{"id":12354,"date":"2019-06-18T12:16:40","date_gmt":"2019-06-18T17:16:40","guid":{"rendered":"https:\/\/rud.is\/b\/?p=12354"},"modified":"2019-06-19T04:53:51","modified_gmt":"2019-06-19T09:53:51","slug":"quick-hit-some-ggplot2-stat","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2019\/06\/18\/quick-hit-some-ggplot2-stat\/","title":{"rendered":"Quick hit: Some ggplot2 Stat ? for {logspline}"},"content":{"rendered":"

I’ve become a big fan of the {logspline} package<\/a> over the past ~6 months and decided to wrap up a manual ggplot2 plotting process (well, it was at least in an RStudio snippet) into a small {ggplot2} Stat<\/code> to make it easier to visualize various components of the fitted model.<\/p>\n

If you’re new to logspline density estimation this paper by Stone, Hansen, Kooperberg, and Truong<\/a> is a pretty accessible introduction to the methodology and the (primary) algorithm used in the {logspline} package.<\/p>\n

If you’re already a user of the {logspline} package and have been just using the package’s base S3 plot function but wanted the functionality in {ggplot2} this was pretty much designed for you.<\/p>\n

If you hit up https:\/\/git.rud.is\/hrbrmstr\/gglogspline<\/a> there are links to all your fav social code collaboration services plus installation instructions. There’s a single Stat<\/code> — stat_logspline()<\/code> — which does all the hard work. By default it will make y<\/code> the stat(density)<\/code> computed field but there are other computed fields you can use as well:<\/p>\n