Christopher Gandrud
(간드루드 크리스토파)

Reproducibility has come a long way in political science. Many major journals now require replication materials be made available either on their websites or some service such as the Dataverse Network . Most of the top journals in political science have formally committed to reproducible research best practices by signing up to the The (DA-RT) Data Access and Research Transparency Joint Statement . This is certainly progress. But what are political scientists actually supposed to do with this new information? Data and code availability does help avoid effort duplication--researchers don't need to gather data or program statistical procedures that have already been gathered or programmed. It promotes better research habits . It definitely provides '' procedural oversight ''. We would be highly suspect of results from authors that were unable or unwilling to produce their code/data. However, there are lots of problems that data/code availability requirements do no...

Note to self: after updating R with Homebrew remember to cleanup old versions: brew cleanup r Otherwise I'm liable to get a segfault . ( see also )

Just wanted to put up the script I've been using to create an Amazon EC2 Ubuntu instance for running RStudio, Git, and Jags. There isn't anything really new in here, but it it has been serving me well. The script begins after the basic instance has been set up in the Amazon EC2 console ( yhat has a nice post on how to do this, though some of their screenshots are a little old). Just SSH into the instance and get started.

Here is the slide deck from a simple introductory talk I gave yesterday on Bayesian data analysis:

Over the past few months I’ve added a few improvements to the repmis –miscellaneous functions for reproducible research–R package. I just want to briefly highlight two of them: Caching downloaded data sets. source_XlsxData for downloading data in Excel formatted files. Both of these capabilities are in repmis version 0.2.9 and greater. Caching When working with data sourced directly from the internet, it can be time consuming (and make the data hoster angry) to repeatedly download the data. So, repmis ’s source functions ( source_data , source_DropboxData , and source_XlsxData ) can now cache a downloaded data set by setting the argument cache = TRUE . For example: DisData <- source_data("http://bit.ly/156oQ7a", cache = TRUE) When the function is run again, the data set at http://bit.ly/156oQ7a will be loaded locally, rather than downloaded. To delete the cached data set, simply run the function again with the argument clearCache = TRUE . source_XlsxDat...

After some delay (and because of helpful prompting by Giles Heywood and code contributions by John Harrison ) d3Network now plays nicely with Shiny web apps . This means you can fully integrate R/D3.js network graphs into your web apps. Here is what one simple example looks like: An explanation of the code is here and you can download the app and play with it using: shiny::runGitHub('d3ShinyExample', 'christophergandrud')

This is reposted from the original on the Hertie School of Governance European Elections blog . The discussion of issues around the European Parliament Elections has been beating around the bush for quite some time now. Karlheinz Reif and Hermann Schmitt famously described European Elections as ” second-order elections ”, in that they are secondary to national elections. A few weeks ago on this blog Andrea Römmele and Yann Lorenz argued that the current election cycle has been characterised by personality politics between candidates vying for the Commission presidency, rather than substantive issues. However, the election campaigns could be an important opportunity for the public to express their views on and even learn more about one of the defining changes to the European Union since the introduction of the Euro: the European Banking Union. Much of the framework for the Banking Union has been established in the past year after intense debate between the EU institut...

In a bit of an aside from what I usually work on, I've put together a small website with a simple purpose: advocating for subway station exits to be numbered. These are really handy for finding your way around and are common in East Asia. But I've never seen them in Western countries. If you're interested check out the site:

A lot of progress has been made on improving political scientists’ ability to access data ‘programmatically’, e.g. data can be downloaded with source code R. Packages such as WDI for World Bank Development Indicator and dvn for many data sets stored on the Dataverse Network make it much easier for political scientists to use this data as part of a highly integrated and reproducible workflow . There are nonetheless still many commonly used political science data sets that aren’t easily accessible to researchers. Recently, I’ve been using the Database of Political Institutions (DPI) , Polity IV democracy indicators, and Reinhart and Rogoff’s (2010) financial crisis occurrence data. All three of these data sets are freely available for download online. However, getting them, cleaning them up, and merging them together is kind of a pain. This is especially true for the Reinhart and Rogoff data, which is in 4 Excel files with over 70 individual shee...

Research is an unpredictable thing. You head in one direction, but end up going another. Here is a recent example: A co-author and I had an idea for a paper. It's a long story, but basically we wanted to compare banks in the US to those in the EU. This was a situation where our desire to explore a theory was egged on by, what we believed, was available data. In the US it's easy to gather data on banks because the regulators have a nice website where they release the filings banks send them. The data is in a really good format for statistical analysis. US done. We thought our next move would be to quickly gather similar data for EU banks and we would be on our way. First we contacted the UK's Financial Conduct Authority . Surprisingly, they told us that not only did they not release this data, but it was actually illegal for them to do so. Pretty frustrating. Answers to one question stymied by a lack of data. Argh. I guess we'll just keep looking to see what kind of...

