# Templating with knit_expand()

## Yihui Xie

### 2026-09-05

A few simple examples:

    library(knitr)
    knit_expand(text = 'The value of pi is {{pi}}.')

    #> [1] "The value of pi is 3.14159265358979."

    knit_expand(text = 'The value of a is {{a}}, so a + 1 is {{a+1}}.', a = rnorm(1))

    #> [1] "The value of a is -0.660194622767742, so a + 1 is 0.339805377232258."

    knit_expand(text = 'The area of a circle with radius {{r}} is {{pi*r^2}}', r = 5)

    #> [1] "The area of a circle with radius 5 is 78.5398163397448"

Any number of variables:

    knit_expand(text = 'a is {{a}} and b is {{b}}, with my own pi being {{pi}} instead of {{base::pi}}', a=1, b=2, pi=3)

    #> [1] "a is 1 and b is 2, with my own pi being 3 instead of 3.14159265358979"

Custom delimiter `<% %>`:

    knit_expand(text = 'I do not like curly braces, so use % with <> instead: a is <% a %>.', a = 8, delim = c("<%", "%>"))

    #> [1] "I do not like curly braces, so use % with <> instead: a is 8."

The pyexpander delimiter:

    knit_expand(text = 'hello $(LETTERS[24]) and $(pi)!', delim = c("$(", ")"))

    #> [1] "hello X and 3.14159265358979!"

Arbitrary R code:

    knit_expand(text = 'you cannot see the value of x {{x=rnorm(1)}}but it is indeed created: x = {{x}}')

    #> [1] "you cannot see the value of x but it is indeed created: x = 0.604712748413103"

    res = knit_expand(text = c(' x | x^2', '{{x=1:5;paste(sprintf("%2d | %3d", x, x^2), collapse = "\n")}}'))
    cat(res)

    #>  x | x^2
    #>  1 |   1
    #>  2 |   4
    #>  3 |   9
    #>  4 |  16
    #>  5 |  25

The m4 example: https://en.wikipedia.org/wiki/M4_(computer_language)

    res = knit_expand(text = c('{{i=0;h2=function(x){i<<-i+1;sprintf("<h2>%d. %s</h2>", i, x)} }}<html>',
    '{{h2("First Section")}}', '{{h2("Second Section")}}', '{{h2("Conclusion")}}', '</html>'))
    cat(res)

    #> <html>
    #> <h2>1. First Section</h2>
    #> <h2>2. Second Section</h2>
    #> <h2>3. Conclusion</h2>
    #> </html>

Build regression models based on a template; loop through some variables in `mtcars`:

    src = lapply(names(mtcars)[2:5], function(i) {
    knit_expand(text=c("# Regression on {{i}}", '```{r lm-{{i}}}', 'lm(mpg~{{i}}, data=mtcars)', '```', ''))
    })
    # knit the source
    litedown::fuse(unlist(src), 'markdown')

    # Regression on cyl
    ``` {.r}
    lm(mpg~cyl, data=mtcars)
    ```

    ```

    Call:
    lm(formula = mpg ~ cyl, data = mtcars)

    Coefficients:
    (Intercept)          cyl
         37.885       -2.876

    ```

    # Regression on disp
    ``` {.r}
    lm(mpg~disp, data=mtcars)
    ```

    ```

    Call:
    lm(formula = mpg ~ disp, data = mtcars)

    Coefficients:
    (Intercept)         disp
       29.59985     -0.04122

    ```

    # Regression on hp
    ``` {.r}
    lm(mpg~hp, data=mtcars)
    ```

    ```

    Call:
    lm(formula = mpg ~ hp, data = mtcars)

    Coefficients:
    (Intercept)           hp
       30.09886     -0.06823

    ```

    # Regression on drat
    ``` {.r}
    lm(mpg~drat, data=mtcars)
    ```

    ```

    Call:
    lm(formula = mpg ~ drat, data = mtcars)

    Coefficients:
    (Intercept)         drat
         -7.525        7.678

    ```