tdavis-ourfuture

#!/usr/bin/php
<?php
/**
 * Checks a list of domains and outputs any that use Google.
 *
 *  usage:  php ./googlecheck.php filewithdomains
 *
 */

stream_set_blocking(STDIN, 0);

zachmayer

cd /Library/Frameworks/R.framework/Resources/lib

# for vecLib use
ln -sf libRblas.vecLib.dylib libRblas.dylib

# for R reference BLAS use
ln -sf libRblas.0.dylib libRblas.dylib

cheuerde

#####################################################
### How to compile R and link it against OpenBLAS ###
#####################################################

# Assuming no root priviledges on the destination machine,
# so we have to compile our software locally

# set default installation directory:
INSTDIR=$HOME/opt

benmarwick

Commonly used scientific symbols in pandoc markdown

encoding is UTF-8, needs xelatex, like this:

---
output:
  pdf_document:
    latex_engine: xelatex
---

domenic

Auto-deploying built products to gh-pages with GitHub Actions

This is a set up for projects which want to check in only their source files, but have their gh-pages branch automatically updated with some compiled output every time they push.

A file below this one contains the steps for doing this with Travis CI. However, these days I recommend GitHub Actions, for the following reasons:

• It is much easier and requires less steps, because you are already authenticated with GitHub, so you don't need to share secret keys across services like you do when coordinate Travis CI and GitHub.
• It is free, with no quotas.
• Anecdotally, builds are much faster with GitHub Actions than with Travis CI, especially in terms of time spent waiting for a builder.

andrie

library(foreach)
library(iterators)
library(doParallel)
library(tcltk)

# Choose number of iterations
n <- 1000

cl <- makeCluster(8)

taylorterry3

dgrapov

#plotly box or lasso select linked to 
# DT data table
# using Wage data
# the out group: is sex:Male, region:Middle Atlantic +


library(ggplot2)
library(plotly)
library(dplyr) 
library(ISLR) 

mcburton

Running Jupyter on a Supercomputer

This quick guide for getting a Jupyter Notebook up and running on Bridges, a supercomputer managed by the Pittsburgh Supercomputing Center. Bridges is a new machine designed to accommodate non-traditional uses of High Performance Computing (HPC) resources like data science and digital humanities. Bridges is available through XSEDE, which is the system that manages access to multiple supercomputing resources. Through XSEDE, Bridges is available researchers or educators at US academic or non-profit research institutions (see the XSEDE eligibility policies) Allocations are free, but there is a somewhat difficult to understand application process filled with jargon and acronyms that take time to understand. See the XSEDE getting started guide for more information about getting acc

ledell

# Set API Key
Sys.setenv(SIGOPT_API_TOKEN="HERE")

# Start a local H2O cluster for training models
library(h2o)
h2o.init(nthreads = -1)

# Load a dataset
data(iris)
y <- "Species"