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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 0.676489790550228, so a + 1 is 1.67648979055023."
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 = -0.320718473271458"
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
```
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.