mrowl

import sqlalchemy as sa

class EnumIntType(sa.types.TypeDecorator):
    impl = sa.types.SmallInteger

    values = None

    def __init__(self, values=None):
        sa.types.TypeDecorator.__init__(self)
        self.values = values

beniwohli

# original XML at http://www.w3.org/Math/characters/unicode.xml
# XSL for conversion: https://gist.github.com/798546


unicode_to_latex = {
    u"\u0020": "\\space ",
    u"\u0023": "\\#",
    u"\u0024": "\\textdollar ",
    u"\u0025": "\\%",
    u"\u0026": "\\&",

endolith

# https://github.com/matplotlib/matplotlib/issues/881
# Several of the ColorBrewer maps are "qualitative", meaning
# they are just a group of colors that can be used together 
# for categories of data.  So I remapped Accent to segments 
# instead of continuous:

# Actually, these should be used with ListedColormap, and 
# the number of colors should depend on the number of 
# categories in the data, with colors removed from the 
# list in a certain order?

ronert

#####################
## R Benchmark 2.5 ##
#####################
# http://r.research.att.com/benchmarks/R-benchmark-25.R
# R Benchmark 2.5 (06/2008) [Simon Urbanek]
# version 2.5: scaled to get roughly 1s per test, R 2.7.0 @ 2.6GHz Mac Pro
# R Benchmark 2.4 (06/2008) [Simon Urbanek]
# version 2.4 adapted to more recent Matrix package
# R Benchmark 2.3 (21 April 2004)
# Warning: changes are not carefully checked yet!

yryozo

;; Usage Example:
;;  
;; <!-- BEGIN RECEIVE ORGTBL ${1:YOUR_TABLE_NAME} -->
;; <!-- END RECEIVE ORGTBL $1 -->
;;  
;; <!-- 
;; #+ORGTBL: SEND $1 orgtbl-to-gfm
;; | $0 | 
;; -->

rage-shadowman

#!/bin/bash
#
# git-svn-diff originally by (http://mojodna.net/2009/02/24/my-work-git-workflow.html)
# modified by mike@mikepearce.net
# modified by aconway@[redacted] - handle diffs that introduce new files
# modified by t.broyer@ltgt.net - fixes diffs that introduce new files
# modified by m@rkj.me - fix sed syntax issue in OS X
# modified by rage-shadowman - cleaned up finding of SVN info and handling of path parameters
# modified by tianyapiaozi - cleaned up some diff context lines
#

fizruk

-- A Pandoc filter to use Pygments for Pandoc
-- Code blocks in HTML output
-- Nickolay Kudasov 2013
-- Requires Pandoc 1.12

import Text.Pandoc.Definition
import Text.Pandoc.JSON (toJSONFilter)
import Text.Pandoc.Shared
import Data.Char(toLower)
import System.Process (readProcess)

xflr6

jennybc

There are packages for this now!

2017-08-03: Since I wrote this in 2014, the universe, specifically Kirill Müller (https://github.com/krlmlr), has provided better solutions to this problem. I now recommend that you use one of these two packages:

• rprojroot: This is the main package with functions to help you express paths in a way that will "just work" when developing interactively in an RStudio Project and when you render your file.
• here: A lightweight wrapper around rprojroot that anticipates the most likely scenario: you want to write paths relative to the top-level directory, defined as an RStudio project or Git repo. TRY THIS FIRST.

I love these packages so much I wrote an ode to here.

I use these packages now instead of what I describe below. I'll leave this gist up for historical interest. 😆

hadley

I think the two most important messages that people can get from a short course are:

a) the material is important and worthwhile to learn (even if it's challenging), and b) it's possible to learn it!

For those reasons, I usually start by diving as quickly as possible into visualisation. I think it's a bad idea to start by explicitly teaching programming concepts (like data structures), because the pay off isn't obvious. If you start with visualisation, the pay off is really obvious and people are more motivated to push past any initial teething problems. In stat405, I used to start with some very basic templates that got people up and running with scatterplots and histograms - they wouldn't necessary understand the code, but they'd know which bits could be varied for different effects.

Apart from visualisation, I think the two most important topics to cover are tidy data (i.e. http://www.jstatsoft.org/v59/i10/ + tidyr) and data manipulation (dplyr). These are both important for when people go off and apply