Merge data frames with partial id - r

Question

Say I have these two data frames:
> df1 <- data.frame(name = c('John Doe',
'Jane F. Doe',
'Mark Smith Simpson',
'Sam Lee'))
> df1
name
1 John Doe
2 Jane F. Doe
3 Mark Smith Simpson
4 Sam Lee
> df2 <- data.frame(family = c('Doe', 'Smith'), size = c(2, 6))
> df2
family size
1 Doe 2
2 Smith 6
I want to merge both data frames in order to get this:
name family size
1 John Doe Doe 2
2 Jane F. Doe Doe 2
3 Mark Smith Simpson Smith 6
4 Sam Lee <NA> NA
But I can't wrap my head around a way to do this apart from the following very convoluted solution, which is becoming very messy with my real data, which has over 100 "family names":
> df3 <- within(df1, {
family <- ifelse(test = grepl('Doe', name),
yes = 'Doe',
no = ifelse(test = grepl('Smith', name),
yes = 'Smith',
no = NA))
})
> merge(df3, df2, all.x = TRUE)
family name size
1 Doe John Doe 2
2 Doe Jane F. Doe 2
3 Smith Mark Smith Simpson 6
4 <NA> Sam Lee NA
I've tried taking a look into pmatch as well as the solutions provided at R partial match in data frame, but still haven't found what I'm looking for.

Answer 1

Rather than attempting to use regular expressions and partial matches, you could split the names up into a lookup-table format, where each component of a person's name is kept in a row, and matched to their full name:
df1 <- data.frame(name = c('John Doe',
'Jane F. Doe',
'Mark Smith Simpson',
'Sam Lee'),
stringsAsFactors = FALSE)
df2 <- data.frame(family = c('Doe', 'Smith'), size = c(2, 6),
stringsAsFactors = FALSE)
library(tidyr)
library(dplyr)
str_df <- function(x) {
ss <- strsplit(unlist(x)," ")
data.frame(family = unlist(ss),stringsAsFactors = FALSE)
}
splitnames <- df1 %>%
group_by(name) %>%
do(str_df(.))
splitnames
name family
1 Jane F. Doe Jane
2 Jane F. Doe F.
3 Jane F. Doe Doe
4 John Doe John
5 John Doe Doe
6 Mark Smith Simpson Mark
7 Mark Smith Simpson Smith
8 Mark Smith Simpson Simpson
9 Sam Lee Sam
10 Sam Lee Lee
Now you can just merge or join this with df2 to get your answer:
left_join(df2,splitnames)
Joining by: "family"
family size name
1 Doe 2 Jane F. Doe
2 Doe 2 John Doe
3 Smith 6 Mark Smith Simpson
Potential problem: if one person's first name is the same as somebody else's last name, you'll get some incorrect matches!

Answer 2

Here is one strategy, you could use lapply with grep match over all the family names. This will find them at any position. First let me define a helper function
transindex<-function(start=1) {
function(x) {
start<<-start+1
ifelse(x, start-1, NA)
}
}
and I will also be using the function coalesce.R to make things a bit simpler. Here the code i'd run to match up df2 to df1
idx<-do.call(coalesce, lapply(lapply(as.character(df2$family),
function(x) grepl(paste0("\\b", x, "\\b"), as.character(df1$name))),
transindex()))
Starting on the inside and working out, i loop over all the family names in df2 and grep for those values (adding "\b" to the pattern so i match entire words). grepl will return a logical vector (TRUE/FALSE). I then apply the above helper function transindex() to change those vector to be either the index of the row in df2 that matched, or NA. Since it's possible that a row may match more than one family, I simply choose the first using the coalesce helper function.
Not that I can match up the rows in df1 to df2, I can bring them together with
cbind(df1, size=df2[idx,])
name family size
# 1 John Doe Doe 2
# 1.1 Jane F. Doe Doe 2
# 2 Mark Smith Simpson Smith 6
# NA Sam Lee <NA> NA

Answer 3

Another apporoach that looks valid, at least with the sample data:
df1name = as.character(df1$name)
df1name
#[1] "John Doe" "Jane F. Doe" "Mark Smith Simpson" "Sam Lee"
regmatches(df1name, regexpr(paste(df2$family, collapse = "|"), df1name), invert = T) <- ""
df1name
#[1] "Doe" "Doe" "Smith" ""
cbind(df1, df2[match(df1name, df2$family), ])
# name family size
#1 John Doe Doe 2
#1.1 Jane F. Doe Doe 2
#2 Mark Smith Simpson Smith 6
#NA Sam Lee <NA> NA