update all variable names

Author: LucyMcGowan

exercises/07-pscores-using-exercises.qmd

@@ -21,7 +21,7 @@ seven_dwarfs <- seven_dwarfs_train_2018 |>
   filter(wait_hour == 9)
 
 propensity_model <- glm(
-  park_extra_magic_morning ~ day_ticket_season + park_close + day_temperature_high,
+  park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
   data = seven_dwarfs,
   family = binomial()
 )

exercises/08-pscores-diagnostics-exercises.qmd

@@ -22,7 +22,7 @@ seven_dwarfs <- seven_dwarfs_train_2018 |>
   filter(wait_hour == 9)
 
 propensity_model <- glm(
-  park_extra_magic_morning ~ day_ticket_season + park_close + day_temperature_high,
+  park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
   data = seven_dwarfs,
   family = binomial()
 )
@@ -62,7 +62,7 @@ ggplot(
 
 ## Your Turn 2
 
-Create an unweighted ECDF for `day_temperature_high` by whether or not the day had Extra Magic Hours.
+Create an unweighted ECDF for `park_temperature_high` by whether or not the day had Extra Magic Hours.
 
 ```{r}
 #| eval: false
@@ -77,7 +77,7 @@ ggplot(seven_dwarfs_ps, aes(x = ____, group = ____, color = factor(____))) +
   ylab("Proportion <= x") 
 ```
 
-Create an weighted ECDF for `day_temperature_high` by whether or not the day had Extra Magic Hours.
+Create an weighted ECDF for `park_temperature_high` by whether or not the day had Extra Magic Hours.
 
 ```{r}
 #| eval: false

exercises/09-outcome-model-exercises.qmd

@@ -57,7 +57,7 @@ ipw_results |>
   mutate(
     estimate = map_dbl(
       boot_fits,
-      # pull the `estimate` for `extra_magic_morning` for each fit
+      # pull the `estimate` for `park_extra_magic_morning` for each fit
       \(.fit) .fit |>
         filter(term == "park_extra_magic_morning") |>
         pull(estimate)

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

@@ -13,7 +13,7 @@ library(splines)
 
 For this set of exercises, we'll use g-computation to calculate a causal effect for continuous exposures.
 
-In the touringplans data set, we have information about the posted waiting times for rides. We also have a limited amount of data on the observed, actual times. The question that we will consider is this: Do posted wait times (`avg_spostmin`) for the Seven Dwarves Mine Train at 8 am affect actual wait times (`avg_sactmin`) at 9 am? Here’s our DAG:
+In the touringplans data set, we have information about the posted waiting times for rides. We also have a limited amount of data on the observed, actual times. The question that we will consider is this: Do posted wait times (`wait_minutes_posted_avg`) for the Seven Dwarves Mine Train at 8 am affect actual wait times (`wait_minutes_actual_avg`) at 9 am? Here’s our DAG:
 
 ```{r}
 #| echo: false
@@ -89,15 +89,15 @@ You don't need to update any code here, so just run this.
 
 ```{r}
 eight <- seven_dwarfs_train_2018 |>
-  filter(hour == 8) |>
+  filter(wait_hour == 8) |>
   select(-wait_minutes_actual_avg)
 
 nine <- seven_dwarfs_train_2018 |>
-  filter(hour == 9) |>
-  select(date, wait_minutes_actual_avg)
+  filter(wait_hour == 9) |>
+  select(park_date, wait_minutes_actual_avg)
 
 wait_times <- eight |>
-  left_join(nine, by = "date") |>
+  left_join(nine, by = "park_date") |>
   drop_na(wait_minutes_actual_avg)
 ```
 

exercises/14-bonus-continuous-pscores-exercises.qmd

@@ -94,10 +94,10 @@ eight <- seven_dwarfs_train_2018 |>
 
 nine <- seven_dwarfs_train_2018 |>
   filter(wait_hour == 9) |>
-  select(date, wait_minutes_actual_avg)
+  select(park_date, wait_minutes_actual_avg)
 
 wait_times <- eight |>
-  left_join(nine, by = "date") |>
+  left_join(nine, by = "park_date") |>
   drop_na(wait_minutes_actual_avg)
 ```
 

slides/raw/08-pscore-diagnostics.qmd

@@ -178,8 +178,8 @@ ggplot(df, aes(x = wt71, color = factor(qsmk))) +
 
 `r countdown::countdown(minutes = 10)`
 
-### Create an unweighted ECDF examining the `weather_wdwhigh` confounder by whether or not the day had Extra Magic Hours.
-### Create a weighted ECDF examining the `weather_wdwhigh` confounder
+### Create an unweighted ECDF examining the `park_temperature_high` confounder by whether or not the day had Extra Magic Hours.
+### Create a weighted ECDF examining the `park_temperature_high` confounder
 
 
 ## {background-color="#23373B" .center .huge}

slides/raw/09-outcome-model.qmd

@@ -143,7 +143,7 @@ boot_estimate <- int_t(ipw_results, boot_fits) |>
 
 `r countdown::countdown(minutes = 12)`
 
-### Create a function called `ipw_fit` that fits the propensity score model and the weighted outcome model for the effect between `extra_magic_morning` and `avg_spostmin`
+### Create a function called `ipw_fit` that fits the propensity score model and the weighted outcome model for the effect between `park_extra_magic_morning` and `wait_minutes_posted_avg`
 
 ### Using the `bootstraps()` and `int_t()` functions to estimate the final effect.
 

slides/raw/14-bonus-continuous-pscores.qmd

@@ -192,9 +192,9 @@ dagify(
 
 ## *Your Turn 1*
 
-### Fit a model using `lm()` with `avg_spostmin` as the outcome and the confounders identified in the DAG.
+### Fit a model using `lm()` with `wait_minutes_posted_avg` as the outcome and the confounders identified in the DAG.
 ### Use `augment()` to add model predictions to the data frame
-### In `wt_ate()`, calculate the weights using `avg_postmin`, `.fitted`, and `.sigma`
+### In `wt_ate()`, calculate the weights using `wait_minutes_posted_avg`, `.fitted`, and `.sigma`
 
 `r countdown::countdown(minutes = 5)`
 
@@ -208,18 +208,18 @@ eight <- seven_dwarfs_train_2018 |>
 
 nine <- seven_dwarfs_train_2018 |>
   filter(wait_hour == 9) |>
-  select(date, wait_minutes_posted_avg)
+  select(park_date, wait_minutes_posted_avg)
 
 wait_times <- eight |>
-  left_join(nine, by = "date") |>
+  left_join(nine, by = "park_date") |>
   drop_na(wait_minutes_posted_avg)
 ```
 
 ```{r}
 post_time_model <- lm(
   wait_minutes_posted_avg ~
     park_close + park_extra_magic_morning + 
-    day_temperature_high + day_ticket_season, 
+    park_temperature_high + park_ticket_season, 
   data = wait_times
 )
 ```
@@ -230,7 +230,7 @@ post_time_model <- lm(
 wait_times_wts <- post_time_model |>
   augment(data = wait_times) |>
   mutate(wts = wt_ate(
-    avg_spostmin, .fitted, .sigma = .sigma
+    wait_minutes_posted_avg, .fitted, .sigma = .sigma
   ))
 ```