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Choosing Your Workflow Applications

Introduction

You can do productive, maintainable and reproducible work with all kinds of different software set-ups.\symbolfootnote[0]{Email: \url{kjhealy@soc.duke.edu}. I thank Jake Bowers for helpful comments.} This is the main reason I don’t go around encouraging everyone to convert to the applications I use. (My rule is that I don’t try to persuade anyone to switch if I can’t commit to offering them technical support during and after their move.) So this discussion is not geared toward convincing you there is One True Way to organize things. I do think, however, that if you’re in the early phase of your career as a graduate student in, say, Sociology, or Economics, or Political Science, you should give some thought to how you’re going to organize and manage your work.[fn:faculty] This is so for two reasons. First, the transition to graduate school is a good time to make changes. Early on, there’s less inertia and cost associated with switching things around than there will be later. Second, in the social sciences, text and data management skills are usually not taught to students explicitly. This means that you may end up adopting the practices of your advisor or mentor, continue to use what you are taught in your methods classes, or just copy whatever your peers are doing. Following these paths may lead you to an arrangement that you will be happy with. But maybe not. It’s worth looking at the options.

[fn:faculty] This may also be true if you are about to move from being a graduate student to starting as a faculty member, though perhaps the rationale is less compelling given the costs.

Although in what follows I advocate you take a look at several applications in particular, it’s not really about the gadgets or utilities. The Zen of Organization is Not to be Found in Fancy Software. Nor shall the true path of Getting Things Done be revealed to you through the purchase of a nice Moleskine Notebook. Instead, it lies within. Unfortunately.

Just Make Sure You Know What You Did

For any kind of formal data analysis that leads to a scholarly paper, however you do it, there are basic principles that you will want to adhere to. Perhaps the most important thing is to do your work in a way that leaves a coherent record of your actions. Instead of doing a bit of statistical work and then just keeping the resulting table of results or graphic that you produced, for instance, write down what you did as a documented piece of code. Rather than figuring out but not recording a solution to a problem you might have again, write down the answer as an explicit procedure. Instead of copying out some archival material without much context, file the source properly, or at least a precise reference to it.

Why should you bother to do any of this? Because when you inevitably return to your table or figure or quotation nine months down the line, your future self will have been saved hours spent wondering what it was you thought you were doing and where in the hell you got that stuff from, anyway.

A second principle is that a document, file or folder should always be able to tell you what it is. Beyond making your work reproducible, you will also need some method for organizing and documenting your draft papers, code, field notes, datasets, output files or whatever it is you’re working with. In a world of easily searchable files, this may mean little more than keeping your work in plain text and giving it a descriptive name. It should generally not mean investing time creating some elaborate classification scheme or catalog that becomes an end in itself to maintain.

A third principle is that repetitive and error-prone processes should be automated if possible. (Software developers call this “DRY”, or Don’t Repeat Yourself.) This makes it easier to check for and correct mistakes. Rather than copying and pasting code over and over to do basically the same thing to different parts of your data, write a general function that can be called whenever it’s needed. Instead of retyping and reformatting the bibliography for each of your papers as you send it out to a journal, use software that can manage this for you automatically.

There are many ways of implementing these principles. You could use Microsoft Word, Endnote and SPSS. Or Textpad and Stata. Or a pile of legal pads, a calculator, a pair of scissors and a box of file folders. Still, software applications are not all created equal, and some make it easier than others to do the Right Thing. For instance, it is possible to produce well-structured, easily-maintainable documents using Microsoft Word. But you have to use its styling and outlining features strictly and responsibly, and most people don’t bother. You can maintain reproducible analyses in SPSS, but the application isn’t set up to do this automatically or efficiently, nor does its design encourage good habits. So, it is probably a good idea to invest some time learning about the alternatives. Many of them are free to use or try out, and you are at a point in your career where you can afford to play with different setups without too much trouble.

What Sort of Computer Should You Use?

