gadenbuie

walk_source <- function(
  file = NULL, 
  delay = 1.5, 
  clear_each_cmd = TRUE,
  style = styler::tidyverse_style
) {
  options("walk_source_cancel" = FALSE)
  if (is.null(file)) {
    txt <- rstudioapi::getSourceEditorContext()$contents
  } else {

noamross

#' Function to partition unequal sized groups into shards of similar size.
#'
#' Based on the greedy algorithm described in [The Wikipedia article on the
#' the partitioning problem](https://en.wikipedia.org/wiki/Partition_problem#The_greedy_algorithm)
#'
#' @param groups_vector An integer vector of group ids, such as a group ID
#'   column in a data frame
#' @param n_shards The number of shards to split groups up into
#' @examples
#' n_groups <- 200

vankesteren

# Try-out of Hastie, Tibshirani, & Friedman (ESL, 2009) p.659

# Generate high-dimensional dataset
beta <- -100:100/100
X <- matrix(rnorm(100*201), 100)
y <- X%*%beta + rnorm(100, 0, sqrt(crossprod(beta)))

# Ridge estimates
bhat_ridge <- solve(crossprod(X) + diag(rep(.1, 201)), crossprod(X, y))

oliviaguest

#!/usr/bin/env python3

import csv
import time
import tweepy
import datetime

# Read http://docs.tweepy.org/en/v3.5.0/auth_tutorial.html to fill in the two lines below.
auth = tweepy.OAuthHandler('XXX', 'YYY')
auth.set_access_token('123', '456')

bearloga

<script language="JavaScript">
$(function() {
  /* Lets the user click on the images to view them in full resolution. */
  $("div.figure img").wrap(function() {
    var link = $('<a/>');
    link.attr('href', $(this).attr('src'));
    link.attr('title', $(this).attr('alt'));
    link.attr('target', '_blank');
    return link;
  });

klmr

#!/usr/bin/env bash

# Configuration
# The remote target branch name needs to be
# 1. DIFFERENT from the local development branch
# 2. Set as the default branch name on GitHub
remote_target_branch=master
generated_contents_dir=_site

get-working-branch() {

DexGroves

strip_glm <- function(cm) {
  cm$y = c()
  cm$model = c()
  
  cm$residuals = c()
  cm$fitted.values = c()
  cm$effects = c()
  cm$qr$qr = c()  
  cm$linear.predictors = c()
  cm$weights = c()

rmflight

If you want to know how many lines of code are in an Rmd file:

rmarkdown::render("file.Rmd", output_format = "md_document")

cat file.md | grep -c '^ \{4\}'

And then how many lines of code in .R files:

gaborcsardi

library(jsonlite)

## Download
pkgs <- fromJSON("http://crandb.r-pkg.org/-/events")

## Filter
na_pkgs <- unique(pkgs$name[ is.na(pkgs$date) ])
events <- pkgs[ ! pkgs$name %in% na_pkgs, c("date", "name", "event")]

hussius

# Download Kallisto and sratools (the latter to be able to download from SRA)
wget https://github.com/pachterlab/kallisto/releases/download/v0.42.3/kallisto_mac-v0.42.3.tar.gz
tar zvxf kallisto_mac-v0.42.3.tar.gz 
wget http://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/2.5.2/sratoolkit.2.5.2-mac64.tar.gz
tar zxvf sratoolkit.2.5.2-mac64.tar.gz

# Download and merge human cDNA and ncDNA files from Ensembl for the index.
wget ftp://ftp.ensembl.org/pub/current_fasta/homo_sapiens/cdna/Homo_sapiens.GRCh38.cdna.all.fa.gz
wget ftp://ftp.ensembl.org/pub/current_fasta/homo_sapiens/ncrna/Homo_sapiens.GRCh38.ncrna.fa.gz
cat Homo_sapiens.GRCh38.cdna.all.fa.gz Homo_sapiens.GRCh38.ncrna.fa.gz > Homo_sapiens.GRCh38.rna.fa.gz