下面是extract的另一个解决方案，它一次性提取Country、City和State，State由可选的捕获组提取（任务的其余部分由@艾伦代码完成）：

library(tidyr)
library(dplyr)
df1 %>%
  extract(City,
          into = c("Country", "City", "State"),
          regex = "([^>]+) > ([^,]+),? ?([A-Z]+)?"
        ) %>%
  # as by @Allen Cameron:
  group_by(Country, City) %>%
  summarize(State = ifelse(all(is.na(State)), NA, State[!is.na(State)]), 
            Population = Population[!is.na(Population)])

您可以使用separate两次来获取国家和州，然后使用group_by Country and City来汇总NA值（如果适用）：

library(tidyverse)

df1 %>%
  separate(City, sep = " > ", into = c("Country", "City")) %>%
  separate(City, sep = ', ', into = c('City', 'State')) %>%
  group_by(Country, City) %>%
  summarize(State = ifelse(all(is.na(State)), NA, State[!is.na(State)]), 
            Population = Population[!is.na(Population)])
#> # A tibble: 6 x 4
#> # Groups:   Country [4]
#>   Country        City                       State Population
#>   <chr>          <chr>                      <chr>      <dbl>
#> 1 Canada         Montreal                   <NA>      250000
#> 2 Canada         Nanaimo                    BC        150000
#> 3 Spain          Las Palmas de Gran Canaria <NA>      200000
#> 4 United Kingdom Leeds                      <NA>     1500000
#> 5 United States  Milwaukee                  WI        400000
#> 6 United States  Minneapolis                MN        700000