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https://github.com/javierluraschi/c19chart

COVID-19 chart tracking per country and per capita cases
https://github.com/javierluraschi/c19chart
Last synced: 19 days ago
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COVID-19 chart tracking per country and per capita cases
Host: GitHub
URL: https://github.com/javierluraschi/c19chart
Owner: javierluraschi
Created: 2020-03-29T16:55:49.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2020-04-25T16:43:32.000Z (over 4 years ago)
Last Synced: 2024-12-23T10:45:53.977Z (28 days ago)
Size: 10.6 MB
Stars: 0
Watchers: 3
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.Rmd
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README

        ---

title: "Coronavirus Deaths by Region per Capita"

output:

  github_document:

    fig_width: 9

    fig_height: 5

---

```{r}

library(dplyr)

library(magrittr)

load(pins::pin("https://github.com/RamiKrispin/coronavirus/raw/master/data/coronavirus.rda"))

coronavirus <- coronavirus %>%

  mutate(country = `Country.Region`) %>%

  mutate(

    country = case_when(

      country == "United Kingdom" ~ "UK",

      country == "Korea, South" ~ "South Korea",

      TRUE ~ country

    )

  )

coronavirus <- coronavirus %>%

  filter(type == "death") %>%

  group_by(country, date) %>%

  summarise(cases = sum(cases))

```

```{r}

since_start <- coronavirus %>%

  group_by(country) %>%

  mutate(total = cumsum(cases)) %>%

  filter(total >= 10) %>%

  group_by(country) %>%

  mutate(since_start = 1:n())

```

```{r}

library(ggplot2)

library(directlabels)

# Validate against current chart https://twitter.com/harryrutter/status/1244253749520084992/photo/1

since_start %>%

  ggplot(aes(x = since_start, y = total, color = country)) +

    geom_point(size = 0.5) +

    geom_line(alpha = 0.3) +

    scale_colour_discrete(guide = 'none') +

    scale_x_discrete(expand = c(0, 8)) +

    scale_y_continuous(trans='log2') +

    geom_dl(aes(label = country), method = list(dl.trans(x = x + 0.2), "last.points", cex = 0.8)) +

    ggtitle(label = "Country by Country: How COVID-19 case trajectories compare",

            subtitle = "Cumulative number of confirmed cases, by number of days since 10th death") +

    theme_light()

```

```{r}

population <- read.csv(pins::pin("https://datahub.io/JohnSnowLabs/population-figures-by-country/r/population-figures-by-country-csv.csv"), stringsAsFactors = F)

population <- population %>%

  mutate(

    country = case_when(

      Country == "United States" ~ "US",

      Country == "United Kingdom" ~ "UK",

      Country == "Czech Republic" ~ "Czechia",

      Country == "Iran, Islamic Rep." ~ "Iran",

      Country == "Korea, Rep." ~ "South Korea",

      Country == "Russian Federation" ~ "Russia",

      Country == "Brunei Darussalam" ~ "Brunei",

      Country == "Congo, Rep." ~ "Congo (Kinshasa)",

      Country == "Egypt, Arab Rep." ~ "Egypt",

      Country == "Kyrgyz Republic" ~ "Kyrgyzstan",

      Country == "Macedonia, FYR" ~ "North Macedonia",

      Country == "Slovak Republic" ~ "Slovakia",

      Country == "Venezuela, RB" ~ "Venezuela",

      Country == "Bahamas, The" ~ "Bahamas",

      Country == "Myanmar"  ~ "Burma",

      Country == "Gambia, The" ~ "Gambia",

      Country == "Syrian Arab Republic" ~ "Syria",

      Country == "Congo, Dem. Rep." ~ "Congo (Brazzaville)",

      TRUE ~ Country

    )

  )

population <- transmute(population, country = country, population = Year_2016)

population <- rbind(population, data.frame(

  country = c("Taiwan*", "Diamond Princess", "MS Zaandam"),

  population = c(23780000, 3700, 136 + 97)

))

countries <- unique(since_start$country)

countries_missing <- countries[!countries %in% unique(population$country)]

if (length(countries_missing) > 0) stop("Countries missing in population table: ", paste0(countries_missing, collapse = "; "))

