OZ FETP: Count Data: Comparing lung cancer rates between four Danish cities

[zkamvar]

https://github.com/ArminderD/Count_data

This exercise was developed for the Australian Field Epidemiology Training Program, The Masters of Philosophy (Applied Epidemiology) offered through the Australian National University. This exercise provides an introduction to analysing count data in R, including understanding distribution patters of count and rate data and performing appropriate analysis of count data.

[zkamvar]

Preview document: https://htmlpreview.github.io/?https://github.com/ArminderD/Count_data/blob/master/Count_data.html

This has questions posed to the participants, so it should be classified as a practical

Things to address:

• Load packages at the top
• Add links to data
• Add stringsAsFactors = FALSE to read.csv() or just use readr::read_csv()
• convert itemized list to level 3 or 4 header sections
• give reason why output from R might differ from STATA.