Data details

In covidnor: Public COVID-19 Data for Norway

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

In this vignette, we provide more detailed information on the data included in covidnor, and demonstrate how to extract the data you need.

Covid data outcomes

We have the following groups of Covid data, for certain combinations of time and location specifications.

There are two type of time granularity: day (date) and week (isoyearweek) in the data. For geo granularity, there are country (nation), county (county)and municipality (municip). Note that not all outcomes of interest have municipality level data.

Population data has been attached to compute number of cases per 100.000 population for a certain location.

In this dataset we only provide total age groups and sex groups.

Cases

Cases are the PCR test confirmed Covid positive cases. We have the following variables:

• number of cases (counts, per 100.000 population) by the date of PCR test, cases_by_testdate_n, cases_by_testdate_vs_pop_pr100000
• number of cases (counts, per 100.000 population) by the date of registration, cases_by_regdate_n, cases_by_regdate_vs_pop_pr100000

Available for

• granularity_time: date, isoyearweek
• granularity_geo: nation, county, municip (only for registration date)

Tests

Tests are the number of testing events. We have the following variables:

• number of testing events (all, positive, negative), testevents_all_n, testevents_pos_n, testevents_neg_n
• percentage of testing events that are positive, testevents_pos_vs_all_pr100

Available for

• granularity_time: date, isoyearweek
• granularity_geo: nation

Hospital admission

We provide two variables related to hospital admissions:

• admission due to Covid as main cause, hospital_admissions_main_cause_n;
• ICU admission, icu_admissions_n.

Available for

• granularity_time: date, isoyearweek
• granularity_geo: nation

Vaccination

For vaccination we provide data for two dates: vaccination date and registration date. We have data on 4 doses. For each dose, we have the following (e.g. dose 1):

• number of vaccinations delivered on the date, vax_dose_1_by_vaxdate_n
• number of vaccinations registered in SYSVAK, vax_dose_1_by_regdate_n
• cumulative number of vaccinations delivered by the date, vax_dose_1_by_vaxdate_sum0_999999_n

Available for

• granularity_time: date, isoyearweek
• granularity_geo: nation, county, municip (only for registration date)

Subsetting data

Instead of working directly on total data, you might want to use a certain combination of time, location, outcome. We recommend using the data.table syntax for data filtering and subsetting.

The way we organize time and location codes is documented in more detail in another csverse package, cstidy. We highly recommend you read through this vignette!

Based on granularity_time and granularity_geo

To get weekly Covid cases and hospital admissions as main cause for Norway (nation):

# load total data (419k rows)
totaldata <- covidnor::total_b2020

# get weekly cases (confirmed) and hospitalisation for Norway
case_hosp <- totaldata[granularity_time == 'isoyearweek' &
                     granularity_geo == 'nation',
                   .(date, 
                     location_name, 
                     cases = cases_by_testdate_n, 
                     hospital_adm = hospital_admissions_main_cause_n)]
case_hosp[1:6,]

Based on specific dates and locations

Get data for a certain date and location combination:

totaldata[date == '2021-12-10' & location_code %in% c('county_nor03', 'county_nor15'), 
          .(date, location_name, 
            cases = cases_by_testdate_n, 
            vax_1 = vax_dose_1_by_vaxdate_n, 
            vaxcum1 = vax_dose_1_by_vaxdate_sum0_999999_n)]

Can also get data for a whole calendar month, such as April 2022,

totaldata[calyearmonth == '2022-M04' & location_code == 'county_nor03', 
          .(date, location_name, 
            cases = cases_by_testdate_n)]

covidnor documentation built on May 31, 2023, 9:19 p.m.