The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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 -0.762876251006842, so a + 1 is 0.237123748993158."
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 = -0.201937141656936"
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.
Health stats visible at Monitor.