Using if_else for string search [closed] - r

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I am trying to replace the name of the US states with the string " United States".
Country<-data.frame(Name=c(" China"," Japan"," Florida"," Canada"," Texas"))
Country$Name<-as.character(Country$Name)
Country
Name
1 China
2 Japan
3 Florida
4 Canada
5 Texas
str(Country)
'data.frame': 5 obs. of 1 variable:
$ Name: chr " China" " Japan" " Florida" " Canada" ...
Below is the DPLYR command I used. It doesn't work. I use state.name for this purpose.
Country%>%mutate(Name=if_else(Name %in% state.name, " United States",Name))
Name
1 China
2 Japan
3 Florida
4 Canada
5 Texas
I tried to use str_detect, but it gives multiple output for the search on state.name. ( FALSE FALSE TRUE...… ). Hence unable to succeed with the condition check.

Answer 1

You can use ifelse from base R to do it
Country <- within(Country, Name <- ifelse(trimws(Name) %in% state.name, "United States",trimws(Name)))

Answer 2

Your problem is that %in% only checks for exact matches. The names in your data.frame have whitespace at the beginning while state.name hasn't. So you need to remove this whitespace before comparing the two.
You can either remove the whitespace (with trimws) from the Name column before comparison:
library(dplyr)
Country %>%
mutate(Name = trimws(Name)) %>%
mutate(Name = if_else(Name %in% state.name, "United States", Name))
#> Name
#> 1 China
#> 2 Japan
#> 3 United States
#> 4 Canada
#> 5 United States
Or just within the comparison, which will preserve the whitespace (I don't see a reason why you would want that but just in case):
Country %>%
mutate(Name = if_else(trimws(Name) %in% state.name, "United States", Name))
#> Name
#> 1 China
#> 2 Japan
#> 3 United States
#> 4 Canada
#> 5 United States
A third possibility would be to use string replacement, for example, with the stringi package:
library(stringi)
Country %>%
mutate(Name = stri_replace_all_fixed(Country$Name, state.name, "United States",
vectorize_all = FALSE, stri_opts_regex(case_insensitive = TRUE)))
#> Name
#> 1 China
#> 2 Japan
#> 3 United States
#> 4 Canada
#> 5 United States
I wouldn't recommend this either but included it since you have a few more options (e.g., case_insensitive) if your strings are more complicated than what's in your sample data.