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Title:

Public COVID-19 Data for Norway

Version:

2023.05.18

Description:

Publicly available COVID-19 data for Norway cleaned and merged into one dataset, including PCR confirmed cases, tests, hospitalisation and vaccination.

Depends:

R (≥ 3.5.0)

License:

MIT + file LICENSE

URL:

https://www.csids.no/covidnor/, https://github.com/csids/covidnor

BugReports:

https://github.com/csids/covidnor/issues

Encoding:

UTF-8

LazyData:

true

Imports:

data.table

Suggests:

testthat, knitr, rmarkdown, rstudioapi, glue, magrittr, ggplot2

RoxygenNote:

7.2.3

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2023-05-25 14:42:44 UTC; andrea

Author:

Chi Zhang

[aut, cre], Richard Aubrey White

[aut], CSIDS [cph], Folkehelseinstituttet [ctb]

Maintainer:

Chi Zhang <andreachizhang@yahoo.com>

Repository:

CRAN

Date/Publication:

2023-05-26 08:00:08 UTC

COVID-19 data for total age/sex in Norway (2020 border).

Description

This dataset contains COVID-19 data in Norway, from 2020 week 8 (2020-02-23) to 2022 week 45 (2022-11-13)

Usage

total_b2020

Format

granularity_time: Time granularity: day, isoyearweek
granularity_geo: Geographical granularity: nation, county, municip
location_code: Location code
border: Redistricting border: 2020
age: Age category: total
sex: Sex category: total
isoyear: Isoyear
isoweek: Isoweek
isoyearweek: Isoyearweek
season: Season
seasonweek: Season week
calyear: Calendar year
calmonth: Calendar month
date: Date
cases_by_testdate_n: Number of cases by date of PCR test
cases_by_testdate_vs_pop_pr100000: Number of cases by date of PCR test per 100.000 population
cases_by_regdate_n: Number of cases by date of registration in MSIS
cases_by_regdate_vs_pop_pr100000: Number of cases by date of registration in MSIS per 100.000 population
icu_admissions_n: Number of ICU admissions
hospital_admissions_main_cause_n: Number of hospital admissions with COVID-19 as the main cause
testevents_neg_n: Number of testing events (all tests of one person within a 7 day period are considered to be one testing event) that were negative
testevents_pos_n: Number of testing events (all tests of one person within a 7 day period are considered to be one testing event) that were positive
testevents_all_n: Number of testing events (all tests of one person within a 7 day period are considered to be one testing event)
testevents_pos_vs_all_pr100: Percent of testing events that were positive
vax_dose_1_by_vaxdate_n: Number of dose 1 vaccines delivered by date of vaccination
vax_dose_2_by_vaxdate_n: Number of dose 2 vaccines delivered by date of vaccination
vax_dose_3_by_vaxdate_n: Number of dose 3 vaccines delivered by date of vaccination
vax_dose_4_by_vaxdate_n: Number of dose 4 vaccines delivered by date of vaccination
vax_dose_1_by_vaxdate_sum0_999999_n: Cumulative number of dose 1 vaccines delivered by date of vaccination
vax_dose_2_by_vaxdate_sum0_999999_n: Cumulative number of dose 2 vaccines delivered by date of vaccination
vax_dose_3_by_vaxdate_sum0_999999_n: Cumulative number of dose 3 vaccines delivered by date of vaccination
vax_dose_4_by_vaxdate_sum0_999999_n: Cumulative number of dose 4 vaccines delivered by date of vaccination
vax_dose_1_by_regdate_n: Number of dose 1 vaccines delivered by date of registration in SYSVAK
vax_dose_2_by_regdate_n: Number of dose 2 vaccines delivered by date of registration in SYSVAK
vax_dose_3_by_regdate_n: Number of dose 3 vaccines delivered by date of registration in SYSVAK
vax_dose_4_by_regdate_n: Number of dose 4 vaccines delivered by date of registration in SYSVAK
pop_jan1_n: Population as per first of January
location_name: Location name (may not be unique)
location_name_description_nb: Location name description (is unique)

Examples

print(covidnor::total_b2020[1,])