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Showing posts from September, 2012

Federal Register API/R Package Ideas?

The other day Critical Juncture put up an API for the Federal Register. I thought it would be great if there was a package that could use this API to download data directly into R (much like the excellent WDI package).This would make it easier to analyse things like:the frequency of regulations issued on a particular issue over a given period of time,the text of the actual regulations.The nice people over at Critical Juncture tweeted me showing interest in the idea and wondering what would be useful.I was thinking that in the package there could be commands such as getFedRegister and getMultiFedRegister that would do pretty much do what the API is set up to help now, except download the data into an R object rather than straight to JSON or CSV.More Ideas?Any other ideas for things that might be useful? Just leave them in the comments at my Tumblr site.

Create Beamer/knitr Lecture Slideshow with Bash, Explain the Script with knitr

Setting up a beamer slideshow is tedious. Creating new slideshows with the same header/footer/style files every week for your course lectures is very very tedious. To solve this problem I created a simple bash shell script. When you run the script in your terminal it asks whether you want to create a "Lecture" or "Seminar" and what number you want it to have. Then it does the rest. You can find the script and all of the necessary files here. To create the README file I used knitr version 0.8's new engine='bash' option. This allows you to knit bash code into your Markdown file the same what you would R code. It's pretty simple. See the R Markdown file for more details.Please feel free to take and modify the files. Also, if you can help streamline them that would be great.Oh kind of related tip: If you want a bash command to show up over more than one line in your knitted document place a backslash (\) at the end of the line. The beamer theme I use…

Graphically Comparing Confidence Intervals From Different Models

In a recent paper on Federal Reserve inflation forecast errors (summary blog post, paper) I wanted a way to easily compare the coefficients for a set of covariates (a) estimated from different types of parametric models using (b) matched and non-matched data. I guess the most basic way to do this would be to have a table of columns showing point estimates and confidence intervals from each estimation model. But making meaningful comparisons with this type of table would be tedious.What I ended up doing was creating a kind of stacked caterpillar plot. Here it is:Comparing 95% Confidence Intervals