Reproducibility in Research

This post by Mario Pineda-Krch complains about the woeful lack of reproducibility in computational sciences.

This reminded me of Jake Bowers's good piece in the Political Methodologist from earlier this year about how to do reproducible computational political science. The article actually inspired me to completely switch over all of my new writing to Sweave. Sweave allows you to combine your R code and LaTeX documents. If you make your Sweave document and data available to readers they can completely reproduce everything in your article: the models, the table, the graphs, everything.

RStudio makes using Sweave really easy (though I still use a text editor for writing much of the code since RStudio doesn't do spellcheck).

Political economy and political science journals don't seem to have been keeping up with these developments. In fact, poli sci journals often require MS Word documents and don't allow you to submit Sweave documents. Few journal submission systems even allow authors to submit data and code appendixes before the paper has been accepted or R&R-ed.

Comments

Showing results from Cox Proportional Hazard Models in R with simPH

Update 2 February 2014: A new version of simPH (Version 1.0) will soon be available for download from CRAN. It allows you to plot using points, ribbons, and (new) lines. See the updated package description paper for examples. Note that the ribbons argument will no longer work as in the examples below. Please use type = 'ribbons' (or 'points' or 'lines' ). Effectively showing estimates and uncertainty from Cox Proportional Hazard (PH) models , especially for interactive and non-linear effects, can be challenging with currently available software. So, researchers often just simply display a results table. These are pretty useless for Cox PH models. It is difficult to decipher a simple linear variable’s estimated effect and basically impossible to understand time interactions, interactions between variables, and nonlinear effects without the reader further calculating quantities of interest for a variety of fitted values. So, I’ve been putting together th

Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame. I've found the various R methods for doing this hard to remember and usually need to look at old blog posts . Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command– slide –to the DataCombine R package (v0.1.5). Building on the shift function TszKin Julian posted on his blog , slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data. Note: your data needs to be in ascending time order with equally spaced time increments. For example 1995, 1996

Dropbox & R Data

I'm always looking for ways to download data from the internet into R. Though I prefer to host and access plain-text data sets (CSV is my personal favourite) from GitHub (see my short paper on the topic) sometimes it's convenient to get data stored on Dropbox . There has been a change in the way Dropbox URLs work and I just added some functionality to the repmis R package. So I though that I'ld write a quick post on how to directly download data from Dropbox into R. The download method is different depending on whether or not your plain-text data is in a Dropbox Public folder or not. Dropbox Public Folder Dropbox is trying to do away with its public folders. New users need to actively create a Public folder. Regardless, sometimes you may want to download data from one. It used to be that files in Public folders were accessible through non-secure (http) URLs. It's easy to download these into R, just use the read.table command, where the URL is the file name

Christopher Gandrud
(간드루드 크리스토파)

Search This Blog