Update index.qmd

_freeze/index/execute-results/html.json

@@ -1,8 +1,8 @@
 {
-  "hash": "612f49f6ca4fc9b3c71614fc22d55d29",
+  "hash": "ce0a92f7bd46d1028b0205152bdfa4b8",
   "result": {
     "engine": "knitr",
-    "markdown": "---\ntitle: \"Intro-tidy\"\nformat: revealjs\nengine: knitr\nexecute: \n  freeze: auto\n---\n\n\n\n\n\n# tidyverse {background-color=\"aquamarine\"}\n\n> The [tidyverse](https://www.tidyverse.org) is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.\n\n## Why should we care about the tidyverse?\n\n> What's the mean of `mpg` (miles per gallon) and the number of observations by `cyl` (cylinders) in the `mtcars` dataset?\n\n::: columns\n\n::: {.column width=\"50%\"}\n#### tidyverse\n\n::: {.cell}\n\n```{.r .cell-code}\nmtcars %>%\n  group_by(cyl) %>%\n  summarise(mean = mean(mpg), n = n())\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n    cyl  mean     n\n  <dbl> <dbl> <int>\n1     4  26.7    11\n2     6  19.7     7\n3     8  15.1    14\n```\n\n\n:::\n:::\n\n:::\n\n::: {.column width=\"50%\"}\n#### base R\n\n::: {.cell}\n\n```{.r .cell-code}\nmtcars_by <- by(mtcars, mtcars$cyl, function(df) {\n  with(df, data.frame(\n    cyl = cyl[[1]],\n    mean = mean(mpg),\n    n = nrow(df)\n  ))\n})\ndo.call(rbind, mtcars_by)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n  cyl     mean  n\n4   4 26.66364 11\n6   6 19.74286  7\n8   8 15.10000 14\n```\n\n\n:::\n:::\n\n:::\n\n:::\n\n::: aside\nAdapted from [dplyr base R](https://dplyr.tidyverse.org/articles/base.html#summarise-reduce-multiple-values-down-to-a-single-value)\n:::",
+    "markdown": "---\ntitle: \"Intro-tidy\"\nformat: revealjs\nengine: knitr\nexecute: \n  freeze: auto\n---\n\n\n\n\n\n# tidyverse {background-color=\"aquamarine\"}\n\n> The [tidyverse](https://www.tidyverse.org) is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.\n\n## Why should we care about the tidyverse?\n\n> What's the mean of `mpg` (miles per gallon) and the number of observations by `cyl` (cylinders) in the `mtcars` dataset?\n\n::: columns\n\n::: {.column width=\"50%\"}\n#### tidyverse\n```{webr-r}\nmtcars |> \n  group_by(cyl) |> \n  summarise(mean = mean(mpg), n = n())\n```\n:::\n\n::: {.column width=\"50%\"}\n\n#### base R\n\n```{webr-r}\nmtcars_by <- by(mtcars, mtcars$cyl, function(df) {\n  with(df, data.frame(\n    cyl = cyl[[1]],\n    mean = mean(mpg),\n    n = nrow(df)\n  ))\n})\ndo.call(rbind, mtcars_by)\n```\n:::\n\n:::\n\n::: aside\nAdapted from [dplyr base R](https://dplyr.tidyverse.org/articles/base.html#summarise-reduce-multiple-values-down-to-a-single-value)\n:::",
     "supporting": [
       "index_files"
     ],

index.qmd

@@ -7,6 +7,7 @@ execute:
 ---
 
 ```{r include=FALSE}
+#| context: setup
 library(ggplot2)
 library(dplyr)
 ```
@@ -25,9 +26,8 @@ library(dplyr)
 ::: {.column width="50%"}
 #### tidyverse
 ```{webr-r}
-#| echo: true
-mtcars %>%
-  group_by(cyl) %>%
+mtcars |> 
+  group_by(cyl) |> 
   summarise(mean = mean(mpg), n = n())
 ```
 :::
@@ -37,7 +37,6 @@ mtcars %>%
 #### base R
 
 ```{webr-r}
-#| echo: true
 mtcars_by <- by(mtcars, mtcars$cyl, function(df) {
   with(df, data.frame(
     cyl = cyl[[1]],