In CBDRH/covidrecon: Pulls and Analyses COVID data

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

library(covidrecon)
library(dplyr)
library(ggplot2)
library(tidyr)
library(purrr)

covid <- covid_latest()

highlight_countries <- c("Japan",
                         "South_Korea",
                         "Singapore",
                         "Italy",
                         "Iran",
                         "China",
                         "France",
                         "United_Kingdom",
                         "Sweden",
                         "Australia")

covid_subset <- covid %>% 
  filter(country_region %in% c(highlight_countries, 
                               "United_States_of_America", 
                               "Germany", 
                               "Denmark"))

covid_changepoint <- covid_subset %>% 
  add_covid_change_point() %>% 
  filter(date >= change_point_date)

Instantaneous effective reproduction number

covid_instant_r <- add_instant_reproduction(covid_changepoint)

gg_effective_repro_facet(covid_instant_r)

gg_effective_repro_all(covid_instant_r)

library(lubridate)

gg + 
  geom_line_interactive(size = 1.2) %>% 
girafe(ggobj = .,
       options = list(
         opts_tooltip(use_stroke = TRUE, opacity = 0.7),
         opts_hover(css = "stroke:black;"),
         opts_toolbar(position = "topright", saveaspng = TRUE)
       ))

hubei_incidence_function_data <- covid %>%
      filter(date >= ymd("2020-01-11")) %>% 
      mutate(HubeiSansWuhan = if_else(
        condition = is.na(HubeiSansWuhan), 
        true = 0, 
        false = HubeiSansWuhan)) %>%
      mutate(incident_cases = ifelse(Date < ymd("2020-02-15"),
                                     Wuhan + HubeiSansWuhan, Hubei)) %>% mutate(date = format(date, 
    "%Y-%m-%d")) %>% 
    select(Date, incident_cases) 
    %>% uncount(incident_cases)
hubei_incidence_object <- incidence(hubei_incidence_function_data$Date)

exploring errors

countries_errors <- country_repro_errors(covid_estimated_repro)

covid_estimated_repro %>% 
  filter(geo_id %in% countries_errors) %>% 
  select(geo_id,
         repro_estimate) %>% 
  unnest(cols = c(repro_estimate)) %>% 
  slice(2) %>% 
  pull(repro_estimate)