Demos of `knit_expand()`

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 1.27316053374727, so a + 1 is 2.27316053374727."
``````
``````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 = 1.09827598737925"
``````
``````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: http://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
##       37.88        -2.88
```
# Regression on disp

```r
lm(mpg ~ disp, data = mtcars)
```

```
##
## Call:
## lm(formula = mpg ~ disp, data = mtcars)
##
## Coefficients:
## (Intercept)         disp
##     29.5999      -0.0412
```
# Regression on hp

```r
lm(mpg ~ hp, data = mtcars)
```

```
##
## Call:
## lm(formula = mpg ~ hp, data = mtcars)
##
## Coefficients:
## (Intercept)           hp
##     30.0989      -0.0682
```
# Regression on drat

```r
lm(mpg ~ drat, data = mtcars)
```

```
##
## Call:
## lm(formula = mpg ~ drat, data = mtcars)
##
## Coefficients:
## (Intercept)         drat
##       -7.52         7.68
```
# Regression on wt

```r
lm(mpg ~ wt, data = mtcars)
```

```
##
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
##
## Coefficients:
## (Intercept)           wt
##       37.29        -5.34
```
# Regression on qsec

```r
lm(mpg ~ qsec, data = mtcars)
```

```
##
## Call:
## lm(formula = mpg ~ qsec, data = mtcars)
##
## Coefficients:
## (Intercept)         qsec
##       -5.11         1.41
```
# 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
##       17.15         7.24
```
# Regression on gear

```r
lm(mpg ~ gear, data = mtcars)
```

```
##
## Call:
## lm(formula = mpg ~ gear, data = mtcars)
##
## Coefficients:
## (Intercept)         gear
##        5.62         3.92
```
# Regression on carb

```r
lm(mpg ~ carb, data = mtcars)
```

```
##
## Call:
## lm(formula = mpg ~ carb, data = mtcars)
##
## Coefficients:
## (Intercept)         carb
##       25.87        -2.06
`````````