```

```{r}

covid_since_start <- since_start %>%

  left_join(population, by = "country") %>%

  mutate(total_percent = total / population)

countries_highlight <- c("San Marino", "Andorra", "Iceland", "India", "Luxembourg", "US", "Spain", "Italy", "Japan", "China", "Iran", "UK", "South Korea", "Singapore", "Diamond Princess", "Vietnam", "Taiwan*", "Germany", "France", "Monaco", "Sweden", "Philippines", "Belgium", "Brazil", "Taiwan*", "Mexico")

highlight_since_start <- filter(covid_since_start, country %in% countries_highlight)

population_us <- population %>% filter(country == "US") %>% pull(population)

total_us <- coronavirus %>% 

  filter(country == "US") %>%

  summarise(total = sum(cases)) %>%

  pull(total)

# https://www.cdc.gov/flu/about/burden/2018-2019.html

deaths_influenza <- data.frame(since_start = 1:120, total_percent = 1:120 * (34200 / 365) / population_us)

# https://www.cdc.gov/nchs/fastats/leading-causes-of-death.htm

deaths_clrd <- data.frame(since_start = 1:120, total_percent = 1:120 * (160201 / 365) / population_us)

# https://www.cdc.gov/heartdisease/facts.htm

deaths_heart <- data.frame( since_start = 1:120, total_percent = 1:120 * (647000 / 365) / population_us)

highlight_since_start %>%

  ggplot(aes(x = since_start, y = total_percent, colour = country)) +

    geom_line(aes(group = country), data = covid_since_start, alpha = 0.3, colour = "grey") +

    geom_line(alpha = 0.8) +

    scale_colour_discrete(guide = 'none') +

    scale_x_continuous(limits = c(0, 95)) +

    scale_y_continuous(labels = scales::percent_format(accuracy = 0.00001), trans='log2') +

    geom_dl(aes(label = country), method = list("last.bumpup", cex = 0.7)) +

    xlab("Number of days since tenth death") + ylab("") +

    labs(title = "Coronavirus Deaths by Region per Capita",

         subtitle = "Cumulative number of deaths as percent of population since tenth death",

         caption = element_text("Source: Johns Hopkins University Center for Systems Science and Engineering. Data Updated: April 25, 2020\nFigure: github.com/javierluraschi/c19chart", color="grey")) +

    theme_bw() + 

    theme(plot.background = element_rect(fill = "#fff1e6"),

          panel.background = element_rect(fill = "#fff1e6",

                                colour = "lightblue",

                                size = 0.5, linetype = "solid"),

          plot.caption = element_text(color="grey60"),

          axis.line.x = element_line(colour = "black"),

          panel.grid.major = element_line(color="grey90"),

          panel.grid.minor = element_line(color="grey90"),

          panel.border = element_blank()) +

    geom_line(data = deaths_influenza, alpha = 0.3, colour="grey70", linetype="dashed") +

    annotate("text", x=87.2, y = max(deaths_influenza$total_percent) + 0.000011, label = "influenza (93/day US)", size = 3, colour="grey70") +

    geom_line(data = deaths_clrd, alpha = 0.3, colour="grey70", linetype="dashed") +

    annotate("text", x=78.0, y = max(deaths_clrd$total_percent) + 0.00007, label = "chronic lower respiratory diseases (438/day US)", size = 3, colour="grey70") +

    geom_line(data = deaths_heart, alpha = 0.3, colour="grey70", linetype="dashed") +

    annotate("text", x=84.7, y = max(deaths_heart$total_percent) + 0.00026, label = "heart disease (1772/day US)", size = 3, colour="grey70") +

    ggsave("covid19-deaths-per-capita.png", device = "png", width = 10, height = 5)

```

```{r}

covid_since_start %>%

  filter(since_start <= 21) %>%

  group_by(country) %>%

  summarise(total = sum(cases), days = max(since_start)) %>%

  left_join(population, by = "country") %>%

  ggplot(aes(x=population, y=total)) +

  scale_x_continuous(trans='log2') +

  scale_y_continuous(trans='log2') +

  geom_dl(aes(label = country), method = list("last.bumpup", cex = 0.7, hjust=-0.2)) +

  geom_smooth(method=lm) +

  labs(title = "Countries deaths vs population after 10th death on 21th day") +

  ggsave("covid19-deaths-population.png", device = "png", width = 12, height = 6)

```

```{r echo=FALSE, eval=FALSE}

covid_since_start %>%

  filter(since_start <= 36) %>%

  filter(country %in% c("Spain", "Netherlands", "Italy", "UK", "France", "China", "Switzerland", "Iran", "US", "South Korea", "Japan")) %>%

  group_by(country) %>%

  summarise(total = sum(cases), days = max(since_start)) %>%

  left_join(population, by = "country") %>%

  ggplot(aes(x=population, y=total)) +

  geom_point() +

  geom_dl(aes(label = country), method = list("last.bumpup", cex = 0.7, hjust=-0.2)) +

  geom_smooth(method=lm) +

  labs(title = "Countries subset deaths vs population after 10th death on 36th day") +

  ggsave("covid19-deaths-population-subset.png", device = "png", width = 12, height = 6)

```

```{r echo=FALSE, eval=FALSE}

covid_since_start %>%

  filter(since_start <= 36) %>%

  filter(country %in% c("Spain", "Netherlands", "Italy", "UK", "France", "Switzerland", "Iran", "US", "South Korea", "Japan")) %>%

  group_by(country) %>%

  summarise(total = sum(cases), days = max(since_start)) %>%

  left_join(population, by = "country") %>%

  ggplot(aes(x=population, y=total)) +

  geom_point() +

  geom_dl(aes(label = country), method = list("last.bumpup", cex = 0.7, hjust=-0.2)) +

  geom_smooth(method=lm) +

  labs(title = "Countries subset deaths vs population after 10th death on 36th day") +

  ggsave("covid19-deaths-population-nochina.png", device = "png", width = 12, height = 6)

```