in Validating Shiny Apps\n\n\n\n\n\n\nSubmission\n\n\nCommunity\n\n\n\nIn this post, we explore the importance of validation in the pharmaceutical industry and how Rhino framework aids in ensuring reliability and accuracy of Shiny applications.\n\n\n\n\n\nMar 1, 2024\n\n\nKamil Żyła, Ege Can Taşlıçukur, APPSILON\n\n\n\n\n\n\n\n\n\n\n\n\nFilter out the noise!\n\n\n\n\n\n\nADaM\n\n\nTechnical\n\n\n\nA brief exposition of the filter_* functions in {admiral} - what they do and how to use them.\n\n\n\n\n\nFeb 26, 2024\n\n\nEdoardo Mancini\n\n\n\n\n\n\n\n\n\n\n\n\nISCR 17th Annual Conference 2024\n\n\n\n\n\n\nConferences\n\n\n\nThis blog highlights my experience of presenting at Indian Society for Clinical Research (ISCR) 17th Annual Conference 2024.\n\n\n\n\n\nFeb 26, 2024\n\n\nPooja Kumari\n\n\n\n\n\n\n\n\n\n\n\n\nteal is now available on CRAN 🎉\n\n\n\n\n\n\nTLG\n\n\nShiny\n\n\n\nAnnouncing the release of teal v0.15.0 on CRAN!\n\n\n\n\n\nFeb 14, 2024\n\n\nDony Unardi\n\n\n\n\n\n\n\n\n\n\n\n\nTesting Containers and WebAssembly in Submissions to the FDA\n\n\n\n\n\n\nSubmission\n\n\n\nIn this post, we dig into the ongoing efforts undertaken to evaluate new technologies for submissions to the Food and Drug Administration (FDA). These transformative approaches, including WebAssembly and containers, hold immense potential to transform the regulatory landscape and streamline the submission process.\n\n\n\n\n\nFeb 1, 2024\n\n\nAndré Veríssimo, Tymoteusz Makowski, Pedro Silva, Vedha Viyash, APPSILON\n\n\n\n\n\n\n\n\n\n\n\n\nPK Examples\n\n\n\n\n\n\nCommunity\n\n\nADaM\n\n\nMetadata\n\n\n\nExplore PK ADaM Examples on Pharmaverse Examples Page\n\n\n\n\n\nJan 26, 2024\n\n\nJeff Dickinson\n\n\n\n\n\n\n\n\n\n\n\n\nadmiral 1.0.0\n\n\n\n\n\n\nADaM\n\n\n\n1.0.0 brings a commitment to stability, new features, a few bug fixes, argument alignment and onboarding resources!\n\n\n\n\n\nJan 4, 2024\n\n\nBen Straub\n\n\n\n\n\n\n\n\n\n\n\n\nEnd of Year Update from the pharmaverse Council\n\n\n\n\n\n\nCommunity\n\n\n\n2023 Was a big year - let’s talk about it!\n\n\n\n\n\nJan 4, 2024\n\n\nMike Stackhouse\n\n\n\n\n\n\n\n\n\n\n\n\nBelieve in a higher order!\n\n\n\n\n\n\nADaM\n\n\nTechnical\n\n\n\nA brief foray into the higher order functions in the {admiral} package.\n\n\n\n\n\nNov 27, 2023\n\n\nEdoardo Mancini\n\n\n\n\n\n\n\n\n\n\n\n\nFloating point\n\n\n\n\n\n\nADaM\n\n\nTechnical\n\n\n\nThe untold story of how admiral deals with floating points.\n\n\n\n\n\nOct 30, 2023\n\n\nStefan Thoma\n\n\n\n\n\n\n\n\n\n\n\n\nReproducing the R Submissions Pilot 2 Shiny Application using Rhino\n\n\n\n\n\n\nSubmission\n\n\nCommunity\n\n\n\nA short blog post about a Rhino pilot submission.