The earliest choice you will face is buying your computer and deciding what operating system to run on it. The leading candidates are Microsoft Windows, Apple’s Mac OS X, and some distribution of Linux. Each of these platforms has gone some of the way — in some cases a long way — toward remedying the main defects stereotypically associated with it. I would characterize the present state of things this way:

  • Windows dominates the market. Because of this, far more viruses and malware target Windows than any other OS. Long-standing design and usability problems have been somewhat ameliorated in recent years. The previous major version, Windows Vista, was not very popular, though its main problems were not primarily related to security. Its successor, Windows 7, is generally accepted to be a solid improvement.
  • Mac OS X runs only on computers made by Apple (the existence of “hackintoshes” notwithstanding). Unlike in the past, Apple computers today have the same basic hardware as computers that run Windows. This has two consequences for those considering Mac OS X. First, one can now make direct price comparisons between Apple computers and PC alternatives (such as Dells, Lenovos, etc). In general, the more similarly kitted-out a PC is to an Apple machine, the more the price difference between the two goes away.[fn:compare] However, Apple does not compete at all price-points in the market, so it will always be possible to configure a cheaper PC (with fewer features) than one Apple sells. For the same reason, it is also easier to find a PC configuration precisely tailored to some particular set of needs or preferences (e.g., with a better display but without some other feature or other) than may be available from Apple.

    Second, because Apple now runs Intel-based hardware, installing and running Windows is easy, and even catered to by Mac OS’s Boot Camp utility. Beyond installing OS X and Windows side-by-side, third-party virtualization software is available (for about \$80 from VMWare or Parallels) that allows you to run Windows or Linux seamlessly within OS X. Thus, Apple hardware is the only setup where you can easily try out each of the main desktop operating systems.

  • Linux is stable, secure, and free. User-oriented distributions such as Ubuntu are much better-integrated and well-organized than in the past. The user environment is friendlier now. Installing, upgrading and updating software — a key point of frustration and time-wasting in older Linux distributions — is also much better than it used to be, as Linux’s package-management systems have matured. It remains true that Linux users are much more likely to be forced at some point to learn more than they might want to about the guts of their operating system.

[fn:compare] Comparisons should still take account of remaining differences in hardware design quality, and of course the OS itself.

These days, I use Mac OS X, and the discussion here reflects that choice to some extent. But the other two options are also perfectly viable alternatives. Rather than try to convince you to plump for one option or another, let’s look at some applications that will run on all of these operating systems.

The dissertation, book, or articles you write will generally consist of the main text, the results of data analysis (perhaps presented in tables or figures) and the scholarly apparatus of notes and references. Thus, as you put a paper or an entire dissertation together you will want to be able to easily edit text, analyze data and minimize error. In the next section I describe some applications and tools designed to let you do this easily. They fit together well (by design) and are all freely available for Windows, Linux and Mac OS X. They are not perfect, by any means — in fact, some of them are kind of a pain in the ass to learn. (I’ll discuss some nicer alternatives, too.) But graduate-level research and writing is kind of a pain in the ass to learn, too. Specialized tasks need specialized tools and, unfortunately, even if they are very good at what they do these tools don’t always go out of their way to be friendly.

Edit Text

If you are going to be doing quantitative analysis of any kind then you should write using a good text editor. The same can be said, I’d argue, for working with any highly structured document subject to a lot of revision, such as a scholarly paper. Text editors are different from word processors. Unlike applications such as Microsoft Word, text editors generally don’t make a big effort to make what you write look like as though it is being written on a printed page.[fn:cottrell] Instead, they focus on manipulating text efficiently and assisting you with visualizing the logical structure of what you’re writing. If you are writing code to do some statistical analysis, for instance, then at a minimum a good editor will highlight keywords and operators in a way that makes the code more readable. Typically, it will also passively signal to you when you’ve done something wrong syntactically (such as forget a closing brace or semicolon or quotation mark), and automagically indent or tidy up your code as you write it. If you are writing a scholarly paper or a dissertation, a good text editor can make it easier to maintain control over the structure of your document, and help ensure that cross-references and other paraphernalia are correct. Just as the actual numbers are crunched by your stats program — not your text editor — the typesetting of your paper is handled by a specialized application, too. Perhaps more importantly, a text editor manipulates plain text as opposed to binary file formats like .doc or .pdf, and plain text is the easiest format to manage, control, manipulate, back up, and come back to later with some other application.

[fn:cottrell] For further argument about the advantages of text-editors over word processors see Allin Cottrell’s polemic, “Word Processors: Stupid and Inefficient.”

Emacs is a text editor, in the same way the blue whale is a mammal. Emacs is very powerful, and can become almost a complete working environment in itself, should you so wish. (I don’t really recommend it.) Combining Emacs with some other applications and add-ons (described below) allows you to manage writing and data-analysis effectively. The Emacs Homepage has links to Windows and Linux versions. The two most easily available versions on the Mac are GNU Emacs itself and Aquamacs. The former is the “purest” version of Emacs and does not implement many Mac conventions out of the box. The latter makes an effort to integrate Emacs with the Mac OS. For Windows users who would like to use Emacs, the developers maintain an extensive FAQ including information on where to download a copy and how to install it.

