Creating Pretty Documents from R Markdown

Themes for R Markdown

With the powerful rmarkdown package, we could easily create nice HTML document by adding some meta information in the header, for example

---
title: Nineteen Years Later
author: Harry Potter
date: July 31, 2016
output:
  rmarkdown::html_document:
    theme: lumen
---

The html_document engine uses the Bootswatch theme library to support different styles of the document. This is a quick and easy way to tune the appearance of your document, yet with the price of a large file size (> 700KB) since the whole Bootstrap library needs to be packed in.

For package vignettes, we can use the html_vignette engine to generate a more lightweight HTML file that is meant to minimize the package size, but the output HTML is less stylish than the html_document ones.

So can we do BOTH, a lightweight yet nice-looking theme for R Markdown?

The prettydoc Engine

The answer is YES! (At least towards that direction)

The prettydoc package provides an alternative engine, html_pretty, to knit your R Markdown document into pretty HTML pages. Its usage is extremely easy: simply replace the rmarkdown::html_document or rmarkdown::html_vignette output engine by prettydoc::html_pretty in your R Markdown header, and use one of the built-in themes and syntax highlighters. For example

---
title: Nineteen Years Later
author: Harry Potter
date: July 31, 2016
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
---

Options and Themes

The options for the html_pretty engine are fully compatible with the default html_document (see the documentation) with two exceptions:

1. The theme option can take value from cayman, tactile and architect. More themes will be added in the future. The themes contained in prettydoc are much inspired by and modified from various Github page themes.
2. The highlight options takes value from github and vignette.

Elements

We demonstrate some commonly used HTML elements here to show the apprearance of themes.

set.seed(123)
n <- 1000
x1  <- matrix(rnorm(n), ncol = 2)
x2  <- matrix(rnorm(n, mean = 3, sd = 1.5), ncol = 2)
x   <- rbind(x1, x2)
smoothScatter(x, xlab = "x1", ylab = "x2")

head(x)

##             [,1]        [,2]
## [1,] -0.56047565 -0.60189285
## [2,] -0.23017749 -0.99369859
## [3,]  1.55870831  1.02678506
## [4,]  0.07050839  0.75106130
## [5,]  0.12928774 -1.50916654
## [6,]  1.71506499 -0.09514745

// [[Rcpp::depends(RcppEigen)]]
// [[Rcpp::depends(RcppNumerical)]]

#include <RcppNumerical.h>

using namespace Numer;

// f = 100 * (x2 - x1^2)^2 + (1 - x1)^2
// True minimum: x1 = x2 = 1
class Rosenbrock: public MFuncGrad
{
public:
    double f_grad(Constvec& x, Refvec grad)
    {
        double t1 = x[1] - x[0] * x[0];
        double t2 = 1 - x[0];
        grad[0] = -400 * x[0] * t1 - 2 * t2;
        grad[1] = 200 * t1;
        return 100 * t1 * t1 + t2 * t2;
    }
};

// [[Rcpp::export]]
Rcpp::List optim_test()
{
    Eigen::VectorXd x(2);
    x[0] = -1.2;
    x[1] = 1;
    double fopt;
    Rosenbrock f;
    int res = optim_lbfgs(f, x, fopt);
    return Rcpp::List::create(
        Rcpp::Named("xopt") = x,
        Rcpp::Named("fopt") = fopt,
        Rcpp::Named("status") = res
    );
}