about free range statistics


free range statistics is the personal blog of Peter Ellis.

I'm an Australasian professional statistician and data scientist with a background running analytics/stats/data science teams and multi million dollar programs in the public sector. I am the Director of the Statistics for Development Division at the Pacific Community.

Much of my career has been in management and evaluation of large overseas aid programmes, but since 2011 I have been working on a range of economic data issues, and on data and statistical capability to support diverse organisational goals. My current role combines the two halves of my career - as an international development professional, and a data and statistics expert and manager - into one.

Accredited Statistician

profile for Peter Ellis on Stack Exchange, a network of free, community-driven Q&A sites

My interests include economics, econometrics, complex surveys, time series, spatial statistics, text mining, R, data visualisation, data warehousing, data governance, and anything that involves putting data and analysis in the hands of as broad a range of people as possible.

The views expressed in this blog are very definitely my own, not those of my organisation or any government. I might be blogging about data and analysis issues that crop up in work, but anything controversial that relates to my work has crept in by mistake and will be removed if and when I notice it.


  • Comments on specific posts please use the comments section.
  • This page is hosted by GitHub and the source code is all available.
  • If you want to make a general comment or request about the blog, feel free to create an issue there and I will definitely respond one way or another.
  • I am no longer on Twitter.
  • Professional contacts should be able to find me on my work email or on LinkedIn easily enough.
  • Personal friends should be able to find me on Facebook.

R packages

I am the author and maintainer of the following R packages:

  • ggseas package which allows you to easily incorporate seasonal adjustment and seasonal decomposition into an exploratory data analysis workflow based around ggplot2.

  • nzelect package which provides conveniently tidied up election results for New Zealand; so far limited to aggregate results from the 2014 general election.

  • nzcensus package which provides a range of calculated demographic variables at meshblock, area unit, territorial authority, and regional council level from the New Zealand census. This package was originally part of the nzelect project but can now be installed and used separately. The source is the Statistics New Zealand census meshblock data. The package is too large to publish on CRAN so is only available from GitHub.

I also host on my GitHub page the forecastHybrid package, which makes it easy to estimate ensemble or hybrid forecasts using a range of the methods in Rob Hyndman's forecast package. I'm a contributor to that package but David Shaub is the primary author and maintainer.

Blogs I read

There is also this post about my ten favourite and twelve second-favourite statistics / data science books.