While very powerful and flexible, Emacs is not particularly easy to learn. Indeed, to many first-time users (especially those used to standard applications on Windows or Mac OS) its conventions seem bizarre any byzantine. As applications go, Emacs is quite ancient: the first version was written by Richard Stallman in the 1970s. Because it evolved in a much earlier era of computing (before decent graphical displays, for instance, and possibly also fire), it doesn’t share many of the conventions of modern applications.[fn:emacs] Emacs offers many opportunities to waste your time learning its particular conventions, tweaking its settings, and generally customizing the bejaysus out of it. There are several good alternatives on each major platform, and I discuss some of them below.

[fn:emacs] One of the reasons that Emacs’ keyboard shortcuts are so strange is that they have their roots in a model of computer that laid out its command and function keys differently from modern keyboards. It’s that old.

At this point it’s reasonable to ask why I am even mentioning it, let alone recommending you try it. The answer is that, despite its shortcomings, Emacs is nevertheless very, very good at managing the typesetting and statistics applications I’m about to discuss. It’s so good, in fact, that Emacs has recently become quite popular amongst a set of software developers pretty much all of whom are much younger than Emacs itself. The upshot is that there has been a run of good, new resources available for learning it and optimizing it easily. Meet Emacs, a screencast from PeepCode, walks you through the basics of the application.

If text editors like Emacs are not concerned with formatting your documents nicely, then how do you produce properly typeset papers? You need a way to take the text you write and turn it into a presentable printed (or PDF) page. This is what LaTeX is for. LaTeX is a freely-available, professional-quality typesetting system. It takes text marked up in a way that describes the structure and formatting of the document (where the sections and subsections are, for example, or whether text should be in bold face or emphasized) and typesets it properly. If you have ever edited the HTML of a web page, you’ll know the general idea of a markup language. If you haven’t, the easiest way to understand what I mean is to look at a segment of LaTeX markup. An example is shown in Listing \ref{lst:latex}. You can get started with LaTeX for Mac OS X by downloading the MacTeX distribution. On Windows, ProTeXt and MiKTeX are both widely-used. Linux vendors have their own distributions, or you can install TeXLive yourself.[fn:distributions]

[fn:distributions] For more about these distributions of TeX, see the LaTeX project page. The proliferation of “-TeX” acronyms and names can be confusing to newcomers, as they may refer to a distribution of an entire TeX/LaTeX platform (as with MikTeX or MacTeX), or to a particular program or utility that comes with these distributions (such as BibTeX, for dealing with bibliographies), or to some bit of software that allows something else to work with or talk to the TeX system.

\subsection{Edit Text} 
This is what \textbf{LaTeX} is for. LaTeX is a freely-available,
professional-quality typesetting system. It takes text marked up in
a way that describes the structure and formatting of the document
(where the sections and subsections are, for example, or whether
text should be \textbf{in bold face} or \emph{emphasized}) and
typesets it properly. If you have ever edited the HTML of a web
page, you'll know the general idea of a markup language. If you
haven't, the easiest way to understand what I mean is to look at a
segment of LaTeX markup. An example is shown in Listing
\ref{lst:latex}.

LaTeX works best with some tools that help you take full advantage of it with a minimum of fuss. You can manage bibliographical references in LaTeX documents using BibTeX. It does the same job as Endnote, the commercial plug-in for managing references in Microsoft Word. BibTeX comes with any standard LaTeX installation. Whichever text editor or word processor you use, you should strongly consider some kind of reference-manager software for your bibliographies. It saves a tremendous amount of time because you can easily switch between bibliographical formats, and you don’t have to worry whether every item referenced in your dissertation or paper is contained in the bibliography.[fn:biblatex]

[fn:biblatex] If you plan to use BibTeX to manage your references, take a look at BibLaTeX, a package by Philipp Lehman, in conjunction with Biber, a replacement for BibTeX. BibLaTeX and Biber are not yet officially stable, but they are very well-documented, in wide use, and will soon jointly replace BibTeX as the standard way to process bibliographies in LaTeX.

AUCTeX and RefTeX are available for Emacs. These packages allow Emacs to understand the ins-and-outs of typesetting LaTeX documents, color-coding the marked-up text to make it easier to read, providing shortcuts to LaTeX’s formatting commands, and helping you manage references to Figures, Tables and bibliographic citations in the text. These packages could also be listed under the “Minimize Error” section below, because they help ensure that, e.g., your references and bibliography will be complete and consistently formatted.[fn:fonts]

[fn:fonts] A note about fonts and LaTeX. It used to be that getting LaTeX to use anything but a relatively small set of fonts was a very tedious business. This is no longer the case. The XeTeX engine makes it trivially easy to use any Postscript, TrueType or OpenType font installed on your system. XeTeX was originally developed for use on the Mac, but is available now for Linux and Windows as well.

