Merge pull request #2 from malcolmbarrett/update_slides

Bump countdown, re-knit slides, add stretch goal

Author: malcolmbarrett

exercises/06-outcome-model-exercises.Rmd

@@ -50,3 +50,5 @@ boot_estimate <- ____(____, ____) %>%
   filter(term == ____)
 ```
 
+
+Stretch goal: Do the same for a model using matching.

slides/00-intro.html

@@ -12,7 +12,7 @@
 class: center, middle, inverse, title-slide
 
 # Causal Inference in R: Introduction
-### 2020-07-29 (updated: 2020-07-28)
+### 2020-07-29 (updated: 2020-11-18)
 
 ---
 

slides/01-causal_modeling_whole_game.html

@@ -12,7 +12,7 @@
 class: center, middle, inverse, title-slide
 
 # Causal Modeling in R: Whole Game
-### 2020-07-29 (updated: 2020-07-28)
+### 2020-07-29 (updated: 2020-11-18)
 
 ---
 

slides/01-causal_modeling_whole_game_files/figure-html/unnamed-chunk-6-1.png

@@ -191,7 +191,7 @@ dagify(
 ### We're going to assume that coffee does not cause cancer, so there's no formula for that. But we still need to declare our causal question. Specify "coffee" as the exposure and "cancer" as the outcome (both in quotations marks).
 ### Plot the DAG using `ggdag()`
 
-`r countdown::countdown(minutes = 3)`
+`r countdown::countdown(minutes = 5)`
 
 ---
 
@@ -298,7 +298,7 @@ smk_wt_dag %>%
 ### Call `tidy_dagitty()` on `coffee_cancer_dag` to create a tidy DAG, then pass the results to `dag_paths()`. What's different about these data?
 ### Plot the open paths with `ggdag_paths()`. (Just give it `coffee_cancer_dag` rather than using `dag_paths()`; the quick plot function will do that for you.) Remember, since we assume there is *no* causal path from coffee to lung cancer, any open paths must be confounding pathways. 
 
-`r countdown::countdown(minutes = 3)`
+`r countdown::countdown(minutes = 5)`
 
 ---
 
@@ -362,7 +362,7 @@ ggdag_adjustment_set(smk_wt_dag)
 #### Use `ggdag_adjustment_set()` to visualize the adjustment sets. Add the arguments `use_labels = "label"` and `text = FALSE`.
 #### Write an R formula for each adjustment set, as you might if you were fitting a model in `lm()` or `glm()`
 
-`r countdown::countdown(minutes = 3)`
+`r countdown::countdown(minutes = 5)`
 
 ---
 

slides/02-dags.Rmd

@@ -14,7 +14,7 @@
 class: center, middle, inverse, title-slide
 
 # Causal Diagrams in R
-### 2020-07-29 (updated: 2020-07-28)
+### 2020-07-29 (updated: 2020-11-18)
 
 ---
 
@@ -144,8 +144,8 @@
 ### We're going to assume that coffee does not cause cancer, so there's no formula for that. But we still need to declare our causal question. Specify "coffee" as the exposure and "cancer" as the outcome (both in quotations marks).
 ### Plot the DAG using `ggdag()`
 
-<div class="countdown" id="timer_5f20b9d1" style="right:0;bottom:0;" data-warnwhen="0">
-<code class="countdown-time"><span class="countdown-digits minutes">03</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
+<div class="countdown" id="timer_5fb59e40" style="right:0;bottom:0;" data-warnwhen="0">
+<code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
 </div>
 
 ---
@@ -225,8 +225,8 @@
 ### Call `tidy_dagitty()` on `coffee_cancer_dag` to create a tidy DAG, then pass the results to `dag_paths()`. What's different about these data?
 ### Plot the open paths with `ggdag_paths()`. (Just give it `coffee_cancer_dag` rather than using `dag_paths()`; the quick plot function will do that for you.) Remember, since we assume there is *no* causal path from coffee to lung cancer, any open paths must be confounding pathways. 
 
-<div class="countdown" id="timer_5f20bc6a" style="right:0;bottom:0;" data-warnwhen="0">
-<code class="countdown-time"><span class="countdown-digits minutes">03</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
+<div class="countdown" id="timer_5fb59e7d" style="right:0;bottom:0;" data-warnwhen="0">
+<code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
 </div>
 
 ---
@@ -312,8 +312,8 @@
