CoronaWatchNL

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.

Installation

You can install the development version of CoronaWatchNL from GitHub with:

# install.packages("devtools")
devtools::install_github("mcnakhaee/CoronaWatchNL")

Usage

At the moment, the following category of datasets are available via this R package:

Geographical datasets

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()###

Descriptive datasets

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()

Intensive care datasets

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()

Mobility 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()

Mortality Rate

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()

Miscellaneous datasets

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()

Example

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 code

province_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()