More information on Emacs and LaTeX is readily available via Google, and there are several excellent books available to help you get started. \textcite{kopka03:_guide_latex} and \textcite{mittlebach04:_latex_compan} are good resources for learning LaTeX.

Analyze Data and Present Results

You will probably be doing some — perhaps a great deal — of quantitative data analysis. R is an environment for statistical computing. It’s exceptionally well-supported, continually improving, and has a very active expert-user community who have produced many add-on packages. R has the ability to produce sophisticated and high-quality statistical graphics. The documentation that comes with the software is complete, if somewhat terse, but there are a large number of excellent reference and teaching texts that illustrate its use. These include \textcite{dalgaard02:_introd_statis_r, venables02:_moder_applied_statis_s_plus, maindonald03:_data_analy_graph_using_r, fox02:_r_s_plus_compan_applied_regres, frank01:_regres_model_strat}, and \textcite{gelmanhill07:data_analysis}. Although it is a command-line tool at its core, it has a good graphical interface as well. You can download it from The R Project Homepage.

R can be used directly within Emacs by way of a package called ESS (for “Emacs Speaks Statistics”). As shown in Figure \ref{fig:ess}, it allows you to work with your code in one Emacs frame and a live R session in another right beside it. Because everything is inside Emacs, it is easy to do things like send a chunk of your code over to R using a keystroke. This is a very efficient way of doing interactive data analysis while building up code you can use again in future.

figures/ess-r-emacs.png

You’ll present your results in papers, but also in talks where you will likely use some kind of presentation software. Microsoft’s PowerPoint is the most common application, but there are better ones. If you wish, you can use LaTeX, too, creating slides with the Beamer document class and displaying them as full-screen PDFs. Alternatively, on Mac OS X Apple’s Keynote is very good. One benefit of using a Mac is that PDF is the operating system’s native display format, so PDF graphics created in R can simply be dropped into Keynote without any compatibility problems. Microsoft’s PowerPoint is less friendly toward the clean integration of PDF files in presentations.[fn:giving]

[fn:giving] The actual business of giving talks based on your work is beyond the scope of this discussion. Suffice to say that there is plenty of good advice available via Google, and you should pay attention to it.

Minimize Error

We have already seen how the right set of tools can save you time by automatically highlighting the syntax of your code, ensuring everything you cite ends up in your bibliography, picking out mistakes in your markup, and providing templates for commonly-used methods or functions. Your time is saved twice over: you don’t repeat yourself, and you make fewer errors you’d otherwise have to fix. When it comes to managing ongoing projects, minimizing error means addressing two related problems. The first is to find ways to further reduce the opportunity for errors to creep in without you noticing. This is especially important when it comes to coding and analyzing data. The second is to find a way to figure out, retrospectively, what it was you did to generate a particular result. These problems are obviously related, in that it’s easy to make a retrospective assessment of well-documented and error-free work. As a practical matter, we want a convenient way to document work as we go, so that we can retrace our steps in order to reproduce our results. We’ll also want to be able to smoothly recover from disaster when it befalls us.

Errors in data analysis often well up out of the gap that typically exists between the procedure used to produce a figure or table in a paper and the subsequent use of that output later. In the ordinary way of doing things, you have the code for your data analysis in one file, the output it produced in another, and the text of your paper in a third file. You do the analysis, collect the output and copy the relevant results into your paper, often manually reformatting them on the way. Each of these transitions introduces the opportunity for error. In particular, it is easy for a table of results to get detached from the sequence of steps that produced it. Almost everyone who has written a quantitative paper has been confronted with the problem of reading an old draft containing results or figures that need to be revisited or reproduced (as a result of the peer-review process, say) but which lack any information about the circumstances of their creation. Academic papers take a long time to get through the cycle of writing, review, revision, and publication, even when you’re working hard the whole time. It is not uncommon to have to return to something you did two years previously in order to answer some question or other from a reviewer. You do not want to have to do everything over from scratch in order to get the right answer. I am not exaggerating when I say that, whatever the challenges of replicating the results of someone else’s quantitative analysis, after a fairly short period of time authors themselves find it hard to replicate their own work. Computer Science people have a term of art for the inevitable process of decay that overtakes a project simply in virtue of its being left alone on the hard drive for six months or more: bit–rot.