\n\n\n\n\n\nOct 10, 2023\n\n\nIsmael Rodriguez, Vedha Viyash, André Veríssimo, APPSILON\n\n\n\n\n\n\n\n\n\n\n\n\nDate/Time Functions and Imputation in {admiral}\n\n\n\n\n\n\nADaM\n\n\nTechnical\n\n\n\nDates, times and imputation can be a frustrating facet of any programming language. Here’s how {admiral} makes all of this easy!\n\n\n\n\n\nSep 26, 2023\n\n\nEdoardo Mancini\n\n\n\n\n\n\n\n\n\n\n\n\nThe pharmaverse (hi)story\n\n\n\n\n\n\nCommunity\n\n\n\n\n\n\n\n\n\nAug 30, 2023\n\n\nNicholas Eugenio\n\n\n\n\n\n\n\n\n\n\n\n\nRounding\n\n\n\n\n\n\nTechnical\n\n\n\nExploration of some commonly used rounding methods and their corresponding functions in SAS and R, with a focus on ‘round half up’ and reliable solutions for numerical precision challenges.\n\n\n\n\n\nAug 22, 2023\n\n\nKangjie Zhang\n\n\n\n\n\n\n\n\n\n\n\n\nIt’s all relative? - Calculating Relative Days using admiral\n\n\n\n\n\n\nADaM\n\n\n\n\n\n\n\n\n\nAug 8, 2023\n\n\nBen Straub\n\n\n\n\n\n\n\n\n\n\n\n\nCross-Industry Open Source Package Development\n\n\n\n\n\n\nCommunity\n\n\nConferences\n\n\nADaM\n\n\n\nThis post is based on a talk given at the regional useR! conference on July 21st 2023 in Basel.\n\n\n\n\n\nJul 25, 2023\n\n\nStefan Thoma\n\n\n\n\n\n\n\n\n\n\n\n\nHow to use Code Sections\n\n\n\n\n\n\nADaM\n\n\nTechnical\n\n\n\nCode sections - are you using them right?\n\n\n\n\n\nJul 14, 2023\n\n\nEdoardo Mancini\n\n\n\n\n\n\n\n\n\n\n\n\nBlanks and NAs\n\n\n\n\n\n\nADaM\n\n\nTechnical\n\n\n\nReading SAS datasets into R. The data is not always as it seems!\n\n\n\n\n\nJul 10, 2023\n\n\nBen Straub\n\n\n\n\n\n\n\n\n\n\n\n\nfalcon\n\n\n\n\n\n\nTLG\n\n\n\nThe {falcon} initiative is an industry collaborative effort under pharmaversewith the aspiration of building and open-sourcing a comprehensive catalog of harmonized TLGs for clinical study reporting.\n\n\n\n\n\nJul 9, 2023\n\n\nVincent Shen\n\n\n\n\n\n\n\n\n\n\n\n\nHello pharmaverse\n\n\n\n\n\n\nCommunity\n\n\n\nShort, fun and user-driven content around the pharmaverse.\n\n\n\n\n\nJun 28, 2023\n\n\nBen Straub\n\n\n\n\n\n\n\n\n\n\n\n\nAdmiral Hackathon 2023 Revisited\n\n\n\n\n\n\nCommunity\n\n\nADaM\n\n\n\nLet’s have a look at the Admiral Hackathon 2023 together.\n\n\n\n\n\nJun 27, 2023\n\n\nStefan Thoma\n\n\n\n\n\n\n\n\n\n\n\n\nHackathon Feedback Application\n\n\n\n\n\n\nShiny\n\n\nCommunity\n\n\nADaM\n\n\n\nGoing through the process of creating a shiny app for the admiral hackathon. The shiny app allows users to check their solutions autonomously, gives feedback, and rates their results.\n\n\n\n\n\nJun 27, 2023\n\n\nStefan Thoma\n\n\n\n\n\n\n\n\n\n\n\n\nDerive a new parameter computed from the value of other parameters\n\n\n\n\n\n\nADaM\n\n\n\nUse admiral::derive_param_computed() like a calculator to derive new parameters/rows based on existing ones\n\n\n\n\n\nJun 27, 2023\n\n\nKangjie Zhang\n\n\n\n\n\n\nNo matching items"
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index 52dc4026..bf6a4c59 100644
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