Manual wrap two painful blocks

Evaluation.Rmd

@@ -993,7 +993,7 @@ There are two basic ways to overcome this challenge:
     \@ref(non-standard-ast)). For modelling functions this means that the 
     captured call is suboptimal:
 
-    ```{r}
+    ```{r, eval = FALSE}
     resample_lm1 <- function(formula, data, env = caller_env()) {
       formula <- enexpr(formula)
       resample_data <- resample(data, n = nrow(data))
@@ -1003,6 +1003,10 @@ There are two basic ways to overcome this challenge:
       eval(lm_call, env)
     }
     resample_lm1(y ~ x, data = df)$call
+    #> lm(y ~ x, data = <data.frame>)
+    #> lm(formula = y ~ x, data = list(x = c(3L, 7L, 4L, 4L, 
+    #> 2L, 7L, 2L, 1L, 8L, 9L), y = c(13.21, 27.04, 18.63, 
+    #> 18.63, 10.99, 27.04, 10.99, 7.83, 28.14, 32.72)))
     ```
 
 1.  Alternatively you can create a new environment that inherits from the
@@ -1028,14 +1032,16 @@ There are two basic ways to overcome this challenge:
 
 1.  Why does this function fail?
 
-    ```{r, error = TRUE}
+    ```{r, eval = FALSE}
     lm3a <- function(formula, data) {
       formula <- enexpr(formula)
 
       lm_call <- expr(lm(!!formula, data = data))
       eval(lm_call, caller_env())
     }
     lm3a(mpg ~ disp, mtcars)$call
+    #> Error in as.data.frame.default(data, optional = TRUE): 
+    #> cannot coerce class ‘"function"’ to a data.frame
     ```
 
 1.  When model building, typically the response and data are relatively