Literate Programming with Sweave

A first step toward closing this gap is to use Sweave when doing quantitative analysis in R. Sweave is a literate programming framework designed to integrate the documentation of a data analysis and its execution. You write the text of your paper (or, more often, your report documenting a data analysis) as normal. Whenever you want to run a model, produce a table or display a figure, rather than paste in the results of your work from elsewhere, you write down the R code that will produce the output you want. These “chunks” of code are distinguished from the regular text by a special delimiter at their beginning and end. A small sample is shown in Listing \ref{lst:codechunk}. The code chunk begins with the line <<load-data, echo=true>>=. The character sequence <<>>= is the marker for the beginning of a chunk: load-data is just a label for the chunk and echo=true is an option. The end of each chunk is marked by the @ symbol.

\subsection{Some exploratory analysis}   
 In this section we do some exploratory analysis of the data. We begin
 by reading in the data file:
 
<<load-data, echo=true>>=
    ## load the data. 
    my.data <- read.csv("data/sampledata.csv",header=TRUE)
@  % The closing delimiter ends the code chunk.

 We should \emph{plot the data} to take a look at it:

<<plot-data, echo=true>>=
    ## make some plots.
    with(my.data, plot(x,y))
@

Maybe run some models, too.

<<ols-model echo=true>>=         
    ## OLS model
    out <- lm(y ~ x1 + x2,data=my.data)
    summary(out)
@ 
       
This concludes the exploratory analysis.

When you’re ready, you “weave” the file: you feed it to R, which processes the code chunks, and spits out a finished version where the code chunks have been replaced by their output. This is now a well-formed LaTeX file that you can then turn into a PDF document in the normal way. Conversely, if you just want to extract the code you’ve written from the surrounding text, then you “tangle” the file, which results in an .R file. It’s pretty straightforward in practice. Sweave files can be edited in Emacs, as ESS understands them.

The strength of this approach is that is makes it much easier to document your work properly. There is just one file for both the data analysis and the writeup. The output of the analysis is created on the fly, and the code to do it is embedded in the paper. If you need to do multiple but identical (or very similar) analyses of different bits of data, Sweave can make generating consistent and reliable reports much easier.

A weakness of the Sweave model is that when you make changes, you have to reprocess the all of the code to reproduce the final LaTeX file. If your analysis is computationally intensive this can take a long time. You can go a little ways toward working around this by designing projects so that they are relatively modular, which is good practice anyway. But for projects that are unavoidably large or computationally intensive, the add-on package cacheSweave, available from the R website, does a good job alleviating the problem.

Literate Programming with Org-mode

Org-mode is an Emacs mode originally designed to make it easier to take notes, make outlines and manage to-do lists. Very much in the spirit of Emacs itself, its users have extended it so that it is capable of all kinds of other things, too, such as calendar management, time-tracking, and so on. One very powerful extension to org-mode is Org-Babel, which is a generalized literate-programming framework for org-mode documents. It works like Sweave, except that instead of writing your papers, reports, or documentation in LaTeX and your code in R, you write text in Org-mode’s lightweight markup syntax and your code in any one of a large number of supported languages. Org-mode has very powerful export capabilities, so it can convert .org files to LaTeX, HTML, and many other formats quite easily. Examples of it in use can be seen at the Org-babel website. This article was written as a plain-text .org file and the raw version is available for inspection as a repository on GitHub. You can treat Org-Babel just as you would Sweave, or you can take advantage of the fact that it’s fully part of org-mode and get all of the latter’s functionality for free.

For example, Figure \ref{fig:org-babel-example} is generated on the fly from source-code blocks included in the .org source for this article. A piece of code can be executed, displayed, or both — as in the case of Listing \ref{lst:babel}. Then the figure can be created directly. I don’t show the code for this here, but you can look in the source file for this article to see how it’s done.

library(ggplot2)
tea <- rnorm(100)
biscuits <- tea + rnorm(100,0,1.3)
qplot(tea, biscuits) + geom_smooth(method="lm") + scale_x_continuous(name="Tea") +  scale_y_continuous(name="Biscuits")

Use Revision Control

The task of documenting your work at the level of particular pieces of code or edits to paragraphs in individual files can become more involved over time, as projects grow and change. This can pose a challenge to the Literate Programming model. Moreover, what if you are not doing statistical analysis at all, but still want to keep track of your work as it develops? The best thing to do is to institute some kind of version control system to keep a complete record of changes to a file, a folder, or a project. This can be used in conjunction with or independently of a documentation method like Sweave. A good version control system allows you to easily “rewind the tape” to earlier incarnations of your notes, drafts, papers and code, and lets you keep track of what’s current without having to keep directories full of files with confusingly similar names like Paper-1.txt, Paper-2.txt, Paper-conferenceversion.txt, and so on.

