• Applications in veterinary epidemiology
  • Preamble
    • Epidemiological Skills for Animal Health Professionals
    • Acknowledgements
    • Copyright
    • Cite
  • 1 Introduction
    • 1.1 Skill requirements
    • 1.2 Structure of this book
      • 1.2.1 Dashboards
    • 1.3 About Ausvet
    • 1.4 TO-DO
  • 2 Reproducible research
    • 2.1 What is reproducible research
    • 2.2 Setting up your R environment
      • 2.2.1 Installing R and RStudio
      • 2.2.2 Use of headers to structure code
      • 2.2.3 Structure of a project directory
    • 2.3 Code repository
      • 2.3.1 Git
      • 2.3.2 Git workflow
      • 2.3.3 What to include in a repository: Git pointers
      • 2.3.4 Collaborating
    • 2.4 Document outputs
      • 2.4.1 Writing out Word documents
      • 2.4.2 Writing out Excel documents
      • 2.4.3 R Markdown
      • 2.4.4 Notebooks
      • 2.4.5 Plumber ??
      • 2.4.6 R packages
    • 2.5 An example of a workflow in practice
  • 3 A framework for data munging
    • 3.1 Data validation
  • 4 R and databasing
  • 5 Spatial data
    • 5.1 Vector data
    • 5.2 Raster data
    • 5.3 Map projections - working with EPSG codes
    • 5.4 Linking to GDAL and other geoprocessing software
      • 5.4.1 installing GDAL
    • 5.5 Constructing Atlantis
      • 5.5.1 Atlantis emerges!
      • 5.5.2 Providing Atlantis with a geography
      • 5.5.3 Populating Atlantis
    • 5.6 Displaying map data
      • 5.6.1 ggplot2
      • 5.6.2 leaflet
      • 5.6.3 interactive leaflet inside bookdown? flexdashboards?
    • 5.7 TODO
  • 6 A regression model stack
    • 6.1 Linear models
    • 6.2 Typical Issues for epidemiological data
    • 6.3 Mixed effect models
    • 6.4 Generalised additive models
    • 6.5 Frequentist vs Bayesian approaches to regression modelling
    • 6.6 A frequentist approach
      • 6.6.1 glmmTMB
      • 6.6.2 mgcv
      • 6.6.3 model selection and averaging
      • 6.6.4 inference with marginal predictions
      • 6.6.5 regression diagnostics
    • 6.7 A Bayesian approach
      • 6.7.1 R-INLA
    • 6.8 regression models for observational studies
  • 7 Clinical trial data
    • 7.1 Non-inferiority tests for ordinal data
    • 7.2 some other case study
  • 8 Case Study
  • 9 Analysis of the epicurve
  • 10 Survival analysis
  • 11 Network Analysis
  • 12 Diagnostic test validation
  • 13 Small area estimation for extrapolating a model
  • 14 Dashboards
    • 14.1 Rationale for dashboards and when to apply them
    • 14.2 Frameworks
    • 14.3 Flex dashboard
  • 15 Speeding up code
    • 15.1 common instances when processing slows down
    • 15.2 code structure
      • 15.2.1 loops
      • 15.2.2 dialect
    • 15.3 use of intermediate objects
    • 15.4 use of pointers
    • 15.5 dynamic loading of compiled code
    • 15.6 scaling up
      • 15.6.1 parallelism
      • 15.6.2 AWS
  • 16 References
  • Published with bookdown

Applications of statistical analysis within veterinary epidemiology

Chapter 4 R and databasing

We have finished a nice book.

https://bookdown.org/gonzalezben81/SQLQueries/introduction.html https://smithjd.github.io/sql-pet/chapter-connect-to-db-with-r-code.html#connect-to-postgresql-1
https://r4ds.had.co.nz/relational-data.html