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data-public Update data-public/raw/IPDLN_Hackathon_Information_August2018.pdf Oct 31, 2018
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LICENSE clean paste from ihacru-analytic-starter Aug 31, 2018
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README.md
ipdln-2018-hackathon.Rproj

README.md

ipdln-2018-hackathon

Demonstrating coloring-book techique of graph production in ggplot2 during data linkage hackathong at IPDLN-2018 conference at Banff, Sep 2018.

part2

Relevant talks

  • April, 2016 - Groningen - Technique orignally developed for 2016 Maelstrom Harmonization Workshop ("Assessing the impact of different harmonization procedures on the analysis results from several real datasets"). View the slides ialsa-2016-groningen presenting the results of the exercise by Andriy Koval and Will Beasley. Groningen, Netherlands, April 22, 2016.
  • September, 2017 - Banff - Presentation of the slide deck of hackathon results to the closing plenary of IDPDL-2018 Conference in Banff, September 17 2018.
  • October, 2017 - Victoria - Matrix Institue colloquium 2018-10-31 - slides for my talk When Notebooks are not Enough at the Matrix Institute colloquium at the University of Victoria on October 31, 2018
  • November, 2017 Victoria - Popultaion Data BC Webinar 2018-11-01 -slides for my webinar Visulizing Logistic Regression at the Power of Population Data Science webinar series at PopDatBC on November 1, 2018.

How to reproduce

  1. Clone this repository (either via git or from the browswer)
  2. Lauch RStudio project via .Rproj file
  3. Execute ./manipulation/0-metador.R to generate an object that would store all the metadata
  4. Examine ./manipulation/stitched_output/1-greeter.html to study the record of how we greeted the data provided to the hackathon participants. This data set is currently unavailable to the public, but please send a friendly tweet @StatCan_eng to let them know there is interest in this data set)
  5. Examine ./reports/technique-demonstration/technique-demonstration-1.html to study the record of how models were estimated on the data provided to hackathon participants (really, please send a friendly tweet @StatCan_eng #letmydatago )
  6. Run ./reports/graphing-phase-only/graphing-phase-only.R to load the model solution and start producing graphs

Background

Dynamic Documentation on Data Cleaning

The product of these two scripts define the foundation of every subsequent analytic report.

ls_guide <- readRDS("./data-unshared/derived/0-metador.rds")
ds0      <- readRDS("./data-unshared/derived/1-greeted.rds")

Analytics during Hackathon

Resulst of these two EDAs informed development of the script to estimate and to graph models of immigrant mortality:

This script yeilded a collection of printed graphs stored in ./reports/coloring-book-mortality/prints/, visualizing three different collection of predictors from the same model. There were put together into this slide deck and presented during the closing plenary of IDPDL-2018 Conference in Banff.

Technique demonstration

Hackathon-2018 Team

  • Stacey Fisher, Ph.D. Candidate, Ottawa Hospital Research Institute; ICES; University of Ottawa
  • Gareth Davies, MSc, Research Data Analyst, SAIL Databank, Swansea University
  • Andriy Koval, Health System Impact Fellow, BC Observatory for Population & Public Health
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