In the social sciences and humanities, you are most likely to have come across the idea of version control by way of the “Track Changes” feature in Microsoft Word, which lets you see the edits you and your collaborators have made to a document. Think of true version control as a way to keep track of whole projects (not just individual documents) in a much better-organized, comprehensive, and transparent fashion. Modern version control systems such as Subversion, Mercurial and Git can, if needed, manage very large projects with many branches spread across multiple users. As such, they require a little time to get comfortable with, mostly because you have to get used to some new concepts related to tracking your files, and then learn how your version control system implements these concepts. Because of their power, these tools might seem like overkill for individual users. (Again, though, many people find Word’s “Track Changes” feature indispensable once they begin using it.) But version control systems can be used quite straightforwardly in a basic fashion, and they increasingly come with front ends that can be easily integrated with your text editor.[fn:magit] Moreover, you can meet these systems half way. The excellent DropBox, for example, allows you to share files between different computers you own, or with collaborators or general public. But it also automatically version-controls the contents of these folders (using Subversion behind the scenes).

[fn:magit] Emacs comes with support for a variety of VCS systems built in. There’s also a very good add-on package, Magit, devoted specifically to Git.

Revision control has significant benefits. A tool like Git or Mercurial combines the virtues of version control with backups, because every repository is a complete, self-contained, cryptographically signed copy of the project. It puts you in the habit of recording (or “committing”) changes to a file or project as you work on it, and (briefly) documenting those changes as you go. It allows you to easily test out alternative lines of development by branching a project. It allows collaborators to work on a project at the same time without sending endless versions of the “master” copy back and forth via email, and it provides powerful tools that allow you to automatically merge or (when necessary) manually compare changes that you or others have made. Perhaps most importantly, it lets you revisit any stage of a project’s development at will and reconstruct what it was you were doing. This can be tremendously useful whether you are writing code for a quantitative analysis, managing field notes, or writing a paper.[fn:dvcs] While you will probably not need to control everything in this way (though some people do), I strongly suggest you consider managing at least the core set of text files that make up your project (e.g., the code that does the analysis and generates your tables and figures; the dataset itself; your notes and working papers, the chapters of your dissertation, etc). As time goes by you will generate a complete, annotated record of your actions that is also a backup of your project at every stage of its development. Services such as GitHub allow you to store public or (for a fee) private project repositories and so can be a way to back up work offsite as well as a platform for collaboration and documentation of your work.

[fn:dvcs] Mercurial and Git are distributed revision control systems (DVCSs) which can handle projects with many contributors and very complex, decentralized structures. Bryan O’Sullivan’s Distributed Version Control with Mercurial is a free, comprehensive guide to one of the main DVCS tools, but also provides a clear account of how modern version-control systems have developed, together with the main concepts behind them. For Git, I recommend starting at this site and following the links to the documentation.

You don’t need backups until you really, really need them

Regardless of whether you choose to use a formal revision control system, you should nevertheless have some kind of systematic method for keeping track of versions of your files. The task of backing up and synchronizing your files is related to the question of version control. Apple’s Time Machine software, for example, backs up and versions your files, allowing you to step back to particular instances of the file you want. Other GUI-based file synchronization tools, such as DropBox and SugarSync, are available across various platforms.

Even if you have no need for a synchronization application, you will still need to back up your work regularly. Because you are lazy and prone to magical thinking, you will not do this responsibly by yourself. This is why the most useful backup systems are the ones that require a minimum amount of work to set up and, once organized, back up everything automatically to an external (or remote) hard disk without you having to remember to do anything. On Macs, Apple’s Time Machine software is built in to the operating system and makes backups very easy. On Linux, you can use rsync for backups. It is also worth looking into a secure, peer-to-peer or offsite backup service like Crashplan or Spider Oak. Offsite backup means that in the event (unlikely, but not unheard of) that your computer and your local backups are stolen or destroyed, you will still have copies of your files.[fn:tornado] As Jamie Zawinski has remarked, when it comes to losing your data “The universe tends toward maximum irony. Don’t push it.”

[fn:tornado] I know of someone whose office building was hit by a tornado. She returned to find her files and computer sitting in a foot of water. You never know.

Pulling it Together: An Emacs Starter Kit for the Social Sciences

A step-by-step guide to downloading and installing every piece of software I’ve mentioned so far is beyond the scope of this discussion. But let’s say you take the plunge and download Emacs, a TeX distribution, R, and maybe even Git. Now what? If you’re going to work in Emacs, there are a variety of tweaks and add-ons that are very helpful but not set by default. To make things a little easier, I maintain an Emacs Starter Kit for the Social Sciences. It’s designed to smooth out Emacs’ rough edges by giving you a drop-in collection of default settings, as well as automatically installing some important add-on packages. It will, I hope, help you skirt the abyss of Setting Things Up Forever. The original version of the kit was written by Phil Hagelberg and was made to go with the ”Meet Emacs” screencast mentioned above. It was aimed at software developers in general. Eric Schulte, one of the authors of Org-babel, modified and further extended the kit. My version adds support for AucTeX, ESS, and other bits and pieces mentioned here. As you can see if you follow the links, the kit is stored on GitHub and you are free to fork it and modify it to your own liking.

Do I Have to Use this Stuff?

Pros and Cons

Using Emacs, LaTeX and R together has four main advantages. First, these applications are all free. You can try them out without much in the way of monetary expense. (Your time may be a different matter, but although you don’t believe me, you have more of that now than you will later.) Second, they are all open-source projects and are all available for Mac OS X, Linux and Windows. Portability is important, as is the long-term viability of the platform you choose to work with. If you change your computing system, your work can move with you easily. Third, they deliberately implement “best practices” in their default configurations. Writing documents in LaTeX encourages you to produce papers with a clear structure, and the output itself is of very high quality aesthetically. Similarly, by default R implements modern statistical methods in a way that discourages you from thinking about statistics in terms of canned solutions to standard problems. It also produces figures that accord with accepted standards of efficient and effective information design. And fourth, the applications are closely integrated. Everything (including version control systems) can work inside Emacs, and all of them talk to or can take advantage of the others. R can output LaTeX tables, for instance, even if you don’t use Sweave.

None of these applications is perfect. They are powerful, but they can be hard to learn. However, you don’t have to start out using all of them at once, or learn everything about them right away — the only thing you really need to start doing immediately is keeping good backups. There are a number of ways to try them out in whole or in part. You could try LaTeX first, using any editor. Or you could try Emacs and LaTeX together. You could begin using R and its GUI.[fn:try] Sweave or Org-babel can be left till last, though I have found these increasingly useful since I’ve started using them, and wish that all of my old project directories had some documentation in one or other of these formats. Revision control is more beneficial when implemented at the beginning of projects, and best of all when committing changes to a project becomes a habit of work. But it can be added at any time.

[fn:try] If you already know Emacs, you should certainly try R using ESS instead of the R GUI, though.

You are not condemned to use these applications forever, either. LaTeX and (especially) Org-mode documents can be converted into many other formats. Your text files are editable in any other text editor. Statistical code is by nature much less portable, but the openness of R means that it is not likely to become obsolete or inaccessible any time soon.

A disadvantage of these particular applications is that I’m in a minority with respect to other people in my field. This is less and less true in the case of R, but remains so for LaTeX. (It also varies across social science disciplines.) Most people use Microsoft Word to write papers, and if you’re collaborating with people (people you can’t boss around, I mean) this can be an issue. Similarly, journals and presses in my field often do not accept material marked up in LaTeX, though again there are important exceptions. Converting files to a format Word understands can be tedious (although it is quite doable).[fn:lightweight] I find these difficulties are outweighed by the day-to-day benefits of using these applications, on the one hand, and their longer-term advantages of portability and simplicity, on the other. Your mileage, as they say, may vary.

[fn:lightweight] Getting from LaTeX to Word is easiest via HTML. But if you really want to maximize the portability of your papers or especially your reading notes or memos, consider writing them in a modern lightweight markup format. Org-mode’s native format is effectively one of these already, and it provides built-in support for export to many others. An org-mode file can also be exported easily to rich-text or HTML, and from there Word or Google Docs will open it. Other options for lightweight markup include Markdown or its close relation, MultiMarkdown. Documents written in these formats are easy to read in their plain-text form but can be simply and directly converted into HTML, Rich Text, LaTeX, Word, or other formats. TextMate has good support for Markdown and MultiMarkdown, allowing you to do these conversions more or less automatically. John MacFarlane’s Pandoc is a powerful tool that can read markdown and (subsets of) reStructuredText, HTML, Org, and LaTeX; and it can write to MarkDown, reStructuredText, HTML, LaTeX, ConTeXt, RTF, DocBook XML, groff man, and S5 HTML slide shows. Pandoc is terrifically useful and I recommend checking it out. Lightweight markup languages like Markdown and Textile have a harder time dealing with some of the requirements of scholarly writing, especially the machinery of bibliographies and citations. If they could handle this task elegantly they would be almost perfect, but in practice this would probably just turn them back into something much less lightweight. Even here, though, good progress is being made as Pandoc will soon include support for citations.

