# 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 0.81517580544331, so a + 1 is 1.81517580544331."

    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 = -1.23805831920307"

    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
    ```