If you have some R code to include in a document, especially a Web-based document like a blog post, the new "Pretty R" feature on inside-R.org can help you make it look its best. Given some raw R code, it will create a HTML version of the code, adding syntax highlighting elements and links. Functions, strings, comments and literals are all color-coded to make reading easier, and function names are linked to the corresponding R help pages in the Language Reference from inside-R.org.

For example, the Freakonometrics blog had an excellent post the other day on using time series methods to forecast pageviews reported on Google Analytics. (It's a great blog if you haven't checked it out before. I also like it because it gives me a chance to brush up on my French. But I digress.) The author, Arthur Charpentier, did include the R code, but I found it a little hard to read. Here's a screenshot:

I cut-and-pasted the code into the Pretty R tool (after removing the > and + prompts, which I recommend as good practice for posting R code — it makes it easier for others to paste it into a script), and then just pasted the resulting HTML into this blog post. Here's the result:

base=read.table("http://perso.univ-rennes1.fr/arthur.charpentier/million2.csv",
sep=";",header=TRUE)
X2=cumsum(base$nombre)
X=X1+X2
kt=which(D==as.Date("01/06/2010","%d/%m/%Y"))
D0=as.Date("08/11/2008","%d/%m/%Y")
D=D0+1:length(X1)
P=rep(NA,(length(X)-kt)+1)
for(h in 0:(length(X)-kt)){
model <- arima(X[1:(kt+h)],c(7 , # partie AR
1, # partie I
7),method="CSS") # partie MA
forecast <- predict(model,200)
u=max(D[1:kt+h])+1:300
k=which(u==as.Date("01/01/2011","%d/%m/%Y"))
(P[h+1]=1-pnorm(1000000,forecast$pred[k],forecast$se[k]))
}

The Pretty R tool is open to everyone, you don't need to set up an account to use it. I hope it's useful to all R authors and bloggers.

inside-R.org: Pretty R Syntax Highlighter

*Related*

To

**leave a comment** for the author, please follow the link and comment on their blog:

** Revolutions**.

R-bloggers.com offers

**daily e-mail updates** about

R news and

tutorials on topics such as:

Data science,

Big Data, R jobs, visualization (

ggplot2,

Boxplots,

maps,

animation), programming (

RStudio,

Sweave,

LaTeX,

SQL,

Eclipse,

git,

hadoop,

Web Scraping) statistics (

regression,

PCA,

time series,

trading) and more...

If you got this far, why not

__subscribe for updates__ from the site? Choose your flavor:

e-mail,

twitter,

RSS, or

facebook...

**Tags:** developer tips, R