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@halpo
halpo / Makefile
Created Nov 17, 2011
Makefile for R Packages
View Makefile
# Makefile for generating R packages.
# 2011 Andrew Redd
#
# Assumes Makefile is in a folder where package contents are in a subfolder pkg.
# Roxygen uses the roxygen2 package, and will run automatically on check and all.
PKG_VERSION=$(shell grep -i ^version pkg/DESCRIPTION | cut -d : -d \ -f 2)
PKG_NAME=$(shell grep -i ^package pkg/DESCRIPTION | cut -d : -d \ -f 2)
R_FILES := $(wildcard pkg/R/*.R)
@romainfrancois
romainfrancois / output.txt
Created Dec 14, 2013
Extract promises and their environments from ...
View output.txt
> f <- function(...) {
+ promises(environment())
+ }
> g <- function(x = 3, ...) {
+ z <- 4
+ f(z = z, ..., x = x)
+ }
> h <- function(..., a = 2) {
@myusuf3
myusuf3 / delete_git_submodule.md
Created Nov 3, 2014
How effectively delete a git submodule.
View delete_git_submodule.md

To remove a submodule you need to:

  • Delete the relevant section from the .gitmodules file.
  • Stage the .gitmodules changes git add .gitmodules
  • Delete the relevant section from .git/config.
  • Run git rm --cached path_to_submodule (no trailing slash).
  • Run rm -rf .git/modules/path_to_submodule (no trailing slash).
  • Commit git commit -m "Removed submodule "
  • Delete the now untracked submodule files rm -rf path_to_submodule
@aortbals
aortbals / squash-and-merge-cli.md
Last active Jan 12, 2021
Squash and Merge on the Command line
View squash-and-merge-cli.md

With the introduction of GitHub's Squash and Merge feature, this has become less prevelant, however it's still useful in scenarios where GitHub's interface is unavailable.

Let's talk through two ways to do a squash and merge on the command line.

Take 1: Interactive Rebase

When to use it

  • When you have not merged master into your feature branch
  • There are no merge conflicts
@flare9x
flare9x / R Simple Back Test Script - 30min ES.R
Last active Apr 11, 2018
R Simple Back Test Script - 30min ES
View R Simple Back Test Script - 30min ES.R
# Back test simple moving average strategy
# Use 30min ES Data
require(TTR)
require(quantmod)
require(lubridate)
require(dplyr)
require(microbenchmark)
i=1
out_results <- data.frame()
View plot.awk
#!/usr/bin/awk -f
# This program is a copy of guff, a plot device. https://github.com/silentbicycle/guff
# My copy here is written in awk instead of C, has no compelling benefit.
# Public domain. @thingskatedid
# Run as awk -v x=xyz ... or env variables for stuff?
# Assumptions: the data is evenly spaced along the x-axis
# TODO: moving average