Author: grant

Sometimes R segfaults with quartz

R plotting crashed a lot on OSX on my system: 

archey
R --version
brew --version

                 ###
               ####                   User: gcr
               ###                    Hostname: orion
       #######    #######             Distro: OS X 10.9.3
     ######################           Kernel: Darwin
    #####################             Uptime: 2 days
    ####################              Shell: /usr/local/bin/bash
    ####################              Terminal: dumb
    #####################             Packages: 78
     ######################           CPU: Intel Core i7-4960HQ CPU @ 2.60GHz
      ####################            Memory: 16 GB
        ################              Disk: 26%
         ####     #####
R version 3.1.1 (2014-07-10) -- "Sock it to Me"
Copyright (C) 2014 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin13.2.0 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under the terms of the
GNU General Public License versions 2 or 3.
For more information about these matters see
http://www.gnu.org/licenses/.
0.9.5

Almost every time that I would use the quartz device, ℝ would segfault.

After changing it to X11, there have not been any segfaults. Perhaps I misunderstood the relationship between how the Brew build interacts with quartz.

How to clean up environment bindings in knitr sub modules?

This post discusses the management of the environment while running different
parts of the code. The author’s desire is to:

  • Obtain a list of bindings before execution sub-module
  • Execute the code in the sub-module
  • Upon completion of the sub-mode code execution remove anything that was added
    to the environment

Can definitely understand the desire. The approach makes total sense.

Perhaps now is the time for me to learn more about packages and whether that would be
a good approach here. Even running everything inside of a local block could be
an option perhaps?

Milwaukee Data Science: Next Steps

The purpose of this Meetup is to provide a safe, pleasant, and convenient venue (physical and virtual) to facilitate the growth and nurturing of the Data Science community in Southeastern Wisconsin.

Since Data Science covers the entirety of all human endeavors, this group serves to facilitate dialogue and discussion between all realms of mastery. Strategic advisers, managers, investors, health-care administrators, and quantitative analysts will feel equally at home here, as will mathematicians, statisticians, ecologists, biologists, and social-scientists. All realms of mastery are invited and welcomed to join.

Given the limitless application of technology here, all members of the Information Technology field are welcomed to join. All roles are welcome ranging from technical (developers, architects, system and devops administrators) to project managers and business analysts.

Experts and neophytes with interest in particular languages, environments, frameworks, and technologies have a home here. Be it R, Python, Java, or Octave, there is a place for everyone to learn and share. Our doors are equally open to practitioners applying specific technological offerings for every industry and platform.

With a laid back approach open to all ideas, this group will reflect the contributions and participation of its members, in whatever form time and resources permit.

How to read a 1 GiB file into memory in R

Here is the start of a small and good discussion on how to read large data
sets into many. They appears frequently on the list, but this one seemed to
touch upon a lot of the recommended approaches: