![]() Most scientists use a typesetting system known as LaTeX to produce beautiful papers with clear mathematics and razor-sharp vector graphics. There is a better way, but you’ll need to leave the safety of wysiwyg word processing behind. If your papers include graphics and math, you will quickly discover that Microsoft Word makes your graphics look blurry or even squashed, and that typesetting equations in Microsoft products leads to daydreams of whether the appropriate collective noun for mouse clicks is a “frustration”. That said, for new users with a solid statistics background, there are at last some good introductory and intermediate R books now on the market.ġ.2 Typesetting with LaTeX. The more flexible R lets you do almost anything, but also reveals, through unfortunately cryptic error messages, whether you know what you are doing. Less flexible statistics packages force you do to statistics right (or at least not creatively wrong), but also force you to do it a certain (possibly inappropriate or suboptimal) way. When a beginner hits an “ R problem”, much of the time, R is doing exactly what they ask, but that, in turn, includes an incoherent application of statistics. The best way to learn R is through a statistics course or two. My students should go ahead and install two packages, tile and simcf, that are available from my webpage see my software page for the package binaries and installation instructions. After you install R, you will notice that you can add new packages – that is, new tools – to R by downloading them from cran or from elsewhere on the web. To install R on your machine, simply download and install the latest binaries from cran. As a result, the gap between the capabilities of R and other statistics packages will most likely continue to grow, so don’t get left behind with another stat package! R is open-source, and uncounted statistical scientists are making it better by adding their code to it as you read this. And while it’s not perfect, R is clearly the best game in town for social scientists aspiring to make the most of their data and willing to learn how to write a little bit of code. This powerful, free, and ubiquitous statistical computing environment is your ticket to cutting edge statistical skills. And if you have your own suggestions, alternatives, or corrections, feel free to leave a comment. ![]() It took me much longer than that, so pace yourself. Naturally, you should feel free to pick and choose: I wouldn’t expect anyone to install and learn all of these tools in a day, or even a single month. Where do you get started? I’ll begin with step-by-step recommendations for essentials, and then follow with some optional extras. Most of the applications below exist for Windows in some form, and many exist for Linux/Unix, but I will leave advice for finding these resources to another day and to commenters. student in political science, sociology, economics, statistics, or a cognate discipline, and that you want to develop solutions for at least a few of the following tasks: modern statistical computing, editing code, typesetting scientific papers, making lecture slides, producing publication quality scientific graphics, and developing websites. Students embarking on quantitative social science careers may be interested in how my own computing environment is set up, so they can get on with the business of using these tools instead of searching for them. As you might imagine, just finding, installing, and configuring scientific software can be quite a distracting quest. ![]() At the same time, I don’t even have a copy of Microsoft Word or PowerPoint on my laptop. Social Science Computing for the Mac in 15 Steps and $29Īs a uantitative social scientist, I use software every day that many people have never even heard of. ![]() Statistical Modeling, Causal Inference, and Social Science We aim for funny, will settle for intriguing, and, the times and the Internet being what they are, resign ourselves to a certain amount of Science Computing for the Mac in 15 Steps and $29 The Waste Book very occasionally collects my passing thoughts on politics, economics, statistics, data visualization, life, culture, and everything. I teach political economy and statistics at a large public university in the US.
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