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  1. library(tidyverse)
  2. library(ggplot2)
  3. library(cowplot)
  4. library(patchwork)
  5. confirmed_df <- read_csv("https://r...content-available-to-author-only...t.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
  6. deaths_df <- read_csv("https://r...content-available-to-author-only...t.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")
  7. recovered_df <- read_csv("https://r...content-available-to-author-only...t.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")
  8.  
  9. sd <- length(confirmed_df) - 21  # start date
  10. ed <- length((confirmed_df))     # end date
  11. dates <- colnames(confirmed_df[, sd:ed])
  12. dates <- as.Date(dates,format = "%m/%d/%y")
  13. dates <- as.POSIXct(dates,tz = "GMT")
  14. getCountrydata <- function(Country,
  15.                            dates = dates,
  16.                            confirmed_df = confirmed_df,
  17.                            deaths_df = deaths_df,
  18.                            recovered_df = recovered_df,
  19.                            sd = sd, ed = ed) {
  20.   if (Country == "all") {
  21.     cases <- confirmed_df %>%
  22.       #select(-(1:400)) %>%
  23.       select(sd:ed) %>%
  24.       colSums()
  25.     death <- deaths_df %>%
  26.       #select(-(1:400)) %>%
  27.       select(sd:ed) %>%
  28.       colSums()
  29.     recovered <- recovered_df %>%
  30.       #select(-(1:400)) %>% 
  31.       select(sd:ed) %>%
  32.       colSums()
  33.   }
  34.   else {
  35.     Country <- enquo(Country)
  36.     cases <- confirmed_df %>%
  37.       filter(`Country/Region` == !! Country) %>%
  38.       #select(-(1:400)) %>%
  39.       select(sd:ed) %>%
  40.       colSums()
  41.     death <- deaths_df %>%
  42.       filter(`Country/Region` == !! Country) %>%
  43.       #select(-(1:400)) %>%
  44.       select(sd:ed) %>%
  45.       colSums()
  46.     recovered <- recovered_df %>%
  47.       filter(`Country/Region` == !! Country) %>%
  48.       #select(-(1:400)) %>% 
  49.       select(sd:ed) %>%
  50.       colSums()
  51.   }
  52.   res.df <- tibble(dates,
  53.                    cases = cases,
  54.                    death = death,
  55.                    recovery = recovered,
  56.                    mortality_rate = death/cases,
  57.                    recovery_rate = recovery/cases)
  58.   return(res.df)
  59. }
  60. world.df <- getCountrydata(Country = "all",
  61.                            dates = dates,
  62.                            confirmed_df = confirmed_df,
  63.                            deaths_df = deaths_df,
  64.                            recovered_df = recovered_df, sd, ed)
  65. #Taiwan
  66. taiwan.df <- getCountrydata(Country = "Taiwan*",
  67.                             dates = dates,
  68.                             confirmed_df = confirmed_df,
  69.                             deaths_df = deaths_df,
  70.                             recovered_df = recovered_df, sd, ed)
  71.  
  72. tmp.cases.plot <- function(df.plot, Country) {
  73.   df.plot %>%
  74.     mutate(cases_k = cases) %>%
  75.     ggplot( aes(x=dates, y=cases_k)) +
  76.     geom_line(color="#69b3a2") +
  77.     geom_point(color="#69b3a2", size=1) +
  78.     scale_x_datetime(breaks = world.df$dates, 
  79.                      date_labels = '%m/%d')+
  80.     ggtitle(paste0(Country," Evolution of COVID-19 cases")) +
  81.     ylab("cases") +
  82.     theme_cowplot() +
  83.     theme(axis.text.x = element_text(size = 10,
  84.                                      vjust = 0.5,
  85.                                      hjust = 0.5,
  86.                                      angle = 90))
  87. }
  88.  
