rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

CristinaSolana

1. Clone your fork:

git clone git@github.com:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream

niw

#!/usr/bin/env bash

# fetch_nike_puls_all_activities.bash
# A simple bash script to fetch all activities and metrics from NikePlus.
# See `nike_plus_api.md` for the API details.

readonly bearer_token="$1"
if [[ -z "$bearer_token" ]]; then
  echo "Usage: $0 bearer_token"
  exit

ctesta01

Quickstart Guide to undertsanding the Qualtrics Survey File

This information is likely to quickly become outdated when Qualtrics next changes the formatting of the QSF file. This guide was started February 2017. I hope that it is a useful introduction to understanding the contents of the QSF file that one can download from Qualtrics.

This document includes:

• An introduction to JSON
• The Anatomy of a Qualtrics Survey File (QSF)
• A Breakdown of Question Structure
• A Guide to Recognizing Question Types

batpigandme

library(tidyverse)

family <- list(
  list(
    "name" = "Bob",
    "age" = 46,
    "mother" = NA,
    "father" = "Big Bob",
    "siblings" = list(NA),
    "children" = list("Tina", "Gene", "Louise"),

Enchufa2

# remotes::install_github("coolbutuseless/eventloop")

Pong <- R6::R6Class(
  "pong",
  
  public = list(
    initialize = function(width=10, height=7, speed=0.02) {
      require(grid)
      private$width <- width
      private$height <- height

moodymudskipper

makeActiveBinding("v", local({
  e <- NULL
  count <- 1
  
  function(value) {
    # increment or reinitialize counter
    exec_env <- sys.frame(-1)
    if(identical(e, exec_env)) {
      count <<- count + 1
    } else {

emitanaka

<script>
(function() {
  var divHTML = document.querySelectorAll(".details-open");
  divHTML.forEach(function (el) {
    var preNodes = el.getElementsByTagName("pre");
    var outputNode = preNodes[1];
    outputNode.outerHTML = "<details open class='output'><summary>Output</summary>" + outputNode.outerHTML + "</details>";
  })
})();
(function() {

ivyleavedtoadflax

Connecting to MS SQL Server using RODBC

You may need to install the following libraries (if you don't already have them)

install.packages("RODBC")
install.packages("dplyr")

Load these packages

mpjdem

library(data.table)
library(keypress)

## Create the universe. Well, sort of.
dims <- c(49, 49)
universe <- CJ(x = seq(dims[1]), y = seq(dims[2]), k = 1, cell = FALSE)
universe[, cell := sample(c(FALSE, TRUE), prod(dims), TRUE)]

## Neighbourhood to consider for each cell
neighbours <- CJ(xd = -1:1, yd = -1:1, k = 1)[xd != 0 | yd != 0]