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Toolchain for macOS Sierra

March 1, 2017

Previously, I’ve described my setup on Windows 7 and macOS 10.9.x (Mavericks). Now that I’ve got a new MacBook Air, it’s time to update these instructions for macOS 10.12.x (Sierra). The setup described below is quite minimal, since I have limited disk space. See the article by Bhaskar Karambelkar‏ for an install based on homebrew that has all the bells & whistles.

The most significant change is the increased security features that were introduced in El Capitan, in an attempt to make OS X less vulnerable to rootkits and other script kiddie exploits. Upgrading caused a lot of headaches for users of R and LaTeX, since folders were magically moved to different locations on the hard drive. This is one of the reasons that I chose to skip Yosemite and El Capitan (OK, mostly it was sheer laziness…)

I use the Outlook email client because my work email is hosted on an Exchange server and I’ve found Mac mail very flaky for that. I also have co-authors that use Excel and Word, so having Office installed makes it easier to collaborate with them. Wherever possible, I use R Markdown with pandoc to generate Word documents programmatically, which saves me a lot of time in the long run (reproducible research FTW!). Other commercial software that I have installed includes MATLAB, Mathematica, Dropbox,  and Skype.

I was very pleased that I was able to install the Xcode command-line tools and the Gnu compilers without any problems. I can switch between clang++ and g++ by editing my ~/.R/Makeconf as described here. I also use XQuartz for X/Windows support. Of course, I have R and RStudio installed, using vecLib instead of the default R BLAS library. The instructions for switching BLAS libraries on OS X in Sect. 10.5 of the installation guide are out of date. On OS X Mavericks or later, type the following commands in Terminal:

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

ln -sf /System/Library/Frameworks/Accelerate.framework/Frameworks/vecLib.framework/Versions/Current/libBLAS.dylib libRblas.dylib

I have limited space on my solid state drive, so I decided to install the minimal BasicTeX with TeXShop for editing and compiling LaTeX documents. Overall, I think this is a better approach. I wouldn’t install all 10,000 packages from CRAN just in case I might need them, so I see no reason why CTAN should be any different. So far, I’ve had to install the following additional packages from CTAN using tlmgr:

  • preprint
  • algorithms
  • subfigure
  • bbm-macros
  • latexmk
  • multirow
  • morefloats
  • framed
  • titling
  • xstring
  • pgfopts
  • inconsolata
  • chicago
  • courier
  • helvetic

Even on Sierra, you need to run tlmgr as root, using:

sudo /usr/local/texlive/2016basic/bin/x86_64-darwin/tlmgr install algorithms

I also needed to install Ghostscript from here, otherwise epstopdf doesn’t work. As with MS Word, you can generate LaTeX programmatically using knitr, but you still need to have pdfLatex installed to compile the PDF. I still use JabRef to manage all of my BibTeX files, since I haven’t found anything that does the job better. The installer for JabRef isn’t signed, so Gatekeeper will complain when you try to install it.

Filesystem      Size   Used  Avail Capacity  Mounted on
/dev/disk1     465Gi   54Gi  411Gi    12%    /
devfs          182Ki  182Ki    0Bi   100%    /dev
map -hosts       0Bi    0Bi    0Bi   100%    /net
map auto_home    0Bi    0Bi    0Bi   100%    /home

No major changes from my previous list. I’ve dropped Stata since the R package foreign does a good enough job of importing .dta files. Instead, I’ve got Mathematica installed to help out with my poor algebra skills. I did consider getting a 15in MacBook Pro instead of the Air, which could have had up to 16GB of RAM and 2TB solid state drive. The Thunderbolt 3 (USB-C) ports and touch bar would have been annoying, but the real deal breaker was Radeon instead of nVidia graphics. If I couldn’t use CUDA, then it just wasn’t worth the extra expense (plus, I’m not sure I’d trust myself with such an expensive laptop!). I’ve got a GeForce GTX 970M in my Windows machine if I need to run any CUDA code. ASUS laptops offer a lot more bang for your buck, but Mac OS is still nicer to use (particularly for developing software and writing academic papers). I think it’s well worth the extra money, if you can afford it.

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From → C++, R, Toolchain

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