  89. tmp.deaths.plot <- function(df.plot, Country) {
  90.   df.plot %>%
  91.     ggplot( aes(x=dates, y=mortality_rate)) +
  92.     geom_line(color="#69b3a2") +
  93.     geom_point(color="#69b3a2", size=1) +
  94.     scale_x_datetime(breaks = world.df$dates, 
  95.                      date_labels = '%m/%d')+
  96.     ggtitle(paste0(Country," Evolution of COVID-19 death rates")) +
  97.     ylab("Mortality rates(Death/Cases)") +
  98.     theme_cowplot()+
  99.     theme(axis.text.x = element_text(size = 10,
  100.                                      vjust = 0.5,
  101.                                      hjust = 0.5,
  102.                                      angle = 90)) 
  103. }
  104. tmp.recover.plot <- function(df.plot, Country) {
  105.   df.plot %>%
  106.     ggplot( aes(x=dates, y=recovery_rate)) +
  107.     geom_line(color="#69b3a2") +
  108.     geom_point(color="#69b3a2", size=1) +
  109.     scale_x_datetime(breaks = world.df$dates, 
  110.                      date_labels = '%m/%d') +
  111.     scale_y_continuous(breaks=seq(0,1,0.2),limits = c(0,1)) +
  112.     ggtitle(paste0(Country," Evolution of COVID-19 recovery rates")) +
  113.     ylab("Recovery rates(Recovery/Cases)") +
  114.     theme_cowplot()+
  115.     theme(axis.text.x = element_text(size = 10,
  116.                                      vjust = 0.5,
  117.                                      hjust = 0.5,
  118.                                      angle = 90)) 
  119. }
  120. #----------
  121. sd <- length(confirmed_df) - 22; sd
  122. ed <- sd; ed
  123. dates <- colnames(confirmed_df[, sd:ed])
  124. df <- getCountrydata(Country = "Taiwan*",
  125.                      dates = dates,
  126.                      confirmed_df = confirmed_df,
  127.                      deaths_df = deaths_df,
  128.                      recovered_df = recovered_df, sd, ed)
  129. first_data <- df$cases[[1]] ; first_data
  130. taiwan.df['daily'] <- NA; taiwan.df
  131. for(i in 1:nrow(taiwan.df)) {
  132.   if(i == 1)
  133.     taiwan.df$daily[i] <- taiwan.df$cases[i]- first_data
  134.   else
  135.     taiwan.df$daily[i] <- taiwan.df$cases[i]- taiwan.df$cases[i - 1] 
  136. }; tail(taiwan.df, 10)
  137.  
  138. tmp.daily.plot <- function(df.plot, Country) {
  139.   df.plot %>%
  140.     mutate(daily = daily) %>%
  141.     ggplot(aes(x = dates, y = daily)) +
  142.     geom_line(color="#69b3a2") +
  143.     geom_point(color="red", size=1) +
  144.     scale_x_datetime(breaks = world.df$dates, date_labels = '%m/%d')+
  145.     ggtitle(paste0(Country," Evolution of COVID-19 daily")) +
  146.     ylab("daily") +
  147.     theme_cowplot() +
  148.     theme(axis.text.x = element_text(size = 10,
  149.                                      vjust = 0.5,
  150.                                      hjust = 0.5,
  151.                                      angle = 90))
  152. }
  153. pic1 <- tmp.daily.plot(df.plot = taiwan.df, Country = "Taiwan"); pic1
  154. pic2 <- tmp.cases.plot(df.plot = taiwan.df, Country = "Taiwan"); pic2
  155. pic3 <- tmp.deaths.plot(df.plot = taiwan.df, Country = "Taiwan"); pic3
  156. pic4 <- tmp.recover.plot(df.plot = taiwan.df, Country = "Taiwan"); pic4
  157. pic1 + pic2 + pic3 + pic4 + plot_layout(ncol = 2)
  158.  
  159. ggplot(taiwan.df, aes(x = dates, y = taiwan$daily ))
  160. #Bar char
  161. ggplot(taiwan.df, aes(x = dates, y = daily)) + 
  162.         geom_bar(stat = "identity", fill = "lightblue") +
  163.         scale_x_datetime(breaks = world.df$dates, date_labels = '%m/%d')
  164.  
Success #stdin #stdout #stderr 0.21s 39088KB
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Error in library(tidyverse) : there is no package called ‘tidyverse’
Execution halted
https://ideone.com/kw4Heg
COVID-19 統計
language:
R (R 3.5.2)
created:
3 years ago
visibility:
public

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