Merge pull request #65 from tgerke/tg-edits

exercises/01-whole-game-exercises.qmd

@@ -36,8 +36,8 @@ Let's look at the distribution of weight gain between the two groups.
 ```{r}
 nhefs_complete_uc |>
   ggplot(aes(wt82_71, fill = factor(qsmk))) + 
-  geom_vline(xintercept = 0, color = "grey60", size = 1) +
-  geom_density(color = "white", alpha = .75, size = .5) +
+  geom_vline(xintercept = 0, color = "grey60", linewidth = 1) +
+  geom_density(color = "white", alpha = .75, linewidth = .5) +
   scale_color_okabe_ito(order = c(1, 5)) + 
   theme_minimal() +
   theme(legend.position = "bottom") + 
@@ -60,19 +60,6 @@ nhefs_complete_uc |>
   )
 ```
 
----
-
-```{r}
-# ~2.5 kg gained for quit vs. not quit
-nhefs_complete_uc |>
-  group_by(qsmk) |>
-  summarize(
-    mean_weight_change = mean(wt82_71), 
-    sd = sd(wt82_71),
-    .groups = "drop"
-  )
-```
-
 Here, it looks like those who quit smoking gained, on average, 2.5 kg. But is there something else that could explain these results? There are many factors associated with both quitting smoking and gaining weight; could one of those factors explain away the results we're seeing here?
 
 To truly answer this question, we need to specify a causal diagram based on domain knowledge. Sadly, for most circumstances, there is no data-driven approach that consistently identify confounders. Only our causal assumptions can help us identify them. Causal diagrams are a visual expression of those assumptions linked to rigorous mathematics that allow us to understand what we need to account for in our model.

exercises/03-ci-with-group-by-and-summarise-exercises.qmd

@@ -72,9 +72,11 @@ sim2 <- tibble(
 sim2 |>
   group_by(_____, _____, _____) |>
   summarise(avg_y = mean(_____)) |>
-  pivot_wider(names_from = exposure,
-              values_from = avg_y,  
-              names_prefix = "x_") |>
+  pivot_wider(
+    names_from = exposure,
+    values_from = avg_y,  
+    names_prefix = "x_"
+  ) |>
   summarise(estimate = x_1 - x_0, .groups = "drop") |>
   summarise(estimate = mean(estimate))  
 ```

exercises/05-quartets-exercises.qmd

@@ -5,6 +5,7 @@ format: html
 
 ```{r}
 #| label: setup
+library(tidyverse)
 library(quartets)
 ```
 

exercises/10-continuous-g-computation-exercises.qmd

@@ -9,6 +9,9 @@ library(tidyverse)
 library(broom)
 library(touringplans)
 library(splines)
+
+seven_dwarfs <- seven_dwarfs_train_2018 |>
+  filter(wait_hour == 9)
 ```
 
 For this set of exercises, we'll use g-computation to calculate a causal effect for continuous exposures.