Visualization of COVID-19 in Canada

covid_alberta_sask.R

library(tidyverse)
library(lubridate)
library(readr)

data_hr <- read_csv("https://raw.githubusercontent.com/ishaberry/Covid19Canada/master/timeseries_hr/cases_timeseries_hr.csv")

# Coerce string to dates and factor provinces
data_cum <- data_hr %>%
    filter(province %in% c("Alberta", "Saskatchewan")) %>% 
    mutate(date_report = lubridate::dmy(date_report)) %>%
    group_by(province, date_report) %>% 
    summarise(cases = sum(cases, na.rm = TRUE)) %>% 
    mutate(cum_cases = cumsum(cases)) %>% 
    filter(cum_cases != 0) 

data_cum %>% 
    mutate(cases = if_else(province == "Alberta",
                           cases/4.371, cases/1.174)) %>% 
    group_by(province) %>% 
    mutate(roll_avg = RcppRoll::roll_mean(cases, n = 5,
                                          fill = NA,
                                          align = "right")) %>% 
    ggplot(aes(date_report, roll_avg, colour = province)) + 
    geom_line() +
    theme_bw() +
    xlab("") + ylab("Daily Cases/1M (5 day average)") +
    theme(legend.position = "top") +
    labs(caption = paste("Data downloaded from",
                         "https://github.com/ishaberry/Covid19Canada"),
         colour = "")

covid_atlantic_swarm.R

library(tidyverse)
library(lubridate)
library(readr)
library(ggbeeswarm)

# Download data on cases by health region
data_hr <- read_csv("https://raw.githubusercontent.com/ishaberry/Covid19Canada/master/timeseries_hr/cases_timeseries_hr.csv")

# Filter for province, coerce string to dates, and add variable
# to highlight certain regions
data_atl <- data_hr %>%
    filter(province %in% c("New Brunswick", "Nova Scotia", 
                           "PEI", "NL")) %>%
    mutate(date_report = lubridate::dmy(date_report))

date_range <- data_atl %>% 
    filter(cases != 0) %>% 
    pull(date_report) %>% 
    range

plot_atl <- purrr::map(c("New Brunswick", "Nova Scotia", 
                         "PEI", "NL"),
                       function(prov) {
                           data_prov <- filter(data_atl,
                                               province == prov)
                           total_cases <-  data_prov %>% pull(cases) %>% sum
                           num_hrs <- data_prov %>% 
                               filter(health_region != "Not Reported") %>%
                               count(health_region) %>% nrow
                           
                           data_prov %>%
                               filter(cases != 0,
                                      health_region != "Not Reported") %>%
                               uncount(cases) %>%
                               ggplot(aes(x = date_report,
                                          y = health_region,
                                          colour = health_region)) +
                               geom_quasirandom(groupOnX = FALSE) +
                               expand_limits(x = today()) +
                               xlim(date_range) +
                               theme_bw() +
                               theme(legend.position = 'none',
                                     panel.grid = element_blank(),
                                     plot.subtitle = element_text(face = "italic")) +
                               geom_hline(yintercept = 0.5 + seq_len(num_hrs - 1),
                                          linetype = 'dashed',
                                          colour = 'grey60',
                                          size = 0.1) +
                               xlab("") + ylab("") +
                               labs(title = paste0(prov, " (Total COVID cases = ", 
                                                   scales::comma(total_cases), ")"))
                       })

cowplot::plot_grid(plot_atl[[1]], plot_atl[[2]], 
                   plot_atl[[3]], plot_atl[[4]], 
                   ncol = 2)

covid_province_swarm.R

library(tidyverse)
library(lubridate)
library(readr)
library(ggbeeswarm)

# Change name of province as required
prov_name <- "Saskatchewan"

# Download data on cases by health region
data_hr <- read_csv("https://raw.githubusercontent.com/ishaberry/Covid19Canada/master/timeseries_hr/cases_timeseries_hr.csv")

# Filter for province, coerce string to dates, and add variable
# to highlight certain regions
data_prov <- data_hr %>%
    filter(province == prov_name) %>%
    mutate(date_report = lubridate::dmy(date_report))

total_cases <-  data_prov %>% pull(cases) %>% sum
num_hrs <- data_prov %>% filter(health_region != "Not Reported") %>%
    count(health_region) %>% nrow
min_date <- data_prov %>%
    filter(cases > 0) %>%
    pull(date_report) %>%
    min

data_prov %>%
    filter(cases != 0,
           health_region != "Not Reported") %>%
    uncount(cases) %>%
    ggplot(aes(x = date_report,
               y = health_region,
               colour = health_region)) +
    geom_quasirandom(groupOnX = FALSE) +
    expand_limits(x = today()) +
    theme_bw() +
    theme(legend.position = 'none',
          panel.grid = element_blank(),
          plot.subtitle = element_text(face = "italic")) +
    geom_hline(yintercept = 0.5 + seq_len(num_hrs - 1),
               linetype = 'dashed',
               colour = 'grey60',
               size = 0.1) +
    xlab("") + ylab("") +
    labs(title = paste0("COVID-19 cases in ", prov_name,
                        " (Total = ", scales::comma(total_cases), ")"),
         subtitle = paste("Effective", data_prov %>% pull(date_report) %>% max),
         caption = paste("Data downloaded from",
                         "https://github.com/ishaberry/Covid19Canada"))