CoronaWatchNL is an R package that allows you to access a wide range of Covid-19 related datasets in the Netherlands. This package is heavily based on the CoronaWatchNL repository on GitHub, which collects and releases this information on a daily basis. However, the R package also provides more datasets that are not included in the original repository, such as the weekly mortality rate data published by CBS.
You can install the development version of CoronaWatchNL from GitHub with:
# install.packages("devtools")
devtools::install_github("mcnakhaee/CoronaWatchNL")At the moment, the following category of datasets are available via this R package:
| Dataset | Source | Variables | Function |
|---|---|---|---|
| Reported case counts by date in NL | RIVM | Date, Type (Total, hospitalized and deceased COVID-19 cases), (Cumulative) Count | get_daily_national_cases() |
| Reported case counts by date in NL per province | RIVM | Date, Province, Type (Total, hospitalized and deceased COVID-19 cases), (Cumulative) Count | get_daily_provincial_cases() |
| Reported case counts by date in NL per municipality | RIVM | Date, Municipality, Province, Type (Total, hospitalized and deceased COVID-19 cases), (Cumulative) Count | get_daily_cases_per_municipality(),get_cumilative_cases_per_municipality()### |
| Dataset | Source | Variables | Function |
|---|---|---|---|
| Case counts in NL per age | RIVM | Date, Age group, Type (Total, hospitalized and deceased COVID-19 cases), (Cumulative) Count | get_cases_by_age() |
| Case counts in NL per sex | RIVM | Date, Sex, Type (Total, hospitalized and deceased COVID-19 cases), (Cumulative) Count | get_cases_by_sex() |
| Deceased case counts in NL per sex and age group | RIVM | Date, Age group, Sex, (Cumulative) Count of deceased cases | get_deceased_cases_by_sex_age() |
| Dataset | Source | Variables | Function |
|---|---|---|---|
| COVID-19 intensive care patient counts in NL (Wide or long Format) | Stichting NICE | Date, New, Total and Cumulative ICU admissions per day, Number of ICUs with at least one COVID-19 case, New and Cumulative fatal, survived and discharged ICU admissions | get_icu_data_wide()/ get_icu_data_long() |
| COVID-19 intensive care patient counts with country of hospitalization | LCPS | Date, Country of Hospitalization, Total COVID-19 ICU admissions | get_lcps_data() |
| Dataset | Source | Variables | Function |
|---|---|---|---|
| Apple’s daily mobility report | GitHub | get_apple_mobility_data() |
|
| Google’s mobility report | GitHub | get_google_mobility_data() |
|
| Waze’s mobility report | GitHub | get_waze_mobility_city_level_data(),get_waze_mobility_country_level_data() |
| Dataset | Source | Variables | Function |
|---|---|---|---|
| Weekly mortality rate | CBS.nl | Sex, Age group of the deceased person on 31 December , Periods, Number of registered death | get_mortality_rate() |
| Dataset | Source | Variables | Function |
|---|---|---|---|
| Population per region (municipality) | CBS.nl | Periods, Regions, Population | get_population_per_region() |
| Suspected patients in NL | National Dashboard | Date, Type of measure, Count | get_suspected_patients() |
| COVID-19 particles in sewage | RIVM | get_sewage_data() |
|
| Reproduction index COVID-19 virus | RIVM | get_reproduction_rate() |
|
| Government financial aid to companies | UWV | Company, Location, Advance | get_economy_data() |
| COVID-19 measures by the government | European Commission Joint Research Centre | Various variables on governmental measures (in English) | get_measures_data() |
| Underlying conditions and/or pregnancy in deceased COVID-19 cased under the age of 70 | RIVM | Date, Type of condition, Cumulative count | get_underlying_conditions() |
| Underlying conditions and/or pregnancy in deceased COVID-19 cased under the age of 70 | RIVM | Date, Type of condition, Cumulative count | get_underlying_statistics() |
| COVID-19 tests in NL per week | RIVM | Year, Calendar week, Start date (Monday), End date (Sunday), Included labs, Type (Total and positive tests), Count | get_testing_data() |