Some Alternatives

There are many other applications you might put at the center of your workflow, depending on need, personal preference, willingness to pay some money, or desire to work on a specific platform. For text editing, especially, there is a plethora of choices. On the Mac, quality editors include BBEdit (beloved of many web developers), Smultron, and TextMate (shown in Figure \ref{fig:tm}). TextMate has strong support for LaTeX and good (meaning, ESS-like) support for R. Because it is a modern application written specifically for the Mac it can take advantage of features of OS X that Emacs cannot, and is much better integrated with the rest of the operating system. It also has good support for many of the ancillary applications discussed above, such as version control systems.[fn:tm2] On Linux, an alternative to Emacs is vi or Vim, but there are many others. For Windows there is Textpad, WinEdt, UltraEdit, and NotePad++ amongst many others. Most of these applications have strong support for LaTeX and some also have good support for statistics programming.

figures/textmate.png

[fn:tm2] Its next major version, TextMate 2, has been in development for a very long time and is awaited with a mixture of near-religious hope, chronic anxiety and deep frustration by users of the original.

For statistical analysis in the social sciences, the main alternative to R is Stata. Stata is not free, but like R it is versatile, powerful, extensible and available for all the main computing platforms. It has a large body of user-contributed software. In recent versions its graphics capabilities have improved a great deal. ESS can run Stata inside Emacs in the same way as it can do for R. Other editors can also be made to work with Stata: Jeremy Freese provides an UltraEdit syntax highlighting file for Stata. There is a Stata mode for WinEdt. Friedrich Huebler has a guide for integrating Stata with programming editors. Gabriel Rossman’s blog Code and Culture has many examples of using Stata in the day-to-day business of analyzing sociological data.

Amongst social scientists, revision control is perhaps the least widely-used of the tools I have discussed. But I am convinced that it is the most important one over the long term. While tools like Git and Mercurial take a little getting used to both conceptually and in practice, the services they provide are extremely useful. It is already quite easy to use version control in conjunction with some of the text editors discussed above: Emacs and TextMate both have support for various DVCSs. On the Mac, CornerStone and Versions are full-featured applications designed to make it easy to use Subversion. Taking a longer view, version control is likely to become more widely available through intermediary services or even as part of the basic functionality of operating systems.

A Broader Perspective

It would be nice if all you needed to do your work was a box software of software tricks and shortcuts. But of course it’s a bit more complicated than that. In order to get to the point where you can write a paper, you need to be organized enough to have read the right literature, maybe collected some data, and most importantly asked an interesting question in the first place. No amount of software is going to solve those problems for you. Too much concern with the details of your setup hinders your work. Indeed — and I speak from experience here — this concern is itself a kind self-imposed distraction that placates work-related anxiety in the short term while storing up more of it for later.[fn:merlin] On the hardware side, there’s the absurd productivity counterpart to the hedonic treadmill, where for some reason it’s hard to get through the to-do list even though the café you’re at contains more computing power than was available to the Pentagon in 1965. On the software side, the besetting vice of productivity-enhancing software is the tendency to waste a lot of your time installing, updating, and generally obsessing about your productivity-enhancing software.[fn:devil] Even more generally, efficient workflow habits are themselves just a means to the end of completing the projects you are really interested in, of making the things you want to make, of finding the answers to the questions that brought you to graduate school. The process of idea generation and project management can be run well, too, and perhaps even the business of choosing what the projects should be in the first place. But this is not the place — and I am not the person — to be giving advice about that.

All of which is just to reiterate that it’s the principles of workflow management that are important. The software is just a means to an end. One of the smartest, most productive people I’ve ever known spent half of his career writing on a typewriter and the other half on an IBM Displaywriter. His backup solution for having hopelessly outdated hardware was to keep a spare Displaywriter in a nearby closet, in case the first one broke. It never did.

[fn:merlin] See Merlin Mann, amongst others, for more on this point.

[fn:devil] Mike Hall’s brilliant ”Org-Mode in your Pocket is a GNU-Shaped Devil” makes this point very well.

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