In the third and last of the ggplot series, this post will go over interesting ways to visualize the distribution of your data.

Conditioning and grouping are two important concepts in graphing that allow us to rapidly refine our understanding of data under consideration. Conditioning, in particular, allows us to view relationships across “panels” with common scales. Each panel contains a plot whose data is “conditional” upon records drawn from the category that supports that particular panel (an

Student’s t-test is a staple of statistical analysis. A quick search on Google Scholar for “t-test” results in 170,000 hits in 2013 alone. In comparison, “Bayesian” gives 130,000 hits while “box plot” results in only 12,500 hits. To be honest, if I had to choose I would most of the time prefer a notched boxplot to...

On a rare, brief holiday (here and here, if you’re interested; both highly-recommended), I make the mistake of checking my Twitter feed: paging @neilfws . . . RT @psudmant: Ground breaking new methods from @naturemethods – boxplots – no rly nature.com/nmeth/journal/…— Chris Miller (@chrisamiller) January 30, 2014 This points me to BoxPlotR. It draws box

Last week, version 5.0 of the forecast package for R was released. There are a few new functions and changes made to the package, which is why I increased the version number to 5.0. Thanks to Earo Wang for helping with this new version. Handling missing values and outliers Data cleaning is often the first step that data scientists...

The last model added to the rugarch package dealt with the modelling of intraday volatility using a multiplicative component GARCH model. The newest addition is the realized GARCH model of Hansen, Huang and Shek (2012) (henceforth HHS2012) which relates the realized volatility measure to the latent volatility using a flexible representation with asymmetric dynamics. This

e-mails with the latest R posts.

(You will not see this message again.)