Have you ever tried to find a lightweight yet nice theme for the R Markdown documents, just like this page?
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 mostly compatible with the default html_document
(see the documentation) with a few exceptions:
- Currently the
theme
option can take the following values. More themes will be added in the future. - The
highlight
option takes value fromgithub
andvignette
. - Options
code_folding
,code_download
andtoc_float
are not applicable.
Elements
We demonstrate some commonly used HTML elements here to show the apprearance of themes.
Headers
Level 4
Level 5
Tables
Df | Sum Sq | Mean Sq | F value | Pr(>F) | ||
---|---|---|---|---|---|---|
Block | 5 | 343.3 | 68.66 | 4.447 | 0.01594 | * |
N | 1 | 189.3 | 189.28 | 12.259 | 0.00437 | ** |
P | 1 | 8.4 | 8.40 | 0.544 | 0.47490 | |
K | 1 | 95.2 | 95.20 | 6.166 | 0.02880 | * |
N:P | 1 | 21.3 | 21.28 | 1.378 | 0.26317 | |
N:K | 1 | 33.1 | 33.14 | 2.146 | 0.16865 | |
P:K | 1 | 0.5 | 0.48 | 0.031 | 0.86275 | |
Residuals | 12 | 185.3 | 15.44 |
Lists
There are three kinds of lies:
- Lies
- Damned lies
- Statistics
- Frequentists
- Bayesian
- …
Supported highlighters in prettydoc
:
github
: Style similar to Githubvignette
: Style used byrmarkdown::html_vignette
Markups
Bold, italic, don’t say this.
Code
Familiar knitr
R code and plots:
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
Also try some other languages, for example C++.
// [[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
);
}