This is a basic example which shows you how to solve a common problem:
library(CoronaWatchNL)
library(tidyverse)
library(ggthemes)
library(geofacet)
library(gghighlight)
## basic example codeprovince_case <- get_daily_provincial_cases()
province_case %>%
filter(Provincienaam == 'Utrecht') %>% count(Type)
#> # A tibble: 3 x 2
#> Type n
#> <chr> <int>
#> 1 Overleden 250
#> 2 Totaal 250
#> 3 Ziekenhuisopname 250
province_case %>%
replace_na(list(Aantal = 0)) %>%
drop_na(Provincienaam) %>%
mutate(name = Provincienaam) %>%
filter(Type == 'Totaal') %>%
ggplot(aes(
x = Datum,
y = Aantal,
,
group = Type,
color = Type
)) +
geom_line(color = 'indianred',
size = 1,
alpha = 1) +
#geom_point(color = 'indianred', size = 2) +
gghighlight(
use_direct_label = FALSE,
unhighlighted_params = list(
size = 1,
width = 0.5,
color = '#F6DAB4',
alpha = 0.5
)
) +
facet_geo( ~ name, grid = 'nl_prov_grid1') +
theme_minimal()
library(sf)
library(gganimate)
library(santoku)
library(foreign)
municipalBoundaries <- st_read("https://geodata.nationaalgeoregister.nl/cbsgebiedsindelingen/wfs?request=GetFeature&service=WFS&version=2.0.0&typeName=cbs_gemeente_2020_gegeneraliseerd&outputFormat=json")
daily_cases_per_municpality <- get_daily_cases_per_municipality()
populatuon_per_region <- get_population_per_region()
daily_cases_per_municpality <- daily_cases_per_municpality %>%
inner_join(populatuon_per_region, by = c('Municipality_name' = 'Regions')) %>%
mutate(
Date_of_publication = lubridate::as_date(Date_of_publication),
day = lubridate::round_date(Date_of_publication , unit = 'day'),
day = lubridate::as_date(day),
avg = 100000 * as.numeric(Total_reported) / as.numeric(`Bevolking op 1 januari (aantal)`),
dis_avg = chop(avg, c(0, 0, 0.5, 1, 5, 12, 20, 35, 55, 80, 100, 200))
)
# merge boundary data
data <- municipalBoundaries%>%
right_join(daily_cases_per_municpality ,by=c(statnaam="Municipality_name"))
### Warning: it will take a lot of time to generate this animation!
data %>%
ggplot() +
geom_sf(aes(fill = dis_avg), color = 'gray95') +
scale_fill_manual(
values = c(
'gray95',
'#fee440',
'#FFBA08',
'#FAA307',
'#F48C06',
'#E85D04',
'#DC2F02',
'#D00000',
'#9D0208',
'#6A040F',
'#370617',
'#03071e'
)
) +
coord_sf(datum = NA) +
labs(
title = 'Covid-19 Cases per 100000 Inhabitants in the Netherlands',
subtitle = 'Date: {current_frame}',
fill = 'Counts per 100000',
caption = 'Source: RIVM'
) +
theme_void() +
theme(
text = element_text(family = 'Poppins Light'),
plot.subtitle = element_text(
family = 'Poppins Light',
size = 13,
margin = margin(b = 10)
) ,
plot.title = element_text(
family = 'Poppins Light',
size = 15,
margin = margin(t = 10, b = 10)
)
) +
transition_manual(day_char_, cumulative = T) +
ease_aes("sine") +
enter_fade(alpha = 0.5) +
exit_fade(alpha = 0.5)
apple_mobility <- get_apple_mobility_data()
glimpse(apple_mobility)
#> Rows: 4,986
#> Columns: 8
#> $ country <chr> "Netherlands", "Netherlands", "Netherlands", "Ne...
#> $ `sub-region` <chr> "Drenthe", "Drenthe", "Drenthe", "Drenthe", "Dre...
#> $ subregion_and_city <chr> "Drenthe", "Drenthe", "Drenthe", "Drenthe", "Dre...
#> $ geo_type <chr> "sub-region", "sub-region", "sub-region", "sub-r...
#> $ date <date> 2020-01-13, 2020-01-14, 2020-01-15, 2020-01-16,...
#> $ driving <dbl> 0.00, 2.80, 8.27, 5.10, 11.09, 11.98, 8.02, 3.11...
#> $ transit <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
#> $ walking <dbl> 0.00, 2.51, 9.32, 15.56, 22.19, 29.07, 10.35, 8....# inspired by: https://kjhealy.github.io/covdata/articles/mobility-data.html
apple_mobility %>%
filter(`sub-region` != 'Total') %>%
mutate(over_under = driving < 0) %>%
ggplot(aes(
x = date,
y = driving,
group = `sub-region` ,
color = over_under
)) +
geom_hline(yintercept = 0, color = "gray40") +
geom_col() +
scale_color_manual(values = c("steelblue" , "firebrick")) +
guides(color = FALSE) +
labs(x = "Date", y = "Relative Mobility", title = "Relative Trends in Apple Maps Usage for Driving in the Netherlands") +
facet_wrap( ~ `sub-region`) +
theme_minimal()