Question

  df <- read.csv ('https://raw.githubusercontent.com/ulklc/covid19- 
 timeseries/master/countryReport/raw/rawReport.csv',
            stringsAsFactors = FALSE) 

 df8 <- read.csv ('https://raw.githubusercontent.com/hirenvadher954/Worldometers- 
  Scraping/master/countries.csv',
             stringsAsFactors = FALSE)

 install.packages("tidyverse")
 library(tidyverse)

   df %>% 
   left_join(df8, by = c("countryName" = "country_name")) %>% 
 mutate(population = as.numeric(str_remove_all(population, ","))) %>% 
  group_by(countryName) %>% 
    group_by(countryName) %>% 
  unique() %>% 
summarize(population = sum(population, na.rm = TRUE),
        confirmed = sum(confirmed, na.rm = TRUE),
        recovered = sum(recovered, na.rm = TRUE),
        death = sum(death, na.rm = TRUE),
        death_prop = paste0(as.character(death), "/", as.character(population))
        )

在此代码中

计算了人口/死亡率。

最高人口/死亡率

查找10个国家/地区。

已确认并恢复

不可用。

10 x 6 countryName population confirmed recovered death death_prop <chr> <dbl> <int> <int> <int> <chr> 1 Afghanistan 4749258212 141652 16505 3796 3796/4749258212 2 Albania 351091234 37233 22518 1501 1501/351091234 3 Algeria 5349827368 206413 88323 20812 20812/5349827368 4 Andorra 9411324 38518 18054 2015 2015/9411324 5 Angola 4009685184 1620 435 115 115/4009685184 6 Anguilla 1814018 161 92 0 0/1814018 7 Antigua and Barbuda 11947338 1230 514 128 128/11947338 8 Argentina 5513884428 232975 66155 10740 10740/5513884428 9 Armenia 361515646 121702 46955 1626 1626/361515646 10 Aruba 13025452 5194 3135 91 91/13025452

数据是一个例子。

信息不正确。

Answer 1

数据为累积格式，表示今天的所有值都具有直到昨天的所有值。因此，每列仅取max的值并计算death_prop。

library(dplyr)

df %>% 
  left_join(df8, by = c("countryName" = "country_name")) %>% 
  mutate(population = as.numeric(str_remove_all(population, ","))) %>%
  group_by(countryName) %>% 
  summarise_at(vars(population:death), max, na.rm = TRUE) %>%
  mutate(death_prop = death/population * 100) %>%
  arrange(desc(death_prop))


# A tibble: 215 x 5
#   countryName    population  year death death_prop
#   <chr>               <dbl> <dbl> <int>      <dbl>
# 1 San Marino          33860  2019    42     0.124 
# 2 Belgium          11589623  2020  9312     0.0803
# 3 Andorra             77142  2019    51     0.0661
# 4 Spain            46754778  2020 28752     0.0615
# 5 Italy            60461826  2020 32877     0.0544
# 6 United Kingdom   67886011  2020 36914     0.0544
# 7 France           65273511  2020 28432     0.0436
# 8 Sweden           10099265  2020  4029     0.0399
# 9 Sint Maarten        42388  2019    15     0.0354
#10 Netherlands      17134872  2020  5830     0.0340
# … with 205 more rows

比率计算并对计算出的比率进行排序

1 个答案: