Function to determine proper group - r

Question

I have the following data:
DF <- data.frame(Members = c("Eva", "Charlie1", "Fred", "Charlie2", "Adam", "Eva", "Charlie2", "David", "Adam", "David", "Charlie1"))
I would like to create a function that returns a specific value if the Members name meets a certain criteria:
Return "Group1" if the Member name is equals Eva or Adam
Return "Group2" if the Member name contains the string "Charlie"
Return "Group3" if the Member name is not either of the first two rules
I'd like to return "Group1", "Group2", "Group3" into a new column in DF called "Teams"
I've accomplished it with the following code, but I'm interested in how to accomplish it with functions
DF$Team <- with(DF, ifelse((DF$Members=="Eva"|DF$Members=="Adam"),"Group1",
ifelse((grepl("Charlie", DF$Members)),"Group2","Group3")))

Answer 1

Do you mean to create a function? Sort of like this:
DF <- data.frame(Members = c("Eva", "Charlie1", "Fred", "Charlie2", "Adam", "Eva", "Charlie2", "David", "Adam", "David", "Charlie1"))
get_group <- function(data=DF, Members=Members) {
with(DF, ifelse((DF$Members=="Eva"| DF$Members=="Adam"),"Group1",
ifelse((grepl("Charlie", DF$Members)),"Group2","Group3")))
}
DF$Group <- get_group(data = DF, Members = Members)

Answer 2

In my own experience, the most challenging part of deal with matters like this has been the "everything else" bucket. I usually have a good sense of what I want elsewhere.
The conventional approach is to use ifelse. This is generally efficient, but I find it difficult to read. My preferred approach is to use something like
levels(DF$Members) <- list(Group1 = c("Eva", "Adam"),
Group2 = c("Charlie1", "Charlie2"),
Group3 = c("David", "Fred"))
The problem with this approach is I have to explicitly name all of the values that map to each group. That doesn't help resolve the "everything else" issue.
We can modify this approach a little to identify the groups programatically.
g1 <- c("Eva", "Adam")
g2 <- levels(DF$Members)[grepl("Charlie", levels(DF$Members))]
g3 <- levels(DF$Members)[!levels(DF$Members) %in% c(g1, g2)]
levels(DF$Members) <- list(Group1 = g1,
Group2 = g2,
Group3 = g3)
This is reasonably tolerable, and helps me understand the group definitions a little better than reading nested ifelse calls.
Since you brought it up, I decided it'd be nice to have a function that handles the "everything else" scenario without my intervention. I came up with the following, which allows you to name as many groups as you want, and then use Other = NULL to indicate "everything else goes into Other".
group_levels <- function(x, ...)
{
x <- as.character(x)
group <- list(...)
which_group_null <- vapply(group, is.null, logical(1))
name_null <- names(group)[which_group_null]
group <- group[!which_group_null]
null_group <- list(unique(x[! x %in% unlist(group)]))
null_group <- setNames(null_group, name_null)
x <- factor(x)
levels(x) <- c(group, null_group)
x
}
group_levels(DF$Members,
Group1 = c("Eva", "Adam"),
Group2 = levels(DF$Members)[grepl("Charlie", levels(DF$Members))],
Group3 = NULL)
If you leave out the Group3 = NULL, the unmatched levels are given NA values.
It's probably slower than using ifelse, but I like how it reads.

Answer 3

Maybe you mean :
group_function <- function(name_string) {
if (name_string == "Eva" | name_string == "Adam")
return("Group 1")
if (grepl("Charlie", name_string))
return("Group 2")
return("Group 3")
}
and then call this function on every member
DF$Team <- sapply(DF$Members, group_function)
DF
# Members Team
#1 Eva Group 1
#2 Charlie1 Group 2
#3 Fred Group 3
#4 Charlie2 Group 2
#5 Adam Group 1
#6 Eva Group 1
#7 Charlie2 Group 2
#8 David Group 3
#9 Adam Group 1
#10 David Group 3
#11 Charlie1 Group 2