Render for correct output in weigh() (#281)

* Render for correct output in `weigh()`

* Make sure we don't run into this again in the future

Author: juliasilge

README.Rmd

@@ -11,6 +11,16 @@ knitr::opts_chunk$set(
   fig.path = "man/figures/README-",
   out.width = "100%"
 )
+
+# `devtools::build_readme()` evaluates in `callr:r_safe()` which causes issues
+# with butcher's memory profiling. Neither RStudio's Knit button nor
+# `rmarkdown::render()` have this issue (#280).
+if (identical(Sys.getenv("CALLR_IS_RUNNING"), "true")) {
+  rlang::abort(c(
+    "Build this README with `rmarkdown::render()` rather than `devtools::build_readme()`.",
+    "See tidymodels/butcher#280 for more info."
+  ))
+}
 ```
 
 # butcher <a href="https://butcher.tidymodels.org"><img src="man/figures/logo.png" align="right" height="138" alt="butcher website" /></a>
@@ -29,7 +39,7 @@ Modeling or machine learning in R can result in fitted model objects that take u
 1. Heavy usage of formulas and closures that capture the enclosing environment in model training
 2. Lack of selectivity in the construction of the model object itself
 
-As a result, fitted model objects contain components that are often redundant and not required for post-fit estimation activities. The butcher package provides tooling to "axe" parts of the fitted output that are no longer needed, without sacrificing prediction functionality from the original model object. 
+As a result, fitted model objects contain components that are often redundant and not required for post-fit estimation activities. The butcher package provides tooling to "axe" parts of the fitted output that are no longer needed, without sacrificing prediction functionality from the original model object.
 
 ## Installation
 
@@ -46,33 +56,33 @@ Or install the development version from [GitHub](https://github.com/):
 pak::pak("tidymodels/butcher")
 ```
 
-## Butchering 
+## Butchering
 
-As an example, let's wrap an `lm` model so it contains a lot of unnecessary stuff: 
+As an example, let's wrap an `lm` model so it contains a lot of unnecessary stuff:
 
 ```{r example}
 library(butcher)
 our_model <- function() {
   some_junk_in_the_environment <- runif(1e6) # we didn't know about
-  lm(mpg ~ ., data = mtcars) 
+  lm(mpg ~ ., data = mtcars)
 }
 ```
 
-This object is unnecessarily large: 
+This object is unnecessarily large:
 
 ```{r}
 library(lobstr)
 obj_size(our_model())
 ```
 
-When, in fact, it should only be: 
+When, in fact, it should only be:
 
 ```{r}
-small_lm <- lm(mpg ~ ., data = mtcars) 
+small_lm <- lm(mpg ~ ., data = mtcars)
 obj_size(small_lm)
 ```
 
-To understand which part of our original model object is taking up the most memory, we leverage the `weigh()` function: 
+To understand which part of our original model object is taking up the most memory, we leverage the `weigh()` function:
 
 ```{r}
 big_lm <- our_model()
@@ -113,8 +123,8 @@ Check out the `vignette("available-axe-methods")` to see butcher's current cover
 
 1. Run `new_model_butcher(model_class = "your_object", package_name = "your_package")`
 2. Use butcher helper functions `weigh()` and `locate()` to decide what to axe
-3. Finalize edits to `R/your_object.R` and `tests/testthat/test-your_object.R` 
-4. Make a pull request! 
+3. Finalize edits to `R/your_object.R` and `tests/testthat/test-your_object.R`
+4. Make a pull request!
 
 ## Contributing
 

README.md

@@ -82,7 +82,7 @@ weigh(big_lm)
 #> # A tibble: 25 × 2
 #>    object            size
 #>    <chr>            <dbl>
-#>  1 terms         8.05    
+#>  1 terms         8.01    
 #>  2 qr.qr         0.00666 
 #>  3 residuals     0.00286 
 #>  4 fitted.values 0.00286 
@@ -102,7 +102,7 @@ remove the (mostly) extraneous component, we can use `butcher()`:
 
 ``` r
 cleaned_lm <- butcher(big_lm, verbose = TRUE)
-#> ✔ Memory released: 8.03 MB
+#> ✔ Memory released: 8.00 MB
 #> ✖ Disabled: `print()`, `summary()`, and `fitted()`
 ```
 
@@ -133,7 +133,7 @@ weigh(small_lm)
 #> # A tibble: 25 × 2
 #>    object            size
 #>    <chr>            <dbl>
-#>  1 terms         8.06    
+#>  1 terms         0.00763 
 #>  2 qr.qr         0.00666 
 #>  3 residuals     0.00286 
 #>  4 fitted.values 0.00286