Version 0.4.0

What's Changed

• Handing HK, Taiwan data moving forward by @beansrowning in #84
• Adding calc_window_pct_change() and updating trend_map() by @beansrowning in #87
• Additional dev changes for 14d+ switch by @beansrowning in #92
• Fixing slice in table_10percentchange by @beansrowning in #97
• Updating calc_window_incidence by @beansrowning in #98
• Updating map_burden by @beansrowning in #99
• Updating pct_change and incidence window calculations by @beansrowning in #100
• quick fix for burden maps to avoid bbox issues introduced by previous commit by @beansrowning in #101
• Changes from dev by @beansrowning in #103
• HOTFIX: Updating trend map to drop infinite values by @beansrowning in #106
• Adding Azure functionality to some data read functions by @beansrowning in #105

Full Changelog: v0.31...v0.4.0

Version 0.3.1

Issues

• Closes #90
• Closes #88
• Closes #89

Changes

Fixed a random Rd issue with get_onetable() due to dropped }

No more warnings.

New Burden Map

burden_map_global_14 <- df_both |>
  filter(date <= sunday_date) |>
  map_burden(time_step = 14)


burden_map_global_14

• Updated API:
  • Updating region and adding time_step argument to allow this to vary
  • Created internal function calc_window_incidence(), which computes average incidence interally
  • No more creating breaks externally and passing in
  • Improved docs

Updated Trend Map

• Refined API to be in-line with map_burden
  • All computation done within function via calc_window_pct_change(), not outside and passed in
• Updated docs

df_both |>
  filter(date <= sunday_date) |>
  map_trend(time_step = 14)

Major overhaul of case tables API

• Combined docs into one shared Rd file
  
• Tables now compute all values internally, greatly reducing complexity in using them and utilizing the same helper functions for the above maps
• Updated documentation in vignette

Examples

df_both |>
  filter(date <= sunday_date) |>
  table_10mostcases(time_step = 14)

df_both |>
  filter(date <= sunday_date) |>
  table_10incidence(time_step = 14)

df_both %>%
  filter(date <= sunday_date, population >= 10000000) |>
  table_10percentchange(time_step = 14)

Caching R dependencies in CI pipeline for faster runs

Version 0.3

Changes

• See #83, #84
• Implemented get_hk_data() and get_taiwan_data() as internal functions to return case and death time series data from HK CHP and Taiwan CDC respectively
• Updated OWID source in data source lookup
• get_covid_df() now accepts a sources argument
  • "all": the default, returns JHU, WHO, Taiwan CDC, HK CHP
  • "WHO": WHO data as-is (china aggregated counts and no supplemental data)
  • "WHO+JHU": the "old" version containing WHO + JHU disaggregated data
  • "WHO+Primary": WHO data plus HK data from HK CHP, Taiwan data from Taiwan CDC, and Macau data from JHU thru Mar 10, 2023
• get_combined_table() was reworked internally to handle new data sources
• type argument for get_combined_table() was changed to accept a new type: "legacy", which returns what "Both" used to return previously (though the JHU data won't be updated and China Mainland data haven't been updated in some time)
• "Both" type for get_combined_table() now returns case and death data from get_covid_df("WHO+Primary")
• Updated docs accordingly

Version 0.2

Changes

• #15
• #18
• #17
• #19
• #21
• #23
• Adding get_combined_table() to return joined onetable, get_covid_df(), get_vax(), and calc_add_risk()
• #35
• #36
• #37

See the high level changes in this article:
https://cdcgov.github.io/SaviR/articles/savir_version_0.2_api_changes.html