### Disproportionality Data

So I was hunting around for some data on disproportional electoral outcomes (when the proportion of voters cast for political parties is not close to the proportion of legislative seats that they win).

Michael Gallagher keeps an updated version of his Least Squares (or Gallagher) Index of electoral disproportionality on his website, however it is in PDF format; very inconvenient for using in any stats project.

John Carey & Simon Hix have some nice data--that includes much of Gallagher's data and some countries he doesn't cover--in easy to use Stata format (here). This is the data from their recent Electoral Sweet Spot paper (see here). However it only goes to 2003.

I combined the best of these two data sets into one .csv file and am making it available so that hopefully others can use their research time for better things than copying and pasting data from a PDF file. You can easily import this data into R or Stata or whatever you may use.

The data set is downloadable HERE. More details on how I combined the data can be found there as well.

I couldn't stop myself from making a few descriptive figures with the data. The first is a map of average disproportionality between 2000 and 2011. The second plots disproportionality over time (you can see there hasn't been much change).

### 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