mschubert

library(stringr)
library(BatchJobs)
library(plyr)

# Rationale
#  This script uses BatchJobs to run functions either locally, on multiple cores, or LSF,
#  depending on your BatchJobs configuration. It has a simpler interface, does more error
#  checking than the library itself, and is able to queue different function calls. The
#  function supplied *MUST* be self-sufficient, i.e. load libraries and scripts.
#  BatchJobs on the EBI cluster is already set up when using the gentoo prefix.

tleonardi

#!/bin/bash
# Wrapper script to run Vim-R-plugin R sessions as interactive jobs on LSF platforms.
# To use it, edit ~/.vimrc and add 'let vimrplugin_r_path = <path_to_script>'

# lets you deicde 
echo What version of R do you want?
echo "1) R vX.X.X"
echo "2) R vZ.Z.Z"
echo "3) R vY.Y.Y"
read -t 60 -p '>' answer

dgrtwo

parsetidy <- function(x, ...) UseMethod("parsetidy")


parsetidy.default <- function(x, ...) {
  # if it's not a call or name (e.g. it's numeric),
  # don't need it
  data.frame()
}

klmr

Vim Notes

Setup

The Vim on the server is horribly outdated. Many of the things below won’t work, or will require extensive setup. In order to ease the pain, I suggest using the version installed and maintained by Micha via the [EBI-predoc config][config].

klmr

#!/usr/bin/env bash

# Inspired by <http://blog.melski.net/2010/11/30/makefile-hacks-print-the-value-of-any-variable/>

usage() {
    echo >&2 "Usage: $0 [-f makefile] variables..."
    echo >&2
    echo >&2 "Optional arguments:"
    echo >&2 "  -f makefile:  path to the makefile"
    echo >&2

klmr

In the following, let’s define sum as

sum = function (a, b, c) a + b +c

Because base::sum’s definition involves ..., which would make the following explanation unnecessarily complex.

---

klmr

library(dplyr)
library(tidyr)
library(ggplot2)

stages = c('Ordered', '?', 'In transit', 'Arrived', 'Returned')

make_stages = function (status)
    factor(status, levels = stages, ordered = TRUE)

pillows = tibble::tribble(

klmr

#!/usr/bin/env bash

while getopts ":n:c:q:" option; do
    case "$option" in
        n)
            cores="$OPTARG"
            ;;
        m)
            mem="$OPTARG"
            ;;

klmr

Who is touching the .Random.seed?

Inspired by a Stack Overflow question, here’s a way of tracking what’s been modifying the .Random.seed.

Since R makes static analysis impossible in general, the following is a runtime tracer that injects itself into the .Random.seed variable via an active binding:

debug_random_seed()
sample(10)

randy3k

importCpp <- function(infile, output_dir="lib", rebuild=FALSE){
    output_dir = ifelse(is.null(output_dir), ".", output_dir)
    dir.create(output_dir, recursive=T, showWarnings=FALSE)
    outfile = file.path(output_dir, paste0(infile, ".R"))

    if (!file.exists(outfile) || file.info(infile)$mtime > file.info(outfile)$mtime || rebuild){
        Rcpp::sourceCpp(infile, rebuild=rebuild)
        context = .Call("sourceCppContext", PACKAGE = "Rcpp",
            normalizePath(infile, winslash = "/"), code=NULL, 0L, .Platform)
        scriptfile = file.path(context$buildDirectory, context$rSourceFilename)