extract value and insert in 3 columns

[re123]

Hi.
I have a dataset like the following and I'm trying to add value to 3 columns based on column Country.

Country<-c("Asia","Africa - Benin (Cotonou)",
           "Europe - France (Paris)","Asia - China(Shanghai)", "Europe - United Kingdom (London)", "Europe - France (Orléans)"
           , "Afrique - Togo (Lomé)", "Afrique - Sénégal (Dakar)")

ID<-c(1,2,3,4,5,6,7,8)
mydata<-data.frame(ID,Country)

 > mydata
>   ID                          Country         col1     col2     col3 
> 1  1                             Asia
> 2  2         Africa - Benin (Cotonou)
> 3  3          Europe - France (Paris)
> 4  4           Asia - China(Shanghai)
> 5  5 Europe - United Kingdom (London)
> 6  6        Europe - France (Orléans)
> 7  7            Afrique - Togo (Lomé)
> 8  8        Afrique - Sénégal (Dakar)
         

I tried the following but im having issue with the regular expression 
> mydata[c("Col1", "col2", "col3")] <- str_split_fixed(mydata$Country, '-d' , 3)

I want the result to be like the following:

> mydata
>   ID                          Country    Col1    Col2             Col3
> 1  1                             Asia    Asia    <NA>             <NA>
> 2  2         Africa - Benin (Cotonou)  Africa   Benin             Cotonou
> 3  3          Europe - France (Paris)  Europe  France             Paris
> 4  4           Asia - China(Shanghai)    Asia   China             Shanghai
> 5  5 Europe - United Kingdom (London)  Europe  United Kingdom     London
> 6  6        Europe - France (Orléans)  Europe  France             Orléans
> 7  7            Afrique - Togo (Lomé) Afrique    Togo             Lomé
> 8  8        Afrique - Sénégal (Dakar) Afrique Sénégal             Dakar

Any suggestion on how to do this?
Thanks in advance

[FJCC]

I would do this with the separate() function from the tidyr package. It throws a warning because of the trailing parenthesis in rows 2 and 3 but it gets the right result.

Country<-c("Asia","Africa - Benin (Cotonou)",
           "Europe - France (Paris)","Asia - China(Shanghai")
ID<-c(1,2,3,4)
mydata<-data.frame(ID,Country)
library(tidyr)
mydata <- mydata %>% separate(col = "Country", into = c("Col1", "Col2", "Col3"), remove = FALSE, fill = "right")
#> Warning: Expected 3 pieces. Additional pieces discarded in 2 rows [2, 3].
mydata
#>   ID                  Country   Col1   Col2     Col3
#> 1  1                     Asia   Asia   <NA>     <NA>
#> 2  2 Africa - Benin (Cotonou) Africa  Benin  Cotonou
#> 3  3  Europe - France (Paris) Europe France    Paris
#> 4  4    Asia - China(Shanghai   Asia  China Shanghai

^{Created on 2022-04-27 by the reprex package (v0.2.1)}

[re123]

@FJCC
It does not work properly in my original large data.
It looks like the following. Some pieces are missing. For example in column col3 row 5,6,7 and 8 som part are missing

ID     Country                          Col1           Col2     Col3
 1    Asia                              Asia           <NA>     <NA>
 2    Africa - Benin (Cotonou)          Africa         Benin  Cotonou
 3    Europe - France (Paris)           Europe         France  Paris
 4    Asia - China(Shanghai)            Asia           China   Shanghai
 5    Europe - United Kingdom (London)  Europe         United  Kingdom
 6    Europe - France (Orléans)         Europe         France  Orl
 7    Afrique - Togo (Lomé)             Afrique        Togo    L
 8     Afrique - Sénégal (Dakar)         Afrique        S      n

[FJCC]

Does this work for you?

Country<-c("Asia","Africa - Benin (Cotonou)",
           "Europe - France (Paris)","Asia - China(Shanghai)", "Europe - United Kingdom (London)")
ID<-c(1,2,3,4,5)
mydata<-data.frame(ID,Country)
library(tidyr)
library(stringr)
library(dplyr)

mydata <- mydata %>% mutate(col1 = str_extract(Country, "^[:alpha:]+"),
                    col2 = str_extract(Country, "(?<=- ).+(?=\\()"),
                    col3 = str_extract(Country, "(?<=\\().+(?=\\))")) %>% 
  mutate(across(col1:col3, str_trim))
mydata
#>   ID                          Country   col1           col2     col3
#> 1  1                             Asia   Asia           <NA>     <NA>
#> 2  2         Africa - Benin (Cotonou) Africa          Benin  Cotonou
#> 3  3          Europe - France (Paris) Europe         France    Paris
#> 4  4           Asia - China(Shanghai)   Asia          China Shanghai
#> 5  5 Europe - United Kingdom (London) Europe United Kingdom   London

^{Created on 2022-04-28 by the reprex package (v0.2.1)}