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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 -0.53420885071924, so a + 1 is 0.46579114928076."
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 = -1.43591569627506"
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 all variables in mtcars:
src = lapply(names(mtcars)[-1], function(i) {
knit_expand(text=c("# Regression on {{i}}", '```{r lm-{{i}}}', 'lm(mpg~{{i}}, data=mtcars)', '```'))
})
# knit the source
res = knit_child(text = unlist(src))
res = paste('<pre><code>', gsub('^\\s*|\\s*$', '', res), '</code></pre>', sep = '')
# 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
```
# Regression on wt
```r
lm(mpg~wt, data=mtcars)
```
```
##
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
##
## Coefficients:
## (Intercept) wt
## 37.285 -5.344
```
# Regression on qsec
```r
lm(mpg~qsec, data=mtcars)
```
```
##
## Call:
## lm(formula = mpg ~ qsec, data = mtcars)
##
## Coefficients:
## (Intercept) qsec
## -5.114 1.412
```
# Regression on vs
```r
lm(mpg~vs, data=mtcars)
```
```
##
## Call:
## lm(formula = mpg ~ vs, data = mtcars)
##
## Coefficients:
## (Intercept) vs
## 16.62 7.94
```
# Regression on am
```r
lm(mpg~am, data=mtcars)
```
```
##
## Call:
## lm(formula = mpg ~ am, data = mtcars)
##
## Coefficients:
## (Intercept) am
## 17.147 7.245
```
# Regression on gear
```r
lm(mpg~gear, data=mtcars)
```
```
##
## Call:
## lm(formula = mpg ~ gear, data = mtcars)
##
## Coefficients:
## (Intercept) gear
## 5.623 3.923
```
# Regression on carb
```r
lm(mpg~carb, data=mtcars)
```
```
##
## Call:
## lm(formula = mpg ~ carb, data = mtcars)
##
## Coefficients:
## (Intercept) carb
## 25.872 -2.056
```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.
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