andreyvit

tmux cheat sheet

(C-x means ctrl+x, M-x means alt+x)

Prefix key

The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf:

remap prefix to Control + a

fightingtheboss

upstream haproxy {
  server 127.0.0.1:9000;
}

upstream stats {
	server 127.0.0.1:9999;
}

server {
	listen   80; ## listen for ipv4; this line is default and implied

jimhester

.Rproj.user
.Rhistory
.RData
*.Rproj
*.html

kevinushey

.Rproj.user
.Rhistory
.RData
*.Rproj
*.html

skranz

# Helper functions that allow string arguments for  dplyr's data modification functions like arrange, select etc. 
# Author: Sebastian Kranz

# Examples are below

#' Modified version of dplyr's filter that uses string arguments
#' @export
s_filter = function(.data, ...) {
  eval.string.dplyr(.data,"filter", ...)
}

arunsrinivasan

Update: The timings are now updated with runs from R v3.2.2 along with the new 'on=' syntax

A small note on this tweet from @KevinUshey and this tweet from @ChengHLee:

The number of rows, while is important, is only one of the factors that influence the time taken to perform the join. From my benchmarking experience, the two features that I found to influence join speed, especially on hash table based approaches (ex: dplyr), much more are:

• The number of unique groups.
• The number of columns to perform the join based on - note that this is also related to the previous point as in most cases, more the columns, more the number of unique groups.

That is, these features influence join speed in spite of having the same number of rows.

LeCoupa

/* *******************************************************************************************
 * THE UPDATED VERSION IS AVAILABLE AT
 * https://github.com/LeCoupa/awesome-cheatsheets
 * ******************************************************************************************* */


// 0. Synopsis.
// http://nodejs.org/api/synopsis.html

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

thertrader

This Shiny application is designed to help analysing trading strategies. It is an ongoing project that I improve when time allows. Feel free to get in touch should you have any suggestion.

*How to use the App as it is?
The App uses as input several csv files (one for each strategy). Each file has two columns: date and daily return. There is an example of such a file in the Github repository. The code is essentially made of 3 files.
-ui.R: controls the layout and appearance of the app
-server.R: contains the instructions needed to build the app. You can load as much strategies as you want as long as the corresponding csv file has the right format (see below).
-shinyStrategyGeneral.R: loads the required packages and launches the app
put ui.R and server.R file in a separate directory

In the server.R file change the inputPath, inputFile and keepColumns parameters to match your setting. The first two are self explanatory the third one is a list of column names within the csv file. Keep only date and daily return

michaelneale

#!/bin/sh                                                                                                                                                                            

remove_dangling() {
   echo "Removing dangling images ..."
   docker rmi $(docker images -f dangling=true -q)
}

remove_stopped_containers() {
    echo "Removing stopped containers ..."
    docker rm $(docker ps -qa)