m14t

#/bin/bash
#-- Script to automate https://help.github.com/articles/why-is-git-always-asking-for-my-password

REPO_URL=`git remote -v | grep -m1 '^origin' | sed -Ene's#.*(https://[^[:space:]]*).*#\1#p'`
if [ -z "$REPO_URL" ]; then
  echo "-- ERROR:  Could not identify Repo url."
  echo "   It is possible this repo is already using SSH instead of HTTPS."
  exit
fi

hieblmedia

#
# If all files excluded and you will include only specific sub-directories
# the parent path must matched before.
#

/**
!/.gitignore

###############################
# Un-ignore the affected subdirectory

Pakillo

---
title: "Untitled"
author: "Francisco Rodriguez-Sanchez"
date: "Thursday, January 22, 2015"
output: html_document
---

<style type="text/css">

body, td {

josephhainline

#!/bin/bash

echo
echo "Processing all *.md files in current directory..."
echo

# Loop through all .md files in directory and rename spaces to dashes
for file in *" "*; do
  echo "Renaming $file"
  mv -- "$file" "${file// /-}"

StevenMMortimer

Automate Chunks of Analysis in R Markdown 
========================================================
 
```{r setup, echo=FALSE}
library(knitr)
library(DT)
```
 
```{r create-datasets, echo=FALSE}
 

dannvix

// Python-like template string in ECMAScript 6 "tagged template"
// inspired from http://stackoverflow.com/a/22619256
function formatter(literals, ...substitutions) {
  return {
    format: function() {
      let out = [], i = 0, k = 0;
      for (i,k; i < literals.length; i++) {
        out[k++] = literals[i];
        out[k++] = Number.isInteger(substitutions[i]) ?
          arguments[substitutions[i]] :

conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

ramhiser

library(dplyr)
iris_char <- iris %>%
  mutate(Species=as.character(Species),
         char_column=sample(letters[1:5], nrow(iris), replace=TRUE))
sum(sapply(iris_char, is.character)) # 2

iris_factor <- iris_char %>%
  mutate_if(sapply(iris_char, is.character), as.factor)
# Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species  char_column 
# "numeric"    "numeric"    "numeric"    "numeric"  "character"  "character" 

simecek

Mukei

#One workaround is to create clone environment, and then remove original one:
#(remember about deactivating current environment with deactivate on Windows and source deactivate on macOS/Linux)

conda create --name new_name --clone old_name --offline #use --offline flag to disable the redownload of all your packages
conda remove --name old_name --all # or its alias: `conda env remove --name old_name`

#There are several drawbacks of this method:
# time consumed on copying environment's files,
# temporary double disk usage.