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Fix weird `r chunk`s introduced at some point

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mine-cetinkaya-rundel 2023-05-26 13:30:22 -04:00
parent 4ce20cd465
commit fa76f8154d
1 changed files with 5 additions and 11 deletions
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quarto.qmd
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@ -601,28 +601,25 @@ On subsequent runs, knitr will check to see if the code has changed, and if it h
The caching system must be used with care, because by default it is based on the code only, not its dependencies. The caching system must be used with care, because by default it is based on the code only, not its dependencies.
For example, here the `processed_data` chunk depends on the `raw-data` chunk: For example, here the `processed_data` chunk depends on the `raw-data` chunk:
``` ```{r}
`r chunk`{r}
#| label: raw-data #| label: raw-data
rawdata <- readr::read_csv("a_very_large_file.csv") rawdata <- readr::read_csv("a_very_large_file.csv")
`r chunk` ```
`r chunk`{r} ```{r}
#| label: processed_data #| label: processed_data
#| cache: true #| cache: true
processed_data <- rawdata |> processed_data <- rawdata |>
filter(!is.na(import_var)) |> filter(!is.na(import_var)) |>
mutate(new_variable = complicated_transformation(x, y, z)) mutate(new_variable = complicated_transformation(x, y, z))
`r chunk`
``` ```
Caching the `processed_data` chunk means that it will get re-run if the dplyr pipeline is changed, but it won't get rerun if the `read_csv()` call changes. Caching the `processed_data` chunk means that it will get re-run if the dplyr pipeline is changed, but it won't get rerun if the `read_csv()` call changes.
You can avoid that problem with the `dependson` chunk option: You can avoid that problem with the `dependson` chunk option:
``` ```{r}
`r chunk`{r}
#| label: processed-data #| label: processed-data
#| cache: true #| cache: true
#| dependson: "raw-data" #| dependson: "raw-data"
@ -630,7 +627,6 @@ You can avoid that problem with the `dependson` chunk option:
processed_data <- rawdata |> processed_data <- rawdata |>
filter(!is.na(import_var)) |> filter(!is.na(import_var)) |>
mutate(new_variable = complicated_transformation(x, y, z)) mutate(new_variable = complicated_transformation(x, y, z))
`r chunk`
``` ```
`dependson` should contain a character vector of *every* chunk that the cached chunk depends on. `dependson` should contain a character vector of *every* chunk that the cached chunk depends on.
@ -642,13 +638,11 @@ This is an arbitrary R expression that will invalidate the cache whenever it cha
A good function to use is `file.info()`: it returns a bunch of information about the file including when it was last modified. A good function to use is `file.info()`: it returns a bunch of information about the file including when it was last modified.
Then you can write: Then you can write:
``` ```{r}
`r chunk`{r}
#| label: raw-data #| label: raw-data
#| cache.extra: file.info("a_very_large_file.csv") #| cache.extra: file.info("a_very_large_file.csv")
rawdata <- readr::read_csv("a_very_large_file.csv") rawdata <- readr::read_csv("a_very_large_file.csv")
`r chunk`
``` ```
We've followed the advice of [David Robinson](https://twitter.com/drob/status/738786604731490304) to name these chunks: each chunk is named after the primary object that it creates. We've followed the advice of [David Robinson](https://twitter.com/drob/status/738786604731490304) to name these chunks: each chunk is named after the primary object that it creates.