wereHamster

This script has a new home: https://github.com/wereHamster/ghup

ramnathv

% Full Screen Environment for Beamer Slides
\newenvironment{changemargin}[2]{%
\begin{list}{}{%
  \setlength{\topsep}{0pt}%
  \setlength{\leftmargin}{#1}%
  \setlength{\rightmargin}{#2}%
  \setlength{\listparindent}{\parindent}%
  \setlength{\itemindent}{\parindent}%
  \setlength{\parsep}{\parskip}%
}%

ramnathv

Here is how you can make a table in GFM format using knitr + ascii

render_gfm()
gfm_table <- function(x, ...) {
    require(ascii)
    y <- capture.output(print(ascii(x, ...), type = "org"))
 # substitute + with | for table markup

richfitz

#!/usr/bin/Rscript
library(tools)
library(knitr)
library(sowsear)
opts_knit$set(progress = FALSE, verbose = FALSE)

## We need a list of files and a package to start.
args <- commandArgs(TRUE)
package <- args[[1]]
files <- args[-1]

kohske

#' # Rスクリプトからレポート生成
#' # `spin`ning in knitr package
#'
#' ### @kohske
#' 
#' Rスクリプトはこちら
#' https://gist.github.com/gists/2975392
#' 
#' ---
#' 

even4void

RMDFILE=demo-rmd-pandoc
PANDOC=~/.cabal/bin/pandoc

all:
	Rscript -e "require(knitr); require(markdown); knit('$(RMDFILE).rmd', '$(RMDFILE).md'); purl('$(RMDFILE).rmd')"
	${PANDOC} --mathjax --toc -B header.html -A footer.html --bibliography refs.bib --css markdown.css -s $(RMDFILE).md -o $(RMDFILE).html
 

kbroman

# a function to deal with sprintf("%.2f", ...) returning -0.00
# see https://twitter.com/hspter/status/314858331598626816

f <- function(..., dig=2) {
  g <- sprintf(paste0("%.", dig, "f"), ...)
  z <- paste0("0.", paste(rep("0", dig), collapse=""))
  g[g==paste0("-",z)] <- z
  g
}

johnmyleswhite

# Generate (x, y) data with a sparse set of active predictors
# prob controls the frequency of predictors having zero effect
function simulate_date(n::Integer, p::Integer, prob::Real)
  x = randn(n, p)
	beta = randn(p)
	for j in 1:p
		if rand() < prob
			beta[j] = 0.0
		end
	end

ramhiser

#' Finds the local maxima (peaks) in the given vector after smoothing the data
#' with a kernel density estimator.
#'
#' First, we smooth the data using kernel density estimation (KDE) with the
#' \code{\link{density}} function. Then, we find all the local maxima such that
#' the density is concave (downward).
#'
#' Effectively, we find the local maxima with a discrete analogue to a second
#' derivative applied to the KDE. For details, see this StackOverflow post:
#' \url{http://bit.ly/Zbl7LV}.

kleinschmidt

#!/bin/bash
# knit.sh -- Dave Kleinschmidt, April 2013
# streamline knitting of Rnw files from the command line.

usage="Usage: $0 input-filename.Rnw [-nolatex] [-notangle]"

if [ $# -lt 1 ]; then
    echo $usage
    exit 1
fi