In this vignette, we provide more detailed information on the data
included in covidnor, and demonstrate how to extract the
data you need.
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 are the PCR test confirmed Covid positive cases. We have the following variables:
cases_by_testdate_n,
cases_by_testdate_vs_pop_pr100000cases_by_regdate_n,
cases_by_regdate_vs_pop_pr100000Available for
Tests are the number of testing events. We have the following variables:
testevents_all_n, testevents_pos_n,
testevents_neg_ntestevents_pos_vs_all_pr100Available for
We provide two variables related to hospital admissions:
hospital_admissions_main_cause_n;icu_admissions_n.Available for
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):
vax_dose_1_by_vaxdate_nvax_dose_1_by_regdate_nvax_dose_1_by_vaxdate_sum0_999999_nAvailable for
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!
granularity_time and
granularity_geoTo 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,]
#> date location_name cases hospital_adm
#> 1: 2022-11-13 Norge 534 60
#> 2: 2022-11-06 Norge 876 174
#> 3: 2022-10-30 Norge 780 151
#> 4: 2022-10-23 Norge 561 120
#> 5: 2022-10-16 Norge 499 117
#> 6: 2022-10-09 Norge 511 101Get 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)]
#> date location_name cases vax_1 vaxcum1
#> 1: 2021-12-10 Oslo 1134 131 541045
#> 2: 2021-12-10 Møre og Romsdal 105 103 210189Can 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)]
#> date location_name cases
#> 1: 2022-04-30 Oslo 35
#> 2: 2022-04-29 Oslo 49
#> 3: 2022-04-28 Oslo 54
#> 4: 2022-04-27 Oslo 57
#> 5: 2022-04-26 Oslo 57
#> 6: 2022-04-25 Oslo 65
#> 7: 2022-04-24 Oslo 37
#> 8: 2022-04-23 Oslo 54
#> 9: 2022-04-22 Oslo 90
#> 10: 2022-04-21 Oslo 92
#> 11: 2022-04-20 Oslo 84
#> 12: 2022-04-19 Oslo 172
#> 13: 2022-04-18 Oslo 92
#> 14: 2022-04-17 Oslo 55
#> 15: 2022-04-16 Oslo 53
#> 16: 2022-04-15 Oslo 60
#> 17: 2022-04-14 Oslo 50
#> 18: 2022-04-13 Oslo 94
#> 19: 2022-04-12 Oslo 102
#> 20: 2022-04-11 Oslo 134
#> 21: 2022-04-10 Oslo 65
#> 22: 2022-04-09 Oslo 86
#> 23: 2022-04-08 Oslo 125
#> 24: 2022-04-07 Oslo 132
#> 25: 2022-04-06 Oslo 187
#> 26: 2022-04-05 Oslo 197
#> 27: 2022-04-04 Oslo 231
#> 28: 2022-04-03 Oslo 126
#> 29: 2022-04-02 Oslo 133
#> 30: 2022-04-01 Oslo 210
#> date location_name cases