Imagine dataset:
df1 <- tibble::tribble(~City, ~Population,
"United Kingdom > Leeds", 1500000,
"Spain > Las Palmas de Gran Canaria", 200000,
"Canada > Nanaimo, BC", 150000,
"Canada > Montreal", 250000,
"United States > Minneapolis, MN", 700000,
"United States > Milwaukee, WI", NA,
"United States > Milwaukee", 400000)
The same dataset for visual representation:
I would like to:
- Split column City into three columns: City, Country, State (if available, NA otherwise)
- Check that Milwaukee has data in state and population (the NA for Milwaukee should have a value of 400000 and then split [City-State-Country] :).
Could you, please, suggest the easiest method to do so :)
CodePudding user response：
Here's another solution with extract to do the extraction of Country, City, and State in a single go with State extracted by an optional capture group (the remainder of the task is done as by @Allen's code):
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)])
CodePudding user response：
You can use separate twice to get the country and state, then group_by Country and City to summarize away the NA values where appropriate:
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