How to update name based on other column's condition (Cleaning Data) - r

Question

I have a df below
df <- data.frame(LASTNAME = c("Robinson", "Anderson", "Beckham", "Wickham", "Carlos", "Robinson", "Beckham", "Anderson", "Carlos"),
FIRSTNAME = c("David", "Adi", "Joan", "Kesley", "Anberto", "Dave", "Joana", "Adien", "An"))
df <- data.frame(lapply(df, as.character), stringsAsFactors = FALSE)
There are some first names are not consistent. I want to find and replace these ones. But when I put it in the function, it doesn't work. One more thing is my data is big. There are hundred of names, so are there any better ways to do it.
My code works well when it is alone (not in function), but I failed to find a way to do it if I have 100 names need to find and replace. I found a reference here, but does not resolve my problem. Any suggestions would be appreciated.
fil_name <- function(last,first,alternative){
df %>%
mutate(FIRSTNAME = ifelse(LASTNAME == "last" & FIRSTNAME == "first", "alternative", FIRSTNAME))
}
fil_name(Robinson,Dave,David)
Expected output:
LASTNAME FIRSTNAME
1 Robinson David
2 Anderson Adien
3 Beckham Joana
4 Wickham Kesley
5 Carlos Anberto
6 Robinson David
7 Beckham Joana
8 Anderson Adien
9 Carlos Anberto

Answer 1

We can convert to character inside the function, and it should work
fil_name <- function(df, last,first,alternative){
last <- rlang::as_string(rlang::ensym(last))
first <- rlang::as_string(rlang::ensym(first))
alternative <- rlang::as_string(rlang::ensym(alternative))
df %>%
dplyr::mutate(FIRSTNAME = case_when(LASTNAME == last &
FIRSTNAME == first ~ alternative, TRUE ~ FIRSTNAME))
}
fil_name(df, Robinson,Dave,David)

Answer 2

Another approach is to create a separate data frame including the FIRSTNAME alternative name pairings, merge it into the original data, and update FIRSTNAME for those rows where ALTNAME is not NA.
This allows one to update the data with a vectorized process, rather than changing the names one by one.
# create data frame with a column to maintain original sort order
df <- data.frame(obs = 1:9,
LASTNAME = c("Robinson", "Anderson", "Beckham", "Wickham", "Carlos", "Robinson", "Beckham", "Anderson", "Carlos"),
FIRSTNAME = c("David", "Adi", "Joan", "Kesley", "Anberto", "Dave", "Joana", "Adien", "An"),
stringsAsFactors = FALSE)
# create firstname / altname pairs
altnames <- data.frame(FIRSTNAME = c("Dave","Adi","Joan","An"),
ALTNAME = c("David","Adien","Joana","Anberto"),
stringsAsFactors = FALSE)
# merge by firstname, keeping all rows from original data frame
combined <- merge(df,altnames,by="FIRSTNAME",all.x=TRUE)
# update rows where ALTNAME is not NA
combined[!is.na(combined$ALTNAME),"FIRSTNAME"] <- combined[!is.na(combined$ALTNAME),"ALTNAME"]
# print the result, ordered by sequence in original data frame
combined[order(combined$obs),c("LASTNAME","FIRSTNAME")]
...and the output:
> combined[order(combined$obs),c("LASTNAME","FIRSTNAME")]
LASTNAME FIRSTNAME
6 Robinson David
1 Anderson Adien
7 Beckham Joana
9 Wickham Kesley
4 Carlos Anberto
5 Robinson David
8 Beckham Joana
2 Anderson Adien
3 Carlos Anberto
>