 #### Use `ggdag_adjustment_set()` to visualize the adjustment sets. Add the arguments `use_labels = "label"` and `text = FALSE`.
 #### Write an R formula for each adjustment set, as you might if you were fitting a model in `lm()` or `glm()`
 
-<div class="countdown" id="timer_5f20bae1" style="right:0;bottom:0;" data-warnwhen="0">
-<code class="countdown-time"><span class="countdown-digits minutes">03</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
+<div class="countdown" id="timer_5fb59da2" style="right:0;bottom:0;" data-warnwhen="0">
+<code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
 </div>
 
 ---

slides/02-dags.html

@@ -193,7 +193,7 @@ class: inverse
 
 ## Your turn
 
-`r countdown::countdown(minutes = 5)`
+`r countdown::countdown(minutes = 7)`
 
 1. Using the **confounders** identified in the previous DAG, fit a propensity score model for `qsmk`
 2. Stretch: Create two histograms, one of the propensity scores for those that quit smoking and one for those that do not

slides/02-dags_files/figure-html/unnamed-chunk-19-1.png

@@ -1,9 +1,10 @@
 <!DOCTYPE html>
-<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
+<html lang="" xml:lang="">
   <head>
     <title>Propensity Scores</title>
     <meta charset="utf-8" />
     <meta name="author" content="Lucy D’Agostino McGowan" />
+    <script src="libs/header-attrs-2.5/header-attrs.js"></script>
     <link href="libs/remark-css-0.0.1/default.css" rel="stylesheet" />
     <link href="libs/countdown-0.3.5/countdown.css" rel="stylesheet" />
     <script src="libs/countdown-0.3.5/countdown.js"></script>
@@ -16,7 +17,7 @@
 # Propensity Scores
 ### Lucy D’Agostino McGowan
 ### Wake Forest University
-### 2020-07-29 (updated: 2020-07-29)
+### 2020-07-29 (updated: 2020-11-18)
 
 ---
 
@@ -167,8 +168,8 @@
 
 ## Your turn
 
-<div class="countdown" id="timer_5f218f59" style="right:0;bottom:0;" data-warnwhen="0">
-<code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
+<div class="countdown" id="timer_5fb59d1d" style="right:0;bottom:0;" data-warnwhen="0">
+<code class="countdown-time"><span class="countdown-digits minutes">07</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
 </div>
 
 1. Using the **confounders** identified in the previous DAG, fit a propensity score model for `qsmk`
@@ -224,6 +225,32 @@
     deleted = true;
   });
 })();
+(function() {
+  "use strict"
+  // Replace <script> tags in slides area to make them executable
+  var scripts = document.querySelectorAll(
+    '.remark-slides-area .remark-slide-container script'
+  );
+  if (!scripts.length) return;
+  for (var i = 0; i < scripts.length; i++) {
+    var s = document.createElement('script');
+    var code = document.createTextNode(scripts[i].textContent);
+    s.appendChild(code);
+    var scriptAttrs = scripts[i].attributes;
+    for (var j = 0; j < scriptAttrs.length; j++) {
+      s.setAttribute(scriptAttrs[j].name, scriptAttrs[j].value);
+    }
+    scripts[i].parentElement.replaceChild(s, scripts[i]);
+  }
+})();
+(function() {
+  var links = document.getElementsByTagName('a');
+  for (var i = 0; i < links.length; i++) {
+    if (/^(https?:)?\/\//.test(links[i].getAttribute('href'))) {
+      links[i].target = '_blank';
+    }
+  }
+})();
 // adds .remark-code-has-line-highlighted class to <pre> parent elements
 // of code chunks containing highlighted lines with class .remark-code-line-highlighted
 (function(d) {
@@ -242,17 +269,6 @@
   preParents.forEach(p => p.classList.add("remark-code-has-line-highlighted"));
 })(document);</script>
 
-<script>
-(function() {
-  var links = document.getElementsByTagName('a');
-  for (var i = 0; i < links.length; i++) {
-    if (/^(https?:)?\/\//.test(links[i].getAttribute('href'))) {
-      links[i].target = '_blank';
-    }
-  }
-})();
-</script>
-
 <script>
 slideshow._releaseMath = function(el) {
   var i, text, code, codes = el.getElementsByTagName('code');

slides/03-pscores.Rmd

@@ -238,7 +238,7 @@ class: inverse
 
 ## Your Turn
 
-`r countdown::countdown(minutes = 5)`
+`r countdown::countdown(minutes = 7)`
 
 1. Using the propensity scores you created in the previous exercise, add the ATE weights to your data frame `df`