As I go about cleaning and merging data sets with R I often end up creating and using simple functions over and over. When this happens, I stick them in the DataCombine package. This makes it easier for me to remember how to do an operation and others can possibly benefit from simplified and (hopefully) more intuitive code. I've talked about some of the commands in DataCombine in previous posts . In this post I'll give examples for a few more that I've added over the past couple of months. Note: these examples are based on DataCombine version 0.1.11. Here is a brief run down of the functions covered in this post: FindReplace : a function to replace multiple patterns found in a character string column of a data frame. MoveFront : moves variables to the front of a data frame. This can be useful if you have a data frame with many variables and want to move a variable or variables to the front. rmExcept : removes all objects from a work space except those specified...

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...

I've written in a number of places about how GitHub can be a great place to store data. Unlike basically all other web data storage sites (many of which I really like such as Dataverse and FigShare ) GitHub enables deep social data set development and fits nicely into a reproducible research workflow with R. One negative though, especially compared to FigShare, was that there was no easy way to view CSV or TSV data files in the browser. Unless you downloaded the data and opened it in Excel or an R viewer or whatever, you had to look at the raw data file in the browser. It's basically impossible to make sense of a data set of any size like this. However, from at least today, GitHub now renders the data set in the browser as you would expect. Take a look at their blog post for the details.

This is an abridged excerpt from Chapter 2 of my new book Reproducible Research with R and RStudio . It's published by Chapman & Hall/CRC Press . You can purchase it on Amazon . "Search inside this book" includes a complete table of contents. Researchers often start thinking about making their work reproducible near the end of the research process when they write up their results or maybe even later when a journal requires their data and code be made available for publication. Or maybe even later when another researcher asks if they can use the data from a published article to reproduce the findings. By then there may be numerous versions of the data set and records of the analyses stored across multiple folders on the researcher’s computers. It can be difficult and time consuming to sift through these files to create an accurate account of how the results were reached. Waiting until near the end of the research process to start thinking about reproducibility ca...

Sometimes I just want to quickly make a simple D3 JavaScript directed network graph with data in R. Because D3 network graphs can be manipulated in the browser–i.e. nodes can be moved around and highlighted–they're really nice for data exploration. They're also really nice in HTML presentations . So I put together a bare-bones simple function–called d3SimpleNetwork for turning an R data frame into a D3 network graph. Arguments By bare-bones I mean other than the arguments indicating the Data data frame, as well as the Source and Target variables it only has three arguments: height , width , and file . The data frame you use should have two columns that contain the source and target variables. Here's an example using fake data: Source <- c("A", "A", "A", "A", "B", "B", "C", "C", "D") Target <- c("B", "C", "D", "J...

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 199...

You may have already seen a lot written on the replication of Reinhart & Rogoff’s (R &amp R) much cited 2010 paper done by Herndon, Ash, and Pollin . If you haven’t, here is a round up of some of some of what has been written: Konczal , Yglesias , Krugman , Cowen , Peng , FT Alphaville . This is an interesting issue for me because it involves three topics I really like: political economy, reproducibility, and communicating uncertainty. Others have already commented on these topics in detail. I just wanted to add to this discussion by (a) talking about how this event highlights a real need for researchers to use systems that make finding and correcting mistakes easy, (b) incentivising mistake finding/correction rather than penalising it, and (c) showing uncertainty . Systems for Finding and Correcting Mistakes One of the problems Herndon, Ash, and Pollin found in R&R’s analysis was and Excel coding error . I love to hate on Excel as much as the next R ...

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...

Update (10 March 2013): FillIn is now part of the budding DataCombine package. Sometimes I want to use R to fill in values that are missing in one data frame with values from another. For example, I have data from the World Bank on government deficits. However, there are some country-years with missing data. I gathered data from Eurostat on deficits and want to use this data to fill in some of the values that are missing from my World Bank data. Doing this is kind of a pain so I created a function that would do it for me. It's called FillIn . An Example Here is an example using some fake data. (This example and part of the function was inspired by a Stack Exchange conversation between JD Long and Josh O'Brien.) First let's make two data frames: one with missing values in a variable called fNA . And a data frame with a more complete variable called fFull . # Create data set with missing values naDF <- data.frame(a = sample(c(1,2), 100, rep=TRUE), ...

A big problem in reproducible research is that software changes. The code you used to do a piece of research may depend on a specific version of software that has since been changed. This is an annoying problem in R because install.packages only installs the most recent version of a package. It can be tedious to collect the old versions. On Toby Dylan Hocking 's suggestion, I added tools to the repmis package so that you can install, load, and cite specific R package versions. It should work for any package version that is stored on the CRAN archive ( http://cran.r-project.org ). To only install old package versions use the new repmis command InstallOldPackages . For example: # Install old versions of the e1071 and gtools packages. # Create vectors of the package names and versions to install # Note the names and version numbers must be in the same order Names &lt- c("e1071", "gtools") Vers &lt- c("1.6", "2.6.1") # Install...